Oncogenic Viruses, Volume 2: Medical Applications of Viral Oncology Research 012824156X, 9780128241561

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Oncogenic Viruses Volume 2 Medical Applications of Viral Oncology Research

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Moulay Mustapha Ennaji

Oncogenic Viruses Volume 2 Medical Applications of Viral Oncology Research

Edited by

Moulay Mustapha Ennaji Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). MATLABs is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLABs software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLABs software. Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-12-824156-1 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

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Contents List of contributors About the editor Preface—Oncogenic Viruses: Up To Recent Knowledge Acknowledgments

1.

Vitamin D new therapy for breast cancer prevention

xv xix xxi xxv 1

Marwah Labyed, Najwa Hassou, Mohammed El Mzibri and Moulay Mustapha Ennaji

2.

1.1 Introduction 1.2 Breast cancer 1.2.1 Generality 1.2.2 Anatomy 1.2.3 Symptoms 1.2.4 Breast cancer types 1.2.5 Risk factors 1.3 Viral etiology of breast cancer 1.4 Mouse mammary tumor virus like 1.5 Human papilloma virus 1.6 Epstein
Barr virus 1.7 Vitamin D 1.7.1 Generality 1.7.2 Biosynthesis 1.8 Food needs and sources 1.9 Storage sites 1.10 Vitamin D receptors 1.11 Vitamin D new therapy for breast cancers prevention 1.11.1 Relationship between vitamin D and breast cancer 1.12 Mechanism of action 1.13 Vitamin D and breast cancer prevention 1.14 Conclusion References

1 2 2 2 3 4 5 8 8 9 9 10 10 11 12 13 13 14 14 14 15 16 16

Molecular diagnosis of human papillomavirus related to cervical cancer

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Kaoutar Anouar Tadlaoui and Moulay Mustapha Ennaji 2.1 Introduction

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2.2 Etiopathogenesis of human papillomavirus infection 2.2.1 Human papillomavirus genome structure 2.2.2 Mechanism of human papillomavirus infection in the cervix and carcinogenesis 2.3 Diagnosis of human papillomavirus viral genome 2.3.1 Identification of human papillomavirus without genotyping 2.3.2 Human papillomavirus genotyping 2.3.3 Human papillomavirus E6/E7 mRNA and protein detection 2.4 Conclusion Acknowledgment References

3.

Risk of the development of cancers induced by the consumption of mussels accumulating metallic trace elements

24 24 25 26 27 30 34 34 35 35

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Hanaaˆ Bazir, Najwa Hassou, Mohammed Nabil Benchekroun, Hlima Bessi and Moulay Mustapha Ennaji

4.

39 41

3.1 Introduction 3.2 Trace metal elements 3.2.1 Origin and cycle of trace metal elements in the natural environment 3.2.2 Properties of trace metal elements 3.2.3 Transfer of trace metal elements in the trophic chain 3.2.4 Effects of metal toxicity on human health 3.3 Bivalve molluscs 3.3.1 Classification of lamellibranchs (bivalves) 3.3.2 The mytilidae as bioindicators 3.3.3 Response of marine organisms to trace metal elements 3.4 Oxidative stress and cancer 3.5 Conclusion Acknowledgments References

41 41 48 49 49 50 51 52 54 55 55 56

Oncolytic virus cancer therapeutic options and integration of artificial intelligence into virus cancer research

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Vaishak Kaviarasan, Barath Ragunath and Ramakrishnan Veerabathiran 4.1 4.2 4.3 4.4

Introduction History General properties of oncovirus Oncolytic viral therapy: a new era of treatment

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Contents

4.4.1 Cancer immunoediting hypothesis 4.4.2 Pharmacokinetics of oncolytic viral therapy 4.5 Applications of oncolytic viral therapy 4.5.1 Diagnosis 4.5.2 Tumor targeted cell delivery by oncolytic virotherapy 4.5.3 Genetically modified oncolytic virus 4.5.4 Integration of oncolytic viral therapy in radiotherapy 4.5.5 Integration of oncolytic viral therapy in chemotherapy 4.5.6 Integration of oncolytic viral therapy with immune inhibitor checkpoints 4.6 Limitations 4.7 Integration of artificial intelligence or machine learning into cancer research 4.8 Future concerns 4.9 Conclusion Acknowledgment References

5.

Oncoviruses: future prospects of molecular mechanisms and therapeutic strategies

vii 63 64 66 67 69 70 70 71 71 71 72 73 74 74 74

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Iyshwarya Bhaskar Kalarani, Kamila Thasneem and Ramakrishnan Veerabathiran 5.1 Introduction 5.2 Mechanism of oncovirus 5.3 Types and mechanism of oncoviruses 5.3.1 Epstein
Barr virus 5.3.2 Hepatitis B virus 5.3.3 Aviadenovirus 5.3.4 Human immunodeficiency virus 5.3.5 Human papillomavirus 5.3.6 Polyomavirus 5.3.7 Herpes simplex virus 5.3.8 Parvovirus 5.3.9 Leporipoxvirus 5.3.10 Orthopoxvirus 5.4 Genetics of virus 5.5 Types of treatment 5.5.1 Immunotherapy 5.5.2 Chemotherapy 5.5.3 Targeted therapy 5.5.4 Radiation therapy 5.5.5 Hormonal therapy 5.5.6 Surgery 5.6 Stem cell transplant therapy 5.6.1 Oncotherapy

81 83 86 86 87 88 88 89 90 90 91 92 93 94 96 96 98 98 99 100 100 101 101

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5.7 Future of oncotherapy 5.8 Conclusion Acknowledgment References

103 104 104 104

Multi-omics methods and tools in dissecting the oncovirus behavior in human host

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Sheik S.S.J. Ahmed, Ramakrishnan Veerabathiran, Mookkandi Sudhan, Harsh Panwar and Prabu Pramasivam

7.

6.1 Introduction 6.1.1 Definition 6.2 Types of omics 6.2.1 Genomics 6.2.2 Transcriptomics 6.2.3 Proteomics 6.2.4 Metabolomics 6.2.5 Bioinformatics resources 6.2.6 Databases and tools 6.3 Conclusions References

109 109 111 111 113 122 127 135 139 141 141

Role of viral human oncogenesis: recent developments in molecular approaches

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ChandraLekha Saravanan, Mahalakshmi Baskar, Sheik S.S.J. Ahmed and Ramakrishnan Veerabathiran 7.1 Introduction 7.2 Prevalence of oncovirus 7.3 Classification of oncovirus 7.3.1 DNA tumor viruses 7.3.2 RNA tumor viruses 7.4 Molecular tools used for oncovirus detection 7.5 Vaccines available for oncovirus 7.6 Statistical analysis of oncovirus 7.6.1 Epstein
Barr virus 7.6.2 Hepatitis B virus 7.6.3 Human papillomavirus 7.6.4 Hepatitis C virus 7.6.5 Kaposi sarcoma-associated herpesvirus 7.6.6 Human immunodeficiency virus 7.6.7 Human T-cell lymphotropic virus type 1 7.6.8 Merkel cell polyomavirus 7.7 Oncovirus and cancer progression 7.7.1 Human papillomavirus on cancer progression

147 148 149 150 151 151 155 157 157 158 158 158 159 159 160 160 161 161

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7.7.2 Hepatitis B virus on cancer progression 7.7.3 Hepatitis C virus on cancer progression 7.7.4 Human papillomavirus on cancer progression 7.8 Oncolytic virotherapy 7.9 Conclusion Acknowledgment References

162 162 162 163 165 165 165

Strategies for the development of hepatitis B virus vaccines

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Fadoua El Battioui, Fatima El Malki and Said Barrijal

9.

8.1 Introduction 8.2 Virus-like particle-based hepatitis B vaccines 8.3 Therapeutic vaccines 8.4 DNA-based vaccines 8.5 mRNA-based vaccines 8.6 Proteins/peptides vaccines 8.7 Cell-based vaccines 8.8 Nanovaccines 8.9 Efficacy of therapeutic vaccines 8.10 Harmlessness 8.11 Immunization coverage 8.12 Conclusion References

173 173 178 179 180 181 181 182 183 183 183 184 184

MYC oncogenes as potential anticancer targets

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ˇ Radostina Alexandrova and Crtomir Podlipnik 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8

Introduction Biological role of MYC genes MYC in normal tissues and cancer MYC signal transduction pathway Structure of MYC The MYC
Max interaction MYC as a potential target for antitumor therapy Targeting the MYC
Max interaction with small molecule inhibitors 9.9 Indirect targeting of the MYC 9.10 Targeting MYC transcription 9.11 Targeting of MYC expression 9.12 Targeting MYC stability 9.13 Synthetic lethality with MYC 9.14 G-quadruplexes and expression of c-MYC 9.15 Conclusions and perspective Acknowledgments References

191 192 193 195 197 198 200 201 203 204 205 205 206 207 210 210 211

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10. Current status of viral biomarkers for oncogenic viruses

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Kazim Yalcin Arga and Medi Kori 10.1 Introduction 10.2 Epstein-Barr virus 10.2.1 Epstein-Barr virus-associated cancers 10.2.2 Epstein Bar virus-associated cancer biomarkers 10.3 Hepatitis B virus and hepatitis C virus 10.3.1 Hepatitis B virus- and hepatitis C virus-associated cancers 10.3.2 Hepatitis B virus-associated cancer biomarkers 10.3.3 Hepatitis C virus-associated cancer biomarkers 10.4 Human T-cell lymphotropic virus-1 10.4.1 HTLV-1-associated cancers 10.4.2 HTLV-1-associated cancer biomarkers 10.5 Human Herpesvirus-8 10.5.1 HHV-8-associated cancers 10.5.2 HHV-8-associated cancer biomarkers 10.6 Human papillomavirus 10.6.1 Human papillomaviruses-associated cancers 10.6.2 Human papillomaviruses-associated cancer biomarkers 10.7 Conclusions References

11. Bioinformatics serving oncoviral studies

221 224 224 225 226 228 229 230 233 233 234 235 236 236 238 239 239 243 243 253

Virupaksha Ajit Bastikar, Pramodkumar Pyarelal Gupta, Alpana Bastikar, Santosh Subhash Chhajed and Santosh Ajabrao Bothe 11.1 Biological database 11.2 Sequence analysis 11.3 Molecular dynamics simulations 11.4 Computer-aided drug discovery 11.5 Systems biology approach 11.6 Artificial intelligence approaches 11.7 Conclusion References

12. QSAR approach for combating cancer cells

255 256 257 259 261 263 263 264 267

ˇ Said Byadi, Aziz Aboulmouhajir and Crtomir Podlipnik 12.1 12.2 12.3 12.4 12.5

Introduction Handling and curation of chemical and biological data Structures drawing and database building Molecular descriptors Multivariate analysis

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Multiple linear regression analysis Principal component regression Partial least squares Kernel partial least squares Artificial neural network Other methods Classification-based QSAR approaches QSAR model generation Model examination and validation Internal validation External validation Applicability domain Model application for the prediction of compounds activity References

272 272 272 273 273 274 274 275 275 275 276 276

13. Human papillomaviruses and their carcinogens effect

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12.6 12.7 12.8 12.9 12.10 12.11 12.12 12.13 12.14 12.15 12.16 12.17 12.18

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Elamrani Elhassani Salma and Bahia Bennani 13.1 13.2 13.3 13.4

Introduction Epidemiology of human papillomaviruse Human papillomaviruse classification Human papillomavirus transmission 13.4.1 Vertical transmission 13.4.2 Horizontal transmission 13.5 Structure, genomic organization, and viral proteins 13.5.1 The long control region 13.5.2 The early region 13.5.3 The late region: L1 and L2 13.6 Human papillomavirus replication cycle 13.7 Infection evolution 13.8 Molecular mechanisms of HPV-induced carcinogenesis 13.9 Mechanisms of cell transformation 13.10 Conclusion Acknowledgments References

14. Progress in the development of vaccines against human papillomavirus

281 281 282 283 284 284 284 285 285 287 288 289 290 291 292 292 292

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Fadoua El Battioui, Fatima El Malki, Hassan Ghazal and Said Barrijal 14.1 Introduction 14.2 Virus-like particle vaccination strategy 14.3 Vaccines prophylactic against human papillomavirus 14.3.1 Types of vaccines 14.4 Immunization procedures and doses

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14.5 Efficacy and safety of human papillomavirus vaccines 14.5.1 Efficacy 14.5.2 Safety and security 14.6 L2-based human papillomavirus prophylactic vaccines 14.7 Human papillomavirus vaccine coverage 14.8 Factors influencing vaccination coverage 14.9 Therapeutic vaccines 14.9.1 Bacterial vector vaccines 14.9.2 Viral vector vaccines 14.9.3 Vaccinia virus Efficacy and safety 14.9.4 DNA vaccines 14.9.5 RNA-based vaccines 14.9.6 Peptide-based vaccines 14.9.7 Protein vaccines 14.9.8 Cellular vaccines (Dendritic cell-based vaccines) 14.10 Conclusion References

300 300 302 303 304 305 305 308 308 308 308 309 309 310 310 311 311 311

15. Development and characterization of an electrochemical sensor using molecularly imprinted polymer based on a gold screen-printed electrode for the detection of creatinine and glucose in human urine and saliva

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Benachir Bouchikhi, Alassane Diouf, Moulay Mustapha Ennaji and Nezha El Bari 15.1 Introduction 15.2 Urine and saliva as noninvasive sources of biomarkers 15.3 Biomarkers in the bloodstream can infiltrate the acini and eventually be secreted into the saliva 15.3.1 Recognition of particular compounds as an indicator of diseases 15.4 Current electrochemical sensor devices 15.5 Applications of gas sensors in oncology or virology as tools for the detection of biomarkers 15.6 Experimental 15.6.1 Chemicals and reagents 15.6.2 Polymer synthesis 15.6.3 Electrochemical sensors fabrication steps 15.6.4 Physicochemical characterization 15.6.5 Electrochemical measurements 15.7 Results and discussion 15.7.1 Morphological characterization of the fabricated sensor 15.7.2 Voltammetric array and electrochemical impedance spectroscopy responses

317 318 319 319 320 321 321 321 322 323 325 326 327 327 333

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15.7.3 Repeatability, reproducibility, selectivity, and stability of the sensor 15.7.4 Real samples detection 15.8 Conclusion Acknowledgments Declaration of competing interest References

336 339 343 344 344 344

16. Detection of triclosan and sodium lauryl sulfate in environmental samples and cosmetic product by electrochemical sensor based on biomimetic recognition combined with electronic nose

349

Nezha EL Bari, Soukaina Motia, Moulay Mustapha Ennaji and Benachir Bouchikhi 16.1 Introduction 16.1.1 Wastewater as sources of micropollutants 16.1.2 Current electrochemical sensors for environmental residues 16.1.3 Potential sensors for applications in the fields of virology and oncology 16.1.4 Electronic nose technology 16.2 Experimental 16.2.1 Chemicals and reagents 16.2.2 Polymer synthesis 16.2.3 Electrochemical sensors fabrication steps 16.2.4 Surface morphotogical analysis 16.2.5 Electrochemical measurements 16.2.6 E-nose setup and measurement 16.3 Results and discussion 16.3.1 Morphological characterization of the fabricated sensors 16.3.2 Electrochemical characterization of the sensors’ fabrication stages 16.3.3 Reproducibility, selectivity, and stability of the sensor 16.3.4 Practical application 16.4 Conclusion Acknowledgments Declaration of competing interest Appendix A References Index

349 351 351 352 353 353 353 354 355 357 357 358 359 359 361 365 368 373 375 375 375 375 379

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List of contributors Aziz Aboulmouhajir Organic Synthesis, Extraction and Valorization Laboratory, Team of Extraction, Spectroscopy and Valorization, Faculty of Sciences Ain Chock, Hassan II University, Morocco, North Africa Sheik S.S.J. Ahmed Multi-omics and Drug Discovery Lab, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Radostina Alexandrova Department of Pathology, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, Sofia, Bulgaria Kaoutar Anouar Tadlaoui Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco Kazim Yalcin Arga Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey; Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey Said Barrijal Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco Mahalakshmi Baskar Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Alpana Bastikar Department of Computer-Aided Drug Design, Navin Saxena Research and Technology Pvt. Ltd, Gandhidham, Gujarat, India Virupaksha Ajit Bastikar Amity Institute of Biotechnology, Amity University Mumbai, Maharashtra, India Hanaaˆ Bazir Laboratory of Virology, Microbiology, Quality and Biotechnology/ Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques– Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco Mohammed Nabil Benchekroun Laboratory of Virology, Microbiology, Quality and Biotechnology/Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques–Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco

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List of contributors

Bahia Bennani Laboratory of Human Pathology, Biomedicine and Environment, University Sidi Mohammed Ben Abdellah of Fez, Fez, Morocco Hlima Bessi Laboratory of Virology, Microbiology, Quality and Biotechnology/ Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques– Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco Iyshwarya Bhaskar Kalarani Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Santosh Ajabrao Bothe Saraswati College, Gaulkhed Road, Shegaon, Dist Buldhana, Maharashtra, India Benachir Bouchikhi Biosensors and Nanotechnology Group, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco; Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Moulay Ismaı¨l University, Meknes, Morocco Said Byadi Organic Synthesis, Extraction and Valorization Laboratory, Team of Extraction, Spectroscopy and Valorization, Faculty of Sciences Ain Chock, Hassan II University, Morocco, North Africa Santosh Subhash Chhajed Department of Pharmaceutical Chemistry, METs Institute of Pharmacy, Nashik, Maharashtra, India Alassane Diouf Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Department of Biology, Moulay Ismaı¨l University, Meknes, Morocco Nezha El Bari Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Department of Biology, Moulay Ismaı¨l University, Meknes, Morocco; Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco Fadoua El Battioui Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco Fatima El Malki Institute of Nursing, Tangier, Morocco Moulay Mustapha Ennaji Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco Hassan Ghazal National Center for Scientific and Technical Research (CNRST), Rabat, Morocco Pramodkumar Pyarelal Gupta School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Navi Mumbai, Maharashtra, India

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Najwa Hassou Laboratory of Virology, Microbiology, Quality and Biotechnology/ Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques– Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco; Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco Vaishak Kaviarasan Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Medi Kori Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey Marwah Labyed Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco Soukaina Motia Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco Mohammed El Mzibri Laboratory of Biology and Medical Research, National Center for Energy Sciences and Nuclear Techniques Rabat, Mohammedia, Morocco Harsh Panwar Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, Punjab, India ˇ rtomir Podlipnik Faculty of Chemistry and Chemical Technology, University of C Ljubljana, Ljubljana, Slovenia Prabu Pramasivam Department of Neurology, University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM, United States Barath Ragunath Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Elamrani Elhassani Salma Laboratory of Human Pathology, Biomedicine and Environment, University Sidi Mohammed Ben Abdellah of Fez, Fez, Morocco ChandraLekha Saravanan Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Mookkandi Sudhan Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chennai, Tamil Nadu, India

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Kamila Thasneem Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India Ramakrishnan Veerabathiran Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India

About the editor Prof. Dr. Moulay Mustapha Ennaji is a Moroccan and Canadian citizen and native of Marrakesh (Morocco). He is a scientist specialized in the fields of virology, hygiene, and microbiology. He received his master’s degree in science in 1986 and PhD in virology in 1993 from Armand Frappier Institute, University of Quebec (Canada). Between 1991 and 1993, he completed his postdoctorate at the Canadian Red Cross. From 1993 to 1995, he was a research associate (RA) and from 1995 to 1996 a research officer (RO) at the National Council of Research of Canada (CNRC). He was also a visiting researcher at the University of California, Irvine, United States, and abroad lecturer at the Histochemistry Institutes of Paris, France. He was a guest researcher of the Franklin Foundation in US NIH Bethesda. He was recruited as a lecturer and enabled professor in 1996 to the Faculty of Sciences and Techniques Mohammedia (FSTM), falling under the University Hassan II of Casablanca (UH2C), where he was the head of the biology department from 1997 to 2000. He is currently a professor of Higher Education (PES C) in the same faculty. Being a scientist who is concerned with research development, he has participated in numerous conferences and delivered lectures on virology, cancerology, hygiene, and microbiology since 1986 at Moroccan, Canadian, and American universities. Since 2010, he has been the director of the Laboratory of Virology, Oncology, Biosciences, Environment and New Energies, Faculty of Sciences and Techniques, Mohammedia, UH2C, Morocco; a leader of Virology Oncology and Biotechnology Team; and deputy director of the Research Centre of Health and Biotechnologies of UH2C and will continue to hold the positions until 2024. From 2010 to 2016, he was the director of the Laboratory of Virology, Microbiology, Quality, Biotechnologies/Ecotoxicology and Biodiversity, Faculty of Sciences and Techniques, Mohammedia, UH2C, Morocco; leader of Virology, Oncology, Total Quality and Medical Biotechnology Team; and deputy director of the Research Centre of Natural Resources and Food (Rensa) of UH2C.

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Between 2005 and 2010, he was appointed as the director of Virology, Hygiene, and Microbiology and coordinator of the Consortium of Biomedical and Environmental Sciences laboratories at UH2C-FSTM. He was also responsible for the master’s programs in biotechnology and biomedical technologies (2000
03), DESA of microbiology and bioengineering (2005
10), and master of science and technology (MST Microbiology, Applied Virology, and Bio-industry Engineering and MST of Livings) (Immuno-Virology and Applied Microbiology) from 2010 to 2015. He has been a member of the Council of the Center of Doctoral Studies (CEDoc) at FSTM-UH2C since 2008. Between 2005 and 2010, he was the deputy head of the UFR DESA Biomedical Sciences and from 2000 to 2005 deputy leaders of the UFR PhD in health and environment. From 2005 to present, he has been the deputy head of Life and Environment Sciences Doctoral UFR. From 2010 to present, he has been a national expert at CNRST and member of the National Commission for Scholarships. Previously, from 2012 to 2014, he was also a UNESCO expert on governance reform of university systems. Throughout his career, he has been rewarded with 24 awards. He has organized numerous national and international meetings in the fields of virology, microbiology, and hygiene. At present, he is the Vice-President of the Moroccan Association of Biosafety, Cancer, and Microbiology.

Preface—Oncogenic Viruses: Up To Recent Knowledge Several research works concerning the tumorigenesis factors have confirmed the oconogenic viruses as a major cause of some cancers and other neoplastic growths. This field is full of arguments and controversial conclusions. Some of these arguments and conclusions are even related to a single virus. There are different study designs, populations, clinical and pathological criteria, social statuses of probands studied, etc., involved in these studies. Some of these studies even implicated other viruses responsible for cancers. These differences even led to contradictory conclusions, which can be noticed in today’s literature. However, there is a general agreement on the suspected roles played by viruses in initiating the development of cancer. Let me consider one of the most prominent oncogenic viruses: human papilloma virus (HPV). It is responsible for the majority of cervical cancer causalities, especially in the developing countries (World Health Organization, 2016). HPV 16 and 18 contribute to malignancy and are classified under the high-risk genital types with their prevalence in 70% of all cases and 20% of the adult population in western countries (Faridi et al., 2011). Talking about other tumors and oncogenic viruses’ relations, for instance, approximately 4% of HTLV-I-infected individuals develop adult T-cell leukemia (ATL)/lymphoma (ATLL) (Shuh & Beilke, 2005). HTLV-1 clinically causes two major diseases: ATLL and tropical spastic paraparesis/HTLV-1associated myelopathy. This virus was discovered as the first retrovirus that is associated with human diseases, including an aggressive leukemia derived from CD41 T cells (ATL), according to Satou et al. (2011). Another virus associated with lymphoma is Epstein
Barr virus (EBV), and many studies linked it to HIV casualties (Shibata et al., 1993). This association was also linked to HIV and development of non-Hodgkin’s lymphoma (NHL) in HIV-infected patients, with immunodeficiency patients at substantial risk of developing NHL and particularly primary CNS lymphoma, as concluded by Pluda et al. (1993). A positive association between hepatitis C virus (HCV) and risk of NHL was also suggested in the beginning of the second millennium (Matsuo et al., 2004).

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Other studies reported different degrees of association of lymphoma with HCV (Negri et al., 2004), but HCV prevalence in patients with B-cell NHL (B-NHL) has been found to be approximately 15%, higher than that reported in a general population (1.5%), suggesting a role of HCV in the etiology of B-NHL. It is important to note that some clinical and pathologic features of the NHL disease are associated with HCV infection, but the virus does not seem to affect prognosis. In addition, a positive association between hepatitis B virus (HBV) infection and B-NHL raises the probability that HBV may play an oncogenic role in the initiation of B-NHL (Gisbert et al., 2003; Keegan et al., 2005; Marcucci et al., 2006; Vallisa et al., 1999). According to WHO, regions with maximum exposure to human papilloma virus include Eastern Africa, Melanesia, and Southern and Central Africa. Unless cervical cancer prevention and control measures are successfully implemented, it is estimated that by 2030, approximately 800,000 new cases of cervical cancer will be annually diagnosed. The vast majority of these cases will be in developing countries. Since 2009, WHO has been promoting the inclusion of HPV vaccination into national immunization programs in countries where cervical cancer is a public health priority and where cost-effective and sustainable implementation of the vaccine is feasible (WHO, 2020). In the last 2 years, the world suffered from a unique pandemic situation (COVID-19) caused by one of zoonetic viruses, that is, coronavirus. This disease continues to have an impact on human health even after treatment, with new symptoms coming to picture every passing day. This pandemic situation has compelled the scientific community to take care of two important aspects of medical biology: the seriousness of viral infections on human health and the importance of an effective vaccination process. Neutralizing antibodies are crucial for vaccine-mediated protection against viral diseases. They probably act by suppressing the infection, which is then dealt with by cellular immunity. The protective effects of neutralizing antibodies can be achieved by neutralization of free virus particles, and also by several activities directed against infected cells. Several viruses have evolved mechanisms to evade neutralizing-antibody responses, and these viruses pose challenges for vaccine design (Burton, 2002). There are two types of immune system: innate immunity possessed by most organisms and the adaptive system found in vertebrates, which inspires the design of vaccines. The former involve cells such as dendritic cells and macrophages, which also have important roles to play in the adaptive system. The latter are characterized by lymphocytes, which possess specific receptors that recognize foreign antigens (Gordon, 2007). Vaccine as a means of preventing infectious diseases is widely applied and appreciated. Amongst them, vectors based on recombinant viruses have shown great promise and play an important role in the development of new

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vaccines. The ideal viral vector is safe and enables availability of required pathogen-specific antigens to the immune system (Souza et al., 2005). Scientists very well realize the importance of the development of vaccine for cancer treatment. Vaccine therapy for cancer is less toxic than chemotherapy or radiation and could, therefore, be especially effective in older, more frail cancer patients. However, it has been shown that older individuals do not respond to vaccine therapy as well as younger adults (Gravekamp, 2009). In this book, we also focus on some concepts of vaccination against viruses for cancer patients. Based on current shreds of evidence generated by clinical and epidemiological studies of various tumors, and a level of familiarity with oncoviruses’ mechanism of action, oncogenic viruses’ role in the initiation and progression of tumors could be confirmed. In addition, the underlying tumorigenesis mechanisms in a vast majority of human cancers could be accurately identified. For this reason, it is recommended to develop HPV vaccines or other such vaccines aimed at neutralizing oncogenic viruses responsible for human tumors, thus saving uncountable lives every hour of every day. This work, as a book, is dedicated to providing an update on oncogenic virus’s molecular biology and recent findings. It is presented in two volumes: the basic knowledge of fundamentals of oncogenic viruses (Volume 1: Fundamental of Oncovirus) and oncogenic viruses’ applications in mitigating threats posed by human cancer (Volume 2: Medical Applications of Viral Oncology Research). Moulay Mustapha Ennaji

References Burton, D. (2002). Antibodies, viruses and vaccines. Nature Reviews Immunology, 2, 706
713. Faridi, R., Zahra, A., Khan, K., et al. (2011). Oncogenic potential of Human Papillomavirus (HPV) and its relation with cervical cancer. Virology Journal, 8, 269. Available from https://doi.org/10.1186/1743-422X-8-269. Gisbert, J. P., Garc´ıa-Buey, L., Pajares, J. M., & Moreno-Otero, R. (2003). Prevalence of hepatitis C virus infection in B-cell non-Hodgkin’s lymphoma: Systematic review and metaanalysis. Gastroenterology, 125(6), 1723
1732. Gordon, A. (2007). The importance of vaccination. Frontiers in Bioscience: A Journal and Virtual Library, 12, 1278
1290. Gravekamp, C. (2009). The importance of the age factor in cancer vaccination at older age. Cancer Immunology, Immunotherapy: CII, 58, 1969. Keegan, T. H., Glaser, S. L., Clarke, C. A., Gulley, M. L., Craig, F. E., DiGiuseppe, J. A., . . . Ambinder, R. F. (2005). Epstein-Barr virus as a marker of survival after Hodgkin’s lymphoma: A population-based study. Journal of Clinical Oncology, 23(30), 7604
7613.

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Marcucci, F., Mele, A., Spada, E., Candido, A., Bianco, E., Pulsoni, A., . . . De Renzo, A. (2006). ). High prevalence of hepatitis B virus infection in B-cell non-Hodgkin’s lymphoma. Haematologica, 91(4), 554
557. Matsuo, K., Kusano, A., Sugumar, A., Nakamura, S., Tajima, K., & Mueller, N. E. (2004). Effect of hepatitis C virus infection on the risk of non-Hodgkin’s lymphoma: A meta-analysis of epidemiological studies. Cancer Science, 95(9), 745
752. Negri, E., Little, D. A., Boiocchi, M., La Vecchia, C., & Franceschi, S. (2004). B-cell nonHodgkin’s lymphoma and hepatitis C virus infection: a systematic review. International Journal of Cancer, 111(1), 1
8. Pluda, J. M., Venzon, D. J., Tosato, G., Lietzau, J., Wyvill, K., Nelson, D. L., . . . Yarchoan, R. (1993). ). Parameters affecting the development of non-Hodgkin’s lymphoma in patients with severe human immunodeficiency virus infection receiving antiretroviral therapy. Journal of Clinical Oncology, 11(6), 1099
1107. Satou, Y., Yasunaga, J. I., Zhao, T., Yoshida, M., Miyazato, P., Takai, K., . . . Yamaguchi, T. (2011). HTLV-1 bZIP factor induces T-cell lymphoma and systemic inflammation in vivo. PLoS Pathogens, 7(2), e1001274. Shuh, M., & Beilke, M. (2005). The human T-cell leukemia virus type 1 (HTLV-1): New insights into the clinical aspects and molecular pathogenesis of adult t-cell leukemia/lymphoma (ATLL) and tropical spastic paraparesis/HTLV-associated myelopathy (TSP/HAM). Microscopy Research and Technique, 68(3-4), 176
196. Shibata, D., Weiss, L. M., Hernandez, A. M., Nathwani, B. N., Bernstein, L., & Levine, A. M. (1993). Epstein-Barr virus-associated non-Hodgkin’s lymphoma in patients infected with the human immunodeficiency virus. Blood, 81(8), 2102
2109. Souza, A. P. D., et al. (2005). Recombinant viruses as vaccines against viral diseases. Brazilian Journal of Medical and Biological Research, 38, 509
522. Vallisa, D., Berte`, R., Rocca, A., Civardi, G., Giangregorio, F., Ferrari, B., . . . Cavanna, L. (1999). Association between hepatitis C virus and non-Hodgkin’s lymphoma, and effects of viral infection on histologic subtype and clinical course. The American Journal of Medicine, 106(5), 556
560. WHO, Guide to introducing HPV vaccine into national immunization programmes. 2020. Who. org (Accessed on 12 September 2020). World Health Organization. (2016). World health statistics 2016: monitoring health for the SDGs sustainable development goals. World Health Organization.

Acknowledgments If the only prayer you say in your whole life is thank you, that will be enough. Maıˆtre Eckhart

The birth of this book entitled “Oncogenic Viruses: Volume 2, Medical Applications of Viral Oncology Research” would not have taken place without the participation of a group of eminent/prominent scientists, clinicians, and academicians. I would like to acknowledge the extraordinary debt I owe to the authors who have contributed their chapters in this book to share their knowledge with the scientific and technical community working on oncoviruses and the new applications of viruses in the oncological field. We would not be able to complete this book without the continuous support and vision of the reviewers, who have contributed significantly with their expertise towards adding value to the chapters dealing with the current state of the art in the field of applications of viruses in cancer treatment. Special thanks to Elsevier publishing house, and all the Elsevier staff who have contributed significantly to the publication of this book in time. I would also like to thank the members of the editorial board who invested a significant amount of time in the different stages of publication of this book. Moulay Mustapha Ennaji

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Chapter 1

Vitamin D new therapy for breast cancer prevention Marwah Labyed1, Najwa Hassou1, Mohammed El Mzibri2 and Moulay Mustapha Ennaji3 1

Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco, 2Laboratory of Biology and Medical Research, National Center for Energy Sciences and Nuclear Techniques Rabat, Mohammedia, Morocco, 3Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco

1.1

Introduction

Breast cancer is a major public health problem worldwide because it accounts for more than half of the cancers that occur in the female population. Its incidence and mortality rates continue to rise each year. This leads us to look for protective factors against this scourge. Vitamin D is important for the development, growth, and repair of bones; the normal absorption of calcium; and the proper functioning of the immune system. However, many recent scientific studies have shown an extra-bony protective role of vitamin D in multiple pathologies. Furthermore, the scientists confirmed a link between vitamin D absorption, that is, significant level of vitamin D in the blood, and a decrease in the risk of developing certain cancers, including breast cancer (Esterle et al., 2014). Other studies have shown that women with vitamin D deficiency are nearly three times more likely to develop the deadliest form of breast cancer than women without vitamin D deficiency. In addition, women in countries in the Northern Hemisphere, which are least exposed to the sun, are among the most affected by breast cancer, as are African women. In the United States, the coloring of the skin is an obstacle to the synthesis of vitamin D in regions of low luminous intensity. Vitamin D has been shown to affect more than 200 genes that influence in particular the proliferation and differentiation of cancer cells (Ebert et al., Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00010-8 Copyright © 2023 Elsevier Inc. All rights reserved.

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Oncogenic Viruses Volume 2

2006). Similarly, they inhibit the growth of certain cancer cells in culture. This could lead us to a new treatment path.

1.2 1.2.1

Breast cancer Generality

Breast cancer is a multifactorial chronic disease that constitutes a major problem of public health in the world. It represents the most common cancer among the female population and presents mostly in women over the age of 50 years. It is the main cause of mortality in women aged between 35 and 65 years. The therapeutic options for breast cancer include surgery, radiotherapy, chemotherapy, hormonal therapy, and biological therapy (targeted therapy). These treatments can be used alone or in combination. The treatment choice depends on several factors such as the cancer stage, tumor type and subtype (including hormone receptor status), patient age, etc.

1.2.2

Anatomy

The breast is an even globular organ placed on the anterior and lateral parts of the thorax, between the clavicle, the armpit, and the middle of the sternum; it is placed on the muscle of the large pectoral and has the main function of producing milk in order to feed a newborn. Each breast contains a mammary gland (itself composed of 15
20 compartments separated by fatty tissue) and supporting tissue that contains vessels, fibers, and fat (Fig. 1.1). Each compartment of the mammary gland consists of: 1. Lobules: a group of glands that produce breast milk during pregnancy. There are between 15 and 25 of these;

FIGURE 1.1 Breast anatomy.

Vitamin D new therapy for breast cancer prevention Chapter | 1

3

FIGURE 1.2 The lymph nodes.

2. Ducts: these convey milk from the lobules to the nipple; 3. Nipple: it is composed of muscle fibers that can contract and harden and is located at the center of the areola, the most pigmented part of the breast. The nipple is used to remove breast milk; 4. Areola: it is the region that surrounds the nipple. It is the most pigmented part of the breast and may be brownish or pinkish. Forming a circle, it contains sebaceous glands called areolar glands; 5. Ligaments: these allow the fixation and support of the breasts to the thorax muscles, and are composed of connective tissue; 6. The lymphatic system: it helps fight infections and is composed of lymph nodes and lymph vessels (Fig. 1.2).

1.2.3

Symptoms

There are approximately 35 lymph nodes around each breast. These are mainly located at the axilla (axillary nodes); above the clavicle (supraclavicular nodes); below the clavicle (subclavian or infraclavicular nodes); and inside the thorax, around the sternum (internal breast nodes). The initial symptoms most commonly seen in breast cancer are detectable abnormalities in the form such as a change in the size or shape of a breast, a lumen/ball in a breast, a hard ganglion in the armpit, a flow by the nipple (especially if it is bloody), or a change in pigmentation or texture. But there are always long delays between the birth of breast cancer and the moment when it becomes clinically detectable. If the cancer is not diagnosed early after the onset of the initial symptoms, the tumor may grow and spread to other parts of the body. The result is the expression of later symptoms such as bone pain, nausea, loss of appetite, weight loss,

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Oncogenic Viruses Volume 2

jaundice, shortness of breath, cough, an accumulation of fluid around the lungs, headache, double vision, and muscle weakness (Bower, 2008).

1.2.4

Breast cancer types

There are several types of breast cancer, although some of them are quite rare. These include noninvasive or in situ cancer, invasive cancer, inflammatory cancer, nipple disease, and triple-negative breast cancer (Caliskan et al., 2008; Kanitakis, 2007) (Table 1.1).

TABLE 1.1 Types of breast cancer. Types of breast cancer

Description

The noninvasive breast cancer or in situ breast cancer

The ductal carcinoma in situ (DCIS)

It is the more current, noninfiltrating type. A DCIS means that the cancer is located only in the walls of the milk ducts. It may not extend to the lymph nodes or to other parts of the body

The lobular carcinoma in situ (LCIS)

Appears when abnormal cells develop in the lobules. It does not spread to the outside walls of the lobules, nor to other parts of the body. Although not fatal, the LCIS indicates that a woman presents an increased risk of developing later an invasive breast cancer in the one or the other of the breasts

The infiltrating ductal carcinoma (IDC)

Also called ductal adenocarcinoma, it is the most current (80%) type of invasive breast cancer. It takes birth in the channels, passes through the wall of the channels, and invades the breast tissue around it. It can extend to the lymph nodes and other parts of the body

The lobular carcinoma infiltrating (LCI)

It represents approximately 10% of invasive breast cancer cases. It takes birth in the lobules, spreads to the outside of the lobules, and invades the breast tissue around it. It can extend to the lymph nodes and other parts of the body

The invasive breast cancer

(Continued )

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5

TABLE 1.1 (Continued) Types of breast cancer

Description

Inflammatory breast cancer

It is a rare and very aggressive cancer representing approximately 1%
3% of all breast cancer cases. It takes birth in the milk ducts and spreads to the lymph vessels. It tends to develop in layers or lamellae and not in the form of solid tumor

The Paget’s disease of the nipple

It is the less evident type of breast cancers and represents less than 4% of all cases. It is more common among adolescents and among women older than 80 years. In this cancer cells develop inside and around the nipple

Triple-negative breast cancer

These types of cancers get their name from the fact that they have no estrogen receptors, no progesterone receptors, and no HER-2 receptors. This means that hormone therapy and targeted anti-HER-2 therapy cannot be used to treat these cancers. Most triple-negative breast cancers are IDCs

1.2.5

Risk factors

Breast cancer is a multifactorial disease. This means that several factors influence the risk of its occurrence, that is, there are many risk factors. We now know a number of risk factors for breast cancer, although there are still uncertainties about the implication and weight of many of these factors.

1.2.5.1 Endogenous hormonal factors 1.2.5.1.1 Early age of first menstruation Many studies show that the onset of the first period before the age of 12 increases the risk of breast cancer (Collaborative Group on Hormonal Factors in Breast Cancer, 1996).

1.2.5.1.2 Late menopause Women who have menopause after 50 years of age are at an increased risk for breast cancer compared to those who stop menstruating early. This risk increases by about 3% for each additional year from the presumed age of menopause (Rossouw, 2002). The mechanism by which late menopause increases the risk of breast cancer appears to be the result of prolonged production of ovarian hormones.

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1.2.5.2 Exogenous hormonal factors 1.2.5.2.1 Oral contraceptives The risk of breast cancer is highly related to the regular use of oral contraceptives. This risk increases by about 25% for women who use them routinely. However, this increase in risk drops as soon as consumption stops. Therefore 10 years after cessation of use, no significant increase in risk is evident (Layde et al., 1989). 1.2.5.2.2

Hormone replacement therapy

Women on hormone replacement therapy (HRT) have an increased risk of breast cancer compared to women who have never used it (Hinkula et al., 2001; Rossouw, 2002). This risk increases with the duration of use.

1.2.5.3 Reproductive factors 1.2.5.3.1 Parity and early age at first motherhood Parity is defined as the number of times a woman has given birth to a child; a close relationship has been established between parity and breast cancer. Women who have had at least one full-term pregnancy before the age of 30 years have, on average, 25% lower risk of breast cancer compared to nulliparous women (Collaborative Group on Hormonal Factors in Breast Cancer, 2002). The protective effect of multiparity seems to increase in proportion to the number of deliveries. Women who have had eight to nine births are about 30% less likely to have breast cancer than women who have had five births (Pavelka et al., 2007). 1.2.5.3.2

Breastfeeding

In general, the risk of breast cancer decreases through breastfeeding. Women who breastfeed for a total of at least 25 months have a 33% lower risk of breast cancer compared to women who have never breastfed (Salehi et al., 2008).

1.2.5.4 Genetic, family, demographic, and health factors 1.2.5.4.1 History of genetic mutations Some genetic mutations are likely to increase the risk of breast cancer. Two genes, BRCA1 and BCRA2, appear to be the most involved (Satagopan et al., 2001; Wolpert et al., 2000). The risk associated with mutations of these genes exceeds 80% for women and 6% for men, when the person carrying these genes reaches the age of 70 years (Parazzini et al., 1991; Schildkraut & Thompson, 1988).

Vitamin D new therapy for breast cancer prevention Chapter | 1

1.2.5.4.2

7

Family history

The risk of breast cancer in a woman is two or more times greater if she has a first-degree relative (mother, sister, or daughter) who developed the disease before the age of 50 years, and the older the relative when she developed breast cancer, the greater the risk. For example, this risk can increase four to six times if two first-degree relatives develop breast cancer (Little et al., 1999) (Fig. 1.3). 1.2.5.4.3

Age

Age is the most important risk factor for breast cancer (Boyd et al., 1998). This risk increases between the ages of 50 and 75 years (almost two-thirds of breast cancers) (Narod et al., 1994). Breast cancer is rare in women under the age of 30 years. 1.2.5.4.4

Ionizing radiation

Exposure of breast tissue to ionizing radiation before the age of 40 years is likely to cause breast cancer in later years. The risk of breast cancer is similar for single exposure or multiple exposures of equal total intensity (IARC, 2002). Ionizing radiation increases the risk of breast cancer as it damages DNA and its constituents.

FIGURE 1.3 Family tree of a family with genetically inherited breast cancer (Calle et al., 1996).

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1.2.5.4.5

Mammography density

The risk of breast cancer increases with the density of breast tissue in mammography. For women with dense mammogram of breasts, the risk is multiplied by two to six times (Marmot et al., 2007).

1.2.5.5 Lifestyle and nutrition factors 1.2.5.5.1 Overweight Overweight and obesity are known factors to promote the development of cancer (Johnson et al., 2000; Wenten et al., 2002). Women who are overweight by more than 20 kg by the age of 18 have a doubling risk of breast cancer after menopause (Collaborative Group on Hormonal Factors in Breast Cancer, 2002). 1.2.5.5.2 Smoking Cigarette smoke contains chemicals that can damage the genetic structure (DNA) of body cells, and turn the normal cells into cancer cells. This truth does not stop for smokers only because it also concerns passive smokers. For example, passive smoking appears to be associated with an increased risk of breast cancer of about 60%; this risk is tripled in women after menopause (Brown et al., 2001). 1.2.5.5.3

Alcohol

Alcohol is a nutritional factor that increases the risk of breast cancer by about 7% for an average consumption of one alcoholic beverage per day (Collaborative Group on Hormonal Factors in Breast Cancer, 2002).

1.3

Viral etiology of breast cancer

Experimental and biological data now indicate that around 20% of human cancers worldwide can be attributed to viruses (Howley, 2006). This is an important part. It is shown that a wide variety of infectious agents, such as DNA and RNA viruses, constitute one of the main causes of cancer in humans (Pisani et al., 1997). However, virus infection is generally not sufficient for cancer; additional events and host factors, such as immunosuppression, somatic mutations, genetic predisposition, and exposure to carcinogens, must also play a role. Mouse mammary tumor virus like (MMTV-like) and Epstein
Barr virus (EBV) are among the viruses implicated in the etiology of breast cancer.

1.4

Mouse mammary tumor virus like

MMTV belongs to the retrovirus family, β-retrovirus group; in 1936, Bittner demonstrated that spontaneous laboratory mouse mammary tumors were due

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9

to the presence of this virus (Bittner, 1936). MMTV has been extensively implicated in the pathogenesis of breast tumors in mice. It is transmitted either endogenously in germ lines as provirus or exogenously through breast milk as an infectious virion. DNA sequences showing homology with those of the MMTV have been detected in human breast cancer, suggesting that this virus called MMTVlike could be the human form of the mouse breast tumor virus (Wang et al., 1995; Wang et al., 1998). The 9.9-Kb proviral sequence of the MMTV-like virus has been demonstrated by PCR techniques in two cases of human breast cancer, the proviral DNA has 95% homology to the MMTV (Liu, Klimberg, et al., 2001; Liu, Wang, et al., 2001; Melana et al., 2007). Wang et al. detected MMTV env gene sequences in 30%
40% of breast cancers in women among various populations (Wang et al., 1995). Other names for this virus include HMTV for human mammary tumor virus (Melana et al., 2002) and HHMMTV for human homolog of the mouse mammary tumor virus (Lawson et al., 2001).

1.5

Human papilloma virus

Human papilloma viruses (HPVs) are small, naked viruses consisting of a 55-nm diameter icosahedric capsid and a bistranded, circular DNA of 8000 base pairs. They belong to the family Papillomavirus (Papillomaviridae). They are classified into types and subtypes, based on the degree of nucleotide sequence homology (Bernard et al., 2010). HPV infection is one of the most common sexually transmitted infections. However, in most cases, the infection disappears or becomes undetectable within 1 or 2 years (Moscicki et al., 2001). High-risk HPVs are present in about 50% of human breast cancers worldwide (Glenn et al., 2012). More than 200 types of HPV are suspected, two of them (types 16, 18) are considered to have high oncogenic potential for their cancers in humans and these are correlated with invasive carcinomas (Glenn et al., 2012; Kroupis et al., 2006). Now, it has been shown that HPV type 16 E6/E7 onco-proteins coat noninvasive and metastatic breast cancer cells in the invasive and metastatic form (Yasmeen et al., 2007).

1.6

Epstein
Barr virus

EBV is a member of the Herpesvirus family (Herpesviridae), a very large family of more than 120 known viruses with a relatively broad host spectrum, including mammals, birds, amphibians, reptiles, and fish (Strauss & Strauss, 2002). It is associated with a number of lymphoma-type cancers (Burkitt and Hodgkin lymphomas) and carcinoma-type cancers (gastric and undifferentiated rhinopharyngeal carcinomas). Ire´ne Joab’s team highlighted

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Oncogenic Viruses Volume 2

the presence of the VBS genome in breast cancer biopsies (Bonnet et al., 1999).

1.7 1.7.1

Vitamin D Generality

Vitamin D was discovered in 1919 by Sir Edward Mellanby; it is not really a vitamin. It is considered a steroid hormone (Mellanby, 1919). It actually contains a set of five different D vitamins, numbered from 1 to 5. Two of which are important for humans: D2 (ergocalciferol) and D3 (cholecalciferol) (Fig. 1.4). Vitamin D2 is synthesized naturally by plants and fungi, thus found in humans through diet. Vitamin D3 is of animal or human origin. It is synthesized in the epidermis by the action of ultraviolet rays (Brown et al., 1999). The object forms can be chemically produced and used as supplements. Vitamins D2 and D3 have different activities depending on the species; however, their activity is similar in humans. It is therefore customary to consider all vitamin D reserves (D2 1 D3) to assess the vitamin D status in humans (Fig. 1.4). Vitamin D presents a double origin: exogenous, which corresponds to the dietary intake but also endogenous, resulting from a neosynthesis at the level of the epidermis (Holick, 2007). Its endogenous synthesis is influenced by season, exposure schedule, and latitude (Holick & Chen, 2008). Vitamin D of endogenous origin is transported in the blood by DBP (vitamin D-binding protein: carrier protein) and vitamin D of exogenous origin is absorbed by the small intestine with bile salts then transported by DBP. These two vitamins are transported to the liver where they are hydroxylated into 25 hydroxyvitamin D, calcidiol, biologically inactive form, then to the kidney for a second hydroxylation into 1,25(OH)2 vitamin D, calcitriol, biologically active form (Souberbielle et al., 2008).

FIGURE 1.4 Structure of vitamins D2 and D3.

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11

The (1,25(OH)2D) action on its target cells requires the presence of an intracellular specific receptor, the vitamin D receptor (VDR) and the transfer of the 1,25(OH)2D
VDR couple to the cell nucleus. In 1988, Minghetti and Norman cataloged the known actions of vitamin D and postulated a key role of vitamin D in the growth and differentiation of mammalian cells (Minghetti & Norman, 1988).

1.7.2

Biosynthesis

The biosynthesis of vitamin D3 is initiated mainly in the skin in the deep layers of the epidermis by the action of ultraviolet radiations (290
310 nm), which transform 7-dehydroliterol (provitamin D) into previtamin D3 (Madhok & DeLuca, 1979; Stamp et al., 1977). Previtamine D3, in turn, isomerizes to vitamin D3. Its activation is catalyzed by CYP localized in liver and kidney cells (Fig. 1.5) (Brown et al., 1999). The first step is a hydroxylation of position 25 which leads to the formation of 25-hydroxyvitamin D3 (25(OH)D3), a form of vitamin D3 reserve whose plasma half-life is two to three weeks. This hepatic hydroxylation is performed by CYP located in the endoplasmic reticulum or mitochondria. Today, localized CYP2R1 in microsomes appears as the major candidate in

FIGURE 1.5 Diagram of vitamin metabolism D3 (Tissandie´ et al., 2006).

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Oncogenic Viruses Volume 2

the synthesis of 25(OH)D3. Individuals carrying a mutation in the CYP2R1 gene have an abnormally low circulating rate of 25(OH)D3 (Cheng et al., 2004). 25(OH)D3 is then taken over by the plasma protein DBP to be transported to the kidney. The second stage is a hydroxylation in position 1 by the mitochondrial CYP27B1 which leads to the biologically active 1,25-dihydroxyvitamin D3 (1,25(OH)2D3), with a plasma half-life of about 4 hours. In addition to this major renal production, minor production sites of 1,25 (OH)2D3 have been identified in the placenta, brain, prostate, keratinocytes, osteoblasts, and macrophages that express CYP27B1. However, this extrarenal production does not usually contribute to the formation of 1,25(OH)2D3 plasmatic (Garabe´dian, 2000). Once synthesized, active vitamin D3 diffuses into the body and binds to target tissues by a receptor, VDR, to act on its target organs (Miller & Portale, 2000; Schuessler et al., 2001).

1.8

Food needs and sources

The recommended nutritional requirement for vitamin D may be defined by the RDA (recommended nutritional intake) which is the amount of a nutrient required daily for good health and must not exceed the tolerable upper intake (TMA); they depend on age and stage of life (Table 1.2).

TABLE 1.2 Recommended nutritional intake and tolerable daily maximum intake for vitamin D. Age range

Recommended nutritional intake (RNI) per day

Tolerable daily maximum intake (TDMI) per day

Infants 0
6 month

400 UI (10 mcg)

1000 UI (25 mcg)

Infants 7
12 month

400 UI (10 mcg)

1500 UI (38 mcg)

Children 1
3 years

600 UI (15 mcg)

2500 UI (63 mcg)

Children 4
8 years

600 UI (15 mcg)

3000 UI (75 mcg)

Children and adults 9
70 years

600 UI (15 mcg)

4000 UI (100 mcg)

Adults .70 years

800 UI (20 mcg)

4000 UI (100 mcg)

Pregnancy and lactation

600 UI (15 mcg)

4000 UI (100 mcg)

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TABLE 1.3 Foods naturally rich in vitamin D. Food

Quantity

Vitamin D content (IU)

Cod liver oil

15 ml

1400

Fresh wild salmon

100 g

600
1000

Farmed salmon

100 g

100
250

Sardine, herring, canned tuna

100 g

224
332

Dried chiitake mushrooms

100 g

1600

Boletus/dried morels

100 g

130

Margarine

15 mL

65
110

Butter

100 g

50

Egg yolk

1

40

Yogurt

100 g

89

Hard cheese

100 g

44

Parmesan cheese

100 g

28

Vitamin D is found in some foods consumed by humans but in tiny doses: egg yolk, yogurt, parmesan, hard cheese, margarine, butter, boletus. And at higher concentrations in dry shiitake mushrooms and fatty marine fish (cod, salmon, etc.) (Table 1.3).

1.9

Storage sites

Vitamin D, like other vitamins, can be stored in different major vitamin D storage tissues in native form or 25(OH)D (Heaney et al., 2009; Landrier et al., 2012) (Table 1.4). Concentrations of 25(OH)D are directly dependent on dietary intake of vitamin D and sun exposure, but are little influenced by age (Bjo¨rkhem & Holmberg, 1978; Heaney et al., 2009). Its dosage is that it reflects the stock and availability of vitamin D in the body. According to official standards, there is vitamin D deficiency if 25(OH) D is at 25 nmoles/L (10 ng/mL), deficiency: between 25 and 75 nmoles/L (10
30 ng/mL), normality: between 75 and 250 nmoles/L (30
100 ng/mL).

1.10 Vitamin D receptors The discovery of the VDR was made in 1969 by Haussler and Norman (1969). The discovery of this receptor firmly establishes vitamin D as a

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Oncogenic Viruses Volume 2

TABLE 1.4 Major vitamin D storage sites (Garland et al., 2009). Vitamin D (IU)

25(OH)D (IU)

Total (IU)

Adipose tissue

6960

1763

8723

Muscle

1527

1055

2581

Liver

168

214

382

Serum

271

1559

1830

Other

571

578

1149

Total

9496

5169

14,665

member of the steroid hormone family. The human vitamin D receptor was cloned and sequenced in 1988 by Baker et al. (1988). The VDR gene has more than 470 single nucleotide polymorphisms (McCullough et al., 2009; Zmuda et al., 2000). The important and intensively studied one-dimensional polymorphisms of VDR are FokI, VDR-BsmI, VDR-TaqI, VDR ApaI, and Poly (A) (Alimirah et al., 2011). Using biochemical and fluorescent immunohistochemistry analyses, the studies demonstrated that VDR are present in a wide range of tissues and cell types (Pike et al., 1980), such as breast, bone, prostate, intestine, activated B and T lymphocytes, monocytes and keratinocytes with distinct mitochondrial, membranous, cytosolic and perinuclear localization (Gombart et al., 2006; Silvagno et al., 2010).

1.11 Vitamin D new therapy for breast cancers prevention 1.11.1 Relationship between vitamin D and breast cancer Several epidemiological studies have shown that the incidence and mortality rates of certain cancers are lower in people living in southern latitudes, where levels of sun exposure are relatively high, compared to those living in northern latitudes (Grant & Mohr, 2009). Experimental evidence also suggested a strong association between vitamin D and cancer risk. In studies on cancer cells and tumors in mice, they found that vitamin D had several activities that could slow down or prevent the development of cancer, including the promotion of cell differentiation, decreased growth of cancer cells, stimulation of apoptosis, and reduction of angiogenesis (Deeb et al., 2007; Thorne & Campbell, 2008).

1.12 Mechanism of action Vitamin D acts on a range of tumor cells that express VDR in several processes such as innate immunity, apoptosis, proliferation, and cell differentiation

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FIGURE 1.6 Proposed mechanism of action of 1,25(OH) 2D in target cells (McCullough et al., 2009).

through its active form. 1,25(OH)2D is supposed to act locally through multiple mechanisms (Fleet, 2007). A rapid response can occur through a plasma membrane receptor and second messengers involved in regulating various cellular activities, including cell cycle control. Genomic effects are mediated by a 1.25(OH)2D link to the nuclear VDR. VDR then binds to the target DNA sequences as a heterodimer with the retinoid receptor X (RXR). This complex binds to the vitamin D response element (VDRE) to induce or suppress the expression of the target genes. VDREs are located on more than 200 genes and can influence a number of biological processes, including cell proliferation, differentiation, apoptosis, growth factor signaling, inflammation, and immunomodulation (Ebert et al., 2006) (Fig. 1.6).

1.13 Vitamin D and breast cancer prevention Epidemiological studies have shown an inverse correlation between average annual sun exposure and the incidence of breast cancer (Garland et al., 1990;

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Gorham et al., 1990; Moukayed & Grant, 2013), suggesting an association between endogenous vitamin D production and breast cancer. Several epidemiological surveys have analyzed possible associations between circulating levels of 25-(OH) D and the risk of developing cancer (Cui & Rohan, 2006; Garland et al., 2007; Lappe et al., 2007; Gorham et al., 2007; Wactawski-Wende et al., 2006). However, the 25-(OH) D threshold above which the risk of cancer decreases varies from study to study. Generally above 32 ng/mL or 80 nmol/L, the risk of developing cancer decreases, whatever its type (Gorham et al., 2007). Jeong and his colleagues showed that supplementation with 1,25 (OH)2D3 to mice with breast tumor-initiating stem cells significantly delayed tumor onset and growth, while mice fed on diets deficient in vitamin D showed increased tumor growth and appearance (Jeong et al., 2015).

1.14 Conclusion These results indicate that 1,25 (OH)2D3 may be a means of prevention and a useful therapeutic strategy in the targeting of some gyneco-mammary cancer. It is useful to consider this evidence in the context of previous studies. It is also helpful to incorporate the genetic variation of the VDR in people with breast cancer in a review, as it is increasingly clear that vitamin D can work together.

References Alimirah, F., Peng, X., Murillo, G., & Mehta, R. G. (2011). Functional significance of vitamin D receptor Foki polymorphism in human breast cancer cells. PLoS One, 6(1), e16024. Available from https://doi.org/10.1371/journal.pone.0016024. Baker, A. R., McDonnell, D. P., Hughes, M., Crisp, T. M., Mangelsdorf, D. J., Haussler, M. R., Pike, J. W., Shine, J., & O’Malley, B. W. (1988). Cloning and expression of full-length cDNA encoding human vitamin D receptor. Proceedings of the National Academy of Sciences, 85(10), 3294
3298. Available from https://doi.org/10.1073/pnas.85.10.3294. Bernard, H. U., Burk, R. D., Chen, Z., van Doorslaer, K., Hausen, H. z, & de Villiers, E. M. (2010). Classification of papillomaviruses (PVs) based on 189 PV types and proposal of taxonomic amendments. Virology, 401(1), 70
79. Available from https://doi.org/10.1016/j. virol.2010.02.002. Bittner, J. J. (1936). Some possible effects of nursing on the mammary gland tumor incidence in mice. Science (New York, N.Y.), 84(2172), 162. Available from https://doi.org/10.1126/ science.84.2172.162. Bjo¨rkhem, I., & Holmberg, I. (1978). Assay and properties of a mitochondrial 25-hydroxylase active on vitamine D3. Journal of Biological Chemistry, 253(3), 842
849. Available from https://doi.org/10.1016/s0021-9258(17)38181-4. Bonnet, M., Guinebretiere, J. M., Kremmer, E., Grunewald, V., Benhamou, E., Contesso, G., & Joab, I. (1999). Detection of Epstein-Barr virus in invasive breast cancers. Journal of the National Cancer Institute, 91(16), 1376
1381. Available from https://doi.org/10.1093/jnci/ 91.16.1376.

Vitamin D new therapy for breast cancer prevention Chapter | 1

17

Bower, J. E. (2008). Behavioral symptoms in patients with breast cancer and survivors. Journal of Clinical Oncology, 26(5), 768
777. Available from https://doi.org/10.1200/JCO.2007.14.3248. Boyd, N. F., Lockwood, G. A., Martin, L. J., Knight, J. A., Byng, J. W., Yaffe, M. J., & Tritchler, D. L. (1998). Mammographic densities and breast cancer risk. Breast Disease, 10(3
4), 113
126. Available from https://doi.org/10.3233/BD-1998-103-412. Brown, A., Dusso, A., & Slatopolsky, E. (1999). Vitamin D. The American Journal of Physiology, 277, 157
175. Brown, G. J. E., St., John, D. J. B., Macrae, F. A., & Aittoma¨ki, K. (2001). Cancer risk in young women at risk of hereditary nonpolyposis colorectal cancer: Implications for gynecologic surveillance. Gynecologic Oncology, 80(3), 346
349. Available from https://doi.org/ 10.1006/gyno.2000.6065. Caliskan, M., Gatti, G., Sosnovskikh, I., Rotmensz, N., Botteri, E., Musmeci, S., Rosali dos Santos, G., Viale, G., & Luini, A. (2008). Paget’s disease of the breast: The experience of the European institute of oncology and review of the literature. Breast Cancer Research and Treatment, 112, 513
521. Available from https://doi.org/10.1007/s10549-007-9880-5. Calle, E. E., Heath, C. W., Miracle-McMahill, H. L., Coates, R. J., Liff, J. M., Franceschi, S., Talamini, R., Chantarakul, N., Koetsawang, S., RachawatRachawat, D., Morabia, A., Schuman, L., Stewart, W., Szklo, M., Bain, C., Schofield, F., Siskind, V., Band, P., Coldman, A. J., & Meirik, O. (1996). Breast cancer and hormonal contraceptives: Collaborative reanalysis of individual data on 53 297 women with breast cancer and 100 239 women without breast cancer from 54 epidemiological studies. Lancet, 347(9017), 1713
1727. Available from https://doi.org/10.1016/S0140-6736(96)90806-5. Cheng, J. B., Levine, M. A., Bell, N. H., Mangelsdorf, D. J., & Russell, D. W. (2004). Genetic evidence that the human CYP2R1 enzyme is a key vitamin D 25-hydroxylase. Proceedings of the National Academy of Sciences of the United States of America, 101(20), 7711
7715. Available from https://doi.org/10.1073/pnas.0402490101. Collaborative Group on Hormonal Factors in Breast Cancer. (1996). Breast cancer and hormonal contraceptives: collaborative reanalysis of individual data on 53 297 women with breast cancer and 100 239 women without breast cancer from 54 epidemiological studies. The Lancet, 347(9017), 1713
1727. Cui, Y., & Rohan, T. E. (2006). Vitamin D, calcium, and breast cancer risk: A review. Cancer Epidemiology Biomarkers and Prevention, 15(8), 1427
1437. Available from https://doi. org/10.1158/1055-9965.EPI-06-0075. Deeb, K. K., Trump, D. L., & Johnson, C. S. (2007). Vitamin D signalling pathways in cancer: Potential for anticancer therapeutics. Nature Reviews. Cancer, 7(9), 684
700. Available from https://doi.org/10.1038/nrc2196. Ebert, R., Schu¨tze, N., Adamski, J., & Jakob, F. (2006). Vitamin D signaling is modulated on multiple levels in health and disease. Molecular and Cellular Endocrinology, 248(1
2), 149
159. Available from https://doi.org/10.1016/j.mce.2005.11.039. Esterle, L., Rothenbuhler, A., & Linglart, A. (2014). Roˆle de la vitamine D et risque de maladies auto-immunes/cancers. OCL, 21, D309. Available from https://doi.org/10.1051/ocl/2013056. Fleet, J. C. (2007). What have genomic and proteomic approaches told us about vitamin D and cancer? Nutrition Reviews, 65(2), S127
S130. Available from https://doi.org/10.1111/ j.1753-4887.2007.tb00340.x. Garabe´dian, M. (2000). La 1,25dihyroxyvitamine D et son re´cepteur. Revue du Rhumatisme, 67, 39
41. Garland, C. F., Gorham, E. D., Mohr, S. B., Grant, W. B., Giovannucci, E. L., Lipkin, M., Newmark, H., Holick, M. F., & Garland, F. C. (2007). Vitamin D and prevention of breast

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cancer: Pooled analysis. Journal of Steroid Biochemistry and Molecular Biology, 103(3
5), 708
711. Available from https://doi.org/10.1016/j.jsbmb.2006.12.007. Garland, F. C., Garland, C. F., Gorham, E. D., & Young, J. F. (1990). Geographic variation in breast cancer mortality in the United States: A hypothesis involving exposure to solar radiation. Preventive Medicine, 19(6), 614
622. Available from https://doi.org/10.1016/00917435(90)90058-R. Garland, C. F., Grant, W. B., Boucher, B. J., Cross, H. S., Garland, F. C., Gillie, O., Gorham, E. D., Heaney, R. P., Holick, M. F., Hollis, B. W., Moan, J. E., Peterlik, M., Reichrath, J., & Zittermann, A. (2009). Open letter to IARC Director Christopher P. Wild: Re IARC Working Group Report 5—Vitamin D and Cancer. Dermato-Endocrinology, 1(2), 119
120. Glenn, W. K., Heng, B., Delprado, W., Iacopetta, B., Whitaker, N. J., & Lawson, J. S. (2012). Epstein-Barr virus, human papillomavirus and mouse mammary tumour virus as multiple viruses in breast cancer. PLoS One, 7(11), e48788. Available from https://doi.org/10.1371/ journal.pone.0048788. Gombart, A. F., Luong, Q. T., & Koeffler, H. P. (2006). Vitamin D compounds: Activity against microbes and cancer. Anticancer Research, 26(4 A), 2531
2542. Gorham, E. D., Garland, C. F., Garland, F. C., Grant, W. B., Mohr, S. B., Lipkin, M., Newmark, H. L., Giovannucci, E., Wei, M., & Holick, M. F. (2007). Optimal Vitamin D status for colorectal cancer prevention. A quantitative meta analysis. American Journal of Preventive Medicine, 32(3), 210
216. Available from https://doi.org/10.1016/j.amepre.2006.11.004. Gorham, E. D., Garland, F. C., & Garland, C. F. (1990). Sunlight and breast cancer incidence in the USSR. International Journal of Epidemiology, 19(4), 820
824. Available from https:// doi.org/10.1093/ije/19.4.820. Grant, W. B., & Mohr, S. B. (2009). Ecological studies of ultraviolet B, vitamin D and cancer since 2000. Annals of Epidemiology, 19(7), 446
454. Available from https://doi.org/ 10.1016/j.annepidem.2008.12.014. Haussler, M. R., & Norman, A. W. (1969). Chromosomal receptor for a vitamin D metabolite. Proceedings of the National Academy of Sciences, 62(1), 155
162. Available from https:// doi.org/10.1073/pnas.62.1.155. Heaney, R. P., Horst, R. L., Cullen, D. M., & Armas, L. A. G. (2009). Vitamin D3 distribution and status in the body. Journal of the American College of Nutrition, 28(3), 252
256. Available from https://doi.org/10.1080/07315724.2009.10719779. Hinkula, M., Pukkala, E., Kyyro¨nen, P., & Kauppila, A. (2001). Grand multiparity and the risk of breast cancer: Population-based study in Finland. Cancer Causes and Control, 12(6), 491
500. Available from https://doi.org/10.1023/A:1011253527605. Holick, M. F. (2007). Vitamin D deficiency. New England Journal of Medicine, 357, 266
281. Available from https://doi.org/10.1056/nejmra070553. Holick, M. F., & Chen, T. C. (2008). Vitamin D deficiency: A worldwide problem with health consequences. American Journal of Clinical Nutrition, 87(4). Available from https://doi.org/ 10.1093/ajcn/87.4.1080s. Howley, P. M. (2006). Warts, cancer and ubiquitylation: Lessons from the papillomaviruses. Transactions of the American Clinical and Climatological Association, 117, 113
127. IARC. (2002). working Weight control and physical activity (Vol. 6). Lyon: IARC (. Jeong, Y., Swami, S., Krishnan, A. V., Williams, J. D., Martin, S., Horst, R. L., Albertelli, M. A., Feldman, B. J., Feldman, D., & Diehn, M. (2015). Inhibition of mouse breast tumorinitiating cells by calcitriol and dietary vitamin D. Molecular Cancer Therapeutics, 14(8), 1951
1961. Available from https://doi.org/10.1158/1535-7163.MCT-15-0066.

Vitamin D new therapy for breast cancer prevention Chapter | 1

19

Johnson, K. C., Hu, J., & Mao, Y. (2000). Passive and active smoking and breast cancer risk in Canada, 1994-97. Cancer Causes and Control, 11(3), 211
221. Available from https://doi. org/10.1023/A:1008906105790. Kanitakis, J. (2007). Mammary and extramammary Paget’s disease. Journal of the European Academy of Dermatology and Venereology, 21(5), 581
590. Available from https://doi.org/ 10.1111/j.1468-3083.2007.02154.x. Kroupis, C., Markou, A., Vourlidis, N., Dionyssiou-Asteriou, A., & Lianidou, E. S. (2006). Presence of high-risk human papillomavirus sequences in breast cancer tissues and association with histopathological characteristics. Clinical Biochemistry, 39(7), 727
731. Available from https://doi.org/10.1016/j.clinbiochem.2006.03.005. Landrier, J. F., Marcotorchino, J., & Tourniaire, F. (2012). Lipophilic micronutrients and adipose tissue biology. Nutrients, 4(11), 1622
1649. Available from https://doi.org/10.3390/ nu4111622. Lappe, J. M., Travers-Gustafson, D., Davies, K. M., Recker, R. R., & Heaney, R. P. (2007). Vitamin D and calcium supplementation reduces cancer risk: Results of a randomized trial. American Journal of Clinical Nutrition, 85(6), 1586
1591. Available from https://doi.org/ 10.1093/ajcn/85.6.1586. Lawson, J. S., Tran, D., & Rawlinson, W. D. (2001). From Bittner to Barr: A viral, diet and hormone breast cancer aetiology hypothesis. Breast Cancer Research, 3(2), 81
85. Available from https://doi.org/10.1186/bcr275. Layde, P. M., Webster, L. A., Baughman, A. L., Wingo, P. A., Rubin, G. L., & Ory, H. W. (1989). The independent associations of parity, age at first full term pregnancy, and duration of breastfeeding with the risk of breast cancer. Journal of Clinical Epidemiology, 42(10), 963
973. Available from https://doi.org/10.1016/0895-4356(89)90161-3. Little, M. P., Muirhead, C. R., Haylock, R. G. E., & Thomas, J. M. (1999). Relative risks of radiation-associated cancer: Comparison of second cancer in therapeutically irradiated populations with the Japanese atomic bomb survivors. Radiation and Environmental Biophysics, 38(4), 267
283. Available from https://doi.org/10.1007/s004110050167. Liu, B., Wang, Y., Melana, S. M., Pelisson, I., Najfeld, V., Holland, J. F., & Pogo, B. G. T. (2001). Identification of a proviral structure in human breast cancer. Cancer Research, 61(4), 1754
1759. Liu, Y., Klimberg, V. S., Andrews, N. R., Hicks, C. R., Peng, H., Chiriva-Internati, M., HenryTillman, R., & Hermonat, P. L. (2001). Human papillomavirus DNA is present in a subset of unselected breast cancers. Journal of Human Virology, 4(6), 329
334. Madhok, T. C., & DeLuca, H. F. (1979). Characteristics of the rat liver microsomal enzyme system converting cholecalciferol into 25 hydroxycholecalciferol. Evidence for the participation of cytochrome P-450. Biochemical Journal, 184(3), 491
499. Available from https://doi. org/10.1042/bj1840491. Marmot, M., Atinmo, T., Byers, T., Chen, J., Hirohata, T., Jackson, A., & Mann, J. (2007). Food, nutrition, physical activity, and the prevention of cancer: A global perspective. Washington, DC: World Cancer Research Fund/American Institute for Cancer Research. McCullough, M. L., Bostick, R. M., & Mayo, T. L. (2009). Vitamin D gene pathway polymorphisms and risk of colorectal, breast, and prostate cancer. Annual Review of Nutrition, 29, 111
132. Available from https://doi.org/10.1146/annurev-nutr-080508-141248. Melana, S. M., Nepomnaschy, I., Sakalian, M., Abbott, A., Hasa, J., Holland, J. F., & Pogo, B. G. T. (2007). Characterization of viral particles isolated from primary cultures of human breast cancer cells. Cancer Research, 67(18), 8960
8965. Available from https://doi.org/ 10.1158/0008-5472.CAN-06-3892.

20

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Melana, S. M., Picconi, M. A., Rossi, C., Mural, J., Alonio, L. V., Teyssie, A., Holland, J. F., & Pogo, B. G. T. (2002). Deteccion de secuencias homologas al gen env del virus del tumor mamario murino (MMTV) en cancer de mama de pacientes Argentinas. Medicina, 62(4), 323
327. Mellanby, E. (1919). The present state of knowledge concerning accessory food factors. Med Res Spec Rep Ser, 38, 995a. Miller, W. L., & Portale, A. A. (2000). Vitamin D 1α-hydroxylase. Trends in Endocrinology and Metabolism, 11(8), 315
319. Available from https://doi.org/10.1016/S1043-2760(00) 00287-3. Minghetti, P. P., & Norman, A. W. (1988). 1,25(OH)2-vitamin D3 receptors: Gene regulation and genetic circuitry. FASEB Journal, 2(15), 3043
3053. Available from https://doi.org/ 10.1096/fasebj.2.15.2847948. Moscicki, A. B., Hills, N., Shiboski, S., Powell, K., Jay, N., Hanson, E., Miller, S., Clayton, L., Farhat, S., Broering, J., Darragh, T., & Palefsky, J. (2001). Risks for incident human papillomavirus infection and low-grade squamous intraepithelial lesion development in young females. Journal of the American Medical Association, 285(23), 2995
3002. Available from https://doi.org/10.1001/jama.285.23.2995. Moukayed, M., & Grant, W. B. (2013). Molecular link between vitamin D and cancer prevention. Nutrients, 5(10), 3993
4021. Available from https://doi.org/10.3390/nu5103993. Narod, S. A., Madlensky, L., Tonin, P., Bradley, L., Rosen, B., Cole, D., & Risch, H. A. (1994). Hereditary and familial ovarian cancer in southern ontario. Cancer, 74(8), 2341
2346. Available from https://doi.org/10.1002/1097-0142(19941015)74:8 , 2341:: AID-CNCR2820740819 . 3.0.CO;2-Z. Parazzini, F., Franceschi, S., La Vecchia, C., & Fasoli, M. (1991). The epidemiology of ovarian cancer. Gynecologic Oncology, 43(1), 9
23. Available from https://doi.org/10.1016/00908258(91)90003-N. Pavelka, J. C., Li, A. J., & Karlan, B. Y. (2007). Hereditary ovarian cancer-assessing risk and prevention strategies. Obstetrics and Gynecology Clinics of North America, 34(4), 651
665. Available from https://doi.org/10.1016/j.ogc.2007.09.005. Pike, J. W., Gooze´, L. L., & Haussler, M. R. (1980). Biochemical evidence for 1, 25dihydroxyvitamin D receptor macromolecules in parathyroid, pancreatic, pituitary, and placental tissues. Life Sciences, 26(5), 407
414. Available from https://doi.org/10.1016/00243205(80)90158-7. Pisani, P., Parkin, D. M., Mun˜oz, N., & Ferlay, J. (1997). Cancer and infection: Estimates of the attributable fraction in 1990. Cancer Epidemiology Biomarkers and Prevention, 6(6), 387
400. Rossouw, J. E.Writing Group for the Women’s Health Initiative Investigators. (2002). Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. JAMA, 288, 321
333. Salehi, F., Dunfield, L., Phillips, K. P., Krewski, D., & Vanderhyden, B. C. (2008). Risk factors for ovarian cancer: An overview with emphasis on hormonal factors. Journal of Toxicology and Environmental Health, Part B, 11(3
4), 301
321. Available from https://doi.org/ 10.1080/10937400701876095. Satagopan, J. M., Offit, K., Foulkes, W., Robson, M. E., Wacholder, S., Eng, C. M., Karp, S. E., & Begg, C. B. (2001). The lifetime risks of breast cancer in ashkenazi jewish carriers of BRCA1 and BRCA2 mutations. Cancer Epidemiology Biomarkers and Prevention, 10(5), 467
473. Schildkraut, J. M., & Thompson, W. D. (1988). Familial ovarian cancer: A population-based case-control study. American Journal of Epidemiology, 128(3), 456
466. Available from https://doi.org/10.1093/oxfordjournals.aje.a114994.

Vitamin D new therapy for breast cancer prevention Chapter | 1

21

Schuessler, M., Astecker, N., Herzig, G., Vorisek, G., & Schuster, I. (2001). Skin is an autonomous organ in synthesis, two-step activation and degradation of vitamin D3: CYP27 in epidermis completes the set of essential vitamin D3-hydroxylases. Steroids, 66(3
5), 399
408. Available from https://doi.org/10.1016/S0039-128X(00)00229-4. Silvagno, F., De Vivo, E., Attanasio, A., Gallo, V., Mazzucco, G., Pescarmona, G., & Kowaltowski, A. J. (2010). Mitochondrial localization of vitamin D receptor in human platelets and differentiated megakaryocytes. PLoS One, 5(1), e8670. Available from https://doi. org/10.1371/journal.pone.0008670. Souberbielle, J. C., Prie´, D., Courbebaisse, M., Friedlander, G., Houillier, P., Maruani, G., Cavalier, E., & Cormier, C. (2008). Actualite´ sur les effets de la vitamine D et l’e´valuation du statut vitaminique D. Annales d’Endocrinologie, 69(6), 501
510. Available from https:// doi.org/10.1016/j.ando.2008.07.010. Stamp, T. C. B., Haddad, J. G., & Twigg, C. A. (1977). Comparison of oral 25hydroxycholecalciferol, vitamin D, and ultraviolet light as determinants of circulating 25-hydroxyvitamin D. The Lancet, 1, 1341
1343. Available from https://doi.org/10.1016/ s0140-6736(77)92553-3. Strauss, J. H., & Strauss, E. G. (2002). Viruses and human disease. Elsevier. Thorne, J., & Campbell, M. J. (2008). The vitamin D receptor in cancer. Proceedings of the Nutrition Society, 67((2)), 115
127. Available from https://doi.org/10.1017/S0029665108006964. Tissandie´, E., Gue´guen, Y., A.Lobaccaro, J.-M., Aigueperse, J., & Souidi, M. (2006). Vitamine D: Me´tabolisme, re´gulation et maladies associe´es. Me´decine/Sciences, 22(12), 1095
1100. Available from https://doi.org/10.1051/medsci/200622121095. Wactawski-Wende, J., Morley Kotchen, J., Anderson, G. L., Assaf, A. R., Brunner, R. L., O’Sullivan, M. J., Margolis, K. L., Ockene, J. K., Phillips, L., Pottern, L., Prentice, R. L., Robbins, J., Rohan, T. E., Sarto, G. E., Sharma, S., Stefanick, M. L., Van Horn, L., Wallace, R. B., Whitlock, E., . . . Manson, J. A. E. (2006). Calcium plus vitamin D supplementation and the risk of colorectal cancer. New England Journal of Medicine, 354(7), 684
696. Available from https://doi.org/10.1056/NEJMoa055222. Wang, Y., Go, V., Holland, J. F., Melana, S. M., & Pogo, B. G. T. (1998). Expression of mouse mammary tumor virus-like env gene sequences in human breast cancer. Clinical Cancer Research, 4(10), 2565
2568. Wang, Y., Holland, J. F., Melana, S., Liu, X., Pelisson, I., Stellrecht, K., Mani, S., Pogo, B. G. T., Bleiweiss, I. J., Cantarella, A., & Pogo, B. G. T. (1995). Detection of mammary tumor virus ENV gene-like sequences in human breast cancer. Cancer Research, 55(22), 5173
5179. Wenten, M., Gilliland, F. D., Baumgartner, K., & Samet, J. M. (2002). Associations of weight, weight change, and body mass with breast cancer risk in Hispanic and Non-Hispanic white women. Annals of Epidemiology, 12(6), 435
444. Available from https://doi.org/10.1016/ S1047-2797(01)00293-9. Wolpert, N., Warner, E., Seminsky, M. F., Futreal, A., & Narod, S. A. (2000). Prevalence of BRCA1 and BRCA2 mutations in male breast cancer patients in Canada. Clinical Breast Cancer, 1(1), 57
65. Available from https://doi.org/10.3816/cbc.2000.n.005. Yasmeen, A., Bismar, T. A., Kandouz, M., Foulkes, W. D., Desprez, P. Y., & Al Moustafa, A. E. (2007). E6/E7 of HPV type 16 promotes cell invasion and metastasisof human breast cancer cells. Cell Cycle (Georgetown, Tex.), 6(16), 2038
2042. Available from https://doi. org/10.4161/cc.6.16.4555. Zmuda, J. M., Cauley, J. A., & Ferrell, R. E. (2000). Molecular epidemiology of vitamin D receptor gene variants. Epidemiologic Reviews, 22(2), 203
217. Available from https://doi. org/10.1093/oxfordjournals.epirev.a018033.

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Chapter 2

Molecular diagnosis of human papillomavirus related to cervical cancer Kaoutar Anouar Tadlaoui1 and Moulay Mustapha Ennaji2 1

Team Research of Virology, Oncology, and Biotechnologies, Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Mohammedia, Morocco, 2Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco

2.1

Introduction

Human papillomavirus (HPV) is a small deoxyribonucleic acid (DNA) virus that belongs to the Papillomaviridae family (Zheng & Baker, 2006). The circular double-stranded viral genome is approximately 8000 base pairs in length (Mirabello et al., 2018). Over the past 20 years, HPV has included more than 200 genotypes (Lizano et al., 2009), of which more than 120 have been identified following complete genome sequencing (De Villiers et al., 2004). Some of these infect skin and mucous cells, and about 50 genotypes cause genitourinary infections (Ljubojevic & Skerlev, 2014), which leads to the development of specific cancers. Among them, more than 13 genotypes (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68) are considered high-risk oncogenic viruses, of which the two main high-risk oncogenic HPV (HR-HPV) are HPV-16 and HPV-18, these are involved in 70% of cervical cancers (CCs) (Graham, 2010). Because of these infections, benign warts appear, such as cervical neoplasia or benign verruciform epidermodysplasia, which can potentially progress to invasive malignant tumors, including precancerous lesions and CC (Tadlaoui et al., 2020). HPV is the most common cause of sexually transmitted genital infections worldwide. It is also the main causal factor of CC, which is one of the most common cancers in women. According to global estimated data in 2012, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00013-3 Copyright © 2023 Elsevier Inc. All rights reserved.

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Oncogenic Viruses Volume 2

530,000 cases of cancer in women (approximately 84% of female cancers) were linked to HPV, and it took a toll of 266,000 deaths (whether 8% of deaths from HPV-related cancers in women) (de Martel et al., 2017). However, not every woman infected with HR-HPV will develop CC. Various factors are involved such as infection persistence, genotype, immunosuppressor, age over 30 years, viral variants, viral load, integration, coinfection, smoking, prophylactic use, long-term use of oral contraceptives, etc. (Melo´n et al., 2013). Oncogenic HPV is also involved in other types of cancer, such as penis, anal, neck, head, or oropharynx cancer (Georgieva et al., 2009; Spano et al., 2005). However, the number of HPV infections in developing and developed countries is considerably increasing, due frequently to late diagnosis and poor prognosis. Following this increase, Kroupis and Vourlidis expected that the number of HPV-related cancer cases will double by 2050, due also to population and life expectancy increases (Kroupis & Vourlidis, 2011). Furthermore, prophylactic vaccination against genotypes 16 and 18, responsible for CC, allows to prevent the course of these infections and eventually CC (Monsonego, 2006) but this is not enough. Further investigations are required to develop a faster and more accurate diagnostic evaluation of HPV.

2.2 2.2.1

Etiopathogenesis of human papillomavirus infection Human papillomavirus genome structure

The HPV virion has a circular double-stranded DNA genome of about 8 kbp (Zheng & Baker, 2006). This small genome contains eight to ten overlapping open reading frames (ORFs) that are present in the early (E) and late (L) regions (Kroupis & Vourlidis, 2011). The viral genome of HPV is divided into three main regions: the early region (E), the late region (L), and the long control region (LCR) within an icosahedral capsid of 45
55 nm in size (de Villiers et al., 2005). Thus, the early polyadenylation (PAE) and late polyadenylation (PAL) sites separate these three regions (Zheng & Baker, 2006).

2.2.1.1 The long control region LCR, also known as noncoding upstream regulatory region (URR), is located between L1 and E6/E7 HPV proteins and has a size between 400 and 1000 bp (Tadlaoui et al., 2020). It contains the origin of replication (Ori), the promoters of viral transcription, four DNA-binding regions for E2, and about 20 other binding regions for host transcription factors specific for epithelial cells (Kroupis & Vourlidis, 2011). Therefore, LCR plays a crucial role in the regulation of virus replication, transcription, and assembly (Tadlaoui et al., 2020).

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2.2.1.2 The early control region (E) The early region is approximately 4 kbp in size (half the genome size) and encodes six early nonstructural viral proteins E1, E2, E4, E5, E6, and E7 (Tadlaoui et al., 2020). In some HPV types, E region also encodes two other proteins E3 and E8 (Lambert et al., 1987). Function of E3 is not yet defined. While E8, fused with E2, downregulates genome replication and viral transcription (Han et al., 1998). E1 protein has an ATP-dependent helicase function. Involving the E2 protein, it binds to Ori and initiates the viral genome replication (Wilson et al., 2002). It also allows to maintain the viral genome in episomal form in cells (Wilson et al., 2002). E2 performs other viral functions such as being a viral transcription factor and downregulator of E6 and E7 oncogenes expression (Kim et al., 2000). E4 is expressed as E1^E4 spliced fusion protein and its role is not well understood, since its presence or absence has no effect on viral DNA replication (Wang et al., 2004). However, E4 plays a role in genome amplification and viral synthesis (Doorbar, 2013). E4 is highly expressed in epithelial cells infected with HPV, suggesting other roles in viral release and/or transmission (Doorbar, 2013). Recent studies have shown that the E4 expression correlates with the levels of viral DNA uptake by the host. Thereby, E4 can serve as a potential biomarker of the viral carcinogenesis stages (Yajid et al., 2017). The putative oncoprotein, E5, stimulates the cellular proliferation by interacting with the E7 protein (Pim et al., 1992), abolishes apoptosis, induces pore formation (Kabsch et al., 2004), as well as facilitates the genome amplification involving epidermal growth factor (EGF) receptor and mitogen-activated protein (MAP) kinases activation (Pim et al., 1992; Yajid et al., 2017). As for the HPV’s oncoproteins, E6 and E7 of HR-HPV degrade the proteins tumor suppressor p53 and retinoblastoma protein (pRb), respectively; this disrupts the cell cycle inducing proliferation and transformation of infected cells, thus leading to viral persistence and carcinogenesis (see below) (Tadlaoui et al., 2020). 2.2.1.3 The late region (L) The late region is approximately 3 kb in size and encodes for major and minor structural proteins of the capsid: L1 (55
60 kDa) and L2 (70 kDa) (Tadlaoui et al., 2020). L1 and L2 are respectively involved in virion assembly and viral conditioning (Tadlaoui et al., 2020). 2.2.2 Mechanism of human papillomavirus infection in the cervix and carcinogenesis CC is the fourth most common cancer in women. It is mainly due to the persistence of high-risk oncogenic HPV infection in cervical cells, inducing

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precancerous lesions of the cervix, which can develop into squamous cell carcinoma over the years (Tadlaoui et al., 2020). HPV infection and integration of viral particles into epithelial cervical cells are key precocious events in the progression of cervical lesions, while many genetic and cellular events appear to be necessary for cervical carcinogenesis. After the viral genome crosses the epithelial membrane and integrates the host cell nucleus, the E1 and E2 genes are expressed, triggering viral replication (Doorbar, 2006). During host cell differentiation, the E4 and E5 genes regulate the EGF, thus promoting viral genomic production. While the E6 and E7 oncogenes allow the E2F transcription factor to drive cell proliferation processes (Melo´n et al., 2013). PRb, being a tumor suppressor protein, blocks the cell cycle in G1 phase via sequestration of the transcription factor E2F. As a result of the expression of the oncoprotein E7, which acts to bind to regulatory proteins of the cell cycle, especially pRb, the release of E2F1 from pRb and the activation of the cell cycle are induced. Thus, pRb is functionally inactivated from the early stages of cervical carcinogenesis (Griffiths et al., 2006; Leversha et al., 2003). Disruption of the pRb-E2F1 pathway by E7 induces overexpression of p16, which is a tumor suppressor protein, known as a cyclin-dependent kinase inhibitor 2A (CDKN2A), and encoded by the CDKN2A gene (MTS1, INK4A) located on chromosome 9p21 (Kalof & Cooper, 2006; Serrano et al., 1993). It slows down the cell cycle by inhibiting the function of the complex-cdk4 and cdk6
cyclin D. This complex phosphorylates and subsequently inactivates the retinoblastoma protein (pRb), which E2F released, thereby preventing binding between E2F and pRb. This means that the CDK4/CDK6
cyclin D complex allows the regulation of the G1 phase checkpoint of the cell cycle by activating the transcription of numerous genes favoring the entry of cells into S phase and the subsequent advancement of the cell cycle in this phase (Kalof & Cooper, 2006; Melo´n et al., 2013; Pauck et al., 2014). This correlation between p16 and pRb regulates negatively the progression of the cell cycle. Therefore, the intervention of the E6 and E7 genes deletes two main cellular defense mechanisms and leads the cell replication machinery to produce new viral particles, which instigate oncogenesis of cervical carcinoma.

2.3

Diagnosis of human papillomavirus viral genome

The diagnosis of HPV infection is essentially based on viral genome screening. Considering the specific character of this virus which is difficult to cultivate in cell culture and is undetectable by serological methods, the qualitative and quantitative viral detection is based on molecular biology techniques.

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Using these molecular techniques for HPV detection, the viral genome is revealed by sample analysis of blood, mucous, or biopsies cervico-uterine from infected people.

2.3.1

Identification of human papillomavirus without genotyping

These techniques allow detecting the presence of all the HPV genotypes without specifying the infecting genotype present in the sample.

2.3.1.1 Signal amplification method: liquid phase in situ hybridization The principle of this technique is based on a step of hybridization in solution of a known nucleotide sequence by a complementary DNA or RNA probe: this is a liquid phase immunocapture (Eide & Debaque, 2012). This involves denaturing the target viral DNA extracted, then carrying out the hybridization in liquid phase by affixing two different known complementary RNA probe cocktails or “aggregates” on the denatured DNA. The two cocktail probes used in Table 2.1 correspond to 13 HR-HPV genotypes (HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59 and 68), or to 5 LRHPV genotypes (HPV 6, 11, 42,43 and 44) (Melo´n et al., 2013; Ollier & Giordanengo, 2008). Among the main tests used for signal amplification or so called “cocktail” or “aggregate” detection, there are the Amplicor polymerase chain reaction (PCR) kit (Roche Diagnostics), the Hybrid Capture II (HC2) test (Qiagen ex Digene), and the Hybrid Capture III (HC3) (Digene, United States) (Eide & Debaque, 2012; Melo´n et al., 2013). The product of the hybridization reaction will be visualized through a set of immunoenzymatic reactions as follows: the DNA/RNA hybrids formed are stable and are captured on the membrane of the microplate wells, by dint of polyclonal antiduplex DNA/RNA antibodies attached thereto. During the following stage of revelation, anti-duplex DNA/RNA antibodies coupled to alkaline phosphatase (ALP) react with the free part of the DNA/RNA duplexes. Then in the presence of a chemiluminescent substrate, they trigger a light signal detected by a luminometer. This means that the test result is positive: the presence of HPV at high oncogenic risk (Eide & Debaque, 2012; Ollier & Giordanengo, 2008). This technique provides a semiquantitative measurement of DNA-HPV. It detects the presence or absence of HR-HPV but does not allow the identification of the genotypes of HPV. Although it is rapid, reproducible, and extensively applicable, it is sensitive just like PCR.

TABLE 2.1 HPV (human papillomavirus) DNA detection tests of known cocktails or aggregates. Type of test

Detection by a mixture of probes

Kit name

Amplicor HPV test

Hybrid capture II

Test Probe Set RUO

Abbott RealTime High Risk HPV

Cervista HPV HR

BD HPV-GT

Manufacturers

Roche Diagnostics

Qiagen

Qiagen

Abbott

Hologic

Becton Dickinson

Protocol: Amplification

PCR with PGM Y09/11 primers

—

—

RT-PCR amplification with GP5 1 /GP6 1 primers

Invader technology

PCR consensus Primers

Protocol: Hybridization

On microplate

On microplate with RNA probes

On microplate with RNA probes

On microplate

On microplate

On microplate

Protocol: Detection

Colorimetric

Chemiluminescence

Chemiluminescence

Fluorescence

Fluorescent probes Freight

Fluorescence

Target region

L1

Whole genome

Whole genome

L1




E6/E7













HPV detected

High risk: 16 /18 / 31/33/35/39/45/ 51/52/56/58/59/ 66/68

High risk: 16/18/31/ 33/35/39/45/51/52/ 56/58/59/68

High risk: 16/18/45

High risk: 16 /18 / 31/33/35/39/45/ 51/52/56/58/59/ 66/68

High risk: 16 /18 / 31/33/35/39/45/ 51/52/56/58/59/ 66/68

High risk: 16 /18 / 45 /31 /51 /52 /(33/ 58), (56/59/66) et 35/ 39/68

Certification

CE-IVD

FDA approved et CE-IVD

CE-IVD

CE-IVD

FDA approved et CE-IVD

FDA in progress CEIVD in progress

Automating

Cobas 4800

Rapid Capture System

Rapid Capture System

Abbott m2000

Yes

BD ViperTM LT

Many tests are used for signal amplification or so-called cocktail or aggregate detection, and to each his specialty and specificity according to the recommendations of the manufacturers. Source: From Eide, M. L. & Debaque, H. (2012). Me´thodes de de´tection des HPVs et techniques de ge´notypage dans le de´pistage du cancer du col ute´rin. Annales de Pathologie, 32(6), 401
409. https://doi.org/10.1016/j.annpat.2012.09.200.

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2.3.1.2 Polymerase chain reaction amplification technique Gene amplification or PCR techniques initially require a small amount of a sequence (DNA or RNA) characteristic of the HPV genome. This known DNA or RNA sequence undergoes an exponential amplification step in vitro which allows duplicating it in order to identify it. This method is sensitive, inexpensive, and rapid, and it relies on the validation of the specificity of the types of PCR primers. However, several genome amplification techniques using different pairs of primers have been developed to cope with the diversity of HPV genotypes.

2.3.1.2.1

Polymerase chain reaction consensus

This is a sensitive and specific technique, allowing the amplification of a genomic sequence of a single HPV genotype, using pairs of specific primers of types designed. This allows the detection of numerous amplified viral fragments of different sizes depending on the genotype, which are then identified using type-specific probes (Melo´n et al., 2013). Consensus PCR exclusively amplifies a highly conserved region of viral DNA, which targets the L1 gene fragment of the viral capsid. The protocol of this technique involves two successive PCR steps, with the use of two pairs of different primers on the same target. This improves the sensitivity of the diagnosis. The main consensus primers targeting the detection of the L1 region of HPV (Table 2.2) are MY09/MY11, GP5 1 /GP6 1 and SPF10, which respectively lead to amplicons of 450, 150, and 65 bp (Eide & Debaque, 2012; Kleter et al., 1999; Tadlaoui et al., 2020).

TABLE 2.2 The primers sequences MY09/MY11 & GP5 1 /GP6 (Tadlaoui et al., 2020). Primers

Sequences

MY09

50 CGTCCMARRGGAWACTGATC30

MY11

50 GCMCAGGGWCATAAYAATGG30

GP5 1

5TTTGTTACTGTGGTAGATACTAC30

GP6 1

50 GAAAAATAAACTGTAAATCATATTC30

Source: From Tadlaoui, K. A., Hassou, N., Bennani, B. & Ennaji, M. M. (2019). Emergence of oncogenic high-risk human papillomavirus types and cervical cancer. In Emerging and reemerging viral pathogens: Volume 1: Fundamental and basic virology aspects of human, animal and plant pathogens (pp. 539
570). Elsevier. https://doi.org/10.1016/B978-0-12-819400-3.00024-7.

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Oncogenic Viruses Volume 2

2.3.2

Human papillomavirus genotyping

Until now, around 50 HPV genotypes, causing human genitourinary infections, have been identified by analyzing the DNA sequence (Ljubojevic & Skerlev, 2014), with more than 13 types of them classified as HR-HPVs and involved in CCs (Tadlaoui et al., 2020). However, the patient may be infected with only one type of HPV or may have multiple infections. In front of the genotypic diversity of HPV and the incidence of multiple infections, different methods of HPV genotyping have been developed, allowing the identification of the viral DNA of each type of HPV present in an analyzed biological sample (Melo´n et al., 2013; Ollier & Giordanengo, 2008). These genotyping techniques are based on a PCR amplification step followed by an in situ hybridization step with type-specific oligonucleotide probes (Table 2.3). Two constraints must be checked to choose the region to amplify: the ends of the targeted DNA sequence must be highly conserved in order to allow gender PCR, and then the internal fragment of the sequence must be divergent in order to permit genotyping by comparing with sequences of recognized genotypes (Ollier & Giordanengo, 2008). However, the quality of the sample and the handling and the good running of the amplification and revelation steps require validation by a control PCR (Ollier & Giordanengo, 2008). Genotyping can be done by different methods: sequencing, Luminex flow cytometry technology, or in situ hybridization, using oligonucleotide probes specific for HPV types (Eide & Debaque, 2012; Ollier & Giordanengo, 2008). Then, the revelation of the hybrids is carried out by enzymatic reactions or using fluorophores (reverse hybridization). Nevertheless, while awaiting prodigious sequencing, automated methods are now the most promising, as they simplify the test procedure, increase the capacity, speed and quality of sample processing, reduce costs, minimize human handling errors, and can be developed in several laboratories (Melo´n et al., 2013). As already reported and despite its limitations, sequencing could be considered the gold standard for HPV genotyping, due to the possibility of identifying virtually all virus types without mistaken classifications through cross-reactions among similar types, which can occur using tests based on hybridization. Nevertheless, it was disadvantaged at identifying genotypes in samples with multiple infections, in which viral sequences overlap and it is not possible to distinguish the various types (Melo´n et al., 2013).

2.3.2.1 Genotyping by sequencing Sequencing is the reference method since the different HPV genotypes are defined by a complete analysis of the DNA sequence encoding the L1 region of the viral capsid of HPV.

TABLE 2.3 Main genotyping tests. Type of test

Genotyping

Kit name

Linear array genotyping test

INNO-LIPA genotyping extra

HPV Genotyping RH

Seeplex HPV4 ACE screening

Papillo check

Clart HPV 2 HPV

Genotyping LQ

Manufacturers

Roche diagnostics

Innogenetics

Qiagen

BioNoBisSeegene

Greiner Bio One GmbH

Genomica

Qiagen

Protocol: Amplification

PCR Consensus PGM Y primers (L1) fragment of 170 bp

PCR Consensus primers GP5 1 / GP6 1

PCR Consensus primers GP5 1 / GP6 1

PCR DPO (dual priming oligonucleotide)

PCR Consensus primers E1 fragment de 350 pb

PCR Consensus primers L1 fragment de 450 pb

PCR Consensus primers GP5 1 /6 1 fragment de 150 pb

Protocol: Hybridization

Reverse on strip

Reverse on strip

Reverse on strip

On plastic slide

On tube bottom

On polystyrene microbeads

Detection

Chromogenic

Chromogenic

Chromogenic

fluorescent

Chromogenic

fluorescence

Automated agarose or capillary gel electrophoresis

(Continued )

TABLE 2.3 (Continued) Type of test

Genotyping

Target region

L1

L1

L1

?

E1

L1

L1

HPV detected

Low risk: 6, 11, 40, 42, 53, 54, 55, 61, 62, 64, 67, 69, 70, 71, 72, 81, 84, IS39, CP6108 High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68, 73, 82, 83

Low risk: 6, 11, 40, 43, 44, 54, 70, 69, 71, 74 High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82

High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51,52, 53, 56, 58, 59, 66, 68, 73, 82

Low risk: 6, 11, 42, 43, 44 High risk: 16, 18, 31, 33, 35, 45, 51, 56, 58, 59, 66, 67, 70

Low risk: 6, 11, 40, 42, 43, 44 High risk: 16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 70, 73, 82

Low risk: 6, 11, 40, 42, 43, 44, 54, 61, 62, 70, 71, 72, 81, 83, 84, 85, 89 High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82

High risk: 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82

Certification

CE-IVD

CE-IVD

CE-IVD

CE-IVD

CE-IVD

CE-IVD

CE-IVD

Automating

_

_

YES

YES

YES

_

YES Liquichip

Source: From Eide, M. L. & Debaque, H. (2012). Me´thodes de de´tection des HPVs et techniques de ge´notypage dans le de´pistage du cancer du col ute´rin. Annales de Pathologie, 32(6), 401
409. https://doi.org/10.1016/j.annpat.2012.09.200.

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Regularly, the genotype is produced from a small DNA sequence, that of amplicons: a variable region depending on the type, which will be aligned with reference DNA fragments of the different HPV genotypes (Ollier & Giordanengo, 2008). Although sequencing is the most efficient technique for genotyping viruses, it does not identify the viral types present in multiple infections, due to the overlap of the sample viral sequences, which does not allow the different types to be distinguished. On the other hand, this technique makes it possible to detect HPV variants (mainly for HPV 16 and 18), the distribution of which varies according to geographic areas (Choi et al., 2005; Melo´n et al., 2013; Ollier & Giordanengo, 2008).

2.3.2.2 DNA microarray genotyping The principle of microarray technology is to increase the number of hybridizations in a small space, and therefore ensure rapid genotyping (Albrecht et al., 2006; Hwang et al., 2003; Ollier & Giordanengo, 2008). It also allows detecting the different types of HPV present in multiple infections (Ollier & Giordanengo, 2008). It is a simple, sensitive, and rapid technique. The protocol of this technique is based on the use of fluorescent primers, and then the phenomenon of hybridization of the complementary DNA strands is carried out on DNA chips, the support of which is in the form of slides. These contain specific probes of the desired HPV genotypes deposited in the form of micro-drops. The chips involved in this analysis have a low density, with 5
100 different probes attached to each slide (Ollier & Giordanengo, 2008). Only the hybridized target strands remain. The nonhybridized strands will be eliminated after washing, before reading the chip. Thus, since the PCR product is fluorescent, it is read directly on a fluorescence scanner that locates the target
probe hybrid and deduces the desired genotype therefrom (Ollier & Giordanengo, 2008). 2.3.2.3 Genotyping using Luminex technology Luminex technology is a molecular biology technology founded on the principle of two-laser flow cytometry, which is based on the use of fluorescent microspheres coupled to specific oligonucleotide probes and a double analysis after excitation by two lasers (Moalic et al., 2004). Thus, this multianalytical system allows the detection of multiple reactions in one tube (Ollier & Giordanengo, 2008). After amplification of the fluorescent primers with HPV DNA, the latter is denatured and then hybridized on the microspheres each labeled with a fluorophore specific for an HPV genotype. And each type of microsphere is encoded in flow cytometry by a specific color code. Thus, during the analysis, carried out by flow cytometry with two lasers, the first detects the

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Oncogenic Viruses Volume 2

fluorophore indicating the presence of an HPV amplicon, while the second detects the color of the microsphere and therefore the HPV genotype (Eide & Debaque, 2012; Ollier & Giordanengo, 2008). This technique can simultaneously analyze up to 100 types of microspheres and therefore analyze the different types of HR-HPV present in the sample (Oh et al., 2007). Furthermore, HPV Genotyping LQ test (Qiagen) is a marketed test for genotyping using Luminex technology, which allows the detection and genotyping of 18 HR-HPV (Eide & Debaque, 2012).

2.3.3

Human papillomavirus E6/E7 mRNA and protein detection

The involvement of the oncogenic proteins E6 and E7 in the carcinogenesis induced by HR-HPV is clearly demonstrated by numerous molecular studies of CC. These studies have shown that when the viral genome integrates within the genome of the host cell, the overexpression of the oncoproteins E6 and E7 is highly induced, consequently causing the malignant transformation of cervical cells and therefore of HPV-dependent cervical carcinomas (Tadlaoui et al., 2020). Therefore, the detection of E6/E7 messenger RNAs is a potential biomarker allowing the early detection of precancerous lesions before their evolution into an active cancerous pathology (high expression), and the differentiation between these ones and transient lesions (low expression) (Eide & Debaque, 2012).

2.4

Conclusion

HPV is responsible for cancer of the cervix and others. HPV infection in the genitourinary area is very common and is often asymptomatic, making early viral detection difficult. However, the detection of HPV, in particular HRHPV, remains an essential tool for the management of lesions of the uterine cervix. Currently, the rapid development of different molecular biology techniques allows for multiple means of HPV screening, typing, and quantification. Eventually, these techniques will be used in the follow-up of cervical lesions after treatment and in the early diagnosis of other precancerous lesions. Faced with the multiplicity of HPV detection and typing techniques, it is now necessary to develop method validation guidelines with appropriate reference materials and laboratory accreditation, in order to ensure the analytical value of HPV molecular detection tests.

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Acknowledgment I would like to extend my deep thanks and gratitude to Faculty of Sciences and Techniques of Mohammedia, University Hassan II of Casablanca, Morocco, my Professor My Mustapha ENNAJI, and my research team of virology, oncology, and biotechnologies for their support, encouragement, and contribution.

References Albrecht, V., Chevallier, A., Magnone, V., Barbry, P., Vandenbos, F., Bongain, A., Lefebvre, J. C., & Giordanengo, V. (2006). Easy and fast detection and genotyping of high-risk human papillomavirus by dedicated DNA microarrays. Journal of Virological Methods, 137(2), 236
244. Available from https://doi.org/10.1016/j.jviromet.2006.06.023. Choi, Y. D., Jung, W. W., Nam, J. H., Choi, H. S., & Park, C. S. (2005). Detection of HPV genotypes in cervical lesions by the HPV DNA chip and sequencing. Gynecologic Oncology, 98 (3), 369
375. Available from https://doi.org/10.1016/j.ygyno.2005.04.044. de Martel, C., Plummer, M., Vignat, J., & Franceschi, S. (2017). Worldwide burden of cancer attributable to HPV by site, country and HPV type. International Journal of Cancer, 141(4), 664
670. Available from https://doi.org/10.1002/ijc.30716. De Villiers, E. M., Fauquet, C., Broker, T. R., Bernard, H. U., & Zur Hausen, H. (2004). Classification of papillomaviruses. Virology, 324(1), 17
27. Available from https://doi.org/ 10.1016/j.virol.2004.03.033. de Villiers, E. M., Whitley, C., & Gunst, K. (2005). Identification of new papillomavirus types. Methods in Molecular Medicine, 119, 1
13. Doorbar, J. (2006). Molecular biology of human papillomavirus infection and cervical cancer. Clinical Science, 110(5), 525
541. Available from https://doi.org/10.1042/CS20050369. Doorbar, J. (2013). The E4 protein; structure, function and patterns of expression. Virology, 445 (1
2), 80
98. Available from https://doi.org/10.1016/j.virol.2013.07.008. Eide, M. L., & Debaque, H. (2012). Me´thodes de de´tection des HPVs et techniques de ge´notypage dans le de´pistage du cancer du col ute´rin. Annales de Pathologie, 32(6), 401
409. Available from https://doi.org/10.1016/j.annpat.2012.09.200. Georgieva, S., Iordanov, V., & Sergieva, S. (2009). Nature of cervical cancer and other HPV
Associated cancers. Journal of B.U.ON, 14(3), 391
398. Graham, S. V. (2010). Human papillomavirus: Gene expression, regulation and prospects for novel diagnostic methods and antiviral therapies. Future Microbiology, 5(10), 1493
1506. Available from https://doi.org/10.2217/fmb.10.107. Griffiths, W., Lewontin, G., Suzuki, & Miller. (2006). Introduction a` l’analyse ge´ne´tique. In La re´gulation ge´ne´tique du nombre de cellules: Les cellules normales et les cellules cance´reuses (Vol. 17, pp. 549
550). Han, R., Cladel, N. M., Reed, C. A., & Christensen, N. D. (1998). Characterization of transformation function of cottontail rabbit papillomavirus E5 and E8 genes. Virology, 251(2), 253
263. Available from https://doi.org/10.1006/viro.1998.9416. Hwang, T. S., Jeong, J. K., Park, M., Han, H. S., Choi, H. K., & Park, T. S. (2003). Detection and typing of HPV genotypes in various cervical lesions by HPV oligonucleotide microarray. Gynecologic Oncology, 90(1), 51
56. Available from https://doi.org/10.1016/S00908258(03)00201-4.

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Kabsch, K., Mossadegh, N., Kohl, A., Komposch, G., Schenkel, J., Alonso, A., & Tomakidi, P. (2004). The HPV-16 E5 protein inhibits TRAIL- and FasL-mediated apoptosis in human keratinocyte raft cultures. Intervirology, 47(1), 48
56. Available from https://doi.org/ 10.1159/000076642. Kalof, A. N., & Cooper, K. (2006). p16INK4a immunoexpression: Surrogate marker of high-risk HPV and high-grade cervical intraepithelial neoplasia. Advances in Anatomic Pathology, 13 (4), 190
194. Available from https://doi.org/10.1097/00125480-200607000-00006. Kim, C. J., Um, S. J., Kim, T. Y., Kim, E. J., Park, T. C., Kim, S. J., Namkoong, S. E., & Park, J. S. (2000). Regulation of cell growth and HPV genes by exogenous estrogen in cervical cancer cells. International Journal of Gynecological Cancer, 10(2), 157
164. Available from https://doi.org/10.1046/j.1525-1438.2000.00016.x. Kleter, B., Van Doorn, L. J., Schrauwen, L., Molijn, A., Sastrowijoto, S., Ter Schegget, J., Lindeman, J., Ter Harmsel, B., Burger, M., & Quint, W. (1999). Development and clinical evaluation of a highly sensitive PCR-reverse hybridization line probe assay for detection and identification of anogenital human papillomavirus. Journal of Clinical Microbiology, 37 (8), 2508
2517. Available from https://doi.org/10.1128/jcm.37.8.2508-2517.1999. Kroupis, C., & Vourlidis, N. (2011). Human papilloma virus (HPV) molecular diagnostics. Clinical Chemistry and Laboratory Medicine, 49(11), 1783
1799. Available from https:// doi.org/10.1515/CCLM.2011.685. Lambert, P. F., Spalholz, B. A., & Howley, P. M. (1987). A transcriptional repressor encoded by BPV-1 shares a common carboxy-terminal domain with the E2 transactivator. Cell, 50(1), 69
78. Available from https://doi.org/10.1016/0092-8674(87)90663-5. Leversha, M. A., Fielding, P., Watson, S., Gosney, J. R., & Field, J. K. (2003). Expression of p53, pRB, and p16 in lung tumours: A validation study on tissue microarrays. Journal of Pathology, 200(5), 610
619. Available from https://doi.org/10.1002/path.1374. Lizano, M., Berumen, J., & Garc´ıa-Carranc´a, A. (2009). HPV-related carcinogenesis: Basic concepts, viral types and variants. Archives of Medical Research, 40(6), 428
434. Available from https://doi.org/10.1016/j.arcmed.2009.06.001. Ljubojevic, S., & Skerlev, M. (2014). HPV-associated diseases. Clinics in Dermatology, 32(2), 227
234. Available from https://doi.org/10.1016/j.clindermatol.2013.08.007. Melo´n, S., Alvarez-Argu¨elles, M., & de On˜a, M. (2013). Molecular Diagnosis of Human Papillomavirus Infections. Human papillomavirus and related diseases
From bench to bedside a diagnostic and preventive perspective (pp. 1
26), IntechOpen. Mirabello, L., Clarke, M. A., Nelson, C. W., Dean, M., Wentzensen, N., Yeager, M., Cullen, M., Boland, J. F., Alemany, L., Banks, L., Bass, S., Buck, C., Burdett, L., Chaturvedi, A., Clifford, G., DiMaio, D., Doorbar, J., Hildesheim, A., Laimins, L., . . . Burk, R. D. (2018). The intersection of HPV epidemiology, genomics and mechanistic studies of HPV-mediated carcinogenesis. Viruses, 10(2), 80. Available from https://doi.org/10.3390/v10020080. Moalic, V., Mercier, B., & Ferec, C. (2004). Technologie LuminexTM: Principe, applications, et perspectives. Immuno-Analyse & Biologie Spe´cialise´e, 19(4), 181
187. Available from https://doi.org/10.1016/j.immbio.2004.05.004. Monsonego, J. (2006). Prevention of cervical cancer: Challenges and perspectives of hpv prophylactic vaccines. Emerging issues on HPV infections: From science to practice (pp. 184
205). S. Karger AG. Available from https://doi.org/10.1159/000092755. Oh, Y., Bae, S. M., Kim, Y. W., Choi, H. S., Nam, G. H., Han, S. J., Park, C. H., Cho, Y., Han, B. D., & Ahn, W. S. (2007). Polymerase chain reaction-based fluorescent Luminex assay to detect the presence of human papillomavirus types. Cancer Science, 98(4), 549
554. Available from https://doi.org/10.1111/j.1349-7006.2007.00427.x.

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Ollier, L., & Giordanengo, V. (2008). Me´thodes de de´tection et d’identification des HPV. Revue Francophone Des Laboratoires, 38(405), 51
55. Available from https://doi.org/10.1016/ s1773-035x(08)74278-3. Pauck, A., Lener, B., Hoell, M., Kaiser, A., Kaufmann, A. M., Zwerschke, W., & Jansen-Durr, P. (2014). Depletion of the cdk inhibitor p16INK4a differentially affects proliferation of established cervical carcinoma cells. Journal of Virology, 88(10), 5256
5262. Available from https://doi.org/10.1128/jvi.03817-13. Pim, D., Collins, M., & Banks, L. (1992). Human papillomavirus type 16 E5 gene stimulates the transforming activity of the epidermal growth factor receptor. Oncogene, 7(1), 27
32. Serrano, M., Hannon, G. J., & Beach, D. (1993). A new regulatory motif in cell-cycle control causing specific inhibition of cyclin D/CDK4. Nature, 366(6456), 704
707. Available from https://doi.org/10.1038/366704a0. Spano, J. P., Marcelin, A. G., & Carcelin, G. (2005). Cancer et infection a` papillomavirus humain. Bulletin du Cancer, 92(1), 59
64. Tadlaoui, K. A., Hassou, N., Bennani, B., & Ennaji, M. M. (2020). Emergence of oncogenic high-risk human papillomavirus types and cervical cancer. In Emerging and Reemerging Viral Pathogens, (pp. 539
570). Academic Press. Wang, Q., Griffin, H., Southern, S., Jackson, D., Martin, A., McIntosh, P., Davy, C., Masterson, P. J., Walker, P. A., Laskey, P., Omary, M. B., & Doorbar, J. (2004). Functional analysis of the human papillomavirus type 16 E1 5 E4 protein provides a mechanism for in vivo and in vitro keratin filament reorganization. Journal of Virology, 78(2), 821
833. Available from https://doi.org/10.1128/JVI.78.2.821-833.2004. Wilson, V. G., West, M., Woytek, K., & Rangasamy, D. (2002). Papillomavirus E1 proteins: Form, function, and features. Virus Genes, 24(3), 275
290. Available from https://doi.org/ 10.1023/A:1015336817836. Yajid, A. I., Zakariah, M. A., Zin, A. A. M., & Othman, N. H. (2017). Potential role of E4 protein in human papillomavirus screening: A review. Asian Pacific Journal of Cancer Prevention, 18(2), 315
319. Available from https://doi.org/10.22034/APJCP.2017.18.2.315. Zheng, Z. M., & Baker, C. C. (2006). Papillomavirus genome structure, expression, and posttranscriptional regulation. Frontiers in Bioscience, 11(1), 2286
2302. Available from https://doi.org/10.2741/1971.

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Chapter 3

Risk of the development of cancers induced by the consumption of mussels accumulating metallic trace elements Hanaaˆ Bazir1, Najwa Hassou1, Mohammed Nabil Benchekroun1, Hlima Bessi1 and Moulay Mustapha Ennaji2 1

Laboratory of Virology, Microbiology, Quality and Biotechnology/Ecotoxicology and Biodiversity, Faculty of Sciences & Techniques
Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco, 2Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco

3.1

Introduction

Water is an indispensable and essential element of life. Its presence covers more than 71% of the earth’s surface (China et al., 2003). Only 2.6% of this water is in the form of fresh water, of which less than 1% is accessible to the population, while the rest is in the form of ice (Kadouche, 2013). However, it is involved in all biological composition and activity. Water is being increasingly contaminated with different types of pollutants, generated by human and natural activities, such as heavy metals. The harmfulness of these pollutants is linked to their speciations. Their concentrations are generally very low, which explains their designation of “trace metallic elements” (TMEs), such as lead, cadmium, and mercury (Rahal, 2012). The long-term toxic effects of excessive ingestion of potentially toxic nonessential metals can be chronic or acute; they can be mutagenic (genetic mutation), carcinogenic, and teratogenic (developmental abnormalities), and also affect metabolism.

Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00015-7 Copyright © 2023 Elsevier Inc. All rights reserved.

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These elements are not biodegradable, so they can be transferred in the food chain, and accumulate and concentrate in living organisms, such as bivalve molluscs. Bivalve molluscs are the first cultivated marine organisms, recognized for a long time as a consumable protein source. These filter feeder species select their food from fine food particles, unlike most molluscs, which use their radula for food, which is why they are considered to be good indicators of metal pollution (Medhioub, 2011). In addition, they are a food to be monitored for two main reasons: (1) they are excellent sensors of pollutants, including metallic trace elements; and (2) the concentration is fixed in the organs of assimilation such as the equivalents of the liver and kidney, which are precisely what humans eat. Aerobic systems are continuously subjected to oxidative pressures, which sometimes lead, during a disruption of the antioxidant balance, to oxidative damage involving lipids, proteins, and DNA. It is mainly the free radicals resulting from these oxidative pressures that degrade the cellular components. Among the many substances responsible for the excessive production of oxidizing species are metals. Some are purely toxic to living beings (cadmium, lead, mercury, etc.), while others are essential for the body and for many biological functions, but they generate toxic effects on the body, beyond a certain concentration threshold (copper and zinc). In either case, the accumulation of these TMEs inside an organism can trigger a defense reaction. Among these transition metals are those that have the ability to interact directly with oxygen (copper) and those that have an indirect action (cadmium and zinc). Free radicals are chemical species (atoms or molecules) with an unpaired electron. These free radicals have important biochemical and cellular consequences. By generating abnormal biological molecules and causing the overexpression of certain genes, oxidative stress potentiates the appearance of multifactorial diseases (diabetes, Alzheimer’s, etc.) and is the main cause of many diseases often linked to aging (cancer, cataracts, etc.). In addition, cells have developed defense systems to metabolize oxidative species and thus limit the damage they cause. These antioxidant systems protect cellular constituents from radical attacks by interacting directly with these radicals or indirectly by producing peptides such as metallothioneins or glutathione. Repair systems help eliminate product damage. However, the massive production of oxidant species and the inhibition of the activities of major antioxidant enzymes in a cell can promote excessive cell death or tumor development. The objective of this chapter is to study the risk generated following the consumption of mussels contaminated with metallic trace elements, leading to the development of cancers in humans, in order to better control the risks of carcinogenesis associated with involuntary consumption of chemical contaminants.

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Trace metal elements

TMEs belong to different families of substances based on metals or metalloids, but in small quantities. Their main characteristic is prevalence in the environment because those substances are neither created nor degraded in the environment. However, these substances can change their physical or chemical form and valence; this is done under the influence of chemical and/or biological reactions. The TMEs comprise 80 constituent chemical elements found in the earth’s crust (Baize, 1998). They are said to be in traces when their concentration in the soil is less than 1 g/kg of dry matter (Gouzy & Ducos, 2008). Some ecosystems are naturally very rich in metallic elements, while others are less so. The trace elements are those that are toxic to humans and the environment but in low doses. They include arsenic (Ar), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Mg), nickel (Ni), lead (Pb), and zinc (Zn) (Varrault, 2011). Most of them have the dual property of both trace elements and toxic elements, for both the animal and plant kingdoms (Doelsch, 2004).

3.2.1 Origin and cycle of trace metal elements in the natural environment The TMEs that enter the aquatic environment are largely governed by atmospheric and continental inputs. TMEs of terrestrial origin are mainly deposited in particulate form, while those of anthropogenic origin are much more soluble (Gouzy & Ducos, 2008). However, continental inputs, those from rivers and streams, are estimated from injections of water in the sea. It is an important source of nutrients, chemicals, and TMEs. However, the knowledge about the contributions of submarine volcanoes to TMEs is still lacking (Oursel, 2013).

3.2.2

Properties of trace metal elements

Some metals are trace elements. For this reason, the periodic table can be classified according to their physical and chemical characteristics into essential and nonessential elements, with the exception of a small number denoting metalloids. A metal is a chemical element most often derived from an ore, with a special luster. It is a good conductor of heat and electricity, with a level of hardness and malleability. The elements combine with other elements to form alloys used by humans since time immemorial (Rouane, 2013). Heavy metals are generally called natural metallic elements characterized by a high density, more than 5 g/cm3. Heavy metals are present in all compartments of the environment but in small quantities. They are basic constituents of the environment, found in air, water, and soil. They accumulate in living organisms as well as in the trophic chain (Merzouki et al., 2009).

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3.2.2.1 The essential elements The essential elements are those that meet the criteria laid down by COTZIAS: G G

G

be present in living tissue at a relatively constant concentration; cause, by their removal from the body, related structural and physiological abnormalities in several species; prevent or cure these disorders through only the element (Burnol et al., 2006).

Some heavy metals are essential elements required in the human body. Their presence in the body is less than 1 mg/kg of body weight, due to which these essential elements are also said to be present in a trace state. This is why they are referred to as trace elements. They differ from other mineral elements by their presence in larger quantities (of the order of a few ten to a few hundred grams) in the body, which are called major mineral elements (Rahal, 2012). At high concentrations, metals like iron (Fe), copper (Cu), zinc (Zn), cobalt (Co), and manganese (Mn) are toxic to human body.

3.2.2.2 Nonessential elements Nonessential or toxic elements are micropollutants. They have no known beneficial role in the cell. Their toxicities develop by bioaccumulation along the food chain. It is impossible to detect the presence of TMEs in water due to their low concentration, which is why sediments and marine organisms are looked into, where these TMEs accumulate (Doelsch, 2004). 3.2.2.2.1

Lead

Lead is a metal that has been mined for 5000 years. It is one of the toxic metals found in great abundance in the earth’s crust. It is present in all terrestrial compartments. Its use is directly linked to metallurgy, industry, and printing. It is also present in paints and automotive fuels, which are mainly responsible for the prevalence of lead in the environment today (Poe¨y & Philibert, 2000). It is a bluish-gray chemical element with the symbol Pb and atomic number 82. It exists in four naturally occurring isotopes: 204Pb, 206Pb, 207 Pb, and 208Pb. Elemental lead has low electrical conductivity and its high mass contributes to its significant absorption capacity for X, γ, and electromagnetic radiation (Garnier, 2005b). It is one of the elements forming TMEs; it can be bio-amplified in biological systems, becoming a potential contaminant for the various trophic links. In the environment, the highest influx of lead in the ocean comes from the atmosphere. Lead appears to be less toxic than copper at an equal molar concentration, due to the formation of complexes with hydroxides or silicates in the medium.

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Sources of lead G Natural sources: Lead is present in the earth’s crust where its average concentration is between 10 and 20 mg/kg in all compartments of the biosphere. In air, lead emissions from wind-blown volcanic dust are recognized to be minor contributors. Other natural processes, such as soil degradation and erosion and forest fires, contribute significantly to the release of Pb. But, generally, these natural processes rarely lead to high concentrations of Pb in the environment (Garnier, 2005b). G Food sources: The contamination of food by lead is an outcome of atmospheric contamination. The techniques used for the preparation of the food, in particular for the cooking and the conservation, also contribute to lead generation in the atmosphere. The food products that are most important vectors of Pb include bread, vegetables, fruits, and sugary drinks. These foods form a major part of the ingested lead-contaminated foods, including crustaceans, molluscs, and offal (kidneys) (Labat & Lhermitte, 2007). Another source of lead is drinking water. The contamination is mainly linked to tap water pipes with physicochemical characteristics (low pH, high temperature) conducive to the dissolution of lead (Barkouch, 2007). G Anthropogenic sources: Lead is mainly used in electric batteries, as an additive in gasoline, in the steel industry, and in the pickling and metal treatment industries. It is also used in waste incineration, wood combustion, cement factories, and battery manufacturing industries (Sabouraud et al., 2009). 3.2.2.2.1.1 Behavior of lead in aquatic environments According to Batley and Florence (1976), approximately 66% of lead in seawater is found in the form of organic complexes or biologically methylated by bacteria. Most inorganic lead compounds are poorly soluble in water (e.g., PbS, PbCO3, PbSO4), halogenated lead compounds (chloride, bromide), or lead acetates being more soluble. 3.2.2.2.1.2 Lead toxicity Lead is a metal that affects all the metabolic functions of the human body. It causes damage to the hematopoietic, nervous, and reproductive systems, as well as the urinary tract (Landrigan et al., 1990). Pb poisoning usually results from oral ingestion and absorption of Pb through the intestinal wall. Lead can also be transmitted during pregnancy from the mother to the embryo or fetus, which may lead to premature abortion, fetal death, or psychomotor delays and teratogenic conditions in children (Graziano et al., 1990). 3.2.2.2.1.3 Carcinogenic effect of lead Lead is only weakly mutagenic, but in vitro it inhibits DNA repair and acts synergistically with other mutagens. Lead acetate administered by mouth, skin, or intraperitoneally causes kidney, brain (glioma), and lung cancers in rodents, and works

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synergistically with other carcinogens. Most cytogenetic studies on workers with exposure to lead have shown an increase in chromosomal aberrations or sister chromatid exchanges, besides studies with positive exposure response trends. There are eight studies conducted on the mortality or incidence of cancer in highly exposed workers; most are cohort studies of lead smelters or battery workers exposed to lead conducted decades ago (Steenland & Boffetta, 2000). 3.2.2.2.2

Cadmium

Cadmium is a metal that is not commonly found in its natural state and occurs as an impurity in various minerals, including zinc, lead, and copper (Rousselet, 2007). Its name originates from cadmia in Latin. It was discovered from calamine in 1817 by the German chemist Friedrich Stromeyer in Thebes, Greece (Andujar et al., 2010). Cadmium is a metal from group IIb of the family of transition metals. It has eight naturally occurring isotopes. In its pure state, it is a soft, ductile metal with a bluish-white color. This metal can sublimate at relatively low temperatures. The most common form of cadmium is the Cd21 ion. This cation can combine with many anions to form salts with different physical and chemical properties. Cadmium is not essential for the development of animal or plant organisms and does not participate in cell metabolism. 3.2.2.2.2.1 Properties of cadmium Cadmium is a nonessential toxic transition metal, an indicator of oxidative stress. The most stable state of cadmium found in nature is Cd21, which allows a high solubility of lipids, a high bioaccumulation, and, consequently, a high degree of toxicity, which results essentially from the similarity of the metabolism of Cd to that of Zn: Cd replaces Zn in many enzymatic reactions (Khalid & Brhimi, 2009). On the other hand, its physical and chemical properties, similar to those of calcium, allow it to cross biological barriers and accumulate in tissues (Rousselet, 2007). 3.2.2.2.2.2 Sources of cadmium Natural sources: Cadmium naturally occurs in most rocks and in the earth’s crust at an average concentration of 0.2 part per million (ppm), as well as in coal and petroleum. It is generally present in zinc or lead ores (Brignon & Malherbe, 2005). It is formed following an alteration and erosion of cadmiferous rocks and also constitutes a refining product for other metals (Zn, Cu, Pb) (Rousselet, 2007). Cadmium inputs to the marine environment, mainly via rivers and rains, are linked to the zinc industry, coal combustion, iron and steel industry, and the manufacture and use of phosphate fertilizers (Burnol et al., 2006).

G

Risk of the development of cancers Chapter | 3 G

G

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Anthropogenic sources: Cadmium is widely used in electronics: its high resistance to corrosion and its shiny appearance enable its wide use in the automotive, aircraft, ship, construction, and communications industries. Cd sulfides are used as colorants in various industries: plastics, ceramics, paints, and textiles. Cd is also a staple product in the battery industry, thanks to its perfect reversibility in electrochemical reactions over a wide temperature range, its low self-discharge rate, and its easy recovery from used batteries (Andujar et al., 2010). Food sources: Cadmium is a metal that is toxic to humans, and the main source of exposure to cadmium in the general nonsmoking population is from food (Filippini et al., 2020). Cadmium is present in a significant amount in certain foods, such as seafood, offal, certain cereals (rice, wheat, etc.), mushrooms, and vegetables and, to a lesser extent, in fish, fruits, and meat (Andujar et al., 2010). The average daily intake is around 3
10 μg in nonsmoker adults. Smoking contributes to a significant intake of cadmium (approximately 1 μg per cigarette).

3.2.2.2.2.3 Behavior of cadmium in the aquatic environment Cadmium is found in the aquatic environment in various forms: dissolved, colloidal, and particulate. The dissolved forms of this element are the free species of Cd21 and those formed by associations of Cd with mineral or organic compounds (Brignon & Malherbe, 2005) and those found in chemical forms: mineral or organic. A set of physicochemical properties of the medium (salinity, pH, etc.) govern the transformation of Cd in the environment (Burnol et al., 2006). It is bioaccumulative and listed as toxic by the National Research and Safety Institute (INRS) in its sulfide and oxide forms. It is a substance classified as “priority dangerous” by European Directive 2000/60/EC (Khalid & Brhimi, 2009). 3.2.2.2.2.4 Cadmium toxicity There are two main routes of cadmium entry into the body: the respiratory tract by inhalation and the digestive tract by consumption of contaminated food. Acute intoxication by the digestive route (ingestion of 3 mg) may lead to bloody vomiting, intense abdominal pain, diarrhea, and myalgia (Andujar et al., 2010). Chronic cadmium poisoning can cause osteomalacia and sometimes osteoporosis. The bone toxicity of cadmium is thought to be the result of renal phosphocalcic leakage and also the disturbance by cadmium of vitamin D3, leading to a decrease in the digestive absorption of calcium (Andujar et al., 2010). 3.2.2.2.2.5 Carcinogenic effect of cadmium In its 1993 report in Lyon, the international cancer research center IARC classified cadmium in group I as a definite carcinogen for humans and animals (IARC, 1997). In this context, three

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cancer sites due to cadmium have been mentioned in the literature: bronchopulmonary cancer, kidney cancer, and pancreatic cancer (Andujar et al., 2010). G

G

Inhalation exposure Cadmium has been classified in group 1 by the IARC. The associated cancers are those of the respiratory system. In the respiratory environment, the results of cohort studies have established a link between exposure to cadmium and the occurrence of lung cancer (Kazantzis et al., 1988). In 2006, a study carried out in the general population near industrial sites, with exposure to cadmium, confirmed an increased risk of lung cancer with high urinary cadmium concentrations (Nawrot et al., 2006). Oral exposure Oral exposure to cadmium can induce the development of other cancers. A meta-analysis carried out in 2013 suggests a positive association (RR 5 1,15; 95% CI: 1.08
1.23) between the consumption of cadmium from food and the risk of cancer in Western countries, in particular certain hormone-dependent cancers such as prostate, breast, and endometrial cancers (Cho et al., 2013).

In addition, a case
control study conducted in the United States in 2006, on a population of 246 women aged 20
69 years with elevated urinary cadmium levels, presented a double risk of developing breast cancer (OR 5 2.29; CI 95% 1.3
4.2) compared with those with low urinary cadmium levels after adjustment for certain risk factors such as exposure to other metals, in particular arsenic (McElroy et al., 2006). Another prospective study on a population of 32,210 Swedish menopausal women also demonstrated an association between exposure to cadmium via diet and the incidence of the development of endometrial cancer ˚ kesson et al., 2008). In 2014, Jancic and his team established a link between (A the occurrence of thyroid cancer and chronic exposure to cadmium. 3.2.2.2.3 Mercury Mercury is found in all compartments of the environment (air, water, soil), where it can be present in different chemical forms. It has no known beneficial role in living things. According to the WHO, mercury is among the most toxic pollutants for humans and the environment. Several regulations have also emerged in order to limit its use and prevent its risks. Understanding the distribution of mercury on a global scale requires knowledge of its sources (natural and anthropogenic) and of the biogeochemical transformations (biogeochemical cycle) governing its speciation and its transfer into the environment (Sharma & Sohn, 2009). 3.2.2.2.3.1 Property of mercury Mercury is a ubiquitous contaminant, with its presence in the atmosphere, pedosphere, hydrosphere, and biosphere (Engstrom, 2007). Anthropogenic Hg emissions are mainly carried out via the atmosphere in the form of elemental mercury Hg0 gas, which is then transported over long distances. This elemental mercury Hg0 can then be

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oxidized to HgII, which can then be deposited in the pedosphere and hydrosphere in a dry or wet form via precipitation. In aquatic environments, anerobic bacteria convert a small portion of HgII into methylmercury (MeHg), which can then bioaccumulate in the food chain (up to a factor of 106). Mercury is a contaminant whose toxicity and bioavailability depend on the chemical speciation of its dissolved form Sharma and Sohn (2009). Various physicochemical and biological processes are involved in the complex biogeochemical cycle of mercury: oxidation, reduction, precipitation, solubilization, adsorption, desorption, and biotransformations via various organisms (bacteria, microorganisms, algae, crustaceans, fish, etc.), leading to the interconversion of mercury between its different chemical forms and its transfer between different environmental compartments. 3.2.2.2.3.2 Sources of mercury Natural sources Mercury is a natural constituent of the earth’s crust and is released into the environment through different routes, especially from volcanoes in the form of vapor (Hg0). Weathering of rocks is also a potential and primary source of mercury, which is the reason mercury is naturally present in soils and constitutes the geochemical background. After being deposited on the continents and the oceans, a good part of the natural mercury volatilizes again, then redistributes itself in another form, both in continental and oceanic environments: these are the secondary emissions of Hg. These re-emissions occur from soils (often by wind erosion), water bodies, vegetation, and forest fires (Carmouze et al., 2001). G Anthropogenic sources There are many sources and anthropogenic emissions of mercury. Almost 80% of these emissions come from fossil fuels, mining activities, mineral purification, and the incineration and treatment of solid waste. Direct additions to the soil in the form of fertilizers and fungicides are responsible for 15% of emissions, and about 5% is contributed by industrial and/or mining effluents. In addition, there is a wide variety of diffuse sources of mercury, including used batteries, thermometers, and various industrial wastes and materials (Carmouze et al., 2001). Also, soils treated by certain agricultural practices (forest fires and deforestation) are subject to wind erosion and lead to an increase in aerosols rich in mercury (Lacerda et al., 2004). Due to its high vapor pressure and relatively long residence time in the atmosphere, mercury is constantly redistributed by air through the general atmospheric circulation. As a result, its distribution remains relatively homogeneous on a planetary scale (Carmouze et al., 2001; Mason et al., 1994). G

3.2.2.2.3.3 Mercury toxicity Mercury is particularly toxic in all its forms and chemical states, but the most toxic form is MeHg. This element is

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responsible for many harmful effects on natural ecosystems and on all living organisms (Peralta-Videa et al., 2009). Mercury has been enlisted by WHO as one of the 10 chemicals of very high public health concern. There are different modes of exposure to mercury for humans, including inhalation of mercury vapors and ingestion of contaminated water or food and consumption of fishery products contaminated with mercury (Bravo et al., 2010; Ekino et al., 2007; Veiga et al., 1999). The toxicity of mercury in its gaseous form (Hg0) is first demonstrated in the respiratory tract, then dissolving in plasma, blood and hemoglobin. Via the blood, it reaches the kidneys, the brain, and nervous system. Pregnant women are also at risk on exposure to mercury and it easily travels through the placenta to reach the fetus. Even after birth the risks persist because breast milk can also be contaminated. 3.2.2.2.3.4 Behavior of mercury in aquatic environments The impact of mercury on the aquatic food chain has been studied from aquatic invertebrates to carnivorous fish at the end of the food chain. It is mostly accumulated in the muscle tissues of aquatic organisms, all along the trophic chain (Peralta-Videa et al., 2009). Methylmercury is accumulated through direct uptake of water by certain organisms low in the food chain, such as bivalve molluscs, and biomagnifies along the food chain. The significant accumulation of mercury in aquatic organisms has led to their use as bioindicators of contamination of the aquatic environment by mercury. 3.2.2.2.3.5 Carcinogenic effect of mercury Very little information is available regarding the carcinogenic potential of mercury and its compounds (WHO, 2003). Some benign and malignant tumors have been reported in the kidneys following exposure to mercuric chloride. No data are available on a possible carcinogenic effect of elemental mercury. A study (IARC, 1997) carried out in rats and mice exposed for 2 years to gavage with mercuric chloride at 1.9 mg/kg/day revealed the following: 2 2 2 2

Male mouse: some renal adenomas and adenocarcinomas. Female rats: some renal adenocarcinomas. Male rats: an increased incidence of malpigial papillomas of the anterior estoma. Both sexes: dose-dependent hyperplasia of the anterior estoma.

3.2.3

Transfer of trace metal elements in the trophic chain

The transfer of metals between individuals follow a process called “trophic transfers.” The pollutant, present in algae and microorganisms, is ingested by a herbivore, which is a prey to a carnivore, which, in turn, is a prey to a

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supercarnivore, animal, or human being. At the end of the food chain, the final consumer will have bioaccumulated the soluble forms of metals (Barkouch, 2007).

3.2.4

Effects of metal toxicity on human health

The toxicity of a metal can be influenced by a number of factors, including those related to the contaminant (or extrinsic factors) such as the nature of the metal, chemical speciation, organotropism, and bioavailability (Burnol et al., 2006). These metals can act by binding to various ligands containing thiol groups, which can lead to the blocking of the functional groups of important molecules such as enzymes and polynucleotides. Several intrinsic factors such as age, sex, nutritional status, exposure conditions, and genetic variability can influence the bioavailability of the metal and its tissue distribution (Andujar et al., 2010).

3.3

Bivalve molluscs

Molluscs are one of the major branches of the animal kingdom, with more than 100,000 known living species. Eight classes are currently distinguished: Caudofoveata, Solenogastres, Polyplacophores, Monoplacophores, Scaphopods, Gastropods, Cephalopods, and Marine Bivalves. The latter class of species have a soft body with different characteristics. Some bivalves have a shell that is calcareous in nature and is opaque white in color. In the snail, the shell is more developed; it tightens for protection and support for the whole body. In other species, the shell consists of two valves, which cover the entire body, while other species lack an outer shell entirely. Seafood products, in particular bivalve molluscs or seafood, considered as food products, are consumed by humans either raw or undercooked, which may cause food poisoning. These are foodborne illnesses caused by the ingestion of pathogenic microorganisms or toxic products (heavy metals) (Myrand et al., 2007). These animals filter water and concentrate microorganisms and toxins (China et al., 2003). Bivalves or (lamellibranchs) form a class of molluscs. This class includes in particular the Pectinidae, mussels, oysters, clams, and many other families of shellfish. Their laterally flattened body is covered with a calcareous shell (calcium carbonate) consisting of two valves, due to which they are named bivalves. These valves present different characteristics, which facilitates in the identification of these species. Its size is one of the most important characters linked to the bioaccumulation of metallic elements in bivalves (Merzouki et al., 2009), as well as the shape (round or elongated, two identical or different valves), the color (white, gray or others), and ornamentation (smooth or wrinkled shell, presence of thorns).

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The water, laden with nutrient particles and oxygen, enters through the inhaling siphon, and is pumped by the beating of the millions of small cilia of the gill-like gills. The food sorted by the gills is transported to the mouth and then digested. The excrements are evacuated through breathing out by the exhaling siphon; hence, the bivalve molluscs are considered as filter feeders. The foot, which alternately lengthens and shortens, acts like a hydrostatic skeleton. This action is also done by fixing the adhesion of the species to the substrate via the shell or by the byssus secreted by the foot. The byssus is made of protein filaments, which solidify like a glue to adhere the species to the rock. Mussels, in particular Mytilus galloprovincialis, are widely used as bioindicators of metal pollution, because it is a marine organism widely integrated in biomonitoring programs of aquatic environments because of its qualities as a sentinel species, given its wide geographical distribution, being sessile, tolerant to environmental variations, and excellent bio-accumulators of many xenobiotics ((Dumas et al., 2020). Xenobiotics are substances with toxic properties, like metals. The metal particles contained in water and associated with food penetrate the gills and the skin barrier, which leads to the bioaccumulation of trace elements in the body. This transfer from the environment to the organism depends on the concentrations present in these sources and the influence of several biotic and abiotic factors (Cheggourm, 1989).

3.3.1

Classification of lamellibranchs (bivalves)

Modern taxonomy divides bivalves into four subclasses of varying importance: G

G

G

G

Protobranchs (proto: first and branch: gill): These are the most primitive because their gills are simple filaments that are not involved in nutrition. Eulamellibranchs (eu: true, lamelli: lamellae, branch: gill): Their gills form real lamellae connected by tissues. Septibranchs (septum: septum, branch: gill): The septum separates the gills from the palleal cavity. This group brings together species that live at great depths. The filbranches (thread: filament, branch: gill): As the name suggests, their branchial filaments are linked together by stiff threads. The inside of their shell is usually covered with the mother-of-pearl.

Some families live associated with others (oysters, mussels, sea ham), while others are free and can swim by clicking their valves (comb, scallop).

3.3.1.1 Habitat The natural habitats of mussels are found in open coastal ecosystems, rocky shores, and muddy seas. They occupy different types of habitats, from shallow coastal waters to the estuary.

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3.3.1.2 Food Bivalve molluscs are suspensivores, capable of feeding on phytoplankton cells in suspension. They are made of a heterogeneous set of unicellular microalgae, mainly photosynthetic, living in suspension in water and subject to the movements of water masses. The cells can be solitary or grouped in colonies (Medhioub, 2011). The mode of nutrition of bivalves by filtration of sea water inevitably leads to their contamination by bacteria, viruses, toxins, and toxic chemicals present in their environment (Vidal, 2001). The degradation of the particles is possible thanks to the presence of the crystalline stylet, composed of mucoproteins, which projects through the stomach and rubs against the gastric shield. The particles are stirred by the rotations of the stylet, and undergo a start of lysis thanks to the action of enzymes (amylase) released by the stylet (Medhioub, 2011). 3.3.1.3 Metallic pollution bioindicators A bio-indicator is defined as a group of living species (community) that is capable of revealing, through its presence, the transformation of a given ecosystem. A bio-indicator must be available in abundance in the study area, easy to sample, inhabiting a sedentary site, accumulator of trace metals, and especially consumed by the local population. Bioindicators can thus play multiple roles. They are used to detect pollution. 3.3.2

The mytilidae as bioindicators

Various families of bivalves, such as Mytilidae, are recognized as good indicators of heavy metals and the best bio-accumulators of different heavy metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn). They are present in the marine environment mainly in three different forms: (1) dissolved form, present in filtered water; (2) organic form contained in the food ingested; and (3) inorganic, particulate form present in suspension in the water columns. The blue mussel Mytilus edulis is a bivalve mollusk, belonging to the Mytilidae family, widespread on the North Atlantic coasts (Marte & Pe´quignot, 2013). The blue mussel is characterized by an equivalent or in-equilateral shell, dark blue to black in color. It measures 3
12 cm in adults (Callier, 2008). The body is made of the visceral mass enveloped by two mantle lobes, white, beige or orange in color, with which it merges at the hepatopancreas (or digestive gland). The mantle lobes contain the gonads and delimit the palleal cavity, where the lamella-shaped gills are located. The muscular foot allows occasional locomotion. The anterior and posterior adductor muscles keep both valves

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closed. The mouth is surrounded by labial palps, which allow nutrient particles to be transported to the mouth. The gills provide respiration and nutrition (Callier, 2008). The water is sucked and pumped through the ciliature present on the branchial epithelium in the palleal cavity. It then passes through the gills, which filter it, and, finally, expelled through the exhaling orifice. Mussels are considered to be phytophagous and mainly absorb phytoplankton, but they also consume bacteria and zooplankton. This mode of nutrition by filtration allows an accumulation of many toxic chemical compounds from algae and pathogenic bacteria present in the water (Merzouki et al., 2009) in particular, if it is located in a polluted area. Even if these toxins do not affect mussels, their consumption proves dangerous and sometimes even fatal for humans and mammals generally (Callier, 2008).

3.3.3

Response of marine organisms to trace metal elements

3.3.3.1 Bioaccumulation It is defined by the bioconcentration factor (BCF—ratio between the concentration of the element in the tissues of the species at equilibrium and the free concentration of the element in water). Molluscs accumulate heavy metals mainly in two organs: the kidney and the hepatopancreas (Bouhaimi et al., 1997). The first phase of accumulation is based on phenomena of accessibility, interaction, and penetration through biological barriers that are in direct contact with the environment. The metals present in the aquatic environment are mainly found in hydrophilic and hydrated forms, which cannot cross biological membranes by simple diffusion and which involve protein transporters or transmembrane channels (Zegmout et al., 2011). There are four mechanisms by which a toxicant passes through a cell membrane: the most important is passive diffusion through the membrane, then filtration through membrane pores, active transport, and endocytosis (Fournier, 2005). Some species present a particular homeostatic mechanism, which is capable of maintaining the concentration of certain metals in their organism constant, while the external concentration increases within certain limits. So this phenomenon serves as a regulator for the species, in a zone of concentrations situated between the deficiency and the toxicity (Bouhaimi et al., 1997). Bioaccumulation is either due to direct transfer by water or due to desorption of metallic elements attached to the inert or living particles suspended in the water, which serve as their food. Thus, it constitutes a very useful tool for estimating the degree of pollution of the hydro system (Elbekkay & Melhaoui, 2011).

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Penetration of lead into cells is a result of transport as Pb21, although endocytosis in the gill epithelium has also been suggested for this metal. The absorption rate of lead is a direct function of its concentration in the marine environment. In the body, Pb21 ions compete with Ca21 ions. Bivalves initially accumulate cadmium mainly in hepatopancreas and in the kidney, and in lysosomes. This accumulation occurs preferentially in the digestive gland by supply to the mantle (Cooper, 2008). The transfer of TMEs from the medium to the organism depends on the concentrations present in these sources and the influence of several biotic and abiotic factors (Cheggourm, 1989). Bioaccumulation in the body depends on many factors, primarily the nature of the metallic element depending on whether it has a biological role or not and whether or not it is regulated by the body (Belbachir et al., 2014). Bioaccumulation also depends on biotic factors such as life stage, age, sex, and physiological state and several abiotic factors such as degree of pollution and season. In addition, most of these factors are interlinked and thus it is often difficult to distinguish them. Thus, during the life cycle, bioaccumulation can undergo significant variations (Zegmout et al., 2011). The phenomenon of bioaccumulation is maximal before reproduction and minimal after spawning when reserves have been depleted during gametogenesis (Bayed, 1998). The concentrations of the majority of metals increase markedly in winter and decrease in summer under the influence of numerous permanent discharges of urban, industrial, and agricultural origin (Bayed, 1998; Fournier, 2005), which reveals the impact of pollution of marine water by environmental inputs.

3.3.3.2 Sequestration and elimination 3.3.3.2.1 Sequestration Bivalve molluscs can either control the absorption of the metal or store it in a nontoxic form by complexing it with a protein called metallothionein, capable of sequestering metals. On the other hand, metallothioneins have no role in the sequestration and detoxification of Pb21 (Garnier, 2005a). 3.3.3.2.1.1 Metallothionein Definition: Metallothioneins (MTs) belong to the family of intracellular proteins, with molecular weight ,7000 Da. This molecular structure does not contain aromatic amino acids or histidine. It is very rich in cysteine (Cys) and has the ability to bind to metals, especially those that regulate the homeostasis of essential metals like copper and zinc (Letendre, 2009). They would ensure tolerance to heavy metals in certain populations of aquatic organisms, in a potentially contaminating environment (Hardivillier, 2005).

G

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They were isolated and characterized for the first time 57 years ago from horse kidneys. Potentially present in all living organisms, they fascinate many researchers because of their very peculiar chemical structure (Picard et al., 2010). G Structure of metallothioneins and metal sequestration properties The number and position of cysteines in TMs are highly conserved across species. Divalent metals are bonded to sulfur atoms in clusters of tetrahedral geometry. The affinity of metals for sulfur atoms varies among metals (Nzengue, 2008). The stability of the bond with copper is 100 times higher than that with cadmium, itself 1000 times stronger than that with zinc. Mercury and silver have an even greater affinity for TM than copper. G Role of metallothioneins The most important function of MTs is to regulate the intracellular concentrations of certain metals essential to the organism, such as copper and zinc, by sequestering them, in order to prevent their free circulation in the tissues and their binding to other vital proteins (Achard-Joris, 2005). The covalent bond of the divalent metals with the thiol groups of the MTs is dynamic, since the trapped metals can be released any time. TMs provide a protective role against metallic elements, by limiting their accessibility to other cell sites, thereby participating in cell detoxification (Nzengue, 2008). 3.3.3.2.2 Elimination Bivalve molluscs eliminate metals via several routes, including the urine, since the kidney has the capacity to excrete granules rich in metals. A major route of zinc excretion in mussels is renal leakage in granular form and through defecation, which removes metals from the digestive tract. As for the metals sequestered inside the hemocytes of molluscs, they are eliminated by migration from the intestinal tissues, through the epithelial barrier, toward the lumen of the digestive tract or toward the surrounding water by the mechanism of diapedesis.

3.4

Oxidative stress and cancer

The balance between the positive and negative effects of free radicals is particularly fragile. The production of these radicals is regulated by different organisms (Sies, 1991). Regulatory systems are made up of enzymes, proteins, small antioxidant molecules, and trace elements essential for the activity of enzymes. An imbalance in the antioxidant balance, in favor of the production of reactive oxygen species (ROS), constitutes oxidative stress. Oxidative stress will denature lipids, proteins, and DNA and cause pathologies (Curtin et al., 2002; Gutteridge & Halliwell, 1993).

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Numerous studies demonstrate the preponderant place of oxidative stress in the initiation and development of cancers. The existence of oxidative stress in patients can be shown both by the breakdown of antioxidant defenses and by the increase in the products of oxidative stress. Oxidation derivatives of DNA indicative of oxidative stress are found in the blood and tissues of cancer patients (Malins & Haimanot, 1991). Many aldehydes (such as malondialdehyde) are found in large quantities in the blood of children with cancer (Yazdanpanah et al., 1997). The increased lipid peroxidation is even observed in the precancerous stage in women with mammary or cervical dysplasia (Boyd & McGuire, 1991). The activity of the enzyme manganese superoxide dismutase (Mn-SOD), having the most effective antioxidant and anti-tumor activity (Behrend et al., 2003), is increased in the mucosa of patients with adenocarcinoma of the stomach, probably as a reaction to the excessive presence of ROS.

3.5

Conclusion

The collection of bivalve molluscs is a very popular activity that allows local residents to obtain a highly valued food and economic resource while keeping a traditional practice alive. However, the consumption of contaminated shellfish constitutes a major public health risk. The mode of nutrition of bivalves by filtration of sea water inevitably leads to their contamination by bacteria, viruses, toxins, and toxic chemicals present in their environment. Among the toxic pollutants of natural or anthropogenic origin, TMEs pose a serious environmental problem, in particular at the level of aquatic ecosystems and public health. In this chapter, we have tried to understand how bivalve molluscs, especially mussels, react to an environment polluted with TMEs, notably lead, cadmium, and mercury, and transfer them through their consumption to humans. Humans are at the top of the food chain, and their toxic TME levels (Cd, Pb, and Hg) ingested through consumption of contaminated food (bivalve molluscs) are determined through examination of their blood. It is one of the causes of oxidative stress in humans. Stress potentiates the appearance of multifactorial diseases (diabetes, Alzheimer’s, etc.) and is the main cause of many diseases often linked to aging (cancer, cataracts, etc.).

Acknowledgments The authors would like to thank the Minister of National Education, Professional Training, Higher Education and Scientific Research and members of the Virology, Oncology and Medical Biotechnology team at FSTM-Hassan II University of Casablanca, for all their efforts in supporting us during all work stages in this research.

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References Achard-Joris, M. (2005). Etudes biochimiques et ge´ne´tiques de la re´ponse adaptative de mollusques face aux contaminations me´talliques et au stress oxydant (Doctoral dissertation). Bordeaux 1. ˚ kesson, A., Julin, B., & Wolk, A. (2008). Apport alimentaire a` long terme de cadmium et inciA dence du cancer de l’endome`tre post-me´nopausique: Une e´tude de cohorte prospective base´e sur la population. Recherche Sur le Cancer, 68(15), 6435
6441. Andujar, P., Bensefa-Colas, L., & Descatha, A. (2010). Intoxication aigue¨ et chronique au cadmium. La Revue de Me´decine Interne, 31(2), 107
115. Baize, D. (1998). Les e´le´ments traces me´talliques (ETM) dans les SOLS. Orle´ans: Institut National de la Recherche Agronomique - Science du Sol. Barkouch, Y. (2007). Etude du transfert des e´le´ments traces me´talliques (Al, Cd, Cu, Pb, Se et Zn) dans une chaıˆne alimentaire d’une zone minie`re de la re´gion de Marrakech
Maroc (Doctoral dissertation). Nantes. Bayed, A. (1998). Variabilite´ de la croissance de Donax trunculus sur le littoral marocain. Dynamique des populations marines 5 Marine populations dynamics, 11
23. Behrend, L., Henderson, G., & Zwacka, R. M. (2003). Reactive oxygen species in oncogenic transformation. Biochemical Society Transactions, 31, 1441
1444. Belbachir, C., Chafi, A., Aouniti, A., & Khamri, M. (2014). Qualite´ microbiologique de Trois espe`ces de mollusques bivalves a` inte´reˆt commercial re´colte´es sur la coˆte me´diterrane´enne nord-est du Maroc (Microbiological quality of three bivalve mollusks species with commercial interest collected on the North-eastern coast of Morocco). Journal of Materials and Environmental Science, 5(1), 304
309. Bouhaimi, A., Idihalla, M., Lagbouri, A., & Moukrim, A. (1997). Etude de la biologie de trois Mollusques Bivalves (Donax trunculus, Mytilusgalloprovincialis ET Pernaperna) de la baie d’Agadir. Boyd, N. F., & McGuire, V. (1991). The possible role of lipid peroxidation in breast cancer risk. Free Radical Biology & Medicine, 10, 185
190. Bravo, A. G., Loizeau, J.-L., Bouchet, S., Richard, A., Rubin, J. F., Ungureanu, V.-G., Amouroux, D., & Dominik, J. (2010). Mercury human exposure through fish consumption in a reservoir contaminated by a chlor-alkali plant: Babeni reservoir (Romania). Environmental Science and Pollution Research International, 17, 1422
1432. Brignon, J.M., & Malherbe, L. (2005). Cadmium et ses de´rive´s. INERIS
Donne´es technicoe´conomiques sur les substances chimiques en France, Verneuil en Halatte. Burnol, A., Duro, L., & Grive, M. (2006). Ele´ments traces me´talliques: Guide me´thodologique. Rapport d’e´tude, 28(06). Callier, M. (2008). Influence de la mytiliculture (Mytilus edulis L.) sur les caracte´ristiques physico-chimiques du se´diment et sur les communaute´s macrobenthiques (Doctoral dissertation). Universite´ du Que´bec a` Rimouski. Carmouze, J., Lucotte, M., & Boudou, A., (2001). Le mercure en Amazonie: Roˆle de l’homme et de l’environnement, risques sanitaires. IRD Editions. 502 p. Cheggourm. (1989). Bioaccumulation de quelques e´le´ments me´talliques (Cu, Zn, Pb, Ni, Cr, Mn, Fe,) chez un mollusque bivalves scrobiculariaplanan, dans l’estuaire du bouregreg (Coˆte atlantique marocaine). China, B., de Schaetzen, M. A., & Daube, G. (2003). Les mollusques bivalves, des aliments dangereux? Annales de me´decine ve´te´rinaire, 147(6), 413
422, Universite´ de Lie`ge, Faculte´ de me´decine ve´te´rinaire.

Risk of the development of cancers Chapter | 3

57

Cho, Y. A., Kim, J., Woo, H. D., & Kang, M. (2013). Dietary cadmium intake and the risk of cancer: A meta-analysis. PLoS One, 8(9), e75087. Cooper, S. (2008). L’importance relative de l’eau et de la nourriture comme vecteurs d’accumulation du cadmium chez le bivalve d’eau douce Pyganodon grandis (Doctoral dissertation). Universite´ du Que´bec, Institut national de la recherche scientifique. Curtin, J. F., Donovan, M., & Cotter, T. G. (2002). Regulation and measurement of oxidative stress in apoptosis. Journal of immunological methods, 265, 49
72. Doelsch, E. (2004). Metallic trace elements: Inventory for Reunion Island (soil, waste and plants). Dumas, T., Be´nilde, B., Elena, G., Julien, B., Nancy, A. C., He´le`ne, F., & Fre´de´rique, C. (2020). Metabolomics approach reveals disruption of metabolic pathways in the marine bivalve Mytilus galloprovincialis exposed to a WWTP effluent extract. Science of The Total Environment, 712(136551). Available from https://doi.org/10.1016/j.scitotenv.2020.136551. Ekino, S., Susa, M., Ninomiya, T., Imamura, K., & Kitamura, T. (2007). La maladie de Minamata revisite´e: Une mise a` jour sur les manifestations aigue¨s et chroniques de l’intoxication au me´thylmercure. Journal des Sciences Neurologiques, 262(1
2), 131
144. Elbekkay, K., & Melhaoui, M. (2011). Confe´rence Me´diterrane´enne Coˆtie`re et Maritime [en ligne]. Tanger. Maroc Edition, 2, 319
322p. Engstrom, D. R. (2007). Les poissons re´agissent lorsque le mercure monte. Actes de l’Acade´mie Nationale des Sciences, 104(42), 16394
16395. Filippini, T., Duarte, T., Carla, L., Catarina, C., Pedro, M., & Androniki, N. (2020). Exposition au cadmium et risque de cancer du sein : une me´ta-analyse dose-re´ponse d’e´tudes de cohorte. Environnement International, 142 (105879). Available from https://doi.org/10.1016/ j.envint.2020.105879. Fournier, E. (2005). Bioaccumulation du se´le´nium et effets biologiques induits chez le bivalve filtreur Corbicula fluminea: Prise en compte de l’activite´ ventilatoire, de la spe´ciation du se´le´nium et de la voie de contamination (Doctoral dissertation). Bordeaux 1. Garnier, R. (2005a). Toxicite´ du plomb et de ses de´rive´s. EMC-Toxicologie-Pathologie, 2(2), 67
88. Garnier, R. (2005b). Toxicity of lead and lead compounds. EMC - Toxicologie-Pathologie, 2(2), 67
88. Available from https://doi.org/10.1016/j.emctp.2004.10.004. Gouzy, A., & Ducos, G. (2008). La connaissance des e´le´ments traces me´talliques: Un de´fi pour la gestion de l’environnement. Air Pur, 75, 6
10. Graziano, J. H., Popovac, D., Factor-Litvak, P., Shrout, P., Kline, J., Murphy, M. J., . . . Stein, Z. (1990). Determinants of elevated blood lead during pregnancy in a population surrounding a lead smelter in Kosovo, Yugoslavia. Environmental Health Perspectives, 89, 95
100. Gutteridge, J. M., & Halliwell, B. (1993). Revue invite´e sur les radicaux libres dans les processus pathologiques: Une compilation des causes et des conse´quences. Communications de Recherche Sur les Radicaux Libres, 19(3), 141
158. Hardivillier, Y. (2005). Caracte´risation et expression des genes de me´tallothione´ines chez deux modioles hydrothermales: Bathymodiolus thermophilus et Bathymodiolus azoricus (Doctoral dissertation). Le Mans. IARC. (1997). Monographs on the Evaluation of Carcinogenic Risk to Humans. Volume 58. Beryllium, Cadmium, Mercury, and Exposures in the Glass Manufacturing Industry. Lyon. Kadouche, S. (2013). Utilisation des biomatriaux dans le traitement des eaux (Doctoral dissertation). Universite Mouloud Mammeri. Kazantzis, G., Lam, T. H., & Sullivan, K. R. (1988). Mortality of cadmium-exposed workers: A five-year update. Scandinavian Journal of Work, Environment & Health, 14, 220
223.

58

Oncogenic Viruses Volume 2

Khalid, O., & Brhimi, O. (2009). Substances bioactives e´labore´es par des cyanobacte´ries isole´es de certains e´cosyste`mes aquatiques marocaine. Afrique Science: Revue Internationale des Sciences et Technologie, 5, 260
269. Labat, L., & Lhermitte, M. (2007). Sources, exploration et prise en charge de lintoxication par le plomb. Revue Francophone des Laboratoires, 2007(390), 45
49. Lacerda, L. D., de Souza, M., & Ribeiro, M. G. (2004). The effects of land use change on mercury distribution in soils of Alta Floresta, Southern Amazon. Environmental Pollution, 129 (2), 247
255. Landrigan, P. J., Silbergeld, E. K., Froines, J. R., & Pfeffer, R. M. (1990). Lead in the modern workplace. American Journal of Public Health, 80(8), 907
908. Letendre, J. (2009). Effets combine´s de l’intertidalite´ et de la contamination chimique chez Mytilus edulis: Me´canisme enzymatiques anti-oxydants et approche prote´omique (Doctoral dissertation). Le Havre. Malins, D. C., & Haimanot, R. (1991). Major alterations in the nucleotide structure of DNA in cancer of the female breast. Cancer Research, 51(19), 5430
5432. Marte, F., & Pe´quignot, A. (2013). Les amas coquilliers du site Imiwaia I (Canal Beagle, Argentine). E´tude des coquilles Mytilus edulis au moyen de la FTIR-ATR. l’Anthropologie, 117(2), 135
160. Mason, R. P., Fitzgerald, W. F., & Morel, F. M. (1994). The biogeochemical cycling of elemental mercury: Anthropogenic influences. Geochimica et Cosmochimica Acta, 58(15), 3191
3198. McElroy, J. A., Shafer, M. M., Trentham-Dietz, A., Hampton, J. M., & Newcomb, P. A. (2006). Exposition au cadmium et risque de cancer du sein. Journal de l’Institut National du Cancer, 98(12), 869
873. Medhioub, W. (2011). E´tude des me´canismes de contamination des mollusques bivalves par des neurotoxines a` action rapide (FAT) & de´veloppement des proce´de´s de de´toxification (Doctoral dissertation). Brest. Merzouki, M., Talib, N., & Sif, J. (2009). Indice de condition et teneurs de quelques me´taux (Cu, Cd, Zn et Hg) dans les organes de la moule Mytilus galloprovincialis de la coˆte d’El Jadida (Maroc) en mai et juin 2004. Bulletin de l’Institut Scientifique, 31, 21
26. Myrand, B., Proulx, D., & Tremblay, R. (2007). Atelier de travail “Indicateurs de stress chez les mollusques” [ressource e´lectronique]/[organisation de l’atelier], Bruno Myrand, Daniel Proulx, Re´jean Tremblay;[re´daction, Re´jean Tremblay]. Nawrot, T., Plusquin, M., Hogervorst, J., Roels, H. A., Celis, H., Thijs, L., . . . Staessen, J. A. (2006). Environmental exposure to cadmium and risk of cancer: A prospective populationbased study. The Lancet Oncology, 7(2), 119
126. Nzengue, Y. (2008). Comparaison des me´canismes de toxicite´ redox du cadmium, du cuivre et du zinc: Place des me´tallothione´ines et de p53 (Doctoral dissertation). Universite´ JosephFourier-Grenoble I. Oursel, B. (2013). Transferts et dynamique des contaminants me´talliques en zone coˆtie`re.: Impact d’une grande agglome´ration me´diterrane´enne (Doctoral dissertation). Universite´ de Toulon. Peralta-Videa, J. R., Lopez, M. L., Narayan, M., Saupe, G., & Gardea-Torresdey, J. (2009). La biochimie de l’absorption environnementale des me´taux lourds par les plantes: Implications pour la chaıˆne alimentaire. La Revue Internationale de Biochimie et Biologie Cellulaire, 41 (8
9), 1665
1677. Picard, R., Temblay, B., & Myrand, B. (2010). Revue de litte´rature et fiches descriptives des diffe´rents indicateurs de stress et de vitalite´ utilise´s pour caracte´riser les mollusques bivalves. Rapport de recherche- de´veloppement no 188, 26p.

Risk of the development of cancers Chapter | 3

59

Poe¨y, J., & Philibert, C. (2000). Toxicite´ des me´taux. Revue Franc¸aise des Laboratoires, 2000 (323), 35
43. Available from https://doi.org/10.1016/S0338-9898(00)80266-8. Rahal, W. (2012). Contribution a` l’e´tude de la bioaccumulation me´tallique dans les se´diments et diffe´rents maillons de la chaine trophique du littoral extreˆme ouest alge´rien (Doctoral dissertation). Rouane, H. O. (2013). Biosurveillance de la qualite´ des eaux coˆtie`res du littoral occidental Alge´rien, par le suivi des indices biologiques, de la biodisponibilite´ et la bioaccumulation des me´taux lourds (Zn, Cu, Pb et Cd) chez la moule Mytilus galloprovincialis et l’oursin Paracentrotus lividus (Doctoral dissertation). The`se doctorat, Universite´ d’Oran, faculte´ des sciences, Algerie. Rousselet, E. (2007). Re´ponses cellulaires vis-a`-vis de l’exposition au cadmium chez les animaux (Doctoral dissertation). Universite´ Joseph-Fourier-Grenoble I. Sabouraud, S., Coppe´re´, B., Rousseau, C., Testud, F., Pulce, C., Tholly, F., . . . Descotes, J. (2009). Intoxication environnementale par le plomb lie´e a` la consommation de boisson conserve´e dans une cruche artisanale en ce´ramique vernisse´e. La Revue de Me´decine Interne, 30(12), 1038
1043. Sharma, V. K., & Sohn, M. (2009). Aquatic arsenic: Toxicity, speciation, transformations, and remediation. Environment International, 35(4), 743
759. Sies, H. (1991). Oxidative stress: From basic research to clinical application. The American Journal of Medicine, 91, 31S
38S. Steenland, K., & Boffetta, P. (2000). Lead and cancer in humans: Where are we now? American Journal of Industrial Medicine, 38(3), 295
299. Varrault, G. (2011). Les contaminants dans les milieux re´cepteurs sous forte pression urbaine (Doctoral dissertation). Universite´ Paris-Est. Veiga, M. M., Hinton, J., & Lilly, C. (1999). Mercure en Amazonie: un examen complet avec un accent particulier sur la bioaccumulation et les bio-indicateurs. Dans Proc. Forum NIMD (Vol. 99, pp. 19
39). Vidal, M. L. (2001). Etude de marqueurs biochimiques de pollution chez le mollusque bivalve d’eau douce corbicula fluminea (Mu¨ller): Purification et caracte´risation des glutathion S-transfe´rases (Doctoral dissertation). Bordeaux 1. WHO, E.M., & Compounds, I.M. (2003). Human health aspects, concise international chemical assessment document 50. Geneva: World Health Organization. Yazdanpanah, M., Luo, X., Lau, R., Greenberg, M., Fisher, L. J., & Lehotay, D. C. (1997). Alde´hydes cytotoxiques comme marqueurs possibles du cancer de l’enfant. Free Radical Biology and Medicine, 23(6), 870
878. Zegmout, M., El Addouli, J. A. M. A. L., Chahlaoui, A., Salima, D., & Chafi, A. (2011). Bioaccumulation de quelques me´taux lourds (Zn, Fe, Cu, Pb, Cd) chez la petite prairie au niveau de l’embouchure de la moulouya (Maroc Nord Oriental). ScienceLib, 3, 111212, ISSN 2111-4706 (2011).

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Chapter 4

Oncolytic virus cancer therapeutic options and integration of artificial intelligence into virus cancer research Vaishak Kaviarasan, Barath Ragunath and Ramakrishnan Veerabathiran Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India

4.1

Introduction

Oncolytic virus (OV) treatment is viewed as a helpful method for the therapy of malignancy. An OV is a genetically changed or normally happening infection that can purposely duplicate in and kill cancer cells while making no mischief for typical tissues. This is in contrast to gene therapy, in which a virus is only utilized as a vehicle for transgene conveyance (Fukuhara et al., 2016). During the 1950s and 1960s, a few viruses were tried in both research center settings and on people. A vaccine strain of rabies virus was one of the first viruses to be used in a controlled clinical study to treat 30 patients with melanomatous, with eight of them showing tumor regression (Pack, 1950). Clinical trials of immune-oncology substances alone and in compositions are currently ongoing, but most of these blends of drug trials have shown to enhance the survival rate of the patients compared to other therapies (Antonia et al., 2014). Currently, two modified OVs are approved for marketing viz., (1) Oncorine used to treat head, neck, and esophagus cancer was approved in 2005 in China (Garber, 2006; Xia et al., 2004) and (2) T-Vec used to treat melanoma which was approved by FDA in the United States (October 2015), Europe, and Australia (Coffin, 2016; January & May, 20. A few OVs are under clinical trials such as G47 Δ, JX-594, CG0070, and Reolysin (Andtbacka et al., 2015). Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00001-7 Copyright © 2023 Elsevier Inc. All rights reserved.

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Oncogenic Viruses Volume 2

History

In 1804 Seishu Hanaoka directed the first surgical partial mastectomy using local sedation to treat breast cancer patients (Izuo, 2004). The medical procedure has been one of the staple therapies for restricted disease; complete tumor ejection and potential fixes are feasible if the malignant growth is identified early and has not spread fully. With Wilhelm Roentgen’s creation of X-beams in 1895 and Marie and Pierre Curie’s forward leap of radioactive radium and polonium in 1898, radiation treatment developed as a method for disease therapy (Connell & Hellman, 2009). S.W. Goldberg and Efim London utilized radium proficiently in 1903 to accomplish total reactions (CR) in two patients having basal cell carcinoma of the derma (Maron et al., 2006). Until around the last part of the 1940s, when enemies of metabolites (methotrexate) and alkylating specialists (nitrogen mustard) are being utilized as chemotherapeutic medications for disease, medical procedure, and radiation treatment surpassed the field of malignancy therapy (Lichtman, 2008). Notwithstanding the huge impact of mixed chemotherapy in leukemia and lymphoma, clinicians acknowledged by the 1950s that chemotherapy had limitations in accomplishing similar treatment results of a complete reduction in many modern strong tumors (DeVita & Chu, 2008). Subsequently, a coordinated exertion was made to comprehend the tumorigenesis through the examination and development of preclinical tumor models. During the 1970s, scientists created novel medications and medication mixes, such as the utilization of adjuvant chemotherapy after medical procedure to advance better supportability. Chemotherapy turned out to be more focused in the years that followed, taking a gander at explicit pathways like the enemy of angiogenesis, flagging pathways, or explicit changes (Kalyn, 2007). Oncolytic virotherapy is such an accommodating decision that uses replicable contamination for the therapy of malignancies (Russell et al., 2012). Despite the fact that around 3000 remarkable kinds of infections are present, few are sensible as oncolytic specialists. It ought to be nonpathogenic, have unimportant malignancy particular murdering action, or can be intended to impart constricting qualities or outfitting qualities (Maroun et al., 2017). From 1950s to1970s live infections were intentionally implanted into disease-influenced patients and promising results were observed (Kelly & Russell, 2007). Regardless, by using these viral particles poisonousness was noted, especially in cases of safe bargained patients with lymphoma or leukemia. Now, three OVs are economically available for malignant growth treatment. These fuse Rigvir asserted in Georgia, Armenia, and Latvia. Oncorine H101 was embraced in China, and Talimogenelaherparepvec (T-Vec) was supported in the United States (Southam & Moore, 1952). The principal clinical preliminary concerning the oncolytic infection HSV1716 was affirmed in Europe in 1996 (Rampling et al., 2000). Somewhere in the range of 1997 and 2003, strain HSV1716 was mixed into the tumors of patients with glioma, a dangerous mind tumor, with no confirmation of teratogenic or results and

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Cervical carcinoma treatment rabies virus

Genetically modified oncolytic virus concept

1912

1950s-1970s

1991

T-Vec approved for treatment of melanoma- USA

H101 approvedtreatment H&N cancer- China

Japan's first G47 trial for glioblastoma

G207 tested for malignant gliomaUSA

Replication competent viruses in clinical trials

1998

63

2005

2009

2015

FIGURE 4.1 Milestones of oncolytic viral therapy .

some drawn-out survivors (Papanastassiou et al., 2002). Description of milestones between the years 1912 to 2015 in oncolytic viral therapy is given in Fig. 4.1.

4.3

General properties of oncovirus

Oncovirus aids cell transformation and uncontrolled cell growth that causes malignant tumors (¸Sevik, 2012). The infections caused by oncoviruses are quite habitual, but all infections do not lead to cancers or tumor formations. Even if there is a virally infected tumor it will take up to a minimum of 15 and a maximum of 40 years to proliferate and cause complications. Epstein
Barr virus (EBV)-related lymphoproliferative disease is an exception in this scenario because it causes complications within a short period after infection (Zur Hausen, 2009). DNA oncoviruses encode the essential viral protein for viral replication, whereas RNA oncoviruses have altered models of the recipient genes but, they are not helpful in replication of the virus (Springs et al., 2001). Universally all oncoviruses aid carcinogenesis by inhibiting the two major tumor suppressor pathways, namely p53 and retinoblastoma (Rb) (Akram et al., 2017; Levine, 2009; Shackelford & Pagano, 2004), which are given in Fig. 4.2.

4.4

Oncolytic viral therapy: a new era of treatment

In this chapter, we will be focus on the therapeutic method using oncolytic viral therapy by altering the virus according to the required necessities.

4.4.1

Cancer immunoediting hypothesis

Almost every cancer can survive without being lysed by the immune system. Immunoediting is an extrinsic tumor suppressor mechanism that relates between the cancer cells and the immune system of the body. This process has three phases, namely elimination, equilibrium, escape (Schreiber et al., 2011), and the details are mentioned in Fig. 4.3.

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DNA & RNA Oncoviruses DNA-It produce essential viral proteins

Two major tumor suppressor pathways p53 & Retinoblastoma (Rb) Oncovirus

RNA-It has altered model of host gene

Complications occur 15- 40 years after being infected by the virus FIGURE 4.2 General properties of oncoviruses.

In the first phase (elimination), both innate and adaptive immunity act together to lyse the growing tumor before they became clinically engaged. However, in case some variants of cancer cells survive the first phase and then pass on to the second phase (equilibrium), the only function of adaptive immunity works to restrict the protuberance of tumor cell by immunological mechanisms. The second phase is almost an end-stage of the cancer immunoediting process, and tumor immunogenicity editing occurs in this phase. The third phase (escape) in which the tumor cells escape from the equilibrium phase is no longer recognized by the immune system or becomes insensitive to immune mechanisms, so tumor growth is no longer restricted by the immune system. Thus, the tumor cells develop to produce relevant disease that is clinically engaged (Vesely et al., 2011).

4.4.2

Pharmacokinetics of oncolytic viral therapy

According to the definition given in the dictionary, the movement of drugs inside a living organism is known as pharmacokinetics. It is derived from the Ancient Greek words Pharmakon meaning “drugs” and kinetics as “movement.” Cancer or tumor cells allow viruses to easily replicate and progress in the cell because cancer cells have reduced mechanisms (type I IFN pathway) by the host cell to respond to the viral activities (Platanias, 2005). IFNs (interferons) are extensively communicated cytokines that can restrict the

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Intrinsic tumor suppression

Environmental factors and genetic mutations

Immune System

Transformed cells

Escape

Equilibrium

Elimination

Innate & adaptive immunity Normal cell Carcinogenesis/ Tum origenesis

Extremely immunogenic transformed cell

Weak immunogenic & immunoevasion transformed cells

Extrinsic tumor suppression

FIGURE 4.3 Cancer immunoediting hypothesis and its phases. The cancer immunoediting hypothesis has three phases, viz. elimination, equilibrium, and escape. The figure highlights the information about relationship between cancer cells and immune system.

growth and antiviral effects and play a crucial role in immune surveillance for tumor cells. There are two broad divisions of related cytokines: type I IFN and type II IFN (Pestka et al., 1987; Pestka et al., 2004).

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The role of modified OVs is not to lyse the tumor cells, but also plays a key role in the activation of the immune system (antiviral and antitumor responses) to create a long immune memory (Marelli et al., 2018). The modified OVs are injected into the region of the tumor by direct or systemic methods. The benefit of OVs is that they do not infect the normal cells but only infect tumor cells. The OVs start to replicate once they infect the tumor cells, and create immunogenic cell death generally known as apoptosis, causing the lysis of tumor cells. The immune system turns the cold tumor into warm by recognizing the T-lymphocytes, macrophages, and dilation and curettage (DC). These cells produce cytokines into the microenvironment that helps to activate all the other immune cells and kill the tumor completely (Breitbach et al., 2016; Guo et al., 2017) (Fig. 4.4).

4.5

Applications of oncolytic viral therapy

Three viruses viz., RIGVIR, Oncorine, and T-VEC are under clinical trials and have shown promising therapeutic results against cancer. Furthermore, Modified OV’s

Cold tumor is turend to warm by immune system

Direct injection into tumor

Can replicate only in tumors

Oncolysis with production of more virions

Anti tumor & Antiviral response

Reduction of tumor & long immune memory

Normal cells

Infection to new tuomor cells by amplification

Tumor cells T-lymphocytes, Macrophages and Dentite cells

FIGURE 4.4 Pharmacokinetics of OVs. Mechanism of modified OVs targeting cancer cells and immune activation. OVs, Oncolytic virus.

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some of the OVs are undergoing pre-clinical testing; among them, vaccinia, adenovirus, and herpesvirus have shown promising outcomes in animal studies. The applications of various OVs are studied here for the diagnosis and treatment of cancers.

4.5.1

Diagnosis

As of now, a wide range of complex picture procurement for tumor discovery, especially CT and MRI, are basic in precisely situating and surveying a tumor nearby intrusion. Opportune screening of essential tumors and few metastases, yet stays a test, requiring the movement of sequencing procedures with improved accuracy and explicitness (Cao et al., 2020). Exceptionally explicit tumor imaging with OV has snatched a great deal of thought of late. OVs with unmistakable qualities can taint and replicate in tumor cells while additionally communicating qualities of interest, for example, the luciferase reporter 1 gene and the human Na 1 /I-symporter (hNIS) (Dingli et al., 2003). Also, we can identify transcriptomic items, for example, fluorescence in malignancy cells utilizing nonintrusive constant subatomic imaging in vivo (Weissleder, 2006). One of OV executions in tumor exact imaging was fluorescence imaging. green fluorescent protein (GFP) is a protein that could be utilized to perceive tumor practices like intensification, intrusion, and metastasis. It is separated from marine spineless creatures. In contrast with other imaging methods, mouse models effectively decided GFP after the mixture of adenovirus, vesicular stomatitis virus (VSV), vaccinia, and measles infection with the GFP quality (Rojas & Thorne, 2012). Recent developments indicate that malignancy cell tomography with the OV has more prominent unwavering quality and perseverance. Besides, the OV outlined huge benefits in atomic clinical imaging (Barton et al., 2008; Galanis et al., 2015). Atomic clinical hardware can follow the columnist qualities addressed by the OV in disease cells to pinpoint the specific area of tumors. hNIS quality, team kill (TK) quality, and human sort 2 somatostatin receptor quality are utilized as correspondent qualities (hSSTR2). Additionally, they all executed well in mice or clinical preliminaries (Abate-Daga et al., 2011; McCart et al., 2004). Optical subatomic imaging, single-photon outflow SPECT/CT, MRI, and PET are for the most part broadly known reenactments that help screen tumors with OVs. All the above could help in the evaluation of oncolytic virotherapy’s security and remedial intercessions viability, and the headway of a more exact demonstrative method for deciding tumor cause (Jacobs et al., 2001; Touchefeu et al., 2012). OVs have unmistakable benefits for exact imaging of the malignant tissues. The blend of imaging strategies OVs holds a great deal of guarantee for beginning phase tumor analysis (Table 4.1).

TABLE 4.1 The role of OVs (oncolytic virus) in cancer diagnosis. The role of OVs in each field and integration of OV with already existing therapies to make the diagnosis approach more advanced to treat cancer. S. no.

Imaging techniques

Cancer type

Viruses involved

Genes involved

Application

Reference

1.

Bioluminescence imaging

Breast cancer

HSV, HSV-1, HSV—Luc

Luciferase

It can track viral replication cycles

Galanis et al. (2015)

2.

Fluorescence imaging

Breast cancer

Vaccinia virus, Lister strain GLV 1h153

GFP

By infecting GLV-1h153, all positive surgical margins can be seen through FI

Gracia (2013)

3.

PET

Gastric cancer

Vaccinia virus, Lister strain, GLV-1h153

hNIS

Tumor imaging can be interpreted using 99mTc pertechnetate SPECT and 124I PET after the infestation of hNIS exhibiting GLV-1h153

Hacein-BeyAbina et al. (2003)

4.

CT

Prostate cancer

Adenovirus serotype, adenovirus serotype 5, adenovirus serotype 5yCD/mutTKSR3 9rephNIS

hNIS

Almost 78% of tumors can be detected

Guo et al. (2017)

5.

MRI

Prostate cancer

Vaccinia virus, Lister strain, GLV-1h68, 1h312, 1h460, 1h462

Tyrosinase, Tyrosinase (p1, p2) Melanin

After GLV-1 h 462 infections, MRI tumor signal enhancement can be detected. The doxycycline- inducible promoter-system can control melanin expression, reducing inhibition of viral replication caused by melanin overproduction

Hadjipanayis and DeLuca (2005)

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69

Tumor targeted cell delivery by oncolytic virotherapy

OV therapy is an antitumor treatment, yet the blood supply is a contentious setting wherein virions can be quelled by characteristic and versatile insusceptible frameworks, lessening viability (Evgin et al., 2015). Accordingly, discovering suitable transporters to convey the OV to the malignant tissues is pivotal for fruitful treatment. Transporter cell research has moved to incorporate the cells that do not just shield antibodies and enhancements from offtarget organs, yet additionally, de-focuses from askew organs, colonizes tumor destinations, and performs antitumor effector capacities. Antigenexplicit T cells aspartate aminotransferase (AST), mesenchymal immature microorganisms mesenchymal stem cells (MSCs), cytokine-instigated executioner cells cytokine induced killer (CIK), and blood outgrowth endothelial cells (BOECs) are the most regularly utilized cell transporters today (Na et al., 2019; Power et al., 2007; Ram´ırez et al., 2015). An ideal cell transporter should have the accompanying components: first, it should be defenseless against the irresistible specialist; second, it ought to be prepared to help the infection in finding tumor tissue while staying unseen by the insusceptible framework; lastly, it should shed offspring infection to attack far off tumor cells (Wei et al., 2007). The specific antitumor rule of AST is that affected by chemokines, AST enters the tumor site through an unclear surface connection molecule in relationship with the malignant cells. AST receptor then connects to the tumor surface, expresses antigen-significant of histocompatibility complexes, and secretes tumor destruction materials, which kills the malignant cells unequivocally (Ilett, 2020). In the particular event of CIK, it fathoms the tumor cells by annexing the NKG2D receptor to the tumor surface’s NKG2D ligand. Nonintrusive imaging modalities have uncovered that vaccinia infection tainted CIK can move vaccinia infection to tumor locales after intramuscular mixture and have an amazing antitumor impact in an immense number of clinical models (Sampath et al., 2013; Tang et al., 2013; Thorne et al., 2006). Moreover, this system brings about more proficient tumor freedom in different malignancy models, permitting CIK to be used as a phone transporter assaulting disease cells. In a mouse study, Manish R. Patel utilized blood outgrowth endothelial cells to accomplish VSV inspiring IFN to metastatic nonsmall cell lung cancer (NSCLC). The study found that these cells could productively move the OV and furthermore had higher clinical interpretation proficiency (Patel et al., 2020). The ultrasound targeted microbubble destruction (UTMD) methodology for the implantation of virions can help OV in effectively going through the hepatic framework while staying away from immunological freedom. Rupesh Dash used the UTMD to move the infection which is likened as therapeutics onto the tumor site in a mice prostate disease model and gained adequate outcomes from the investigation (Dash et al., 2011). The beginning of a nanocarrier has aroused the curiosity of numerous individuals. Metal,

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natural, polymeric, and lipid nanocarriers have noteworthy conveyance proficiency. They can improve OVs’ ability to uprise the restorative level at tumor destinations, tumor particularity, and assault just as immunological camouflage (Howard & Muthana, 2020).

4.5.3

Genetically modified oncolytic virus

To improve the helpful effect, hereditarily designed adjustments in OVs, like additions and cancellations in the hereditary data, can offer additional restorative particles to malignancy cells, effectively sidestepping the far-reaching protection from single-target anticancer medications (Cao et al., 2020). Right now, about 100 helpful exogenous qualities are being assessed, including apoptotic atoms, hostile to angiogenic particles, and little RNA particles that limit malignancy-related qualities. It is realized to a great extent that tumor affectability to this virotherapy is connected to the overarticulation of the PD-L1 in the disease and insusceptible cell (Zamarin et al., 2018). Recent discoveries indicate developments toward hereditary designing to make an OV that communicates PD-L1 inhibitor with GM-CSF. The designed OVs PD-L1 could keep PD-L1 from restricting malignancy and invulnerable cells (Emdad et al., 2018). Recent developments also indicate that OV can help in the mediation of malignancy neo-antigen explicit the response of T-cells, bringing about improved antitumor impacts, especially in influenced patients who are defenseless to PD-1/PD-L1 bar treatment (Wang et al., 2020). OVs furnished with the expert support of provocative cytokine interleukin 12 or Beclin-1 is likewise said to have unrivaled anti-tumor action compared to the parent OV (Deng et al., 2020; Nakao et al., 2020). Self-destruction quality treatment, additionally called viral-intervened protein hydrolytic drug antecedent treatment, is one more technique for tumor quality treatment virus directed enzyme prodrug therapy (VDEPT). The quality consolidation that encodes a delicate strand in the tumors permits the cells to have a significantly low harmfulness to drugs, bringing about tumor cell demise.

4.5.4

Integration of oncolytic viral therapy in radiotherapy

In different human malignancies, incorporating radiotherapy and oncolytic virotherapy complementarily affects disease treatment (Chen et al., 2001; Dilley et al., 2005). With oncolytic HSV, a synergistic impact among both radiotherapy and virotherapy was determined in one specific case (Adusumilli et al., 2007; Advani et al., 1998; Blank et al., 2002; Bradley et al., 1999; Dai et al., 2010). Radiation-instigated GADD34, p38-intervened viral advertised improvement, and oHSV-interceded DNA fix concealment are the basic cycles in this investigation (Hadjipanayis & DeLuca, 2005; Mezhir et al., 2005). Radiotherapy can crop up with oncolytic vaccinia infection to support proficiency. In one examination, the synergistic impact profoundly depended on

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the concealment of JNK signals brought by radiation (Kyula et al., 2014). Other than this, VACV-scAb-VEGF upgraded the explicitness of tumor cells to radiotherapy (Buckel et al., 2013). VACV-scAb-VEGF improved the anticancer effect in an examination using the mouse xenograft as a model (Young et al., 2013).

4.5.5

Integration of oncolytic viral therapy in chemotherapy

Although there were a few studies on the combination of immunotherapy with OV therapy, safely designated spot inhibitors focusing on PD-1 or/and CTLA-4 implantation with oncolytic virotherapy give modern approach for tumor treatment. Designated spot inhibitors work to pair with OV treatment to trigger or improved invulnerable reactions (Rajani et al., 2016). Such immunotherapy exhibit more noteworthy insurance, expanding the patient’s occupation significantly further (Zamarin et al., 2014). In addition, the mix of the vaccinia infection and paclitaxel had a synergistic impact (Liikanen et al., 2013). Paclitaxel works by permitting cells to get to the period of the cell cycle when the vaccinia infection is bound to influence cells (Huang et al., 2011). Sorafenib incorporated with oncolytic vaccinia infection showed brilliant anticancer outcomes in xenografts, while clinical preliminaries in patients exhibited amazing security and clinical reaction, and they have been supported for foundational use in malignant growth of organs like thyroid, kidney, and liver (Heo et al., 2011).

4.5.6 Integration of oncolytic viral therapy with immune inhibitor checkpoints Despite the fact that there have been few reports on the blend of invulnerable designated spot inhibitors and OV therapy, resistant designated spot inhibitors influencing PD-1 or/and CTLA-4 merged with OV therapy provide a novel malignant growth treatment methodology. Designated spot inhibitors are utilized related to OV treatment to start or improve the insusceptible reaction (Rajani et al., 2016; Zamarin et al., 2014). A few exploration have been directed, and the discoveries show that when incorporated with designated spot inhibitors, OVs had significantly more potential anticancer impacts (Engeland et al., 2014). Unattributed research has found that consolidating resistant designated spot inhibitors with oncolytic virotherapy does not build results. More resistant designated spot inhibitors are anticipated to be found to incorporate with OVs for improved treatment viability (Rojas et al., 2015).

4.6

Limitations

A huge amount of OV is under clinical progression across the globe. They have both advantages and disadvantages, since they pass on the parental attributes of wild-type infections (Andtbacka et al., 2015). A few clinical

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preliminaries have been performed using harmless human diseases like influenza contamination, coxsackievirus, measles, mumps, and adenovirus. Since malignant growth patients profoundly procure artful contaminations from such cold-delivering infections, which can have genuine ramifications, intricacies can happen during virotherapy (Chisholm et al., 2005; Cooksley et al., 2005; Hicks et al., 2003). For example, on account of the OV, HSV-1 like T-Vec and G47Δ is best managed intralesional, though intravenous conveyance isn’t proper (Varghese et al., 2006). One critical concern of oncolytic disease treatment has been that the feasibility may be decreased by the inclusion of orbiting antibodies (Gong et al., 2016). When infections that normally cause viremia are likely delicate to killing antibodies, the antitumor effect of the intravenous association in patients who have recently gotten care or inoculation might be confined. For instance, in a clinical preliminary utilizing MVNIS in patients with numerous myelomas, a troublesome impact of circling antibodies was grounded (Russell et al., 2014). Since all infections are possibly immunogenic, they can cause unfortunate results, especially in high portions. Adenoviruses directed i.v., for example, actuate transient liver exacerbation and poor quality spread intravascular coagulation at restoratively successful dosages of 1012 particles, which is an issue considering the great biosafety information amassed to date (Gomez-Manzano et al., 2004; Nemunaitis et al., 2001). The destruction of a patient with ornithine decarboxylase lacks who was treated with high-portion adenovirus in 1999 fills in as a calming sign of the perils of using infections in people. In France, three children with incredible X-connected immunodeficiency who were treated with retrovirally transduced T cells contacted leukemia due to viral combination and initiation of the LMO2 oncogene which is in like manner saw as another disaster in this strategy (Hacein-Bey-Abina et al., 2003). Prior insusceptibility can limit the feasibility of virotherapy; however, it can likewise prompt expanded harmfulness (Vlachaki et al., 2002). Moreover, high-portion infection treatment may bring about the consumption of cytotoxic T cells or antiviral interferons, trading off common tumor invulnerability, diminishing the adequacy of tumor inoculation, and making the most vulnerable to additional viral disease (a cycle known as resistant depletion) (Alsharifi et al., 2006; Krebs et al., 2005). Organization of adenovirus communicating TK into the CSF joined by i.v. ganciclovir caused viral meningitis in rodents and nonhuman primates in an investigation to target leptomeningeal metastases, and a clinical preliminary was ended after the intrathecal retrovirus-TK treatment caused huge harm in one patient (Driesse et al., 1998).

4.7 Integration of artificial intelligence or machine learning into cancer research Artificial Intelligence (AI) and Machine Learning (ML) are algorithms linked with human intelligence created using coding or programming, the

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more the data provided more the better result they provide. AI software approved by the Food and Drug Administration (FDA) screens breast cancer mammograms to progress images quickly. Integration of AI software with cancer research will improve perfection and help reduce the time taken for diagnosis that can create better health results. There is a paucity of medical experts to diagnose, and it takes a long time to analyze a patient’s history and give reports (Berwick & Gaines, 2018). The ML model understands the given set of information about the health orientation of the patients, but it requires lots of data to learn and perform its function (Bates et al., 2014). The algorithm also chooses patients who are eligible for clinical trials and can save many lives who are at high risks according to the data provided, but the data given should be of good quality (Ford & Norrie, 2016; Gracia, 2013; Schneeweiss, 2014).

4.8

Future concerns

Oncolytic viral therapy will be a new promising therapeutic option to treat various cancers. The other options are radiotherapy, chemotherapy, and drug delivery using nanoparticles. Although these methods are currently used to treat cancers each have their side effects that will be mentioned in Table 4.2.

TABLE 4.2 Comparison between OV (oncolytic virus) therapy and other therapeutic options. Comparison between OV therapy, radiotherapy, chemotherapy, and nanoparticles. S no

Treatment method

Results

1.

Oncolytic viral therapy

The most promising treatment option in the future, but it needs more time, advancements, and many are still in clinical trials. It targets only cancer cells and boosts immunity

2.

Radiotherapy

It will not only kill cancer cells but also affects the healthy cells surrounding the tumor. Common problems are hair loss on the radiotherapy given area because of radiation

3.

Chemotherapy

High dosage chemo drugs are introduced into the body of cancer patients through veins, which also affects the normal cells. Common problems are nerve damage and the body becomes sick and takes time for recovery

4.

Nanoparticles

Drug delivery using Nanoparticles is also a good option to treat cancer but nanoparticles are costlier and have various risk factors inside the human body like pH, compatibility, etc.

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Conclusion

To ensure that oncolytic viral therapy will be a promising therapeutic option, researchers have to make more efforts in concentrating on the advancements because some viruses cause specific issues when introduced into the body. The main aim should be not only to kill the tumor cells but also to increase the stability of the immune system against cancer. Integrating AI or ML into virus cancer research is essential because, considering the lack of medical expertise and the long-time taken for diagnosis, AI or ML can reduce the time taken for diagnosis and give better accuracy compared to human skills but it requires lots of data to process the results.

Acknowledgment The authors thank the Chettinad Academy of Research and Education for the constant support and encouragement for this study.

References Abate-Daga, D., Andreu, N., Camacho-S´anchez, J., Alemany, R., Herance, R., Millan, O., & Fillat, C. (2011). Oncolytic adenoviruses armed with thymidine kinase can be traced by PET imaging and show potent antitumoural effects by ganciclovir dosing. PLoS One, 6(10), e26142. Adusumilli, P. S., Chan, M. K., Hezel, M., Yu, Z., Stiles, B. M., Chou, T. C., . . . Fong, Y. (2007). Radiation-induced cellular DNA damage repair response enhances viral gene therapy efficacy in the treatment of malignant pleural mesothelioma. Annals of Surgical Oncology, 14(1), 258
269. Advani, S. J., Sibley, G. S., Song, P. Y., Hallahan, D. E., Kataoka, Y., Roizman, B., & Weichselbaum, R. R. (1998). Enhancement of replication of genetically engineered herpes simplex viruses by ionizing radiation: A new paradigm for destruction of therapeutically intractable tumors. Gene Therapy, 5(2), 160
165. Akram, N., Imran, M., Noreen, M., Ahmed, F., Atif, M., Fatima, Z., & Bilal Waqar, A. (2017). Oncogenic role of tumor viruses in humans. Viral Immunology, 30(1), 20
27. Alsharifi, M., Regner, M., Blanden, R., Lobigs, M., Lee, E., Koskinen, A., & Mu¨llbacher, A. (2006). Exhaustion of type I interferon response following an acute viral infection. The Journal of Immunology, 177(5), 3235
3241. Andtbacka, R. H., Kaufman, H. L., Collichio, F., Amatruda, T., Senzer, N., Chesney, J., . . . Coffin, R. S. (2015). Talimogene laherparepvec improves durable response rate in patients with advanced melanoma. Journal of Clinical Oncology, 33(25), 2780
2788. Antonia, S. J., Larkin, J., & Ascierto, P. A. (2014). Immuno-oncology combinations: A review of clinical experience and future prospects. Clinical Cancer Research, 20(24), 6258
6268. Artificial Intelligence
Opportunities in Cancer Research. Available from: , https://www.cancer.gov/research/areas/diagnosis/artificial-intelligence . . Barton, K. N., Stricker, H., Brown, S. L., Elshaikh, M., Aref, I., Lu, M., . . . Movsas, B. (2008). Phase I study of noninvasive imaging of adenovirus-mediated gene expression in the human prostate. Molecular Therapy, 16(10), 1761
1769.

Oncolytic virus cancer therapeutic options and integration Chapter | 4

75

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123
1131. Berwick, D. M., & Gaines, M. E. (2018). How HIPAA harms care, and how to stop it. JAMA, 320(3), 229
230. Blank, S. V., Rubin, S. C., Coukos, G., Amin, K. M., Albelda, S. M., & Molnar-Kimber, K. L. (2002). Replication-selective herpes simplex virus type 1 mutant therapy of cervical cancer is enhanced by low-dose radiation. Human Gene Therapy, 13(5), 627
639. Bradley, J. D., Kataoka, Y., Advani, S., Chung, S. M., Arani, R. B., Gillespie, G. Y., . . . Weichselbaum, R. R. (1999). Ionizing radiation improves survival in mice bearing intracranial high-grade gliomas injected with genetically modified herpes simplex virus. Clinical Cancer Research, 5(6), 1517
1522. Breitbach, C. J., Lichty, B. D., & Bell, J. C. (2016). Oncolytic viruses: Therapeutics with an identity crisis. EBioMedicine, 9, 31
36. Buckel, L., Advani, S. J., Frentzen, A., Zhang, Q., Yu, Y. A., Chen, N. G., . . . Szalay, A. A. (2013). Combination of fractionated irradiation with anti-VEGF expressing vaccinia virus therapy enhances tumor control by simultaneous radiosensitization of tumor associated endothelium. International Journal of Cancer, 133(12), 2989
2999. Cao, G. D., He, X. B., Sun, Q., Chen, S., Wan, K., Xu, X., . . . Xiong, M. M. (2020). The oncolytic virus in cancer diagnosis and treatment. Frontiers in Oncology, 10, 1786. Chen, Y., DeWeese, T., Dilley, J., Zhang, Y., Li, Y., Ramesh, N., . . . Yu, D. C. (2001). CV706, a prostate cancer-specific adenovirus variant, in combination with radiotherapy produces synergistic antitumor efficacy without increasing toxicity. Cancer Research, 61(14), 5453
5460. Chisholm, J., Howe, K., Taj, M., & Zambon, M. (2005). Influenza immunisation in children with solid tumours. European Journal of Cancer, 41(15), 2280
2287, Va¨ha¨-Koskela, M. J., Heikkila¨, J. E., & Hinkkanen, A. E. (2007). Oncolytic viruses in cancer therapy. Cancer Letters, 254(2), 178
216. Coffin, R. (2016). Interview with Robert Coffin, inventor of T-VEC: The first oncolytic immunotherapy approved for the treatment of cancer. Immunotherapy, 8(2), 103
106. Connell, P. P., & Hellman, S. (2009). Advances in radiotherapy and implications for the next century: A historical perspective. Cancer Research, 69(2), 383
392. Cooksley, C. D., Avritscher, E. B., Bekele, B. N., Rolston, K. V., Geraci, J. M., & Elting, L. S. (2005). Epidemiology and outcomes of serious influenza-related infections in the cancer population. Cancer: Interdisciplinary International Journal of the American Cancer Society, 104(3), 618
628. Dai, M. H., Zamarin, D., Gao, S. P., Chou, T. C., Gonzalez, L., Lin, S. F., & Fong, Y. (2010). Synergistic action of oncolytic herpes simplex virus and radiotherapy in pancreatic cancer cell lines. British Journal of Surgery, 97(9), 1385
1394. Dash, R., Azab, B., Quinn, B. A., Shen, X., Wang, X. Y., Das, S. K., . . . Fisher, P. B. (2011). Apogossypol derivative BI-97C1 (Sabutoclax) targeting Mcl-1 sensitizes prostate cancer cells to mda-7/IL-24
mediated toxicity. Proceedings of the National Academy of Sciences, 108(21), 8785
8790. Deng, L., Yang, X., Fan, J., Ding, Y., Peng, Y., Xu, D., . . . Hu, Z. (2020). An oncolytic vaccinia virus armed with GM-CSF and IL-24 double genes for cancer targeted therapy. OncoTargets and Therapy, 13, 3535. DeVita, V. T., & Chu, E. (2008). A history of cancer chemotherapy. Cancer Research, 68(21), 8643
8653.

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Dilley, J., Reddy, S., Ko, D., Nguyen, N., Rojas, G., Working, P., & Yu, D. C. (2005). Oncolytic adenovirus CG7870 in combination with radiation demonstrates synergistic enhancements of antitumor efficacy without loss of specificity. Cancer Gene Therapy, 12(8), 715
722. Dingli, D., Russell, S. J., & Morris, J. C., III (2003). In vivo imaging and tumor therapy with the sodium iodide symporter. Journal of Cellular Biochemistry, 90(6), 1079
1086. Driesse, M. J., Vincent, A. J. P. E., Smitt, P. S., Kros, J. M., Hoogerbrugge, P. M., Avezaat, C. J. J., . . . Bout, A. (1998). Intracerebral injection of adenovirus harboring the HSVtk gene combined with ganciclovir administration: Toxicity study in nonhuman primates. Gene Therapy, 5(8), 1122
1129. Emdad, L., Das, S. K., Wang, X. Y., Sarkar, D., & Fisher, P. B. (2018). Cancer terminator viruses (CTV): A better solution for viral-based therapy of cancer. Journal of Cellular Physiology, 233(8), 5684
5695. Engeland, C. E., Grossardt, C., Veinalde, R., Bossow, S., Lutz, D., Kaufmann, J. K., . . . Ungerechts, G. (2014). CTLA-4 and PD-L1 checkpoint blockade enhances oncolytic measles virus therapy. Molecular Therapy, 22(11), 1949
1959. Evgin, L., Acuna, S. A., De Souza, C. T., Marguerie, M., Lemay, C. G., Ilkow, C. S., . . . McCart, J. A. (2015). Complement inhibition prevents oncolytic vaccinia virus neutralization in immune humans and cynomolgus macaques. Molecular Therapy, 23(6), 1066
1076. Ford, I., & Norrie, J. (2016). Pragmatic trials. . The New England Journal of Medicine, 375, 454
463, 10.1056.NEJMra1510059. Fukuhara, H., Ino, Y., & Todo, T. (2016). Oncolytic virus therapy: A new era of cancer treatment at dawn. Cancer Science, 107(10), 1373
1379. Galanis, E., Atherton, P. J., Maurer, M. J., Knutson, K. L., Dowdy, S. C., Cliby, W. A., . . . Hartmann, L. C. (2015). Oncolytic measles virus expressing the sodium iodide symporter to treat drug-resistant ovarian cancer. Cancer Research, 75(1), 22
30. Garber, K. (2006). China approves world’s first oncolytic virus therapy for cancer treatment. Journal-National Cancer Institute, 98(5), 298. Gomez-Manzano, C., Yung, W. A., Alemany, R., & Fueyo, J. (2004). Genetically modified adenoviruses against gliomas: From bench to bedside. Neurology, 63(3), 418
426. Gong, J., Sachdev, E., Mita, A. C., & Mita, M. M. (2016). Clinical development of reovirus for cancer therapy: An oncolytic virus with immune-mediated antitumor activity. World Journal of Methodology, 6(1), 25. Gracia, D. (2013). Institute of medicine (IOM). The learning healthcare system: Workshop summary (pp. 89
91). Washington, DC: The National Academies Press, 2007. Cr´ıtica, 39. Guo, Z. S., Liu, Z., Kowalsky, S., Feist, M., Kalinski, P., Lu, B., . . . Bartlett, D. L. (2017). Oncolytic immunotherapy: Conceptual evolution, current strategies, and future perspectives. Frontiers in Immunology, 8, 555. Hacein-Bey-Abina, S., Von Kalle, C., Schmidt, M., McCormack, M. P., Wulffraat, N., Leboulch, P. A., . . . Cavazzana-Calvo, M. (2003). LMO2-associated clonal T cell proliferation in two patients after gene therapy for SCID-X1. Science, 302(5644), 415
419. Hadjipanayis, C. G., & DeLuca, N. A. (2005). Inhibition of DNA repair by a herpes simplex virus vector enhances the radiosensitivity of human glioblastoma cells. Cancer Research, 65 (12), 5310
5316. Heo, J., Breitbach, C. J., Moon, A., Kim, C. W., Patt, R., Kim, M. K., . . . Hwang, T. H. (2011). Sequential therapy with JX-594, a targeted oncolytic poxvirus, followed by sorafenib in hepatocellular carcinoma: Preclinical and clinical demonstration of combination efficacy. Molecular Therapy, 19(6), 1170
1179.

Oncolytic virus cancer therapeutic options and integration Chapter | 4

77

Hicks, K. L., Chemaly, R. F., & Kontoyiannis, D. P. (2003). Common community respiratory viruses in patients with cancer: More than just “common colds.”. Cancer: Interdisciplinary International Journal of the American Cancer Society, 97(10), 2576
2587. Howard, F., & Muthana, M. (2020). Designer nanocarriers for navigating the systemic delivery of oncolytic viruses. Nanomedicine, 15(1), 93
110. Huang, B., Sikorski, R., Kirn, D. H., & Thorne, S. H. (2011). Synergistic anti-tumor effects between oncolytic vaccinia virus and paclitaxel are mediated by the IFN response and HMGB1. Gene Therapy, 18(2), 164
172. Ilett, E. J. (2020). Delivery of oncolytic reovirus by cell carriers. Oncolytic viruses (pp. 229
236). New York, NY: Humana. Izuo, M. (2004). Medical history: Seishu Hanaoka and his success in breast cancer surgery under general anesthesia two hundred years ago. Breast Cancer, 11(4), 319
324. Jacobs, A., Tjuvajev, J. G., Dubrovin, M., Akhurst, T., Balatoni, J., Beattie, B., . . . Blasberg, R. G. (2001). Positron emission tomography-based imaging of transgene expression mediated by replication-conditional, oncolytic herpes simplex virus type 1 mutant vectors in vivo. Cancer Research, 61(7), 2983
2995. Kalyn, R. (2007). Overview of targeted therapies in oncology. Journal of Oncology Pharmacy Practice, 13(4), 199
205. Kelly, E., & Russell, S. J. (2007). History of oncolytic viruses: Genesis to genetic engineering. Molecular therapy, 15(4), 651
659, 17. Lin, E., & Nemunaitis, J. (2004). Krebs, P., Scandella, E., Odermatt, B., & Ludewig, B. (2005). Rapid functional exhaustion and deletion of CTL following immunization with recombinant adenovirus. The Journal of Immunology, 174(8), 4559
4566. Kyula, J. N., Khan, A. A., Mansfield, D., Karapanagiotou, E. M., McLaughlin, M., Roulstone, V., . . . Harrington, K. J. (2014). Synergistic cytotoxicity of radiation and oncolytic Lister strain vaccinia in V600D/E BRAF mutant melanoma depends on JNK and TNF-α signaling. Oncogene, 33(13), 1700
1712. Levine, A. J. (2009). The common mechanisms of transformation by the small DNA tumor viruses: The inactivation of tumor suppressor gene products: p53. Virology, 384(2), 285
293. Lichtman, M. A. (2008). Battling the hematological malignancies: The 200 years’ war. The Oncologist, 13(2), 126
138. Liikanen, I., Ahtiainen, L., Hirvinen, M. L., Bramante, S., Cerullo, V., Nokisalmi, P., . . . Hemminki, A. (2013). Oncolytic adenovirus with temozolomide induces autophagy and antitumor immune responses in cancer patients. Molecular Therapy, 21(6), 1212
1223. Marelli, G., Howells, A., Lemoine, N. R., & Wang, Y. (2018). Oncolytic viral therapy and the immune system: A double-edged sword against cancer. Frontiers in Immunology, 9, 866. Maron, B. J., Towbin, J. A., Thiene, G., Antzelevitch, C., Corrado, D., Arnett, D., . . . Young, J. B. (2006). Contemporary definitions and classification of the cardiomyopathies: An American Heart Association scientific statement from the council on clinical cardiology, heart failure and transplantation committee; quality of care and outcomes research and functional genomics and translational biology interdisciplinary working groups; and council on epidemiology and prevention. Circulation, 113(14), 1807
1816. Maroun, J., Mun˜oz-Al´ıa, M., Ammayappan, A., Schulze, A., Peng, K. W., & Russell, S. (2017). Designing and building oncolytic viruses. Future Virology, 12(4), 193
213. McCart, J. A., Mehta, N., Scollard, D., Reilly, R. M., Carrasquillo, J. A., Tang, N., . . . Bartlett, D. L. (2004). Oncolytic vaccinia virus expressing the human somatostatin receptor SSTR2:

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Molecular imaging after systemic delivery using 111In-pentetreotide. Molecular Therapy, 10(3), 553
561. Mezhir, J. J., Advani, S. J., Smith, K. D., Darga, T. E., Poon, A. P., Schmidt, H., . . . Weichselbaum, R. R. (2005). Ionizing radiation activates late herpes simplex virus 1 promoters via the p38 pathway in tumors treated with oncolytic viruses. Cancer Research, 65(20), 9479
9484. Na, Y., Nam, J. P., Hong, J., Oh, E., Shin, H. C., Kim, H. S., . . . Yun, C. O. (2019). Systemic administration of human mesenchymal stromal cells infected with polymer-coated oncolytic adenovirus induces efficient pancreatic tumor homing and infiltration. Journal of Controlled Release, 305, 75
88. Nakao, S., Arai, Y., Tasaki, M., Yamashita, M., Murakami, R., Kawase, T., . . . Nakamura, T. (2020). Intratumoral expression of IL-7 and IL-12 using an oncolytic virus increases systemic sensitivity to immune checkpoint blockade. Science Translational Medicine, 12(526), eaax7992. Nemunaitis, J., Khuri, F., Ganly, I., Arseneau, J., Posner, M., Vokes, E., . . . Kirn, D. (2001). Phase II trial of intratumoral administration of ONYX-015, a replication-selective adenovirus, in patients with refractory head and neck cancer. Journal of Clinical Oncology, 19(2), 289
298. Pack, G. T. (1950). Note on the experimental use of rabies vaccine for melanomatosis. AMA Archives of Dermatology and Syphilology, 62(5), 694
695. Papanastassiou, V., Rampling, R., Fraser, M., Petty, R., Hadley, D., Nicoll, J., . . . Brown, M. (2002). The potential for efficacy of the modified (ICP 34.5 2 ) herpes simplex virus HSV1716 following intratumoural injection into human malignant glioma: A proof of principle study. Gene Therapy, 9(6), 398
406. Patel, M. R., Jacobson, B. A., Ji, Y., Hebbel, R. P., & Kratzke, R. A. (2020). Blood outgrowth endothelial cells as a cellular carrier for oncolytic vesicular stomatitis virus expressing interferon-β in preclinical models of non-small cell lung cancer. Translational Oncology, 13(7), 100782. Pestka, S., Krause, C. D., & Walter, M. R. (2004). Interferons, interferon-like cytokines, and their receptors. Immunological Reviews, 202(1), 8
32. Pestka, S., Langer, J. A., Zoon, K. C., & Samuel, C. E. (1987). Interferons and their actions. Annual Review of Biochemistry, 56(1), 727
777. Platanias, L. C. (2005). Mechanisms of type-I-and type-II-interferon-mediated signalling. Nature Reviews Immunology, 5(5), 375
386. Power, A. T., Wang, J., Falls, T. J., Paterson, J. M., Parato, K. A., Lichty, B. D., . . . Bell, J. C. (2007). Carrier cell-based delivery of an oncolytic virus circumvents antiviral immunity. Molecular Therapy, 15(1), 123
130. Rajani, K., Parrish, C., Kottke, T., Thompson, J., Zaidi, S., Ilett, L., . . . Vile, R. (2016). Combination therapy with reovirus and anti-PD-1 blockade controls tumor growth through innate and adaptive immune responses. Molecular Therapy, 24(1), 166
174. ´ ., & Franco-Luzo´n, L. (2015). Ram´ırez, M., Garc´ıa-Castro, J., Melen, G. J., Gonz´alez-Murillo, A Patient-derived mesenchymal stem cells as delivery vehicles for oncolytic virotherapy: Novel state-of-the-art technology. Oncolytic Virotherapy, 4, 149. Rampling, R., Cruickshank, G., Papanastassiou, V., Nicoll, J., Hadley, D., Brennan, D. A., . . . Brown, M. (2000). Toxicity evaluation of replication-competent herpes simplex virus (ICP 34.5 null mutant 1716) in patients with recurrent malignant glioma. Gene Therapy, 7(10), 859
866.

Oncolytic virus cancer therapeutic options and integration Chapter | 4

79

Rojas, J. J., Sampath, P., Hou, W., & Thorne, S. H. (2015). Defining effective combinations of immune checkpoint blockade and oncolytic virotherapy. Clinical Cancer Research, 21(24), 5543
5551. Rojas, J. J., & Thorne, S. H. (2012). Theranostic potential of oncolytic vaccinia virus. Theranostics, 2(4), 363. Russell, S. J., Federspiel, M. J., Peng, K. W., Tong, C., Dingli, D., Morice, W. G., . . . Dispenzieri, A. (2014). Remission of disseminated cancer after systemic oncolytic virotherapy, . Mayo clinic proceedings (Vol. 89, pp. 926
933). Elsevier, No. 7. Russell, S. J., Peng, K. W., & Bell, J. C. (2012). Oncolytic virotherapy. Nature Biotechnology, 30(7), 658. Sampath, P., Li, J., Hou, W., Chen, H., Bartlett, D. L., & Thorne, S. H. (2013). Crosstalk between immune cell and oncolytic vaccinia therapy enhances tumor trafficking and antitumor effects. Molecular Therapy, 21(3), 620
628. Schneeweiss, S. (2014). Learning from big health care data. The New England Journal of Medicine, 370(23), 2161
2163. Schreiber, R. D., Old, L. J., & Smyth, M. J. (2011). Cancer immunoediting: Integrating immunity’s roles in cancer suppression and promotion. Science, 331(6024), 1565
1570. Sevik, ¸ M. (2012). Oncogenic viruses and mechanisms of oncogenesis. Turkish Journal of Veterinary and Animal Sciences, 36(4), 323
329. Shackelford, J., & Pagano, J. S. (2004). Tumor viruses and cell signaling pathways: Deubiquitination vs ubiquitination. Molecular and Cellular Biology, 24(12), 5089
5093. Southam, C. M., & Moore, A. E. (1952). Clinical studies of viruses as antineoplastic agents, with particular reference to Egypt 101 virus. Cancer, 5(5), 1025
1034. Springs, H., Gold, F. S., & too Far, A. B. (2001). Searching for the beginning. The way of the cell: Molecules, organisms, and the order of life (p. 235) Oxford University Press. Tang, H., Sampath, P., Yan, X., & Thorne, S. H. (2013). Potential for enhanced therapeutic activity of biological cancer therapies with doxycycline combination. Gene Therapy, 20(7), 770
778. Thorne, S. H., Negrin, R. S., & Contag, C. H. (2006). Synergistic antitumor effects of immune cell-viral biotherapy. Science, 311(5768), 1780
1784. Touchefeu, Y., Franken, P., & J Harrington, K. (2012). Radiovirotherapy: Principles and prospects in oncology. Current Pharmaceutical Design, 18(22), 3313
3320. Varghese, S., Rabkin, S. D., Liu, R., Nielsen, P. G., Ipe, T., & Martuza, R. L. (2006). Enhanced therapeutic efficacy of IL-12, but not GM-CSF, expressing oncolytic herpes simplex virus for transgenic mouse derived prostate cancers. Cancer Gene Therapy, 13(3), 253
265. Vesely, M. D., Kershaw, M. H., Schreiber, R. D., & Smyth, M. J. (2011). Natural innate and adaptive immunity to cancer. Annual Review of Immunology, 29, 235
271. Vlachaki, M. T., Hernandez-Garcia, A., Ittmann, M., Chhikara, M., Aguilar, L. K., Zhu, X., . . . Aguilar-Cordova, E. (2002). Impact of preimmunization on adenoviral vector expression and toxicity in a subcutaneous mouse cancer model. Molecular Therapy, 6(3), 342
348. Wang, G., Kang, X., Chen, K. S., Jehng, T., Jones, L., Chen, J., . . . Chen, S. Y. (2020). An engineered oncolytic virus expressing PD-L1 inhibitors activates tumor neoantigen-specific T cell responses. Nature Communications, 11(1), 1
14. Wei, J., Wahl, J., Nakamura, T., Stiller, D., Mertens, T., Debatin, K. M., & Beltinger, C. (2007). Targeted release of oncolytic measles virus by blood outgrowth endothelial cells in situ inhibits orthotopic gliomas. Gene Therapy, 14(22), 1573
1586, 50. Weissleder, R. (2006). Molecular imaging in cancer. Science, 312(5777), 1168
1171.

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Xia, Z. J., Chang, J. H., Zhang, L., Jiang, W. Q., Guan, Z. Z., Liu, J. W., . . . Zheng, X. (2004). Phase III randomized clinical trial of intratumoral injection of E1B gene-deleted adenovirus (H101) combined with cisplatin-based chemotherapy in treating squamous cell cancer of head and neck or esophagus. Ai zheng 5 Aizheng 5 Chinese Journal of Cancer, 23(12), 1666
1670. Young, B. A., Spencer, J. F., Ying, B., Toth, K., & Wold, W. S. (2013). The effects of radiation on antitumor efficacy of an oncolytic adenovirus vector in the Syrian hamster model. Cancer Gene Therapy, 20(9), 531
537. Zamarin, D., Holmgaard, R. B., Subudhi, S. K., Park, J. S., Mansour, M., Palese, P., . . . Allison, J. P. (2014). Localized oncolytic virotherapy overcomes systemic tumor resistance to immune checkpoint blockade immunotherapy. Science Translational Medicine, 6(226), 226ra32
226ra32. Zamarin, D., Ricca, J. M., Sadekova, S., Oseledchyk, A., Yu, Y., Blumenschein, W. M., . . . Wolchok, J. D. (2018). PD-L1 in tumor microenvironment mediates resistance to oncolytic immunotherapy. The Journal of Clinical Investigation, 128(4), 1413
1428. Zur Hausen, H. (2009). The search for infectious causes of human cancers: Where and why. Virology, 392(1), 1
10.

Chapter 5

Oncoviruses: future prospects of molecular mechanisms and therapeutic strategies Iyshwarya Bhaskar Kalarani, Kamila Thasneem and Ramakrishnan Veerabathiran Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India

5.1

Introduction

Cancer, a disease caused by uncontrolled cell growth, is associated with chemical carcinogens, such as tobacco, hormonal imbalance, or genetics. Cancer may be caused by different sources, including oncoviruses or cancercausing viruses. Oncovirus infection may have induced cancer in approximately 18.1 million cases worldwide as shown in Fig. 5.1 (Latest Global 1% 6% 21% America Europe Asia Africa 49%

23%

Oceania

FIGURE 5.1 Global cancer incidence. Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00012-1 Copyright © 2023 Elsevier Inc. All rights reserved.

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Cancer Data: Cancer Burden Rises to 18.1 Million New Cases & 9.6 Million Cancer Deaths in 2018. International Agency for Research on Cancer, 2018). Oncoviruses are often overlooked because current preventive measures against cancer focus on modifiable lifestyle risk factors, such as changes in lifestyle, physical activities and proper diet, inflammation, coinfection, immune suppression, or host mutations. The primary tumor causing virus is the Shope papilloma tumor virus (SPV), which was segregated from rabbit papilloma. It was discovered in 1933 and its significant analysis aimed at detecting oncovirus in humans that causes human cancers. Cancer was not thought to be associated with infections at that time. The discovery of oncovirus dates back to the early 20th century, when in 1908 Oluf Bang and Wilhelm Ellermann, the two researchers at the University of Copenhagen, demonstrated that leukemia could be induced in healthy chickens by treating them with a filterable extract containing the avian leukemia virus (Javier & Butel, 2008). The human oncovirus was discovered in 1964 when the Epstein
Barr virus was detected with the help of an electron microscope in Burkitt lymphoma (Epstein, Achong, & Barr, 1964). Studies have found monoclonal proliferation of EBV in tumors, which is a strong evidence in support of a carcinogenic character (Raab-Traub & Flynn, 1986). A turning point in Rous’ cancer research in the 20th century is the discovery of oncoviruses that caused cancers in humans. Epidemiological studies show that people with EBV are at higher risk for EBV-related cancers (De-The´ et al., 1978). About 12% people have at least one oncovirus gene, but only a few individuals suffer from cancer except for when the cause of cancer is an oncovirus, resulting in chronic inflammatory and environmental changes in the innate and adaptive immune response. Direct- and indirect-acting carcinogens are the two main classifications of chemical carcinogens. Direct-acting cancers are induced without the need of metabolism, for example, nitrosamines or ultraviolet light which is an agent that integrates directly with DNA. An indirect-acting carcinogen needs metabolic activation for inducing cancer, which includes heterocyclic aromatic amines and polycyclic aromatic hydrocarbons. Major oncoviruses discovered are EBV, hepatitis B virus (HBV), hepatitis C virus, mastadenovirus, aviadenovirus, rhadinovirus, varicellovirus, polyomavirus, adenoviruses, orthopoxvirus, leporipoxvirus, parvovirus, human immunodeficiency virus (HIV), human papillomavirus (HPV), human herpesvirus 8, and simplex virus (Fig. 5.2). About 12%
15% of cancers in cancers worldwide are caused by the EBV (Center & Room, 2014). According to a 2018 study, EBV is associated with a wide range of malignancies, such as Burkitt lymphoma, and approximately 2.2 million new cases have been affected by EBV infection (Pei et al., 2020). It stimulates tumors using common signal and tumor suppressor pathways such as oncovirus p53 and retinoblastoma, inhibiting and controlling proliferation, differentiation, cell death, and the immune system (Akram et al., 2017).

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FIGURE 5.2 Discovery of oncovirus.

5.2

Mechanism of oncovirus

Oncogenic viruses are catagorized into DNA and RNA viruses. DNA contamination oncogenes, unlike RNA tumor infections, encode viral proteins needed for viral replication. RNA viruses have modified versions of standard infected cell genomes that are not necessary for cell proliferation. Oncogenesis causes cellular alteration, triggering an unregulated cell growth period, and improving malignant tumors. In juvenile leukemia, autoimmune diseases, and a number of healthy tumors, oncogenic defects can be seen. Virus-induced tumor changes in cells are the first step in the complicated oncogenesis process. Viral oncogenes are the genes located within the viral genome that alter host cell proliferation control, induce the synthesis of new proteins, and are responsible for transformation traits (Akram et al., 2017). Oncogenic viruses are classified as DNA or RNA tumor viruses based on their genetic material. There are two life bureaucracies in DNA tumor

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viruses. Viral replication induces cell cleavage and mortality in permissive cells. Viral DNA is predominantly incorporated into the extraordinary positions of their genomes in nonpermissive cells. It regulates amino acids like p53 and retinoblastoma by encoding specific sites and inhibits cellular increase. The interpretation of proteins that manipulate host cell DNA synthesis transforms the cell (Nikitin & Luftig, 2011). Rous sarcoma virus (RSV) was discovered to contain RNA in 1961. Oncogenic retroviruses are RNA tumor viruses; more than 30 oncogenes have been described in retroviruses. Retroviruses have three distinct characteristics (gag stands for group antigens (ag) and Pol stands for reverse transcriptase). Env is an acronym for envelope glycoproteins, which are used to combine underlying proteins, virion-associated chemical compounds, and envelope glycoproteins. Lentivirus-based complex retroviruses have an extra nonstructural gene (vonc). The viral sarcoma gene, for example, is the fourth gene in the RSV. RSV obtains this high-quality cell to taint cells. RNA tumor infections use a variety of oncogenic structures to enhance tumor growth. Some of them encode oncogenic proteins, which are similar to the cell proteins that regulate cellular growth. Cell proliferation is caused by the overproduction or alteration of these oncogenic materials. These RNA viruses have the potential to cause tumor growth. The RNA tumor infection encodes a protein that triggers cell feature outflow. As tolerant cells are infected with RNA tumor pathogens, the contaminated offspring is released from the cell surface through maturation, and perpetual hereditary transformations turn the corrupted cells into the disease. The virus is embedded in the chromosome of the cell, it is subject to manipulation by the cell’s regulator genes, and it may remain in the cells without causing any damage. Endogenous retroviruses are these types of retroviruses. When cells carrying a primary infection are exposed to mutagenic or cancerogenic factors, such as light, mutagens, or cancerogenic artificial materials, hormonal or immunological stimuli, etc., the infection is activated and cells begin to multiply. The interference of antiviral proteins with the host cell’s epigenetic equipment is a common key mechanism underlying virus-brought cell transformation (Doerfler, 2012). Epigenetic modulations are critical for oncovirus-mediated malignant transformation, allowing for different levels of healing intervention (Khoury et al., 2013). Viral oncoproteins use the mobile device to rewrite the epigenetic landscape. Healing opportunities for targeting each viral and host cellular factor that contributes to the epigenetics of transformation have been verified in light of such interactions. The majority of the work done to intervene with virus-added epigenetic reprogramming has focused on host cell proteins and epigenetic regulators (Yoo & Jones, 2006). Targeting the viral elements responsible for these types of changes has no’t been thoroughly investigated in many cases, even though viral proteins offer tremendous therapeutic potential due to their superior abilities and large versions when compared to host mobile proteins (Flanagan, 2007). The presence

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of such elements in essential biochemical processes in various healthy cells is a major issue with epigenetic elements focused on host cells. Based on this research, we can classify epigenetic events linked to the most active oncoviruses, as well as the improvement made in recovery due to interaction with their oncoproteins. Over the past 3 years, excellent studies on the epigenetics of malignant diseases have been published (Dawson et al., 2012). Epigenetic activities as therapeutic targets have sparked a flurry of interest as promising approaches to combating a variety of issues, especially cancers (Ramos & Lossos, 2011). Oncogenic virus epigenetics has also been the subject of both collective articles and reviews, in my opinion, in conjunction with pathways of epigenetic dysregulation in host cells, as shown in Fig. 5.3 (Weber et al., 2015; Zhang et al., 2013).

FIGURE 5.3 Oncoviral mechanism.

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5.3

Types and mechanism of oncoviruses

Oncogene activation necessitates genetic alterations in protooncogenes found in cells oncogenes are turned on. Mutation, gene amplification, and chromosome rearrangements are three genetic pathways that result in a change in protooncogene expression or structure. The oncoviruses are categorized as EBV, HBV, aviadenovirus, HIV, HPV, polyomavirus, herpes simplex virus, parvovirus, leporipoxvirus, and orthopoxvirus (Fig. 5.4).

5.3.1

Epstein
Barr virus

EBV is a common virus that mainly spreads through saliva and causes many infectious diseases. EBV (human herpesvirus 4) is a member of the herpes virus family. Symptoms of EBV infection include fever, rash, fatigue, and swollen lymph nodes in the neck. This virus can infect both children and adults equally. Sometimes in children the symptoms may not appear in the early stage but will be present in the later stage. Mostly EBV spreads through body fluids like saliva, blood, and semen. EBV is spread through some of the known ways, such as sharing drinks and food, eating in the same cups, and using the same toothbrush. EBV was identified using a blood test. There is no vaccine and treatment till now so the only way of protection is not to share eating utensils and toothbrushes with those who are EBV positive (Baumforth et al., 1999). EBV replicates, integrates in host cells and leads to tumor growth. EBV is associated with many cancers causing chronic

Hepatitis B Virus Human Papilloma Virus

Aviadeno Virus

Herpes Simplex Virus

Epstein Barr Virus Oncovirus

Parvo Virus

Orthopox Virus

Leporipox Virus

FIGURE 5.4 Types of oncoviruses.

Polyoma Virus

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inflammation and difficulties. It will integrate into the host cell and decrease the function of inflammation-related genes CDK15, PARK2, and TNFAIP3 (Xu et al., 2019). EBV has terminal repeats that are the actual reason for integration, recombination, and tumor growth in oncoviral cancer induction as illustrated in Fig. 5.5.

5.3.2

Hepatitis B virus

HBV causes a drastic infection that can damage the liver. Most commonly, this virus is transmitted from the mother to child during delivery and also through blood, body fluid, semen, sharing needles, razors, and other medical equipment that have been used by infected persons. Many people do not know that they are affected by this virus and they can spread the infection to others. Chronic hepatitis B is mostly seen in infants and acute in adults. Acute hepatitis B include symptoms such as fever, fatigue, dark urine, vomiting, abdominal pain, joint pain, nausea, and jaundice. People with chronic hepatitis B do not show any symptoms or illness. Blood tests are used to diagnose the infection. There is no treatment for acute and chronic infections. Vaccination can somehow prevent infection, therefore all infants should be vaccinated (Liang, 2009). HBV infection leads to inflammation; it will infect the host and start regeneration and alteration of the normal mechanism of the host. Genetic instability and viral protein accumulation can be seen in the host which later lead to the induction of cancer as shown in Fig. 5.6.

FIGURE 5.5 Epstein Barr virus mechanism of action.

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FIGURE 5.6 HBV mechanism of action. HBV, hepatitis B virus.

5.3.3

Aviadenovirus

Aviadenovirus cause a wide range of illnesses such as fever, bronchitis, pneumonia, pink eye, and diarrhea. An adenovirus infection can occur at any age. People with a history of cardiac or respiratory disease and weakened immune systems are at higher risk of adenovirus infection. These infections can spread through the air by coughing and sneezing, shaking hands with infected people, etc. To prevent this type of infection, we should not touch our mouth, nose, and eyes without washing hands and should regularly wash hands with soap. There is no proper treatment for this infection (Turnell, 2008).

5.3.4

Human immunodeficiency virus

HIV infects the immune system of the body and if not treated properly it can lead to AIDS. There is currently no effective cure for HIV infection. Once people get HIV, then it will remain lifelong. HIV came to humans from a chimpanzee in Africa. In early ages humans hunted infected chimpanzees for meat. Major symptoms of HIV infection are fever, sore throat, rash, muscle aches, etc. HIV infections have three major stages: acute HIV, chronic HIV, and AIDS. It is asexually transmitted disease (STD). An HIV positive mother can transmit the infection to her baby but it is not common. Using needles or syringes and the blood of infected people can also transmit HIV. Some of the rare transmission ways are oral sex, biting, open-mouth kissing, prechewed food, tattoos, etc. HIV can also transmit by body fluids like semen, vaginal fluid, blood, and saliva. The viral genome will be infected to the

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host cells through the capsid and it will prevent the normal function, stop reverse transcription, and regulate the nuclear pathway. Cyclophilin A (cyp A) is a viral protein that causes oncogenesis. HIV will infect the body and start to reduce control of oncogenesis virus function, reducing the immune response to cancer. The virus will attach to the cell and fuse, and the viral RNA will be reverse transcribed and cDNA produced. This cDNA combines with chromosomal DNA and begins to replicate as shown in Fig. 5.7.

5.3.5

Human papillomavirus

HPV spreads through direct skin contact. Sometimes the virus has no symptoms and it goes away on its own. The major symptoms of this virus are

FIGURE 5.7 Mechanism of human immunodeficiency virus.

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genital warts and warts in the throat. HPV infection is a common disease that leads to cervical cancers. Diagnosis of HPV in both men and women is different (Soto et al., 2017). Women should be screened with Pap tests every 3 years and HPV test every 5 years. But in men only screening tests are available. Genital warts can be treated using medications, burning by giving electric current, and freezing with liquid nitrogen. Sometimes HPV infection can go away and sometimes it can lead to cancer. Cancers caused by HPV are treated with chemotherapy, radiation therapy, and surgical method. More number of sexual partners, low immunity, unprotected sex, HPV positive sexual partners are the major causes of HPV infection. HPV can easily be prevented by using condoms and safe sex. Vaccines are given to children at the age of 11 to prevent this disease. HPV infection will inhibit E6 and E7 protein in the interferon pathway. Major histocompatibility complex (MHC) is not expressed on antigen presenting cell (APC) cell surface; later immune response will be altered and cancer is induced as shown in Fig. 5.8.

5.3.6

Polyomavirus

Polyomavirus are small DNA viruses, which are widespread in nature. In immunecompetent hosts, the viruses remain latent after primary infection. Polyomavirus consists of three proteins: VP1, VP2, and VP3. Normally polyomavirus does not infect humans but some of them have been associated with some disease conditions. Polyomavirus can infect people who have low immunity. There are 14 types of polyomaviruses that can infect humans. Polyomavirus infections are very common and usually do not show symptoms also. Polyomavirus can cause nephropathy, AIDS, etc. Normally antibody assay is used to detect the polyomavirus. In nephropathy, renal biopsy will be done to diagnose. When the polyomavirus enters the human body, it will capture the cell into S phase and then it will replicate in the host as illustrated in Fig. 5.9. To manage this, host immune-boosting is done (Ambalathingal et al., 2017).

5.3.7

Herpes simplex virus

Human herpesvirus 8, simplex virus, and varicella virus belong to the herpes virus family. Herpes virus is categorized into three types: alphaherpesviruses, herpes simplex virus, and varicella-zoster. There are more than 100 herpes viruses but only eight affect humans. Of these, the most common are herpesvirus 8 and simplex virus. After affecting the human host, the virus will be transcribed and the viral DNA will replicate. The most common diseases caused by the herpes virus are neonatal herpes, genital herpes, central nervous system infection, and chickenpox. To diagnose the herpes virus, isolate and culture the virus separately and check the desired gene present or not using the PCR method. Therapy and vaccines can be given to treat and

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HPV infection

E6&E7protein inhibits interferon pathway

Major Histocompatibility class 1 is not expressed on surface of antigen presenting cells

CD8 + CTL not activated

1. Viral protein 2. Antigen Presenting Cell 3. MHC antigen

HPV clearance

CD8+ CTL

Viral immune eluding HPV persistence

Induction of cancer

FIGURE 5.8 HPV mechanism of action. HPV, human papillomavirus.

prevent herpes virus infection (Roizman, 1990). Herpes virus glycoprotein attaches to receptors on the cell surface and starts releasing viral particles for replication in the cell as illustrated in Fig. 5.10 (Nolkemper et al., 2010).

5.3.8

Parvovirus

Parvovirus is very dangerous and can spread very fast. Mostly parvovirus spread by dog to dog contact. The infection has a mortality rate of more than 90% in dogs. Parvovirus that mostly infects humans is parvovirus B19. Parvovirus B19 can cause mild rash illness, anemia, joint pain, and fifth disease. Parvovirus commonly spreads through body fluids, such as saliva, mucus, blood, etc. When an infected person coughs or sneezes, it can infect the other person. Also, a pregnant woman who is affected by parvovirus can

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FIGURE 5.9 Mechanism of polyomavirus.

FIGURE 5.10 Mechanism of herpesvirus.

pass it to the baby. The blood test will confirm parvovirus infection. No vaccine or medicines are yet present to treat or prevent infection. The only way of prevention is not to get infected. Washing your hands regularly with soap, covering your mouth, and staying home when affected are the only ways to reduce infection spread. The uncoated virus penetrating into the cell through the plasma membrane replicates DNA followed by RNA transcription and protein translation, as shown in Fig. 5.11 (Heegaard & Brown, 2002).

5.3.9

Leporipoxvirus

Leporipoxvirus has a brick-shaped structure and rabbits and squirrels are the hosts. The major leporipoxvirus are myxoma, rabbit fibroma, and squirrel

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FIGURE 5.11 Mechanism of parvovirus.

fibroma. After the entry of the virus into the host, it can replicate in the cytoplasm which leads to cell death and the virus infection will be preserved till finding the other host. Using this virus ex vivo cancer therapy, the virus is attached to the cell losing its outer membrane, and the uncoated core releases viral DNA and DNA replication in the nucleus, which results in morphogenesis of immature particles released by exocytosis, as shown in Fig. 5.12 (Barrett & McFadden, 2007).

5.3.10 Orthopoxvirus Orthopoxviruses are enveloped in brick-shaped capsid. Mammals, humans, arthropods are its natural hosts. The virus envelope will attach to the plasma membrane and unleash the virus into the cytoplasm after the virus has reached the host. From the cytoplasm, the virus replicates and infection starts. Orthopoxviruses can cause rash illness, skin infections, smallpox, and cowpox. This infection is diagnosed by PCR and electron microscopy. The vaccine is available for treatment (Bennet et al., 2015). Major viruses causing cancers in humans are listed in Table 5.1.

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FIGURE 5.12 Mechanism of leporipoxvirus.

5.4

Genetics of virus

Viruses are simple organisms with no energy-generating equipment and minimal biological abilities. The simplest virus has only a few genomes, while the most important viruses have up to 200 genomes. Viruses, on the other hand, have multiple irregular functions of cells genetically. Viruses are difficult to mutate; however, the genomes of certain viruses may come together to form a new lineage; the activation of the viral gene may be controlled, and viral genetic material may be involved. The two processes by which viruses acquire genetic changes are mutation and recombination. Changes in the genetic material of a deadly disease may change the structure of viral proteins, resulting in the emergence of new viruses with altered virulence. Genetic changes are caused by one of three main mechanisms: the effects of biological mutations on nucleic acids, such as due to ultraviolet rays and Xrays; the normal action of the bases that make up nucleotides; and the inadequacy of the enzymes that reproduce nucleotides (Baron, 1996). Genes

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TABLE 5.1 Oncogenic viruses associated with human cancer. S. no

Viruses

Genome

Cancers in human

Infections

1

Adenovirus

dsDNA

Types are unspecified but the virus has transforming ability

Respiratory transmission via patient contact

2

HPV

dsDNA

Cervical cancer, head and neck carcinoma

Sexual transmission

3

HBV

dsDNA

Hepatocarcinoma

Transplacentally, during child delivery, via lactation, sexually, parentally, percutaneous

4

EBV

dsDNA

Burkitt lymphoma, Hodgkin lymphoma, post-transplantation lymphoma, nasopharyngeal carcinoma

Oropharyngeal secretions (saliva), transplacentally, via lactation, via blood, via organ transplantation

5

KSHV

dsDNA

Kaposi sarcoma, primary effusion lymphoma (PEL), multicentric Castleman disease (MCD), KSHV associated inflammatory syndrome (KICS)

Sexually, vertically, via drug injection, blood transfusion, solid organ transplantation, nonsexual horizontal transmission

6

HTLV1

ssRNAdsDNA

Adult T-cell leukemia (ATL)

Transplacentally, via lactation, sexually, via blood

7

HCV

ssRNA

Hepatocarcinoma

Injection equipment, blood and blood product transfusion, vertically, sexually

produce proteins that enhance cell division and survival. This first category also includes genes leading to tumor growth by inhibiting apoptosis. Genes can inhibit cell division actively or indirectly through their protein products. Protooncogenes are the unmutated versions of the first-class genes. Oncogenes are the corrupted or altered versions of the genes. It is insufficient in some normal cells and it is involved as the name implies in the gene in cell reaction to growth factors. HER2/neu gene amplification is expected to affect the tumor response and the tumor’s ability to mature and expand.

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The tumor may be more lethal with too many versions of the HER2/new gene, but it may also be more responsive to chemotherapy agents. Kinase signal systems control gene transcription and thus cell growth and differentiation (Schedin et al., 2018). RAS gene products play a prominent role. The RAS protein must bind to the cell to “turn on” a particular molecule. Membrane function is associated with the gene-2 proteins in cell-based lymphoma, which are a part of a complex mechanism that controls apoptosis (Vogelstein & Kinzler, 2002). Oncogenes associated with numerous cancer types are given in Table 5.2 (Gupta, 2014).

5.5

Types of treatment

Oncolytic virus therapy is a novel alternative experimental method for cancer treatment that has recently been approved. An oncolytic virus is a genetically modified or naturally occurring virus that can selectively replicate in cancer cells and destroy them while causing no damage to normal tissues. Oncologic treatments also resulted in more precise and complex regimens that aim to have the greatest influence on malignant cells, thus sparing nontumor tissues and minimizing side effects (Fukuhara et al., 2016). Various ways to treat cancer depend on the types and stages of cancer. The most effective treatment methods are radiation therapy, chemotherapy, immunotherapy, targeted therapy, hormone therapy, and stem cell transplant.

5.5.1

Immunotherapy

Our immune system works in complicated manner to combat infection. This process consists of cells, organs, and proteins. Cancer can normally get around a number of the immune device’s natural defenses, allowing cancer cells to maintain growth. There are numerous varieties of immunotherapy involving monoclonal antibodies and tumor-agnostic remedies, which include checkpoint inhibitors, oncolytic virus treatment, T cell therapy. Maximum cancer vaccines are illustrated in Fig. 5.13. A few immunosuppressive therapies can help stop or reduce the growth of most cancer cells in the immune system. Others help the immune system to damage most cancer cells or prevent cancers from spreading. Immunotherapy treatments can be used or mixed with most cancer treatments of one type or another. A certain new material that the immune system dislikes activates an alert, causing the immune system to respond by targeting it. Germs include molecules that contain beneficial proteins not naturally present in the human body. They are regarded as “distant sites” by the immune system, which destroys them. The immune system’s reaction is to kill something that contains a foreign material, such as germs or cancer cells (Bayer et al., 2019). Immunotherapy, also known as biological therapy or biological reaction reinforcement therapy, is a cancer treatment that makes use of the body’s immune system and

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TABLE 5.2 List of cancer associates with oncogenes and their function. S. no

Oncogene

Function

Cancer

1

ABL1

Promotes cell growth through tyrosine kinase activity

Chronic myelogenous leukemia

2

AFF4/ MLLT11

Fusion affects the MLLT11 transcription factor/methyltransferase. MLLT11 is also called HRX, ALL1, and HTRX1

Acute leukemia

3

AKT2

Encodes a protein-serine/ threonine kinase

Ovarian cancer

4

ALK

Encodes a receptor tyrosine kinase

Lymphomas

5

RUNX1 (AML1)

Encodes a transcription factor

Acute myeloid leukemia

6

AXL

Encodes a receptor tyrosine kinase

Hematopoietic cancers

7

MYC (cMYC)

Transcription factor that promotes cell proliferation and DNA synthesis

Leukemia; breast, stomach, lung, cervical, and colon carcinomas; neuroblastomas and glioblastomas

8

EGFR

Cell surface receptor that triggers cell growth through tyrosine kinase activity

Squamous cell carcinoma, glioblastomas, lung cancer

9

ERBB2

Cell surface receptor that triggers cell growth through tyrosine kinase activity; also known as HER2 or neu

Breast, salivary gland, and ovarian carcinomas

10

GNAS (GSP)

Membrane-associated G protein

Thyroid carcinoma

11

LCK

Tyrosine kinase

T-cell lymphoma

12

MYCL

Transcription factor

Lung carcinomas

13

MYB

Transcription factor

Colon carcinoma and leukemia

14

HRAS

G-protein. Signal transduction.

Bladder carcinoma

15

NTRK1

Receptor tyrosine kinase

Colon and thyroid carcinomas

16

TSC2

GTPase activator

Renal and brain tumors

97

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FIGURE 5.13 Immunotherapy in cancer treatment.

antibodies to destroy most cancer cells. Interferons, interleukins, monoclonal antibodies, colony-stimulating factors like cytokines, and vaccines are examples of biological therapies (Fig. 5.13). Researchers have discovered that the immune device may be able to decide the difference between healthful cells and cancer cells in the body and to do away with the cancer cells. Organic treatment options are designed to reinforce the immune device, either directly or circuitously (Tashiro & Brenner, 2017).

5.5.2

Chemotherapy

Chemotherapy is the use of medication to kill or prevent the growth of most cancer cells. Most chemotherapy treatments include a drug or a mixture of drugs administered intravenously or orally in the form of a tablet. Chemotherapy is not like surgical treatment or radiation remedy in that the cancer-stopping capsules flow inside the blood to parts of the frame wherein cancer might also moreover have spread and may kill or put off most cancer cells at websites first-rate distances from authentic cancer. As a result, chemotherapy is taken into consideration as systemic treatment and is illustrated in Fig. 5.14 (Shi et al., 2020).

5.5.3

Targeted therapy

It is designed to treat most cancer cells and reduce number of broken cells in comparison to healthy cells. Cancer remedies that “target” cancer cells can

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FIGURE 5.14 Chemotherapy treatment.

also offer the gain of decreased treatment-associated aspect effects and progressed consequences. Targeted therapy uses tablets to target unique genes and proteins that are involved in the survival and multiplication of cancer cells. This therapy targets tissue surroundings that help cancer growth and targets cells like blood vessel cells that promote cancer growth. Researchers first diagnosed the genetic changes assisting tumor development and then directed their therapy toward preventing specific mutations. Focused healing procedures may block or flip off alerts that inform most cancer cells to grow and divide, prevent the cells from residing longer than usual, and ultimately destroy cancer cells (Gore et al., 2013).

5.5.4

Radiation therapy

Radiation therapy, also known as radiotherapy, uses high-energy radiations to damage or destroy cancer cells by blocking them from developing and separating. Radiation therapy, like surgery, is a community procedure for removing detectable tumors. Radiation treatment has a low success rate in killing tumor cells that have already migrated to other areas of the body. Radiation therapy may be administered either directly or internally. Outside radiation, which comes from a tool outside the body, sends high-voltage rays straight to the tumor site online. The implantation of a small volume of radioactive material in or around most cancers is known as internal radiation or brachytherapy. Radiation may be used to treat or control maximum cancers, or to ease some of the symptoms and signs of most cancers as shown in Fig. 5.15. Now and then radiation is used with different types of other cancers treatment, which include chemotherapy and surgical remedy, and sometimes it is used on its own (Szatkowska & Krupa, 2020).

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FIGURE 5.15 Treatment in radiation therapy.

5.5.5

Hormonal therapy

Hormones are sincerely taking place materials inside the frame that stimulate the increase of hormone-sensitive tissues, collectively with the breast or prostate gland. Even as most cancers occur in breast or prostate tissue, their growth occurs because of the body’s very own hormones. Consequently, drugs that block hormone manufacturing or exchange the way hormones paintings, or the removal of organs that secrete hormones that encompass the endocrine glands, which consist of glands collectively with the thyroid, pancreas, ovaries, or testicles, are techniques of preventing most cancers. Hormone remedy, just like chemotherapy, is a systemic remedy because it can affect most cancer cells in unspecified time in the future within the frame (Fairchild et al., 2015). The estrogen receptor (ER) which regulates cell growth consists of six different functional domains including the ligandbinding domain for estrogen and the DNA-binding domain (Ya¸sar et al., 2017).

5.5.6

Surgery

Surgical treatment is used frequently in case of cancers, such as for diagnosing most cancers, figuring out the stage of cancer, getting rid of the primary tumor, and relieving signs. A biopsy is a rare type of surgical technique that can be used to aid in the diagnosis of cancer. It is a procedure in which a sample of tissue from a suspected cancerous area is taken and examined in a laboratory by a representative. It is effectively completed within the doctor’s workplace. A first-rate biopsy shows the presence of cancer, whereas a terrible biopsy may suggest that no cancer is present in the pattern and vice versa. In the surgical operation, cancer and a few tissues adjoining to the maximum cancers are commonly removed. In addition to promoting the development of different treatment methodologies for cancer, data collected from surgical operations prove beneficial in predicting the risk of most

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cancer recurrences guiding future research. Surgery, as used to cure cancer, is a method by which a skilled surgeon removes the majority of tumors from your body. Huo et al. (2019) studied typical techniques that may be used either during the surgical operation of most cancers or after surgical procedures.

5.6

Stem cell transplant therapy

Stem cells have self-renewal and differentiation capacity that can be used to repair human tissues after chemotherapy. Stem cell transplantation is the method that restores blood-forming stem cells because they grow into one-ofa-kind types of blood cells in most cancer patients who have had their cells destroyed via excessive doses of chemotherapy or radiation therapy. Find out approximately the styles of transplants, element consequences that might arise, and the manner stem cell transplants are implemented in most cancers treatment. White blood cells, which are components of the immune system and make the body resistant to infectious substances; red blood cells, which transport oxygen across the body; and platelets, which aid in blood clotting, are the three main types of blood cells. Stem cells can classified into embryonic stem cells (ESCs) obtained from the inner cell mass of blastocyst and adult stem cells (ASCs) found from any adult tissue. These may be further differentiated into hematopoietic, mesenchymal, and neural stem cells. Using stem cell therapy, cancer treatments have been developed, including hematopoietic stem cells transplantation surgery, mesenchymal stem cell infusion for post-cancer treatment, stem cells for therapeutic carriers, immune cell development, and vaccine production (Zhang et al., 2017).There are six steps involved in stem cell transplantation as shown in Fig. 5.16.

5.6.1

Oncotherapy

Oncotherapy is a method or a treatment by which we can cure cancer. It is a type of oncovirus gene therapy where the modified oncovirus will be sent to the targeted cancerous cells in the host body. Oncolytic virus will replicate in the host cancer cells and start to destroy the cancerous area; also, it won’t damage the normal cells or tissues. Oncotherapy method emerged in the early 1900s but after the discovery of chemotherapy and radiotherapy, viral therapy started to diminish slowly. In the 1990s, again oncolytic viral therapy returned with modified oncoviruses. This method is expected to be an emerging field for cancer treatment. The full method depends on the effectiveness of the oncovirus, as in how it kills cancer cells or how much faster it works. Oncotherapy can be carried out in two ways: the direct method or intratumoral method, where the modified oncovirus is transferred directly to the targeted area in the host, and the systemic or intravenous method, where through an intravenous injection the modified oncovirus is introduced in the

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FIGURE 5.16 Steps involved in stem cells transplantation.

host body. The systemic method was found very useful for metastatic cancers. Oncolytic virus has the potential to destroy all cancerous cells without affecting normal healthy cells through activating the immune system. Normally all cells will show defense when a viral particle enters a normal cell in the body. However, in oncotherapy method, the oncolytic viruses are modified with some kind of deletion or mutation because of which when we induce the oncovirus to the host if it enters a normal cell then the cell will turn on defensive mechanism thus would not be harmed, but if it enters the cancer cell then the cancer cell won’t react and it will be eliminated from the tissue. In another way, if the modified oncovirus enters the cancerous cells, those cells won’t show any defense and will let the virus grow and replicate and our oncovirus will start to kill or destroy the targeted cancerous area. We are selecting the virus by looking at the affinity they show to cells, this might ease out the whole process (Howard et al., 2013).

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Some viruses show more similarity to some cells and some show less. One of the most common examples is that neuronal cells preferred the herpes virus. Recent studies in this field of oncology have shown very promising results. It was very clear that oncoviruses or oncotherapy is a good way to kill cancer almost same as chemotherapy and radiation therapy. Very few studies have been conducted in this field, especially leukemia, which yielded promising results for customized immunotherapy or other oncologies. There are other studies also in radiation therapy and oncoviral therapy ex vivo and in vivo, which showed good results and the treatment is capable of killing tumor cells (Marelli et al., 2018). Oncotherapy started early in the 1900s but at that time it was not well established and did not attract much interest because of the existence of chemotherapy. At that time chemotherapy was most studied and focused area because of its easy methods. Chemotherapy became very friendly to all worldwide very fastly so that oncotherapy remain a little neglected but now again oncotherapy coming gaining interest. Although the oncotherapy field is now an upcoming area, it still has some barriers or difficulties. One of the major problems we are facing in oncotherapy is while we introduce the oncovirus to the host, it is turned off by the immune system of the host body. It was believed that if the oncovirus can cause cancer then it can also destroy cancer if modified properly. In the beginning of oncotherapy there were many side effects because of the overdosage of the virus or the viral toxic content in the body. But later modified oncoviruses were used for clinical trials and there was visible decrease in side effects and an increase in the effective killing of cancer cells. To improve the effect of oncotherapy first factor taken into account is enhanced delivery of oncovirus to the host or targeted area. Then a repeated dosage of the virus is practiced that leads to targeting and retargeting of the cancerous cells. This leads to modified oncolytic virus introduction and inactivation of the defensive mechanism of the host. The clinical and preclinical trials have shown that oncotherapy is one of the best methods other than chemotherapy and radiation therapy (Zhang et al., 2017).

5.7

Future of oncotherapy

Oncotherapy is the most attractive therapy in the cancer treatment field nowadays. Several treatment methods have been practiced in this field to destroy cancerous cells, but only oncotherapy the main method that showed positive results. Using oncolytic-virus-targeted treatment of cancerous cells is emerging in the modern era. The modified oncolytic virus can destroy cancer cells without harming healthy normal cells, and with a combination of chemotherapy and radiation therapy, the amount of oncotherapy that is most appropriate in antiviral and repetitive results is believed to have a bright future for

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cancer treatment in the field of cancer. After looking at the evolution and interest in cancer that many scientists have shown, it is very clear that the field of cancer prevention will be one of the brightest areas in future. Cancer may be treated easily and without any harm to other normal cells with the use of oncolytic viruses. Furthermore, we can learn about the mechanism by which the virus modifies and transitions to anticancer treatment and how it copes with the host immune system. Cancer treatment will be one of the most innovative in the field of oncology medicine in the future (Junaid et al., 2018).

5.8

Conclusion

In this chapter, we have discussed what is an oncovirus, its types, and the mechanisms that can be used in oncotherapy to destroy cancer cells without harming normal cells. This gives a proper insight about oncoviruses and their association with cancer therapy. In the early time when the oncoviruses were identified, all were very scared because of their cancer-causing properties. Later it was found that if an oncovirus can cause cancer then it can also kill cancer. Oncovirus therapy is a very advanced and emerging field for cancer treatment nowadays and is majorly concentrated on studying the interaction of oncolytic viruses and cancer environments. Oncolytic virotherapy is a treatment of cancer cells, with radiation therapy and chemotherapy, by modulation of the oncovirus for cancer treatment such that they target cancer and destroy the cancer-producing stem cells. In many clinical trials, the method has been found successful but the major problem which arises is reappearing or regrowth of tumor. Target-specific tailor-made oncotherapy will be one of the great ideas for developing cancer treatment. Direct induction of oncolytic viruses to a targeted cancer niche by an intrainjection method can increase the success rate of cancer treatment. Major types of advanced treatments using an oncolytic virus for cancer, their results, and obstacles have been discussed here. Now we have an understanding of oncotherapy and major problems of this field. We hope that the future of oncovirus therapy will be successful in the field of cancer therapy.

Acknowledgment The authors thank the Chettinad Academy of Research and Education for the constant support and encouragement during the study.

References Akram, N., Imran, M., Noreen, M., Ahmed, F., Atif, M., Fatima, Z., & Bilal Waqar, A. (2017). Oncogenic role of tumor viruses in humans. Viral Immunology, 30(1), 20
27. Available from https://doi.org/10.1089/vim.2016.0109.

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Ambalathingal, G. R., Francis, R. S., Smyth, M. J., Smith, C., & Khanna, R. (2017). BK polyomavirus: Clinical aspects, immune regulation, and emerging therapies. Clinical Microbiology Reviews, 30(2), 503
528. Available from https://doi.org/10.1128/ CMR.00074-16. Baron, S. (1996). Leptospira
medical microbiology. University of Texas Medical Branch at Galveston. Barrett, J. W., & McFadden, G. (2007). Genus leporipoxvirus (pp. 183
201). Poxviruses. Baumforth, K. R. N., Young, L. S., Flavell, K. J., Constandinou, C., & Murray, P. G. (1999). The Epstein-Barr virus and its association with human cancers. Journal of Clinical Pathology
Molecular Pathology, 52(6), 307
322. Available from https://doi.org/10.1136/ mp.52.6.307. Bayer, V. R., Davis, E., & Gordan, R. A. (2019). Chemotherapy and immunotherapy guidelines and recommendations for practice (2019, pp. 149
189). Oncology Nursing Society. Bennet, J. E., Blaser, M. J., & Dolin, R. (2015). Principles and practice of infectious diseases. Elsevier. Center, D., & Room, M. (2014). Fourth annual neuroscience. Behavior and Health Research Forum. Dawson, M. A., Kouzarides, T., & Huntly, B. J. P. (2012). Targeting epigenetic readers in cancer. New England Journal of Medicine, 367(7), 647
657. Available from https://doi.org/ 10.1056/NEJMra1112635. De-The´, G., Geser, A., Day, N. E., Tukei, P. M., Williams, E. M., Beri, D. P., Smith, P. G., Dean, A. G., Bornkamm, G. W., Feorino, P., & Henle, W. (1978). Epidemiological evidence for causal relationship between Epstein-Barr virus and Burkitt’s lymphoma from Ugandan prospective study. Nature, 274(5673), 756
761. Available from https://doi.org/10.1038/ 274756a0. Doerfler, W. (2012). Impact of foreign DNA integration on tumor biology and on evolution via epigenetic alterations. Epigenomics, 4(1), 41
49. Available from https://doi.org/10.2217/ epi.11.111. Epstein, M. A., Achong, B. G., & Barr, Y. M. (1964). Virus particles in cultured lymphoblasts from burkitt’s lymphoma. The Lancet, 1, 702
703. Available from https://doi.org/10.1016/ s0140-6736(64)91524-7. Fairchild, A., Tirumani, S. H., Rosenthal, M. H., Howard, S. A., Krajewski, K. M., Nishino, M., Shinagare, A. B., Jagannathan, J. P., & Ramaiya, N. H. (2015). Hormonal therapy in oncology: A primer for the radiologist. American Journal of Roentgenology, 204(6), W620
W630. Available from https://doi.org/10.2214/AJR.14.13604. Flanagan, J. M. (2007). Host epigenetic modifications by oncogenic viruses. British Journal of Cancer, 96(2), 183
188. Available from https://doi.org/10.1038/sj.bjc.6603516. Fukuhara, H., Ino, Y., & Todo, T. (2016). Oncolytic virus therapy: A new era of cancer treatment at dawn. Cancer Science, 107(10), 1373
1379. Available from https://doi.org/ 10.1111/cas.13027. Gore, L., DeGregori, J., & Porter, C. C. (2013). Targeting developmental pathways in children with cancer: What price success? The Lancet Oncology, 14(2), e70
e78. Available from https://doi.org/10.1016/S1470-2045(12)70530-2. Gupta, S. P. (2014). Cancer-causing viruses and their inhibitors. Cancer-Causing Viruses and their Inhibitors (pp. 1
482). CRC Press. Available from https://doi.org/10.1201/b16780. Heegaard, E. D., & Brown, K. E. (2002). Human parvovirus B19. Clinical Microbiology Reviews, 15(3), 485
505. Available from https://doi.org/10.1128/CMR.15.3.485-505.2002.

106

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Howard, K., Joe, G., Sara, H., Michael, P., Sebastian, B., H, S. R., Philip, F., Carl, R., & Yvonne, S. (2013). Oncolytic virus therapy for cancer. Oncolytic Virotherapy, 31, 8901. Available from https://doi.org/10.2147/OV.S38901. Huo, Q., Yang, Q., Gorczynski, R. M., & Li, T. (2019). Oncological minimally invasive surgery. Journal of Oncology, 2019, 3297. Available from https://doi.org/10.1155/2019/1903297. International Agency for Research on Cancer. (2018). Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. International Agency for Research on Cancer. Javier, R. T., & Butel, J. S. (2008). The history of tumor virology. Cancer Research, 68(19), 7693
7706. Available from https://doi.org/10.1158/0008-5472.CAN-08-3301. Junaid, R., M., L. J., N., G. S., A., S. K., & S., K. H. (2018). Oncolytic virus immunotherapy: Future prospects for oncology. Journal for ImmunoTherapy of Cancer, 6, 140. Available from https://doi.org/10.1186/s40425-018-0458-z. Khoury, J. D., Tannir, N. M., Williams, M. D., Chen, Y., Yao, H., Zhang, J., Thompson, E. J., Meric-Bernstam, F., Jeffrey Medeiros, L., Weinstein, J. N., & Su, X. (2013). Landscape of DNA virus associations across human malignant cancers: Analysis of 3,775 cases using RNA-seq. Journal of Virology, 87(16), 8916
8926. Available from https://doi.org/10.1128/ JVI.00340-13. Liang, T. J. (2009). Hepatitis B: The virus and disease. Hepatology, 49(5), S13
S21. Available from https://doi.org/10.1002/hep.22881. Marelli, G., Howells, A., Lemoine, N. R., & Wang, Y. (2018). Oncolytic viral therapy and the immune system: A double-edged sword against cancer. Frontiers in Immunology, 9, 866. Available from https://doi.org/10.3389/fimmu.2018.00866. Nikitin, P. A., & Luftig, M. A. (2011). At a crossroads: Human DNA tumor viruses and the host DNA damage response. Future Virology, 6(7), 813
830. Available from https://doi.org/ 10.2217/fvl.11.55. Nolkemper, S., Reichling, J., Sensch, K. H., & Schnitzler, P. (2010). Mechanism of herpes simplex virus type 2 suppression by propolis extracts. Phytomedicine, 17(2), 132
138. Available from https://doi.org/10.1016/j.phymed.2009.07.006. Pei, Y., Wong, J. H. Y., & Robertson, E. S. (2020). Targeted therapies for epstein-barr virusassociated lymphomas. Cancers, 12(9), 1
21. Available from https://doi.org/10.3390/ cancers12092565. Raab-Traub, N., & Flynn, K. (1986). The structure of the termini of the Epstein-Barr virus as a marker of clonal cellular proliferation. Cell, 47(6), 883
889. Available from https://doi.org/ 10.1016/0092-8674(86)90803-2. Ramos, J. C., & Lossos, I. S. (2011). Newly emerging therapies targeting viral-related lymphomas. Current Oncology Reports, 13(5), 416
426. Available from https://doi.org/10.1007/ s11912-011-0186-8. Roizman, B. (1990). Herpesviridae: A brief introduction. Fields Virology. Lippincott Williams & Wilkins, Philadelphia, PA. Schedin, T. B., Borges, V. F., & Shagisultanova, E. (2018). Overcoming therapeutic resistance of triple positive breast cancer with CDK4/6 inhibition. International Journal of Breast Cancer, 2018, 7835095. Available from https://doi.org/10.1155/2018/7835095. Shi, Q., Lee, J. W., Wang, X. S., Fisch, M. J., Chang, V. T., Wagner, L., & Cleeland, C. S. (2020). Testing symptom severity thresholds and potential alerts for clinical intervention in patients with cancer undergoing chemotherapy. JCO Oncology Practice, 16(9), E893
E901. Available from https://doi.org/10.1200/JOP.19.00403.

Oncoviruses: future prospects of molecular mechanisms Chapter | 5

107

Soto, D., Song, C., & McLaughlin-Drubin, M. E. (2017). Epigenetic alterations in human papillomavirus-associated cancers. Viruses, 9(9), 248. Available from https://doi.org/ 10.3390/v9090248. Szatkowska, M., & Krupa, R. (2020). Regulation of DNA damage response and homologous recombination repair by microRNA in human cells exposed to ionizing radiation. Cancers, 12(7), 1
19. Available from https://doi.org/10.3390/cancers12071838. Tashiro, H., & Brenner, M. K. (2017). Immunotherapy against cancer-related viruses. Cell Research, 27(1), 59
73. Available from https://doi.org/10.1038/cr.2016.153. Turnell, A. S. (2008). Adenoviruses: Malignant transformation and oncology. Encyclopedia of virology (pp. 9
16). Elsevier Ltd. Available from https://doi.org/10.1016/B978-0123744104.00357-5. Vogelstein, B., & Kinzler, K. W. (2002). The genetic basis of human cancer. Springer. Weber, S., Hofmann, A., Herms, S., Hoffmann, P., & Doerfler, W. (2015). Destabilization of the human epigenome: Consequences of foreign DNA insertions. Epigenomics, 7(5), 745
755. Available from https://doi.org/10.2217/epi.15.40. Xu, M., Zhang, W. L., Zhu, Q., Zhang, S., Yao, Y. y, Xiang, T., Feng, Q. S., Zhang, Z., Peng, R. J., Jia, W. H., He, G. P., Feng, L., Zeng, Z. L., Luo, B., Xu, R. H., Zeng, M. S., Zhao, W. L., Chen, S. J., Zeng, Y. X., & Jiao, Y. (2019). Genome-wide profiling of Epstein-Barr virus integration by targeted sequencing in Epstein-Barr virus associated malignancies. Theranostics, 9(4), 1115
1124. Available from https://doi.org/10.7150/thno.29622. Ya¸sar, P., Ayaz, G., User, S. D., Gu¨pu¨r, G., & Muyan, M. (2017). Molecular mechanism of estrogen
estrogen receptor signaling. Reproductive Medicine and Biology, 16(1), 4
20. Available from https://doi.org/10.1002/rmb2.12006. Yoo, C. B., & Jones, P. A. (2006). Epigenetic therapy of cancer: Past, present and future. Nature Reviews Drug Discovery, 5(1), 37
50. Available from https://doi.org/10.1038/nrd1930. Zhang, C. L., Huang, T., Wu, B. L., W. X., & Liu, D. (2017). Viral oncotherapy in leukemia Ce´sar Alonso Verdeja-Robles and JearimDanahe´ Rodriguez-Alvarez Oncolytic viruses in cancer therapy. Oncotarget, 8(43). Zhang, X., Zhang, Z., Zheng, B., He, Z., Winberg, G., & Ernberg, I. (2013). An update on viral association of human cancers. Archives of Virology, 158(7), 1433
1443. Available from https://doi.org/10.1007/s00705-013-1623-9.

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Chapter 6

Multi-omics methods and tools in dissecting the oncovirus behavior in human host Sheik S.S.J. Ahmed1, Ramakrishnan Veerabathiran2, Mookkandi Sudhan3, Harsh Panwar4 and Prabu Pramasivam5 1

Multi-omics and Drug Discovery Lab, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India, 2Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India, 3Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Chennai, Tamil Nadu, India, 4Department of Dairy Microbiology, College of Dairy Science and Technology, Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, Punjab, India, 5Department of Neurology, University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM, United States

6.1

Introduction

Recent advancements in biotechnology and high-throughput techniques have generated an enormous amount of biological data. This huge data cannot be processed manually with ease, and hence we depend on computers to extract meaningful biological information (Raja et al., 2017). This led to the evolution of a new domain called bioinformatics which merges biology and information technologies.

6.1.1

Definition

Bioinformatics collects, organizes, and analyzes biological data to retrieve useful biological information on biomolecules, such as DNA, RNA, proteins, and metabolites. Bioinformatics integrates multiple domains such as computer science, engineering, and biostatistics to fill the vital gaps between molecules and pathophysiology (Ma & Dai, 2011). With advancements in omics research, bioinformatics witnessed tremendous growth over the last decade. In the current scenario, most research labs generate massive biological data as the outcome of omics-based experiments. These extensive data Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00007-8 Copyright © 2023 Elsevier Inc. All rights reserved.

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are stored in databases and shared across the world with the help of the internet. Bioinformatics plays a vital role in processing the omics data and helps to understand and address the multifaceted problems associated with biological research (Gomez-Cabrero et al., 2014). Omics is an emerging field of technology that mainly handles a large family of cellular molecules such as genes, proteins, and metabolites (Horgan & Kenny, 2011). This technology helps to understand the interrelationship between the cellular molecules that collectively participate in various biological and molecular functions in cells, tissue, or organs. In addition, molecular profiling using omics helps to differentiate the occurrence of molecular and cellular events between the cell, tissue, organs, or species. Currently, a huge amount of data generated using high-throughput omics techniques have been stored in biological databases (Shulaev, 2006). Furthermore, the data are processed through a bioinformatics approach to retrieve valuable and meaningful information. The omics-based techniques are primarily classified as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metallomics. Genomics deals with the coding and noncoding regions of a genome. Transcriptomics is the study of RNA, and proteomics deals with proteins, while metabolomics explores metabolites in a cell, tissue, and body fluids (e.g., serum, plasma, urine, and cerebrospinal fluids) (Haider & Pal, 2013). At the same time, the assessment of metal homeostasis in cells and tissue is referred to as metallomics (Fig. 6.1). Since

FIGURE 6.1 Various domains of omics. Omics that demonstrate functional behavior of genes, transcript, proteins, and metabolites.

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omics technology deals with a large family of cellular components; it is suggested to have a wide range of applications in the medicine, pharmaceutical, agriculture, and marine sectors. Currently, these omics-based approaches are primarily applied in medicine to understand pathophysiology, thus potentially helping in disease management (Noorbakhsh et al., 2015). In particular, the omics concept helps to determine the etiology, diagnosis, and prognosis of several diseases (Wheelock et al., 2013). This information is the vital resource for biomarkers and drug discovery processes that contribute to developing several domains within omics, including pharmacogenomics, lipidomics, glycomics, and metagenomics.

6.2 6.2.1

Types of omics Genomics

The entire DNA molecule present within a cell is referred to as the genome. The area of genomics is mainly concerned with genome sequencing and its analyses. The current focus of genomics is to perform whole-genome sequencing and genetic mapping, which helps to understand the function, mutation, and evolution of an organism (Krier et al., 2016; M. Perez-deCastro et al., 2012). Based on the potential application, genomics is further classified into functional genomics, structural genomics, and comparative genomics (Fig. 6.2). Functional genomics deals with the functions of genes; structural genomics deals with the genomic regions that contribute to the structural components of cells; whereas comparative genomics deals with the

FIGURE 6.2 Various domains of genomics. Genomics classifies the behavior of genes into functional structural and comparative genomics.

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structural and functional comparison between genes of the same or different organism (Tsai et al., 2016; Wilson et al., 2004). At present, several high-throughput methods are being used for genome analysis that helps in the diagnosis of genetic diseases such as cancer, sickle cell anemia, cystic fibrosis, and Huntington’s disease, which result from a certain abnormality in DNA sequences that encodes for specific proteins (Nishant et al., 2009; Reuter et al., 2015). Recent reports suggest that genomic studies have increased the rate of drug targets research on genes useful in the pharmaceutical field. In addition, genomics showed a potential contribution in the field of cancer research. Approximately 12% of all human cancers are caused by oncoviruses. These cancers are of very distinct types because of the complex nature of the virus
host cell interaction, which leads to cancer development. In cancer, the genetic profile of oncoviruses was led to improve the dissecting of the behavior of human host cells. Applied research has led to the development of diagnostic, therapeutic, and even prophylactic approaches for these viral-mediated cancers. Oncogenic human papillomaviruses (HPV) and anogenital cancers serve as a classic example of this successful story, from the identification of cervical cancer epidemiology to the prophylactic vaccine’s development against the most oncogenic HPV. Oncoviruses are generally classified as direct or indirect carcinogens. Direct carcinogenic viruses possess viral oncogenes that can directly contribute toward neoplastic cellular transformation, whereas indirect carcinogens can result in oncogenic transformation mediated through chronic inflammation. The oncogenic potential of some viruses has been clearly established, and thus they are being targeted for cancer prevention and treatment. Successful vaccines are already available against hepatitis B virus (HBV) and HPV infection. Antiviral malignancy treatments and therapeutic vaccines are currently under investigation. Leads from few metagenomic projects have provided information on oncogenic DNA viruses (Table 6.1) including

TABLE 6.1 Genomics sequencing of oncovirus organisms. Organism

Kingdom

Group

Hepatitis B virus (HBV)

Pararnavirae

Hepadna

Hepatitis C virus (HCV)

Orthornavire

Flaviviridae

Human herpesvirus-8 (HHV-8)

Heunggongvirae

Herpusviridae

Human papillomavirus (HPV)

Shotokuvirae

Papillomaviridae

Merkel cell polyomavirus (MCPyV)

Shotokuvire

Papillomaviridae

Human T-cell lymphotropic virus-1 (HTLV-1)

Pararnavirae

Revtraviridae

Epstein
Barr virus (EPV)

Heunggongvirae

Herpusviridae

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Epstein
Barr virus (EBV), HBV, HPV, human herpesvirus-8 (HHV-8), Merkel cell polyomavirus (MCPyV), hepatitis C virus (HCV), and human Tcell lymphotropic virus-1 (HTLV-1) (Butt & Miggin, 2012). Epigenomics deals with the genome-wide characterization of reversible modifications of DNA or DNA-associated proteins, for example, DNA methylation or histone acetylation. Covalent modifications of DNA and histones regulate gene transcription and subsequently determine cellular fate. Both genetic and environmental factors can influence these modifications, which can be long-lasting and sometimes heritable. While the role of epigenetic modifications as mediators of transgenerational environmental effects remains controversial, their importance in biological processes and disease development is evident from many epigenome-wide association studies that have been reported. For example, differentially methylated regions of DNA can indicate the disease status of metabolic syndrome, cardiovascular disease, cancer, and many other such pathophysiologic states. Epigenetic signatures are often tissue-specific, and several large consortia are focusing on establishing comprehensive epigenomic maps in multiple human tissues. Thus, in addition to the insight gained from identifying epigenetic modifications correlating with different diseases, data generated by several studies have great potential to enhance our functional interpretation of genetic variants residing in those regions or of epigenetic markers associated with the disease. Particularly, epigenomic profiling has been performed to understand the behavior of cancer-causing oncoviruses. Genomics classifies the behavior of genes into functional, structural, and comparative genomics.

6.2.2

Transcriptomics

Transcriptomics deals with the complete set of transcriptomes (RNAs) encoded by a gene of a specific cell or organism. Understanding the transcriptome of an organism gives insight into the time-dependent activation of the gene across various environmental and experimental conditions. Human host transcriptomic behavior upon oncovirus infection has been potentially explored, which helped in understanding and management of virusassociated tumors by demonstrating viral integration patterns and host
pathogen interactions (Slatteryet al. 2012). Advances in transcriptome technology, such as DNA microarray and next-generation sequencing, make it possible to understand the gene regulation in a cell or tissue. These transcriptome-based techniques generate enormous data that have been stored in the databases for enabling biomedical researchers to understand disease pathology, biomarker detection, and drug discovery process (Alizon et al., 2019) (Table 6.2). The development of high-throughput DNA and RNA sequencing, in conjunction with new bioinformatics tools, allows for deep, whole-genome sequencing of the host and pathogen. Recent improvements

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TABLE 6.2 Transcriptome-based profiling of cancer oncogenic virus. S. No

Oncovirus

NCBI GEO DataSet

Title

1.

Hepatitis B virus (HBV)

GSE119072

Microarray upon Sp110 knockdown in HepG2.2.15 cells

2.

HBV

GSE160648

Gene expression profiles in epigenetically reprogrammed HepG2 cells

3.

HBV

GSE114783

Microarray gene expression from HBV infection to hepatocellular carcinoma (HCC)

4.

HBV

GSE132628

Molecular signature of HBV regulation by interferon gamma (IFN)-γ in primary human hepatocyte

5.

HBV

GSE96851

Role of humoral immunity against HBV core antigen in the pathogenesis of acute liver failure

6.

HBV

GSE109824

HBV deregulates cell cycle to promote viral replication and a premalignant phenotype

7.

HBV

GSE66699

Liver gene expression profiles of IFN therapy in chronic hepatitis B patients (Exon)

8.

HBV

GSE66698

Liver gene expression profiles of IFN therapy in chronic hepatitis B patients (mRNA)

9.

HBV

GSE84044

Characterization of gene expression profile in HBV-related liver fibrosis patients

10.

HBV

GSE64878

Global rewiring of p53 transcription regulation by the HBV X (HBx) protein

11.

HBV

GSE72068

Huh-7-Mock and Huh-7-HBV

12.

HBV

GSE72068

Gene expression response to HBV infection in primary human hepatocytes (PHH)

13.

HBV

GSE67764

β-Arrestin1 is involved in hepatocellular carcinogenesis via an inflammation-mediated Akt signal

14.

HBV

GSE55092

Viral expression and molecular profiling in liver tissue vs microdissected hepatocytes in HBVassociated HCC (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

15.

HBV

GSE44074

Gene expression profiling of hepatitis B- and hepatitis C-related HCC using graphical Gaussian modeling

16.

HBV

GSE41737

MicroRNA-27a regulates lipid metabolism and inhibits hepatitis C virus replication in human hepatoma cells

17.

HBV

GSE47197

Human liver infected by HBV: tumoral part vs nontumoral part of the liver

18.

HBV

GSE38941

Liver regeneration gene signature in HBV-associated acute liver failure identified by gene expression profiling

19.

HBV

GDS4387

HBV-associated acute liver failure (ALF) patients: liver explants

20.

HBV

GSE20140

Combining clinical, pathology, and gene expression data to predict recurrence of HCC

21

HBV

GSE19665

Aberrant DNA methylation in hepatitis B and C virus-related HCC

22

HBV

GSE22058

miR-122 as a regulator of mitochondrial metabolic gene network in HCC

23

HBV

GSE14668

B-Cell gene signature with massive intrahepatic production of antibodies to hepatitis B core antigen in HBVassociated acute liver failure

24

HBV

GSE10186

Integrative transcriptome analysis reveals common molecular subtypes of human HCC

25

HBV

GSE10143

Gene expression in fixed tissues and outcome in HCC

26

HBV

GSE10141

Gene expression in fixed tissues and outcome in hepatocellular carcinoma (Training Set, HCC)

27

HBV

GSE11064

AdHBx UV vs AdEasy UV vs HepG2 (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

28

HBV

GSE140114

Gene expression analysis of different passages and subclones of the Huh7 cell line

29

Hepatitis C virus (HCV)

GSE128726

Energy metabolism and cell motility defect in natural killer (NK) cells from patients with hepatocellular carcinoma

30

HCV

GSE117858

Genome-wide comparison of differentially expressed genes in hepatoma cell lines with and without treatment of dimethyl dicarboxylate biphenyl

31

HCV

GSE78737

Infection with HCV depends on TACSTD2, a regulator of claudin-1 and occludin highly downregulated in HCC

32

HCV

GSE78736

Infection with HCV depends on TACSTD2, a regulator of claudin-1 and occludin highly downregulated in HCC [cell line]

33

HCV

GSE69715

Infection with HCV depends on TACSTD2, a regulator of claudin-1 and occludin highly downregulated in HCC [patient]

34

HCV

GSE110312

Expression data from HCV infected hepatoma carcinoma cell line Huh7-MAVSR cells

35

HCV

GSE64605

Transcriptome profiles of differentiated hepatoma cells infected with oncogenic HCV

36

HCV

GSE79340

Genome-wide transcriptomic analysis of hepatocyte-like cells upon ectopic miR-146a-5p expression

37

HCV

GSE71757

HCV upregulates B-cell receptor signaling: a novel mechanism for HCVassociated B-cell lymphoproliferative disorders

38

HCV

GSE68927

Comparison of Huh6 and Huh7 cells under IFN gamma treatment (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

39

HCV

GSE70781

HCV-induced expression profiling of hepatic and nonhepatic cancer cell lines

40

HCV

GSE62546

Quantitative mRNA expression comparison of HCV replicon (2a) on Huh7.5 cell lines

41

HCV

GSE60948

Expression data of genome-length HCV RNA-replicating OL8(3.5Y) cells and OL8(3.5Y)-derived ribavirin-resistant cells

42

HCV

GSE54101

Gene-expression profiles of paired biopsies and explanted liver from HCV-infected individuals

43

HCV

GSE54100

Expression profiles of 186-gene signature in US HCV-related liver cirrhosis

44

HCV

GSE54099

Expression profiles of 186-gene signature in Italian HCV-related liver cirrhosis

45

HCV

GSE51699

Endogenous intrahepatic IFNs and association with IFN-free HCV treatment outcome

46

HCV

GSE44074

Gene expression profiling of hepatitis B- and hepatitis C-related HCC using graphical Gaussian modeling

47

HCV

GSE41737

MicroRNA-27a regulates lipid metabolism and inhibits HCV replication in human hepatoma cells

48

HCV

GSE41804

Hepatic gene expression of HCV related HCC and noncancerous tissue with Il28B rs8099917 TT genotype and TG/GG genotype

49

HCV

GDS4887

IL-28B polymorphism effect on HCV-related HCC: resected live

50

HCV

GSE40812

Impaired TLR3-mediated immune responses from macrophages of patients chronically infected with HCV (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

51

HCV

GSE38226

MicroRNA profiling in TGFβ signaling and development of fibrosis

52

HCV

GSE38542

HIV/HCV coinfection activates a type 1 IFN response in monocytes that correlates with cognitive impairment

53

HCV

GSE32886

HCV-induced expression profiling of hepatic and nonhepatic cancer cell lines [mock-treated]

54

HCV

GSE19665

Aberrant DNA methylation in hepatitis B and C virus-related HCC

55

HCV

GSE17856

Gene expression in nontumoral liver tissue and recurrence-free survival in HCV-positive HCC

56

HCV

GSE14323

RMA expression data for liver samples from subjects with HCV, HCV-HCC, or normal liver

57

HCV

GSE3632

Post-hepatitis C and post-alcoholism HCCs: series 3

58

HCV

GDS2239

HCV core protein effect on hepatocyte cell line

59

HCV

GSE97318

Restoring PU.1 induces apoptosis and modulates viral transactivation via IFNstimulated genes in primary effusion lymphoma

60

Human herpesvirus (HHV)

GSE16547

Kaposi’s sarcoma-associated herpes virus (KSHV) manipulates notch signaling by upregulating Dll4 and JAG1 to alter cell cycle gene expression in lymphatic endothelial cells (LECs)

61

HHV

GSE6489

HHV-8 infection of pulmonary microvascular entdothelialcells

62

HHV

GDS3007

KSHV encodes an ortholog of miR-155

63

HHV

GDS1063

Primary effusion lymphomas and associated viral infections

64

HHV

GSE1880

Role of LANA in KSHV latent infection (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

65

HHV

GDS988

KSHV LANA overexpression in B lymphoid cells: time course

66

HHV

GSE69697

Altered global gene profile in KSHV 1 PEL cells exposure to dhC16-Cer

67

HHV

GSE138554

Characterization of organoid culture

68

Human papillomavirus (HPV)

GSE118776

E5 HPV type 16 oncoprotein modify ST3GAL3 and ST6GAL1 gene expression

69

HPV

GSE90930

Glycogene expression profiles based on microarray data of cervical carcinoma HeLa cells partially silenced in E6 and E7 HPV oncogenes

70

HPV

GSE75132

TMEM45A, SERPINB5 and p16INK4A transcript levels are predictive for development of high-grade cervical lesions

71

HPV

GSE73761

Changes in global gene expression profiles induced by HPV 16 E6 oncoprotein variants in cervical carcinoma C33-A cells

72

HPV

GSE65858

Gene expression patterns and TP53 mutations are associated with HPV RNA status, lymph node metastasis, and survival in head and neck cancer

73

HPV

GSE65166

Whole-genome analysis of cells permissive for late gene expression of HPV-16

74

HPV

GSE46842

Inhibitors of differentiation-1 promotes transformation of human papillomavirus type 16-immortalized cervical epithelial cells

75

HPV

GSE29570

The mtDNA amerindian haplogroup B2 enhances the risk for cervical cancer of HPV: deregulation of mitochondrial genes may be involved

76

HPV

GSE28266

Expression data from BJ-hTERT cells expressing vector, cyclin E, c-Myc or coexpression of both (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

77

HPV

GSE17892

HPV16 E7 expression effect on expression profile mediated by tumor necrosis factor

78

HPV

GSE8288

Identification of markers associated with deregulated hTERT during HPVmediated transformation

79

HPV

GSE11488

Molecular evidence and gene expression profiles implicating involvement of HPV in human retinoblastoma

80

HPV

GSE3292

Gene expression signature of HPV in head and neck squamous cell carcinoma

81

HPV

GDS1667

Head and neck squamous cell carcinoma tumors harboring HPV

82

HPV

GSE137328

MCPyV targets NDRG1 to promote cellular proliferation

83

Merkel cell polyomavirus (MCPyV)

GSE68503

Cellular transcriptional responses to MCPyV T antigens

84

MCPyV

GSE42334

Effect of cigarette smoke extract, cisplatin, nicotine and/or ionizing radiation on mRNA and microRNA expression in the NCI-H460 human lung large cell carcinoma cell line

85

MCPyV

GSE61589

Gene and microRNA expression in normal and tumoral esophageal squamous cell lines

86

MCPyV

GSE50434

Comparative transcriptional profiling of human Merkel cells and Merkel cell carcinoma

87

MCPyV

GSE44843

Involvement of miRNAs in the differentiation of human glioblastoma multiforme brain tumor stem-like cells

88

MCPyV

GSE33127

Comparison of microarray platforms for measuring differential microRNA expression (Continued )

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TABLE 6.2 (Continued) S. No

Oncovirus

NCBI GEO DataSet

Title

89

MCPyV

GSE119072

Microarray upon Sp110 knockdown in HepG2.2.15 cells

90

MCPyV

GSE39612

Distinct gene expression profiles of viral- and nonviral-associated Merkel cell carcinoma revealed by transcriptome analysis

91

MCPyV

GSE26177

Functional evidence that Drosha overexpression in cervical squamous cell carcinoma affects cell phenotype and microRNA profiles

92

Human T-cell lymphotropic virus1 (HTLV-1)

GSE16219

Identification of genes that are regulated by TAK1 in human cutaneous T cell lymphoma HuT-102 cells

93

Epstein
Barr virus (EPV)

GSE108137

Snail-dependent epithelial splicing regulatory protein 1 (ESRP1) silencing drives malignant transformation of human pulmonary epithelial cells

94

EPV

GSE41015

Dynamic transcriptome analysis reveals new prognosis biomarker in colorectal cancer

95

EPV

GSE39807

Gene and microRNA expression data from tumor-induced CD11b 1 myeloid-derived suppressor cells (MDSC)

96

EPV

GSE44843

Involvement of miRNAs in the differentiation of human glioblastoma multiforme brain tumor stem-like cells

97

EPV

GSE32688

Integrative survival-based molecular profiling of human pancreatic cancer

98

EPV

GSE19693

STAR RNA-binding protein, quaking, suppresses cancer via regulation of microRNA

in genomics and transcriptomics have made it possible to be precise. Hence, the transcriptomics profile of oncoviruses will enhance the knowledge of the host response at various critical time points that would be potentially useful for the development of novel anticancer drugs (Hasin et al., 2017).

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Oncogenic Viruses Volume 2

Proteomics

Proteomics is the study of all the proteins expressed in a cell, tissue, or organism. Protein is a complex molecule derived from the mRNA transcript synthesized in the cytoplasm of the cell for performing a molecular function. Understanding the cell proteome has become an important discipline of gene function, which provides insight into the mechanisms and biological processes. In general, proteomics provides information on protein structure, expression, and function that helps to understand the perturbations in molecular pathways of complex diseases, which improves the therapeutic properties of the protein, diagnostic markers, and protein replacement therapy (Slatteryet al. 2012). The current advancement in biotechnology made it possible to examine the expression, structure, and function of the protein using high-throughput technologies such as chromatography, mass spectroscopy, two-dimensional differential gel electrophoresis, protein microarray, X-ray diffraction, and nuclear magnetic resonance (NMR) spectroscopy (Zhang et al., 2014). These techniques contribute to the field of expression, structural, and functional proteomics (Fig. 6.3).

6.2.3.1 Types of proteomics 6.2.3.1.1 Protein expression proteomics It is a quantitative approach to estimate the level of proteins expressed in a cell as a function of stimulation, environmental conditions, or at the state of pathology. This proteomics analysis will be helpful in identifying the

FIGURE 6.3 Domains of proteomics. Proteomics classifies the behavior of proteins as functional, structural, and expression.

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signaling mechanisms under various biological conditions (e.g., time, drug, disease, or environmental toxins) (Graves & Haystead, 2002). Highthroughput techniques such as two-dimensional differential gel electrophoresis and protein microarray are currently used in expression proteomics which helps identify proteins responsible for various diseases, including heart diseases and cancers (Aslam et al., 2017). 6.2.3.1.2

Structural proteomics

The major goal of structural proteomics is to elucidate the three-dimensional (3D) structure of the protein encoded by the genome of an organism. Structural proteomics helps to understand the structure of proteins which provide clues about their biological and cellular functions (Manjasetty et al., 2012). Recent development in X-ray and NMR technologies has enabled the elucidation of the structure of the protein with high resolution in a limited time scale. Such an unprecedented rate of structure elucidation resulted in tremendous growth in the number of protein structures that were being deposited in databases such as Protein Data Bank (PDB). 6.2.3.1.3

Functional proteomics

An increase in the number of proteomic projects generates several protein sequences whose functions are largely unknown. Functional proteomics is an emerging field of proteomics that explores the biological functions of proteins. Investigating the protein functions will unravel the protein mechanisms and their interacting partners within the cell. In addition, the elucidation of protein interactions in the cell will provide the clue on signaling pathways which may be helpful for the treatment of complex diseases (Monti et al., 2007). In general, the protein of interest is expressed with a suitable tag with its specific partner (protein) to enable its separation from the cellular extract. Furthermore, each protein within the multi-protein complex is identified by subsequent mass spectrometric procedures. This helps to retrieve information on protein signaling and understanding the role of protein in various disease mechanisms and protein
drug interactions in a cell. Oncogenic viruses are responsible for around 15% of human cancers. Several recent studies document the prospects and challenges of viral proteomics in the study of oncogenic viruses (Table 6.3). These viruses have coevolved with their hosts and cause persistent infections. Each virus encodes oncoproteins that manipulate key cellular pathways to promote viral replication and evade host immune response. Viral proteomics can identify cellular pathways perturbed by a viral infection, identify cellular proteins involved in viral persistence and oncogenesis, and identify diagnostic and development of therapeutic targets. Conversely, a large number of oncolytic viruses have shown great potential for the treatment of certain types of cancer

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(Dzutsev et al., 2017; Guven-Maiorov et al., 2020; Kuzembayeva et al., 2012; Lupberger et al., 2019; Quiles et al., 2009; Seo et al., 2018) (Table 6.3).

6.2.3.2 Proteomic techniques Proteins are important regulators for biological activity, and their concentrations are influenced by mRNA as well as the host translational process. Proteins are the most reliable or appropriate data to characterize the biological system. Proteomics is the most important methodology for understanding cellular mechanisms, and is even more complicated than genomics. Change in gene expression levels could be measured using RNA or even by proteomics profiling to distinguish between two biological states of the cell. Here we suggest a few methodologies that are adopted for proteomic analysis. 6.2.3.2.1 Chromatography Chromatography-based methods, which include ion-exchange chromatography, size exclusion chromatography, and affinity chromatography, were used to get the purified proteins. The enzyme-linked immunosorbent assay (ELISA) and western blotting could be used to evaluate specific proteins. Both methods are capable of determining specific proteins. For complicated protein separation, two-dimensional gel electrophoresis (2-DE), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and twodimensional differential gel electrophoresis (2D-DIGE) are utilized. Proteins are separated into spots by 2-DE, with each spot representing a single protein. The protein may be enzymatically digested, and the peptide sequence could be determined based on mass using spectrometry. The mass of the degraded proteins was quantified by using a matrix-aided laser desorption ionization time-of-flight (MALDI-TOF). The MALDI-TOF-MS (mass spectroscopy) creates a massive amount of single charged ions, which have been used to determine the number of peptides. It generates protein data based on the masses after processing the data from the MS spectrum. 6.2.3.2.2

Mass spectroscopy

MS is a method for proteome analysis. In proteomic research, MS has become a gold standard technique to differentiate various proteins. By using the high-resolution mass spectrometry, qualitative data re obtained from the protein samples. The advanced high-resolution mass analyzers are mostly utilized to separate, identify, and splice by a mass filter, which will reduce the data complexity on the emitted primary ions. Moreover, MS methods were proven to be highly dependable for identifying and distinguishing proteins in various disease conditions with great accuracy, coverage, and reproducibility.

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TABLE 6.3 Cancer proteomic studies. S. No

Organism

Journal Name

Title of the article

Authors

1.

Hepatitis C virus

Gastroenterology

Combined analysis of metabolomes, proteomes, and transcriptomes of hepatitis C virusinfected cells and liver to identify pathways associated with disease development

Lupberger et al. (2019)

2.

Epstein
Barr virus

Plos One

Comparing proteomics and RISC immune precipitations to identify targets of Epstein
Barr viral miRNAs [RISC-IPseq]

Kuzembayeva et al. (2012)

3.

Epstein
Barr virus

Molecular Endocrinology

Mutational analysis of progesterone receptor delineates different sets of regulated genes

Quiles et al. (2009)

4.

Epstein
Barr virus

Journal of Molecular Biology

HMI-PRED: A web server for structural prediction of host
microbe interactions based on interface mimicry

GuvenMaiorov et al. (2020)

5.

Oncovirus

Annual Review of Immunology

Microbes and cancer

Dzutsev et al. (2017)

6.

Oncovirus

Stem Cells International

Stemness-attenuating miR-503
3p as a paracrine factor to regulate growth of cancer stem cells

Seo et al. (2018)

6.2.3.2.3

X-ray Crystallography

X-ray crystallography (X-ray powder diffraction [XRD]) is an electromagnetic radiation technique, which absorbs and emits radiation in the frequency range of 0.01
10 nm. XRD is mainly used to identify the arrangement of

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atoms within a crystal in diverse crystallized components. XRD technique reveals the three-dimensional structure and function of several biological substances such as proteins and nucleic acids. A purified protein crystal is subjected to an X-ray beam, and the X-ray is diffracted by the atoms in the protein crystals. An X-ray beam is diffracted in a different directions based on the arrangement of atoms into the crystal as well as the number of electrons in the atoms. Diffraction is generated from the angles and sensitivities of these diffracted beams. The electron density map is used to analyze the mean position of the atoms in a crystallized protein and it estimates the three-dimensional structure of proteins. 6.2.3.2.4 Nuclear magnetic resonance spectroscopy NMR spectroscopy works on the principle of electromagnetic radiation, which absorbs and emits radiation in the frequency range of 40
1000 MHz. It identifies molecules by atomic nuclear spin depending on their nature when the magnetic field is applied. Regularly in proteomics, 1H and 13C atoms were analyzed to get the precise structure of the protein. It identifies the major bonds in complex macromolecules, thus by having the structural information one can predict the function of the protein. Nowadays NMR spectroscopy has become a vital tool for elucidating structural proteomics studies. Both XRD- and NMR-based structural proteomics could provide complete structural datasets for predicting the essential biological activities of putative proteins discovered in many studies.

6.2.3.3 Computational process A massive amount of proteomics data are obtained with help of highthroughput technology. Bioinformatics systems are designed to manage massive amounts of data and their storage. Computational biology technologies have been proposed for 3D structural characterization, protein domain and finding motifs, protein
protein interaction analysis, and MS data processing. The evolutionary connection between sequences and structures was determined by alignment techniques (Kuzembayeva et al., 2012; Lupberger et al., 2019). Proteome analysis, utilizing two or more different methods, offers an understanding of the characteristics of protein in the cell, which includes the cellular function in various diseases that gives the opportunity of therapeutic application. Computational proteomics is an inherent part of proteomics, the applications are increased gradually with the emergence of effective techniques that rely on sophisticated data processing. This current and innovative domain is offering unique techniques for managing vast and diverse proteome data, as well as the evolution of the discovery process. The application of bioinformatics for proteomics has grown substantially in the past several years. The establishment of innovative algorithms for the processing of large

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data sets with greater specificity and accuracy aids to detect and measure proteins to enhance high-throughput protein expression data. Proteomics-associated methods have been used in various scientific research, including the identification of several pre-disease diagnoses, drug target identification for candidates, exploring the mechanism of diseases, the changes in transcriptional regulation which react to variations signals, and the analysis of functional protein pathways in various diseases. Bioinformatics is a relatively complex field since it involves the study and identification of a genome’s total protein signatures. MS, LC-MS, and MALDI-TOF are commonly used methods and are the core of recent proteomics. Moreover, the use of proteomics resources, including programs for devices and databases, as well as the necessity of qualified staff, significantly raises the costs, limiting their widespread usage, particularly in the blooming countries. Also, the proteome is very transient due to complex regulatory mechanisms which regulate cellular behavior. Constant efforts have been made in various proteomics approaches to understand the antiviral properties and their application in medical sciences.

6.2.4

Metabolomics

Metabolomics is an emerging field in omics research that links both genomics and proteomics. Metabolomics deals with small molecules with a molecular weight is less than B800 Dalton which includes small peptides, oligonucleotides, carbohydrates, organic acids ketones, aldehydes, amines, amino acids, lipids, steroids, and alkaloids (Fig. 6.4). These molecules, mostly produced during the molecular and cellular processes, perform

FIGURE 6.4 Metabolomics classification. Metabolomics contributes toward the assessment of amino acids, nucleotides, peptides, and carbohydrates.

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TABLE 6.4 Metabolomic profiles of cancer. S. No.

Title of the study

Journal

Author

1

Downregulation of adipose triglyceride lipase by EB viral-encoded LMP2A links lipid accumulation to increased migration in nasopharyngeal carcinoma

Molecular Oncology

Zheng et al. (2020)

2

Cancer biology-causes and biomarkers of cancer

Current Research in Oncology

Bansode (2019)

3

Microbiome and human malignancies

Microbiome and Cancer

Saha and Robertson (2019)

4

Development and evaluation of biomarkers for early detection of cancer

Recent Advancements in Biomarkers and Early Detection of Gastrointestinal Cancers

Challa et al. (2020)

5

Oxidative stress in infection and consequent disease

Oxidative Medicine and Cellular Longevity

Ivanov et al. (2017)

6

The cancer microbiome: distinguishing direct and indirect effects requires a systemic view

Trends in Cancer

Xavier et al. (2020)

7

Single-cell analysis by ambient mass spectrometry

TrAC Trends in Analytical Chemistry

Yunyun et al. (2017)

8

High-sensitivity detection of micrometastases generated by GFP lentivirus-transduced organoids cultured from a patient-derived colon tumor

Jove Journal

Okazawa et al. (2018)

9

DNMT1 mediatesmetabolic reprogramming induced by Epstein
Barr virus latent membrane protein 1 and reversed by grifolin in nasopharyngeal carcinoma

Nature (cell death and disease)

Luo et al. (2018)

10

From super-enhancer noncoding RNA to immune checkpoint: frameworks to functions

Frontier in Oncology

Wu and Shen (2019)

(Continued )

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TABLE 6.4 (Continued) S. No.

Title of the study

Journal

Author

11

Mining inter-relationships in online scientific articles and its visualization: natural language processing for systems biology modeling

Journal of Online and Biomedical Engineering

Melethadathil et al. (2019)

12

Analysis of ligustrum lucidum AIT leaves using leaf spray mass spectrometry

Asian Journal of Pharmaceutical Analysis and Medicinal Chemistry

Zhang et al. (2016)

13

Molecular basis of cervical cancer: a review

European Journal of Pharmaceutical and Medical Research

Singh et al. (2020)

14

The shifting microbiome in surgical stress

Nutrition, Metabolism, and Surgery

Codner and Herron (2017)

15

The interplay between the genetic and immune landscapes of AML: mechanisms and implications for risk stratification and therapy

Frontier in Oncology

Mendez et al. (2019)

16

EDEM3 modulates plasma triglyceride level through its regulation of LRP1 expression

Cell Press

Xu et al. (2020)

several vital functions in the body. Studying cellular metabolomics helps to understand the biochemical and metabolic pathway of the cell at various cellular and environmental conditions. Recent developments in chromatography, NMR, and MS helped in the identification and quantification of metabolites with high sensitivity and specificity in cells, tissues, or biofluids (Ahmed et al., 2009). Thus, metabolomics is an integrative platform that deals with both genetic and proteomics factors, and which has an impact on biomarker discoveries, drug
disease mechanisms, and drug-induced toxicity. There is the potential application of metabolomic profiling in oncology, such as early detection and diagnosis of cancer, and use as a predictive and pharmacodynamic marker to study drug effects. Overall, the application of metabolomics mainly relies on the field of medicine, which helps to understand the real-time mechanism underlying complex biological functions (Bansode, 2019; Challa et al., 2020; Codner & Herron, 2017;

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Gain, C, 2020; Ivanov et al., 2017; Luo et al., 2018; Melethadathil et al., 2019; Mendez et al., 2019; Okazawa et al., 2018; Saha & Robertson, 2019; V´asquez & Chimoy, 2019; Wu & Shen, 2019; Xavier et al., 2020; Xu et al., 2020; Yang et al., 2017; Zheng et al., 2020) (Table 6.4).

6.2.4.1 Metallomics Metallomics, a discipline integrating sciences that address the biometals and metalloids, provides new opportunities for discoveries. As part of a systems biology approach, it draws attention to the importance of many chemical elements in biochemistry. Traditionally, biochemistry has treated life as organic chemistry, separating it from inorganic chemistry, considered a field reserved for investigating the inanimate world. However, inorganic chemistry is part of the chemistry of life, and metallomics contributes by highlighting the importance of a neglected fifth branch of building blocks in biochemistry. Metallomics adds chemical elements/metals to the four building blocks of biomolecules and the fields of their studies: carbohydrates (glycome), lipids (lipidome), proteins (proteome), and nucleotides (genome). The realization that nonessential elements are present in organisms in addition to essential elements represents a certain paradigm shift in our thinking, as it stipulates inquiries into the functional implications of virtually all the natural elements. There is urgent need to understand the role of metallomics for health and TABLE 6.5 Metallomic profile of cancer. S. No.

Organism

Title of the study

Journal

Author

1

Oncovirus

Profiling of immune related genes silenced in EBVpositive gastric carcinoma identified novel restriction factors of human gammaherpesviruses

Plos Pathogens

Fiches et al. (2020)

2

Oncovirus

Zinc metallochaperones as mutant p53 reactivators: a new paradigm in cancer therapeutics

MDPI

Kogan and Carpizo (2018)

3

Oncovirus

Cancer: a nanotechnological approaches

World Journal of Pharmaceutical Research

Kumar et al. (2016)

4

Oncovirus

Microbiome and human malignancies

Microbiome and Cancer

Saha and Robertson (2019)

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TABLE 6.6 List of genomic database and tools. S. No

Databases name

Description

Link

1.

EPD

Collection of eukaryotic promoters

http://epd.vital-t

2

OrthoDB

Hierarchical catalog of eukaryotic orthologs

http://www.orthodb.org/

3

SwissRegulon

Annotations of regulatory sites

http://swissregulon.unibas.ch

4

OMA

Orthology inference among complete genomes

http://omabrowser.org

5

arrayMap

Curated array data repository for cancer genomics

http://cega.ezlab.org/

6

CEGA

Conserved elements from genomic alignments

http://www.clipz.unibas.ch/

7

CLIPZ

Binding sites of RNAbinding proteins

http://www.clipz.unibas.ch

8

ElMMo

Mirna target predictions

http://gpsdb.expasy.org/

9

GPSDB

Gene and protein synonyms

http://gpsdb.expasy.org/

10

ImmunoDB

Insect immunerelated genes and gene families

http://cegg.unige.ch

11

miROrtho

Catalog of animal microRNA genes

http://cegg.unige.ch

12

OpenFlu

Influenza genetic and epidemiological data

http://openflu.vital-it.ch

13

Progenetix

Genomic copy number aberrations in cancer

http://www.progenetix.org/

14

EPD

Collection of eukaryotic promoters

http://epd.vital-it.ch/

15

ALF

Simulation of genome evolution

http://alfsim.org (Continued )

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TABLE 6.6 (Continued) S. No

Databases name

Description

Link

16

arrayMap

Curated array data repository for cancer genomics

http://www.arraymap.org/

17

Association Viewer

SNPs display in a genetic context

http://www.arraymap.org/

18

Scan

Identify natural selection

http://cmpg.unibe.ch

19

Boxshade

MSA pretty printer

http://embnet.vital-it.ch

20

BUSCO

Benchmarking universal single-copy orthologs

http://busco.ezlab.org/

21

CEGA

Conserved elements from genomic alignments

http://cega.ezlab.org/

22

ChIP-Seq

Chip-Seq data analysis tools

http://ccg.vital-it.ch

23

Decrease redundancy

Sequence redundancy reduction

http://web.expasy.org/ decrease_redundancy/

24

DIALIGN

Local multiple sequence alignment

https://bibiserv.cebitec.unibielefeld.de/dialign/

25

ElMMo

MiRNA target predictions

http://www.clipz.unibas.ch/ ElMMo3/

26

EMBOSS translation tools

Sequence translation tools

http://www.ebi.ac.uk/Tools/st/

27

ESTscan

Coding region detection

http://myhits.isb-sib.ch/cgi-bin/ estscan

28

FASTA

Search

http://www.ebi.ac.uk/Tools/sss/ fasta/

29

FastEpistasis

Test for epistasis effects

http://www.vital-it.ch/software/ FastEpistasis

30

Fastsimcoal

Coalescent simulation of genomic data

http://cmpg.unibe.ch/software/ fastsimcoal2/

31

FetchG

Tagger

https://sourceforge.net/projects/ tagger/ (Continued )

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TABLE 6.6 (Continued) S. No

Databases name

Description

Link

32

GENIO

Logo

http://www.biogenio.com/logo/

33

GMM

Copy number variation detection

https://www2.unil.ch/cbg/ index.php?title 5 GMM

34

Graphical Codon Usage Analyzer

Codon bias

http://gcua.schoedl.de/index. html

36

ISMARA

Genome-wide expression data modeling

https://ismara.unibas.ch/fcgi/ mara

37

IsotopIdent

Theoretical isotopic distribution

http://education.expasy.org/ student_projects/isotopident/ htdocs/

38

Kalign - EBI

Fast and accurate multiple sequence alignment

http://www.ebi.ac.uk/Tools/ msa/kalign/

39

LALIGN

Pairwise alignment

http://embnet.vitalit.ch/ software/LALIGN_form.html

40

MADAP

Clustering for genome annotation data

http://ccg.vital-it.ch/madap/

41

MAFFT-CBRC

Multiple sequence alignment

http://mafft.cbrc.jp/alignment/ server/index.html

42

MAFFT-EBI

Multiple sequence alignment

http://www.ebi.ac.uk/Tools/ msa/mafft/

43

MAMOT

HMM models

http://bcf.isb-sib.ch/mamot/

44

MaxAlign

Gap removal from alignments

http://www.cbs.dtu.dk/services/ MaxAlign/

45

miRmap

Search and predict miRNA targets with miRmap

http://mirmap.ezlab.org/

46

Multalin

Multiple sequence alignment

https://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl? page 5 npsa_multalin.html

47

MUSCLE

Multiple alignment server

http://www.ebi.ac.uk/Tools/ msa/muscle/

48

Newick Utilities

High-throughput phylogenetic tree processing

http://cegg.unige.ch/ newick_utils (Continued )

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TABLE 6.6 (Continued) S. No

Databases name

Description

Link

49

PACMAN

PACMAN: Pacific Biosciences methylation analyzer

http://bugfri.vital-it.ch/

50

Phylogibbs

Regulatory sites discovery

http://www.phylogibbs.unibas. ch/cgi-bin/phylogibbs.pl

51

Progenetix

Genomic copy number aberrations in cancer

http://www.progenetix.org/

52

QuasR

Quantify and annotate short reads in R

http://bioconductor.org/ packages/release/bioc/html/ QuasR.html

53

Reverse Transcription and Translation Tool

Transcription, translation, reverse transcription

http://www.attotron.com/ cybertory/analysis/trans.htm

54

Reverse Translate

Reverse translation

http://www.bioinformatics.org/ sms2/rev_trans.html

55

SAMBA

Systolic accelerator for molecular biological Appl

http://www.irisa.fr/SAMBA/

56

Sequence Similarity Maps (SSM)

Data mining of viral isolates

http://ssm.vital-it.ch/

57

Sequerome

BLAST similarity search and sequence profiling

http://sequerome.georgetown. edu/sequerome/

58

SHOPS

Genomic operon context analysis

http://bioinformatics. holstegelab.nl/shops/

59

ShoRAH

Tools for the analysis of NGS data

https://www.bsse.ethz.ch/cbg/ software.html

60

SIBsim4

Spliced sequence alignment

https://sourceforge.net/projects/ sibsim4/

61

SSA

Tools for the analysis of NGS data

http://ccg.vital-it.ch/ssa/

62

T-Coffee

Nucleic acid sequence motifs

http://tcoffee.vital-it.ch/apps/ tcoffee/index.html

63

T-Coffee - EBI

Sequence and structure multiple alignments

http://www.ebi.ac.uk/Tools/ msa/tcoffee/ (Continued )

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135

TABLE 6.6 (Continued) S. No

Databases name

Description

Link

64

T-Coffee - WUR

Multiple sequence alignment program

http://www.bioinformatics.nl/ tools/t_coffee.html

65

TagScan

Genome-wide sequence tag scanner

http://ccg.vital-it.ch/tagger/ tagscan.html

66

Translate

Nucleotide sequence translation

http://web.expasy.org/translate/

67

TriFLe

TRFLP species identification and simulation

http://cegg.unige.ch/trifle_docs

68

Tromer

Transcriptome analyzer

http://ccg.vital-it.ch/tromer/

69

WebLogo

Sequence logos

http://weblogo.berkeley.edu/ logo.cgi

70

Wise2

Genomic sequence to protein sequence comparison

http://www.ebi.ac.uk/Tools/psa/ genewise/

71

WU BLAST

Sequence similarity search in protein databases

http://www.ebi.ac.uk/Tools/sss/ wublast/

72

ZFN-Site

Zinc finger nuclease (off-) target sites search

http://ccg.vital-it.ch/tagger/ targetsearch.html

disease. The biological system consists of 30%
40% metalloproteases (Fiches et al., 2020; Kogan & Carpizo, 2018; Kumar et al., 2016; Saha & Robertson, 2019; Yang et al., 2017) (Table 6.5).

6.2.5

Bioinformatics resources

In recent times, several omics experiments have been carried out that generate a vast amount of biological data. These data are classified into three major classes, i.e., sequence, expression, and structures that are archived in commercial or publically available databases for bioinformatics application. For instance, databases like NCBI (http://www.ncbi.nlm.nih.gov/), EMBL (http://www.embl.org/), and DDBJ (http://www.ddbj.nig.ac.jp/), contain biological information of DNA, RNA, and proteins; whereas the structural

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information of proteins is stored in PDB (http://www.rcsb.org/pdb/home/ home.do) and ExPASy (http://www.expasy.org/), which helps to retrieve useful information using bioinformatics tools (Milanowska et al., 2011). TABLE 6.7 List of transcriptomics database and tools. S. No

Database name

Description

Link

1

Bgee

Gene expression patterns comparison

http://bgee.org/

2

SwissRegulon

Annotations of regulatory sites

http://swissregulon.unibas.ch/sr/ swissregulon

3

CLIPZ

Binding sites of RNAbinding proteins

http://www.clipz.unibas.ch/

4

ElMMo

miRNA target predictions

http://www.clipz.unibas.ch/ ElMMo3/

5

ESTscan

Coding region detection

http://myhits.isb-sib.ch/cgi-bin/ estscan

6

ExpressionView

Exploring biclusters in gene expression data

https://www2.unil.ch/cbg/index. php?title 5 ExpressionView

7

ISA

Gene expression module discovery

https://www2.unil.ch/cbg/index. php?title 5 ISA

8

ISMARA

Genome-wide expression data modeling

https://ismara.unibas.ch/fcgi/ mara

9

Ping pong algorithm

Coherent patterns across paired datasets

https://www2.unil.ch/cbg/index. php?title 5 ISA

10

QuasR

Quantify and annotate short reads in R

http://bioconductor.org/ packages/release/bioc/html/ QuasR.html

11

SIBsim4

Spliced sequence alignment

https://sourceforge.net/projects/ sibsim4/

12

The Miner Suite

Tools for data analysis

https://discover.nci.nih.gov/

13

Translate

Nucleotide sequence translation

http://web.expasy.org/translate/

14

Tromer

Transcriptome analyzer

http://ccg.vital-it.ch/tromer/

15

GEO Datasets

Gene Expression Omnibus (GEO) repository

https://www.ncbi.nlm.nih.gov/ gds

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137

TABLE 6.8 List of proteomics database and tool. S. No

Databases name

Description

Link

1

UniProtKB

Functional information on proteins

http://www.uniprot.org/

2

UniProtKB Swiss-Prot

Protein sequence database

http://web.expasy.org/docs/ swiss-prot_guideline.html

3

STRING

Protein
protein interactions

https://string-db.org/

4

SWISSMODEL Repository

Protein structure homology models

https://swissmodel.expasy. org/repository/

5

PROSITE

Protein domains and families

http://prosite.expasy.org/

6

ViralZone

Portal to viral UniProtKB entries

http://viralzone.expasy.org/

7

neXtProt

Human proteins

https://www.nextprot.org/

8

EMBnet services

Bioinformatics tools, databases, and courses

http://embnet.vital-it.ch/

9

ENZYME

Enzyme nomenclature

http://enzyme.expasy.org/

10

GlyTouCan

International glycan structure repository

https://glytoucan.org/

11

GPSDB

Gene and protein synonyms

http://gpsdb.expasy.org/

12

HAMAP

UniProtKB family classification and annotation

http://hamap.expasy.org/

13

MatrixDB

Protein
glycosaminoglycan interactions

http://matrixdb.ibcp.fr/

14

MetaNetX

Metabolic network repository and analysis

http://www.metanetx.org/

15

MIAPEGelDB

MIAPE document edition

http://miapegeldb.expasy. org/

16

PaxDb

Protein abundance database

http://pax-db.org/

17

Protein Spotlight

Informally written reviews on proteins

http://web.expasy.org/ spotlight/

18

SWISS2DPAGE

Proteins on 2-D and SDS PAGE maps

http://world-2dpage.expasy. org/swiss-2dpage/

19

SwissLipids

Knowledge resource for lipid biology

http://www.swisslipids.org/#/ (Continued )

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TABLE 6.8 (Continued) S. No

Databases name

Description

Link

20

UniPathway

Metabolic pathways for the UniProtKB

http://www.grenoble.prabi. fr/obiwarehouse/ unipathway

21

World2DPAGE Repository

Gel-based proteomics data

http://world-2dpage.expasy. org/repository/

22

SWISSMODEL Workspace

Structure homology modeling

https://swissmodel.expasy. org/

23

3of5

Find user-defined patterns in protein sequences

https://swissmodel.expasy. org/

24

AACompIdent

Protein identification by aa composition

https://www.expasy.org/ proteomics

25

ALF

Simulation of genome evolution

http://alfsim.org/#index

26

big-PI

Predict GPI modification sites

http://mendel.imp.ac.at/sat/ gpi/gpi_server.html

27

BLAST (UniProt)

BLAST search on the UniProt web site

http://imed.med.ucm.es/ Tools/blast2fasta.html

28

Blast2Fasta

BLAST to FASTA conversion

http://imed.med.ucm.es/ Tools/blast2fasta.html

29

Click2Drug

Directory of computational drug design tools

http://www.click2drug.org/

30

CSS-Palm

Prediction of palmitoylation sites in proteins

http://lipid.biocuckoo.org/

31

DictyOGlyc

GlcnacO-glycosylation sites in D. discoideum

http://www.cbs.dtu.dk/ services/DictyOGlyc/

32

Dotlet

Sequence similarity plots

http://myhits.isb-sib.ch/cgibin/dotlet

33

ELM

Eukaryotic linear motifs

http://elm.eu.org/

34

FUGUE

Sequence structure homology recognition

https://www.expasy.org/ proteomics

35

GlycoMod

Oligosaccharide structure prediction

http://web.expasy.org/ glycomod/

36

Mascot

Protein identification from mass spectrometry data

http://www.matrixscience. com/search_form_select. html (Continued )

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139

TABLE 6.8 (Continued) S. No

Databases name

Description

Link

37

NetOGlyc

Mammalian mucintype galnacO-glycosylation sites

http://www.cbs.dtu.dk/ services/NetOGlyc/

38

NQ-Flipper

Correction of unfavorable rotamers of Asn and Gln

https://flipper.services. came.sbg.ac.at/cgi-bin/ flipper.php

39

PLOGO

Sequence logos

http://rth.dk/resources/ plogo/

40

PROPSEARCH

Functional and characters

http://abcis.cbs.cnrs.fr/ propsearch/

41

QMEAN

Estimate quality of protein models

https://swissmodel.expasy. org/qmean/

42

REPRO

De novo repeat detection in protein sequences

http://www.ibi.vu.nl/ programs/reprowww/

43

SAPS

Statistical analysis of protein sequences

http://www.ebi.ac.uk/Tools/ seqstats/saps/

44

Swiss Mass Abacus

Calculator of peptideand glycopeptide masses

http://glycoproteome. expasy.org/swiss-massabacus/

6.2.6

Databases and tools

The advancement in omics research exponentially increases the amount of data that have been generated from various biological experiments. It becomes necessary to store, analyze, and share this information for various biological applications. Hence, the database management systems were used to collect, organize, and store the biological data to gain and share the knowledge worldwide using internet facilities. These biological databases were categorized as primary and secondary databases based on omics experiments. For instance, the nucleic acid-related information was available in databases like GenBank, EMBL Data Library, or the DNA Databank of Japan (Table 6.6). At the same time, the transcriptomic information is accessible in the GEO dataset and GEO profiles (Table 6.7).Similarly, protein information was obtainable from PROSITE and PDB (Table 6.8), and metabolomics information is available in metabolomics databases such as HMDB and PubChem (Table 6.9), and scientific literature. These databases help to

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TABLE 6.9 List of metabolomics database and tools. S. No

Database

Link

1

UCSD Marine Natural Products Database

http://libraries.ucsd.edu/blogs/blog/marinlit/

2

HMDB

http://www.h

3

BiGG

http://bigg.ucsd.edu/

4

MetaboLights database

http://www.ebi.ac.uk/metabolights/index

5

KEGG

http://www.genome.jp/kegg/

6

MetaCyc

https://metacyc.org/

7

HumanCyc

https://humancyc.org/

8

BioCyc

https://biocyc.org/

9

Reactome

http://reactome.org/

10

WikiPathways

http://www.wikipathways.org/index.php/ WikiPathways

11

PubChem

https://pubchem.ncbi.nlm.nih.gov/

12

ChEBI

http://www.chemspider.com/

13

ChemSpider

http://www.ebi.ac.uk/chebi/

14

KEGG Glycan

http://www.genome.jp/kegg/glycan/

15

IIMDB

http://metabolomics.pharm.uconn.edu/iimdb/

16

DrugBank

https://www.drugbank.ca/

17

Therapeutic Target Database

http://bidd.nus.edu.sg/group/cjttd/

18

PharmGKB

https://www.pharmgkb.org/

19

STITCH

http://stitch.embl.de/

20

BMRB

http://www.bmrb.wisc.edu/metabolomics/

21

MMCD

http://mmcd.nmrfam.wisc.edu/

22

MassBank

http://www.massbank.jp/

23

Golm Metabolome Database

http://gmd.mpimp-golm.mpg.de/

24

Metlin

https://metlin.scripps.edu/landing_page.php? pgcontent 5 mainPage

25

Fiehn GC-MS Database

http://fiehnlab.ucdavis.edu/19-projects/153metabolite-library

26

OMIM

https://www.ncbi.nlm.nih.gov/omim/

27

METAGENE

http://www.metagene.de/appl/index.html (Continued )

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141

TABLE 6.9 (Continued) S. No

Database

Link

28

OMMBID

http://ommbid.mhmedical.com/

29

Supernatural database

http://bioinformatics.charite

30

Marine compound database

http://www.progenebio.in/mcdb/index.html

31

NaPDoS

http://www.biokepler.org/use_cases/napdos

retrieved multiple information that helps to make decisions on scientific discoveries.

6.3

Conclusions

Genomics, transcriptomics, proteomics, and metabolomics-based approaches are now being used in biomedical research for the understanding of complex disease pathogenesis and identification of drug targets and markers for therapeutic and diagnostic development. Integration of multi-omics techniques and bioinformatics concepts of analyzing data has gained attention toward storing, systematic analyses, and interpretation of the data. Overall, omics and bioinformatics go hand in hand, where the extensive data generated in the omics tools by experimentation is successfully managed with the help of bioinformatics concepts by storing it in the biological database. Most of the biological databases are open access, which act as platforms for researchers all over the world. Hence, the integration of omics with bioinformatics provides an effective tool that provides novel and important insights about diverse cellular processes such as development, gene-expression dynamics, tissue heterogeneity, and disease pathogenesis.

References Ahmed, S. S., Santosh, W., Kumar, S., & Christlet, H. T. T. (2009). Metabolic profiling of Parkinson’s disease: Evidence of biomarker from gene expression analysis and rapid neural network detection. Journal of Biomedical Science, 16(1), 63. Available from https://doi.org/ 10.1186/1423-0127-16-63. Alizon., Bravo, P. J., Farrell, S., & Roberts. (2019). Towards a multi-level and a multidisciplinary approach to DNA oncovirus virulence. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 374, 20190041. Aslam, B., Basit, M., Nisar, M. A., Khurshid, M., & Rasool, M. H. (2017). Proteomics: Technologies and their applications. Journal of Chromatographic Science, 55(2), 182
196. Available from https://doi.org/10.1093/chromsci/bmw167.

142

Oncogenic Viruses Volume 2

Bansode, S. (2019). Cancer biology-causes & biomarkers of cancer. Current Research in Oncology, 2019, 1
9. Butt, A. Q., & Miggin, S. M. (2012). Cancer and viruses: A double-edged sword. Proteomics, 12 (13), 2127
2138. Available from https://doi.org/10.1002/pmic.201100526. Challa, S., Sri-Tirumala-Peddiniti, R. C. P. K., & Neelapu, N. R. R. (2020). Development and evaluation of biomarkers for early detection of cancer (pp. 27
43). Springer Science and Business Media LLC. Available from https://doi.org/10.1007/978-981-15-4431-6_3. Codner, P. A., & Herron, T. J. (2017). The shifting microbiome in surgical stress. Current Surgery Reports, 5(4
5), 9. Available from https://doi.org/10.1007/s40137-017-0172-7. Dzutsev, A., Badger, J. H., Perez-Chanona, E., Roy, S., Salcedo, R., Smith, C. K., & Trinchieri, G. (2017). Microbes and cancer. Annual Review of Immunology, 35, 199
228. Available from https://doi.org/10.1146/annurev-immunol-051116-052133. Fiches, G. N., Zhou, D., Kong, W., Biswas, A., Ahmed, E. H., Baiocchi, R. A., Zhu, J., & Santoso, N. (2020). Profiling of immune related genes silenced in EBV-positive gastric carcinoma identified novel restriction factors of human gammaherpesviruses. PLoS Pathogens, 16(8), e1008778. Available from https://doi.org/10.1371/journal.ppat.1008778. Gain, C., et al. (2020). Proteasomal inhibition triggers viral oncoprotein degradation via autophagy-lysosomal pathway. PLOS PATHOGENS, 16(2)(e1008105). Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam-Rudloff, E., Conesa, A., & Tegne´r, J. (2014). Data integration in the era of omics: Current and future challenges. BMC Systems Biology, 8, I1. Available from https://doi.org/10.1186/1752-0509-8-S2-I1. Graves, P. R., & Haystead, T. A. J. (2002). Molecular biologist’s guide to proteomics. Microbiology and Molecular Biology Reviews, 66(1), 39
63. Available from https://doi.org/ 10.1128/MMBR.66.1.39-63.2002. Guven-Maiorov, E., Hakouz, A., Valjevac, S., Keskin, O., Tsai, C. J., Gursoy, A., & Nussinov, R. (2020). HMI-PRED: A web server for structural prediction of host-microbe interactions based on interface mimicry. Journal of Molecular Biology, 432(11), 3395
3403. Available from https://doi.org/10.1016/j.jmb.2020.01.025. Haider, S., & Pal, R. (2013). Integrated analysis of transcriptomic and proteomic data. Current Genomics, 14(2), 91
110. Available from https://doi.org/10.2174/1389202911314020003. Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18, 83. Horgan, R. P., & Kenny, L. C. (2011). ‘Omic’ technologies: Genomics, transcriptomics, proteomics and metabolomics. The Obstetrician & Gynaecologist, 13, 189
195. Available from https://doi.org/10.1576/toag.13.3.189.27672. Ivanov, A. V., Bartosch, B., & Isaguliants, M. G. (2017). Oxidative stress in infection and consequent disease. Oxidative Medicine and Cellular Longevity, 2017, 3496043. Available from https://doi.org/10.1155/2017/3496043. Kogan, S., & Carpizo, D. R. (2018). Zinc metallochaperones as Mutant p53 reactivators: A new paradigm in cancer therapeutics. Cancers, 10(6), 166. Available from https://doi.org/ 10.3390/cancers10060166. Krier, J. B., Kalia, S. S., & Green, R. C. (2016). Genomic sequencing in clinical practice: Applications, challenges, and opportunities. Dialogues in Clinical Neuroscience, 18(3), 299
312. Available from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5067147/pdf/ DialoguesClinNeurosci-18-299.pdf. Kumar, V. R., Ketul, M., RajeshandShirsat, V., & Mruna, K. (2016). Cancer: A nanotechnological approaches. World Journal of Pharmaceutical Research, 5(3), 1561
1600.

Multi-omics methods and tools in dissecting the oncovirus Chapter | 6

143

Kuzembayeva, M., Chiu, Y. F., & Sugden, B. (2012). Comparing proteomics and RISC immunoprecipitations to identify targets of Epstein-Barr Viral miRNAs. PLoS One, 7(10), e0047409. Available from https://doi.org/10.1371/journal.pone.0047409. Luo, X., Hong, L., Cheng, C., Li, N., Zhao, X., Shi, F., Liu, J., Fan, J., Zhou, J., Bode, A. M., & Cao, Y. (2018). DNMT1 mediates metabolic reprogramming induced by Epstein-Barr virus latent membrane protein 1 and reversed by grifolin in nasopharyngeal carcinoma. Cell Death and Disease, 9(6), 2. Available from https://doi.org/10.1038/s41419-018-0662-2. Lupberger, J., Croonenborghs, T., Roca Suarez, A. A., Van Renne, N., Ju¨hling, F., Oudot, M. A., Virzı`, A., Bandiera, S., Jamey, C., Meszaros, G., Brumaru, D., Mukherji, A., Durand, S. C., Heydmann, L., Verrier, E. R., El Saghire, H., Hamdane, N., Bartenschlager, R., Fereshetian, S., . . . Baumert, T. F. (2019). Combined Analysis of metabolomes, proteomes, and transcriptomes of hepatitis C virus
infected cells and liver to identify pathways associated with disease development. Gastroenterology, 157(2), 537
551. Available from https:// doi.org/10.1053/j.gastro.2019.04.003, e9. M. Perez-de-Castro, A., Vilanova, S., Canizares, J., Pascual, L., M. Blanca, J., J. Diez, M., Prohens, J., & Pico, B. (2012). Application of genomic tools in plant breeding. Current Genomics, 13(3), 179
195. Available from https://doi.org/10.2174/138920212800543084. Ma, S., & Dai, Y. (2011). Principal component analysis based methods in bioinformatics studies. Briefings in Bioinformatics, 12(6), 714
722. Available from https://doi.org/10.1093/bib/ bbq090. Manjasetty, B. A., Bu¨ssow, K., Panjikar, S., & Turnbull, A. P. (2012). Current methods in structural proteomics and its applications in biological sciences. 3 Biotech, 2, 89
113. Available from https://doi.org/10.1007/s13205-011-0037-1. Melethadathil, N., Heringa, J., Nair, B., & Diwakar, S. (2019). Mining inter-relationships in online scientific articles and its visualization: Natural language processing for systems biology modeling. International Journal of Online and Biomedical Engineering (iJOE), 15(2), 39. Available from https://doi.org/10.3991/ijoe.v15i02.9432. Mendez, L. M., Posey, R. R., & Pandolfi, P. P. (2019). The interplay between the genetic and immune landscapes of AML: Mechanisms and implications for risk stratification and therapy. Frontiers in Oncology, 9, 1162. Available from https://doi.org/10.3389/ fonc.2019.01162. Milanowska, K., Rother, K., & Bujnicki, J. M. (2011). Databases and bioinformatics tools for the study of DNA repair. Molecular Biology International, 2011, 1
9. Available from https://doi.org/10.4061/2011/475718. Monti, M., Cozzolino, M., Cozzolino, F., Tedesco, R., & Pucci, P. (2007). Functional proteomics: Protein-protein interactions in vivo. Italian Journal of Biochemistry, 56((4)), 310
314. Nishant, K. T., Singh, N. D., & Alani, E. (2009). Genomic mutation rates: What high-throughput methods can tell us. BioEssays, 31(9), 912
920. Available from https://doi.org/10.1002/ bies.200900017. Noorbakhsh, F., Aminian, A., & Power, C. (2015). Application of “Omics” technologies for diagnosis and pathogenesis of neurological infections. Current Neurology and Neuroscience Reports, 15(9), 58. Available from https://doi.org/10.1007/s11910-015-0580-y. Okazawa, Y., Mizukoshi, K., Koyama, Y., Okubo, S., Komiyama, H., Kojima, Y., Goto, M., Habu, S., Hino, O., Sakamoto, K., & Orimo, A. (2018). High-sensitivity detection of micrometastases generated by GFP lentivirus-transduced organoids cultured from a patientderived colon tumor. Journal of Visualized Experiments, 2018(136), 57374. Available from https://doi.org/10.3791/57374.

144

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Quiles, I., Mill´an-Arin˜o, L., Subtil-Rodr´ıguez, A., Min˜ana, B., Spinedi, N., Ballare´, C., Beato, M., & Jordan, A. (2009). Mutational analysis of progesterone receptor functional domains in stable cell lines delineates sets of genes regulated by different mechanisms. Molecular Endocrinology, 23(6), 809
826. Available from https://doi.org/10.1210/me.2008-0454. Raja, K., Patrick, M., Gao, Y., Madu, D., Yang, Y., & Tsoi, L. C. (2017). A review of recent advancement in integrating omics data with literature mining towards biomedical discoveries. International Journal of Genomics, 2017, 6213474. Available from https://doi.org/ 10.1155/2017/6213474. Reuter, J. A., Spacek, D. V., & Snyder, M. P. (2015). High-throughput sequencing technologies. Molecular Cell, 58(4), 586
597. Available from https://doi.org/10.1016/j. molcel.2015.05.004. Saha, A., & Robertson, E. S. (2019). Microbiome and human malignancies (pp. 1
22). Springer Science and Business Media LLC. Available from https://doi.org/10.1007/978-3-030-041557_1. Seo, M., Kim, S. M., Woo, E. Y., Han, K.-C., Park, E. J., Ko, S., Choi, E. w, & Jang, M. (2018). Stemness-Attenuating miR-503-3p as a paracrine factor to regulate growth of cancer stem cells. Stem Cells International, 2018, 1
10. Available from https://doi.org/10.1155/ 2018/4851949. Singh, S., Ahmad, S., & Srivastava, A. N. (2020). Molecular basis of cervical cancer: A review. European Journal of Pharmaceutical and Medical Research, 7(9), 150
157. Available from https://www.ejpmr.com/home/abstract_id/7213. Shulaev, V. (2006). Metabolomics technology and bioinformatics. Briefings in Bioinformatics, 7 (2), 128
139. Available from https://doi.org/10.1093/bib/bbl012. Slattery, M., Ankisetty, S., Corrales, J., Marsh-Hunkin, K. E., Gochfeld, D. J., Willett, K. L., and Rimoldi, J. M. (2012). Marine proteomics: A critical assessment of an emerging technology. Journal of Natural Products, 75(10), 1833
1877. Tsai, T. H., Wang, M., & Ressom, H. W. (2016). Preprocessing and analysis of LC-MS-based proteomic data, . Methods in molecular biology (Vol. 1362, pp. 63
76). Humana Press Inc. Available from https://doi.org/10.1007/978-1-4939-3106-4_3. V´asquez, E. F., & Chimoy, P. J. (2019). A localized warburg effect and prostatic cancer: A biochemical approach. International Journal of Cell Biology and Cellular Processes, 5(2), 13
29. Wheelock, C. E., Goss, V. M., Balgoma, D., Nicholas, B., Brandsma, J., Skipp, P. J., Snowden, S., Burg, D., D’Amico, A., Horvath, I., Chaiboonchoe, A., Ahmed, H., Ballereau, S., Rossios, C., Chung, K. F., Montuschi, P., Fowler, S. J., Adcock, I. M., Postle, A. D., . . . Djukanovi´c, R. (2013). Application of omics technologies to biomarker discovery in inflammatory lung diseases. European Respiratory Journal, 42(3), 802
825. Available from https://doi.org/10.1183/09031936.00078812. Wilson, K. E., Ryan, M. M., Prime, J. E., Pashby, D. P., Orange, P. R., O’Beirne, G., Whateley, J. G., Bahn, S., & Morris, C. M. (2004). Functional genomics and proteomics: Application in neurosciences. Journal of Neurology, Neurosurgery and Psychiatry, 75(4), 529
538. Available from https://doi.org/10.1136/jnnp.2003.026260. Wu, M., & Shen, J. (2019). From super-enhancer non-coding RNA to immune checkpoint: Frameworks to functions. Frontiers in Oncology, 9, 1307. Available from https://doi.org/ 10.3389/fonc.2019.01307. Xavier, J. B., Young, V. B., Skufca, J., Ginty, F., Testerman, T., Pearson, A. T., Macklin, P., Mitchell, A., Shmulevich, I., Xie, L., Caporaso, J. G., Crandall, K. A., Simone, N. L., Godoy-Vitorino, F., Griffin, T. J., Whiteson, K. L., Gustafson, H. H., Slade, D. J., Schmidt,

Multi-omics methods and tools in dissecting the oncovirus Chapter | 6

145

T. M., . . . Wargo, J. A. (2020). The cancer microbiome: Distinguishing direct and indirect effects requires a systemic view. Trends in Cancer, 6(3), 192
204. Available from https:// doi.org/10.1016/j.trecan.2020.01.004. Xu, Y. X., Peloso, G. M., Nagai, T. H., Mizoguchi, T., Deik, A., Bullock, K., Lin, H., Musunuru, K., Yang, Q., Vasan, R. S., Gerszten, R. E., Clish, C. B., Rader, D., & Kathiresan, S. (2020). EDEM3 modulates plasma triglyceride level through its regulation of LRP1 expression. iScience, 23(4), 973. Available from https://doi.org/10.1016/j. isci.2020.100973. Yang, Y., Huang, Y., Wu, J., Liu, N., Deng, J., & Luan, T. (2017). Single-cell analysis by ambient mass spectrometry. TrAC - Trends in Analytical Chemistry, 90, 14
26. Available from https://doi.org/10.1016/j.trac.2017.02.009. Yunyun, F, Yu, C., Panpan, Z., Hua, C., Di, W., Lidan, Z., Linyi, P., Li, W., Qingjun, W., Xuan, Z., Yan, Z., Xiaofeng, Z., Fengchun, Z., & Wen, Z. (2017). Efficacy of Cyclophosphamide treatment for immunoglobulin G4-related disease with addition of glucocorticoids. Scientific Reports, 7(1), 6195. Available from https://doi.org/10.1038/s41598-017-06520-5. Zhang, X. L., Hua, R., Zhang, H., Wang, G. F., Wang, N. N., Shi, L., Xiao, G. P., Li, J. Y., & Han, W. (2016). Analysis of ligustrum lucidum AIT leaves using leaf spray mass spectrometry. Analysis of Ligustrum Lucidum AIT Leaves Using Leaf Spray Mass Spectrometry, 4(3), 124
130. Available from http://www.ajpamc.com/article/ANALYSIS%20OF% 20LIGUSTRUM%20LUCIDUM%20AIT%20LEAVES%20USING%20LEAF%20SPRAY% 20MASS%20SPECTROMETRY.pdf. Zhang, Z., Wu, S., Stenoien, D. L., & Paˇsa-Toli´c, L. (2014). High-throughput proteomics. Annual Review of Analytical Chemistry, 7, 427
454. Available from https://doi.org/ 10.1146/annurev-anchem-071213-020216. Zheng, S., Matskova, L., Zhou, X., Xiao, X., Huang, G., Zhang, Z., & Ernberg, I. (2020). Downregulation of adipose triglyceride lipase by EB viral-encoded LMP2A links lipid accumulation to increased migration in nasopharyngeal carcinoma. Molecular Oncology, 14(12), 3234
3252. Available from https://doi.org/10.1002/1878-0261.12824.

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Chapter 7

Role of viral human oncogenesis: recent developments in molecular approaches ChandraLekha Saravanan1, Mahalakshmi Baskar1, Sheik S.S.J. Ahmed2 and Ramakrishnan Veerabathiran1 1

Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India, 2Multi-omics and Drug Discovery Lab, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chennai, Tamil Nadu, India

7.1

Introduction

Viruses that produce tumors in their natural hosts, experimental animals, or cell cultures are called oncogenic viruses. Oncovirus changes the growth properties of cells they infect—approximately 12% of all cancer caused by oncovirus. More than 80% of human cancer cases occur in developing countries (Donald, 2006). Tumors in animals are caused by many viruses, but only seven of them are associated with human oncogenic viruses. ˇ Oncoviruses are of two types, DNA and RNA oncovirus (Sevik, 2012). Epstein
Barr virus (EBV) was the first human oncogenic recognized herpesvirus (EBV) and identified in Burkitt lymphoma cells in 1964 (Epstein et al., 1964); since then, six human oncovirus have been identified: Kaposi’s sarcoma (KS), human T-cell lymphtropic virus, human papillomavirus (HPV), Merkel cell polyomavirus (MCV), and hepatitis B and hepatitis C viruses. EBV, also known as human herpesvirus 4, belongs to the herpes family. EBV is associated with four cancer types: Hodgkin’s lymphoma, nonHodgkin’s lymphoma linked with posttransplant, Burkitt’s tumor, and nasopharyngeal carcinoma (Epstein & Barr, 1964). Kaposi’s sarcoma-associated herpesvirus (KSHV) is also known as human herpesvirus-8 (HHV-8). Immunosuppressed individuals with HHV-8 cause KS, including patients

Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00006-6 Copyright © 2023 Elsevier Inc. All rights reserved.

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2008

2010 2000

1994

1990 1980 1970

1980

1989 1983

1964 1965

Epstein–Barr virus (EBV) hepatitis B virus (HBV) human T-lymphotropic virus-1 (HTLV-1) human genital papillomavirus (HPV) hepatitis C virus (HCV)

1960 1950

Kaposi sarcoma herpesvirus (KSHV/HHV8)

1940

Merkel cell polyomavirus (MCV)

FIGURE 7.1 Year of discovery of human oncovirus.

with HIV. Hepatitis B virus (HBV) is spread through contaminated blood or unsafe medical procedures. HBV is a single-stranded RNA virus that causes liver cancer (HCC). It is the third leading cause of death in the world. The first reported retrovirus was HTLV-1 that is transmitted through breast milk, sexually via semen, vaginal fluids, or blood transfusion. Historically, in 1908, Vilhelm and Bang Danish scientists from Copenhagen University published a paper on viral transmission of avian erythroblastosis (chicken leukemia). In 1911, Peyton Rous first described the association of viruses with malignancy. Edwin Shope, in 1932, isolated rabbit fibroma virus (Lunn et al., 2017). In1957, polyoma virus was discovered by Bernice Eddy and Sarah Stewart. The first recognized human oncovirus was identified from Burkitt lymphoma cells by Anthony Epstein, Yvonne Barr, and Bert Achong in 1964 (Pan et al., 2001). It is also called as human herpesvirus 4 but commonly called EBV, a herpesvirus. Harald zur Hausen proposed HPV as etiologic agent of cervical cancer in 1974, for which he received the Nobel Prize in 2008. The virus discovered based on the PCR technique in 1994 by Chang et al. is KSHV. The other virus discovered by digital transcriptome is MCV (Katze & Korth, 2015). Fig. 7.1 illustrates the year of discovery of oncovirus.

7.2

Prevalence of oncovirus

The IARC is the specialized cancer agency of WHO that estimated in 2002, 17.8% of human cancers were caused by infection, with 11.9% caused by oncovirus. Among 2658 samples collected from 38 various cancer types,16% were associated with oncovirus in 2020. The EBV is estimated to be positive in more than 90% of the world’s population (Gerber et al., 1996). Globally, they have estimated 5.5 billion were infected with this EBV. The incidence

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of EVB associated with Burkitt’s lymphoma is 5
10/100,000 children. EBV is present in all populations but causes Burkitt’s lymphoma in the African equatorial belts, accounting for about 74% of cancers in children (Santpere et al., 2014). HPV is transmitted through sexual contact and causes severe cervical cancer, affecting women 60%
70% worldwide. The high-risk HPV types 16 and 18 are particularly responsible for cervical cancer. According to the US statistics, they have estimated 83% female and 74% males are associated with HPV (Krings et al., 2019). According to the global estimation, HPV showed a high rate of prevalence in these countries, namely SubSaharan Africa (24.0%), Eastern Europe (21.4%), and Latin America (16.1%). A large peak of prevalence in HPV is strongly identified in both genders connected with age. Researchers’ estimate that 2.3 billion people are infected by the hepatitis virus (Ferlay et al., 2018; Stelzle et al., 2021). HBV causes severe chronic and acute diseases. A liver disease associated with HBV includes complications of liver cirrhosis (HCC). Hepatitis C virus (HCV) infection has a high prevalence in Southern Italy, Northern Africa, Mongolia, Pakistan, China, and some areas of Japan (Schweitzer et al., 2015). Genotype 1, genotype 2, genotype 3, genotype 4, genotype 5, and genotype 6 are the six main HCV genotypes. In overlapping areas of West and Central Africa, HCV genotypes 1 and 4 have been identified. A virus that affects our immune system and transmits through sexual intercourse, contact with blood, or transmission between mother to child is HIV. Global statistics estimate that about 3
4 million people were affected and tested positive. KSHV belongs to herpesvirus family. Transmission passes through sexual contact and nonsexual contact (saliva HHV-8, blood, tissue, or organ transplant), but people with healthy immune system will not get affected by this virus (Martin et al., 1998). Researchers have estimated moderate prevalence in countries around the Mediterranean .50% in sub-Saharan Africa and ,10% in Europe and Asia. In 2008, researchers discovered MCPyV at Pittsburgh University. MCPyV belongs to polyomaviruses and it is associated with about 80% of Merkel cell. It is estimated that at least 5
10 million people are affected worldwide. It is rare and causes high aggressive skin cancer and mortality rate in patients (Schowalter et al., 2010) (Fig. 7.2).

7.3

Classification of oncovirus

Oncovirus is divided into two types: DNA virus and RNA virus. Both oncogenic viruses cause cancer in humans. Research has demonstrated that these viruses do not directly cause cancer, their actions promote cancer development in humans (Hausen, 2009). Recently, they have recognized seven human oncoviruses that cause cancer. The known human oncoviruses are HPV, human T-cell lymphtropic viruses, EBV, human herpesvirus-8, KS, Burkitt’s lymphoma, MCPyV, and HBV and HCV viruses (Akram et al., 2017).

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1% 0.3%

Hepas B virus 3.1%

Hepas C virus

15%

Human papillomavirus Epstein-barr virus 1-2%

Human T-cell lymphotropic virus

5.2%

Kaposi's sarcoma-associated herpesvirus

FIGURE 7.2 Percentage of cancer caused in humans by oncovirus.

7.3.1

DNA tumor viruses

Oncogenic DNA viruses cause cancer in humans. They include p53 and retinoblastoma protein, two major tumor suppressor proteins in humans. Major human DNA oncovirus is classified into three categories: small DNA viruses (adenovirus, SV40, HPV), large DNA viruses (EBV, KSHV), and others (HBV). It triggers and inactivates cell lysis and cell death by creating tumors while DNA replicates (Levine, 2009). The family Herpesviridae belongs to large DNA viruses, including herpesvirus such as EBV, which causes cancer in human beings such as Burkitt’s lymphoma, infectious mononucleosis, nasopharyngeal carcinoma, and Hodgkin’s lymphoma (Shimamoto & Yamanishi, 1999). KSHV also belongs to herpesvirus family, which can be transmitted through sexual and nonsexual contact (saliva HHV-8, blood, tissue, or organ transplant), but people with healthy immune system will not get affected by this virus (Encyclopedia of Microbiology, 2019). The family Adenoviridae belongs to a small DNA virus which includes adenovirus. It causes illnesses like fever, coughs, respiratory illness, sore throats, diarrhea, and conjunctivitis (Thinley KalsangBhutia Adenovirus Infections, 2018). The family Papovaviridae is one of the double-stranded DNA viruses, which is small DNA oncovirus that includes papillomavirus and polyomaviruses. Papillomavirus, which causes cervical cancer in women, penile cancer, squamous cell, and carcinoma, is transmitted through sexual intercourse, contact with blood, or transmission between mother to child affecting our immune system. Polyomaviruses include MCP which is implicated in Merkel cell carcinoma, an aggressive skin cancer, and human polyomaviruses species (BKPyV) and (JCPyV) cause prostate and colorectal cancer. SV40 is a virus that affects both humans and animals

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(EH & Murphy, 2012). The family Hepadnaviridae includes HBV, which causes liver cancer in humans, leading to severe chronic and acute diseases (Dowd et al., 2003).

7.3.2

RNA tumor viruses

RNA tumor virus contains ribonucleic acid as their genome, which belongs to retrovirus family. RNA virus contains single-stranded RNA. All cancercausing viruses are said to be oncovirus. This oncovirus, which is associated with RNA, is also known as oncogenic RNA viruses or retroviruses. These oncogenic retroviruses are also known as RNA tumor viruses. In 1961, researchers found that Rous sarcoma virus (RSV) particles contain RNA. HTLV-1 is the first human retrovirus discovered in 1981. Retroviruses are divided into three groups: Oncovirinae, Lentivirinae, and Spumaviruses. RSV, HTLV-1, and HTLV-2 are examples of the first group virus. Human immunodeficiency virus (HIV) is an example of the second group that has the characteristic feature of a long period of latency. A different oncogenic mechanism is used in RNA tumor viruses. RNA has a high oncogenic efficiency than DNA. It contains a reverse transcriptase enzyme that produces DNA from the RNA template (Petropoulos, 1997; Silverthorn, 2015; Sverdlov, 2000; Wilson & Harrison, 1991). There are three basic gene pools in retrovirus, namely gag, pol, and env. The external envelope comes from the covering of the plasma cell. Textured proteins (high antigens) are encased in an env (envelope) type and embodied in glycosylated. One essential hereditary item is created; however, this includes the creation of more than one glycoprotein by a developed bacterium (decontaminated by the Golgi framework catalyst). The transmembrane protein, an essential protein, is connected to the endoplasmic reticulum and produced using ribosomes. Inside layer is an icosahedral capsid that contains proteins embedded by the gag quality (a particular antigen gathering). Gag-instigated proteins additionally incorporate genomic RNA. Likewise, there is one significant hereditary item. This is joined with virally stacked protease (from the pol type) (Dahlberg, 1988; Steinhauer & Holland, 1987; Uyen et al., 2017). Fig. 7.3 illustrates the classification of oncovirus. All the virus details are given in Table 7.1

7.4

Molecular tools used for oncovirus detection

The development of various molecular assays, such as multiplex-PCR, realtime PCR, PCR-ELISA, quantitative-PCR, etc., due to advancements in biotechnology and molecular biology helps identify and characterizepathogens such as oncovirus (Kabir, 2018). EBV, cancer-causing virus, includes the most popular PCR method. RNA in situ hybridization (RISH) method is also used to detect EBV. EBV can also be diagnosed by simple blood test and

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Oncovirus

DNA oncovirus

HBV Liver

HPV Skin

RNA oncovirus

CMV

EBV

HCV

HIV

HTLV-1

Skin

Lymph

Liver

T-cell

T-cell leukemia

FIGURE 7.3 Classification of oncovirus.

antibody test like IgG (Ebell et al., 2016; Morrison et al., 2015). HPV, cancer-causing virus, includes the main methods like PCR, RT-PCR, and FISH methods to detect HPV. They also used Pap test and DNA test to detect HPV (Dixit et al., 2011). HBV, cancer-causing virus, includes quantitative PCR and ELISA test to detect HBV. HBsAg and rapid diagnostic tests (RDTs) are used to detect the chronic HBV, which is used as a marker. HBV can also be detected by a blood test, liver ultrasound, and liver biopsy (Lok & Brain, 2007). HCV, cancer-causing virus, includes RNA PCR method, blood test like anti-HCV test, and quantitative test to detect HCV (Kumar et al., 2018). Immunofluorescence assay (IFA) was the first serological assay used for detecting KSHV antibodies. PCR assays are used to detect DNA sequences in KSHV, which is limited in diagnostic use. At present, a small number of proteins or viral peptides by EIA and ELISA are used to detect KSHV. Southern blotting with a labeled probe is also used to detect KSHV (Nascimento et al., 2007; Pan et al., 2001). PCR has become the best method for diagnosis in all stages of HIV. HIV RNA PCR can be used for monitoring the level of viremia. Screening tests used for HIV are ELISA, simple test, and rapid test. (Lakshmi et al., 2011; Parekh et al., 2018) Western blot test is a confirmatory test in which HIV proteins are separated based on mobility and molecular weight (Cassar & Gessain, 2017; Ruggieri et al., 2019). Clinical consequences of multiple chronic conditions’ viral status are not clearly understood. At present, there is no proper clinical assessment for detecting MCPyV. Immunohistochemistry and quantitative PCR are used for detecting MCPyV in tumor samples which is flawed (Brummer et al., 2016; Eid et al., 2017). Micro (mi) RNAs are 22-base pair nucleic acids that were originally identified in the worm Caenorhabditis elegans. They are tiny, noncoding, and very stable. They are genetic components that can move about and do work. Pancreatic insulin secretions, adipogenesis, oncogenesis, and viral

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TABLE 7.1 Major human oncovirus and its prevalence. Virus

Family

Associated cancer

Prevalence (worldwide estimation)

Genome

EBV

Herpesviridae

Burkitt’s lymphoma, Hodgkin’s lymphoma, nasopharyngeal carcinoma, gastric cancer

.90% of positive cases have been identified around the world’s populations

DNA

HPV

Papillomaviridae

Cancers of penis, cervix, skin, anus, vaginal, vulvar, and head and neck squamous cell carcinoma

According to the US statistics, they have estimated females .(83%) males ,(74%), so females are more affected. Cervical cancer is the fourth most common cancer among women globally, with an estimated 570,000 new cases in 2018

DNA

HBV

Hepadnaviridae

Hepatocellular carcinoma causes liver cancer

2.3 billion people are infected by the hepatitis virus. According to WHO, children ,5 years of age were chronically infected with HBV in 2019

DNA

HCV

Hepadnaviridae

Hepatocellular carcinoma causes liver cancer

Globally, 71 million people have been chronically infected by HCV

RNA

KSHV

Herpesviridae

Kaposi sarcoma, body cavity lymphoma

Prevalence of KSHV varies

DNA

(Continued )

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TABLE 7.1 (Continued) Virus

Family

Associated cancer

Prevalence (worldwide estimation)

Genome

HIV

Retroviridae

Kaposi sarcoma, non-Hodgkin lymphoma (NHL), and cervical cancer

Approximately 38 million people across the globe were infected with HIV in 2019. 36.2 million were adults, and 1.8 million were children ,15 years of age

RNA

HTLV-1

Retroviridae

Adult T-cell leukemia/ lymphoma

HTLV-1 infects approximately 10
20 million people worldwide

RNA

MCPyV

Polyomaviridae

Merkel cell carcinoma

Global estimates at least 5
10 million people infected worldwide

DNA

illnesses are all influenced by miRNA genes. The first regulatory viral miRNA was miR-S1 in simian virus 40 (SV40), which promotes the recognition and destruction of infected cells by cytotoxic T cells. In contrast, the first trans-regulatory viral miRNA was miR-LAT in HSV-1 infection, which targets TGF-β and SMAD3, promoting cellular proliferation and preventing apoptosis. Pfeffer et al. were the first to characterize miRNA in the context of EBV infection, and 44 mature miRNAs derived from precursor miRNA were later identified. According to research, they are encoded in two areas: BART and BHRF1. During viral latency and lytic proliferation, they are expressed in a variety of cell lines and tumors. HIV-1 miRNA has been identified in the U3 region of the 3’-LTR of HIV-associated malignancies, where it suppresses cellular apoptosis, antagonizing transcription factor (AATF) gene production. Pichler et al. and Yeung et al. found that miRNA is either directly activated by tax or linked with HTLV-1-induced cell transformation during HTLV infection. As defined by Robin Holliday, epigenetic modifications in gene expression are heritable changes that are not produced by changes in the DNA sequence, including the methylation state of DNA and the posttranslational

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modification of histones. In mammals, the epigenetic process plays a significant role in carcinogenesis. DNA methylation is the most well-known epigenetic marker, and it, together with particular histone modification and specific miRNA, is thought to constitute a defining molecular landscape that is changed in cancer. By causing insertion mutations and chromosomal rearrangement, oncoviral genomes alter the host genome, predisposing infected cells to malignancy. In human malignancies, viral genes are also linked to an abnormal methylation profile in host-specific genes. Several researchers have sought to understand the epigenetic alterations in viruses. In the life cycle of EBV, epigenetic control of viral genes is a critical process. DNA methylation and histone modification govern the production of latent viral oncogenes, RNA, and miRNA, culminating in the total silence of the EBV gene. The transforming latency gene is subdued by methylation of the EBV DNA, which protects the host cell. HPV infection has also been linked to viral and host epigenetic modifications, such as DNA methylation and histone modification, both involved in pathogenesis and carcinogenesis. More than asymptomatic infections or dysplasia, viral DNA methylation is linked to cancer. For example, regardless of the stage of neoplastic development, the LCR and E6 sequences of HPV 16 and 18 were observed to be unmethylated, but the L1 region was highly methylated. According to methylation studies, the LCR of HPV16 was methylated in several primary cervical carcinomas, particularly at E2-binding sites (E2BS). E2BS has been found to block E2 binding, and methylation has been linked to E6 and E7 viral protein activation. HCV infection has also been linked to epigenetic alterations. HBV replication is linked to epigenetic markers such as H3 and H4 acetylation. Epigenetic changes have been seen in virtually all known oncoviruses.

7.5

Vaccines available for oncovirus

EBV is the first tumor virus that causes mononucleosis which is transmitted through saliva. Mostly children and young teenagers have been infected. It is contagious and can cause infection once again after months or years after treatment. The serious thing is no vaccine has been found, yet research for the vaccine is currently underway. So avoiding direct contact (sex, kissing, and sharing personal stuff) with the infected person is the best way to control the infection. Globally, .90% of positive cases have been identified around the world’s populations (Bu et al., 2019). HPV may cause severe cervical cancer; however, it is curable by the use of vaccine, which is entirely safe for consumption because some may cause side effects. This vaccine helps protect these high-rise type 16 and 18, mainly responsible for cervical cancer, and type 6 and 11 cause genital warts. Gardasil, Gardasil 9, and Cervarix are the vaccines used to prevent HPV. Gardasil 9 is licensed and only used in the United States, and the other vaccines are being used in many other countries, which majorly affects women 60%
70% worldwide

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(Monie et al., 2008). HBV is spread through blood (contaminated) or unsafe medical procedures, which causes liver cancer in humans, leading to severe chronic and acute diseases. HBV vaccines are highly safe and effective but may cause some side effects to prevent the infection. Such vaccines provide greater protection to newborns, children, and adults recommended with three-stage dosage levels. The approved vaccines Engerix-B and Recombivax-HB are used to treat the infection at different stages of age (Alexander & Kowdley, 2006). HCV infects the liver, which tends to cause chronic hepatitis and acute hepatitis. No vaccine has been found to prevent HCV infection, but vaccines are currently available for other hepatitis viruses. HCV viral infection has a high prevalence in Southern Italy, Northern Africa, Mongolia, Pakistan, China, and some areas of Japan (Petruzziello et al., 2016). KSHV has been subjected to little vaccine research compared to other human oncoviruses and herpesviruses. Perhaps because of a better general record for herpesvirus antiviral, there has been much more work to develop pharmacologic agents than anti-KSHV vaccines. KSHV may be considered a second-line vaccine. Efforts to avoid or cure HIV infection would result in a major reduction in direct efforts to prevent KSHV infection. The lack of animal samples of KSHV infection and/or KS development was reported as the second factor in vaccine research (Engels et al., 2008; Parkin, 2006; Sullivan et al., 2009; Westmoreland & Mansfield, 2008). Since there are no symptoms of early HTLV infection and cancer it causes, there is very little investment relative to the HTLV vaccine. Vaccine carriers to prevent the production of ATL and HAM/TSP may have a position in theory. There is a protein tax deduction, and there is some evidence that the multiepitope vaccine peptide vaccine is successful. The hidden period of ATL growth, on the other hand, is generally decades-long, and providence levels do not decrease. The vaccine’s primary use may be in the treatment of ATL. In certain cases, however, the absence of HIV type decreases education for these patients (Hino, 1997; Kannagi et al., 2005; Lynch & Kaumaya, 2006; Sundaram et al., 2004). Priority for the positive results from the RV144 study is that researchers determine the compatibility of protection against a key vaccine compound, that is, they must accurately determine how a highly potent compound is protected from HIV infection. The trial used the “first reinforcement” strategy for two HIV testing targets. The first was a synthetic vaccine using the canarypox virus, genetically engineered with antigenic HIV-coded protein codes. This vaccine was used as a “prime” and intended to stimulate the immune system (T cell response). The “boost” vaccine was made of an extra antigen-derived protein from HIV and intended to stimulate antibody production (B-cell responses) (Benson et al., 2009; Callahan, 2006; Harris et al., 2014; Malteˆz et al., 2011; Nitayaphan et al., 2004). Since the MCPyV compound is combined, the location varies from patient-to-patient, suggesting that the virus was present within the cell prior to or during

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oncogenic mutations. MCPyV genes in tissue do not join open-source T antigen (LT) tests, which are thought to release viral replication, compared to MCPyV for normal tissue. Essentially, the amino terminus of MCPyV T (LT) (aa1
258) is expressed in MCPyV-positive tissues, which plays a crucial role in multiple chronic conditions oncogenesis, providing the ideal vaccine target (Liang, 2009; Zeng et al., 2012).

7.6

Statistical analysis of oncovirus

Hidden Markov models (HMMs) are well known for their ability to represent correlations between nearby symbols, domains, or events, and they have been widely applied in various disciplines, including voice recognition and digital communication. As a result, HMM can discover patterns common to a family of viruses but difficult to detect when compared to a single nucleotide. The HMM design was first used in computational biology about 20 years ago, although it has a limited use than BLAST-based alignments. The HMMER3 HMM technique has been used to find RNA viral sequences in plasma, investigate Ebola virus pathogenicity, and subtype human influenza virus. Using HMMER3 and the viral profile database vFamily, we sought to explore if we could expand the overall viral ecology in human biospecimens from various sources. The novel set of partial differential equations (sPDEs) that describe the HCV RNA cycle overcomes the limitations of multilinear diffusion and reaction coefficients by distinguishing between the functions of HCV RNA and nonstructural virus-encoded proteins (NSPs). The simulation platform UG4 was used to test the model using the finite volume technique (FV). This in silico model was tested on powerful and massively parallel supercomputers using fast multigrid solvers. Despite the more realistic approach and expansion of earlier models, most of the parameters are still heuristically determined. Although the simulations were limited to a portion of the intracellular ER, the new model is now flexible enough to allow for changes in experimental situations, and the interaction between in silico and in vitro/ in vivo research may even suggest new experiments for a better understanding of virus dynamics.

7.6.1

Epstein
Barr virus

Globally, .90% of positive cases have been identified around the world’s populations. It is very common, which may be identified in 9 of 10 people worldwide, but very few can cause cancer. The four cancer types associated with EBV are Hodgkin’s lymphoma, non-Hodgkin’s lymphoma linked with post-transplant, Burkitt’s lymphoma, and nasopharyngeal carcinoma. Globally, 5.5 billion people were infected by EBV. They estimated that seroprevalence is rapidly increasing in adolescence, both male and female. In the

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United Kingdom, seropositivity is high in females. According to statistics, more than 1% of human cancer is linked to EBV worldwide. In 2017, an estimated17 million people were infected, and 9 million people dead worldwide (Khan et al., 1990).

7.6.2

Hepatitis B virus

HBV causes liver cancer in humans and is the second leading cause of severe chronic and acute diseases. Each year 30 million people are infected worldwide. A total of 37 million HBV carriers are found in India, which infects a large population worldwide. WHO estimates that 257 million peoples were infected by HBV in 2015. In 2016, according to researchers, about 250 million people were infected by HBV. Only 10% of people were infected worldwide, and very few were treated. According to the US estimation, the prevalence rate has been controlled. It was about 5.7% during 1999
2002 and 4.3% during 2015
2018, which has been visibly decreased. About 2.3 billion people were infected by HBV worldwide. WHO estimates that children ,5 years of age were chronically infected with HBV in 2019 (Chen et al., 2007).

7.6.3

Human papillomavirus

HPV is a sexually transmitted virus that is associated with cervix, skin, anus, penis, vaginal, vulvar, head, and neck and causes severe cancer. It causes severe cervical cancer, which majorly affects women 60%
70% worldwide. The high-risk types 16 and 18 are particularly responsible for cervical cancer. According to the US statistics, they have estimated females (83%) are more affected than males (74%) (Daneshvar et al., 2016). During 2011
14, 3.3% of adult females and 11.5% of adult males were infected by oral HPV, and 3.9% females and 45.2% males were infected by genital HPV. Females with cervix cancer and males with oropharynx cancer were estimated from 2012 to 2016. In 2017, approximately 70% of oropharynx cancer was caused by HPV (David et al., 2012). Globally, cervical cancer is the fourth most common cancer in women. In 2018, an estimated 570,000 cases were diagnosed with cervical cancer. Global estimation on HPV statistical prevalence was11.7%. Sub-Saharan Africa (24.0%), Eastern Europe (21.4%), and Latin America (16.1%) showed high rate in prevalence. A large peak of prevalence in HPV is strongly identified in both genders connected with age (Fourati et al., 2019).

7.6.4

Hepatitis C virus

The US estimated about 2.4 million people were infected by HCV in 2013
16. In the United States, 1.2 million people were infected with HIV in

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2018. In 2016, WHO estimated that approximately 399,000 people were infected with the primary liver cancer in HCV. Globally, 71 million people have been chronically infected by HCV in 2017, which may cause severe liver cancer and cirrhosis. HCV viral infection has a high prevalence in Southern Italy, Northern Africa, Mongolia, Pakistan, China, and some areas of Japan. In 2018, according to CDC estimation, 3621 cases with acute HCV were reported. Later CDC analyzed that 50,300 cases were identified in the United States. They have about six major types of genotypes in HCV in which most commonly infected is genotype 1 with 44% cases, genotype 3 with 25% cases, and genotype 4 with 15% cases. Geographical distribution of HCV has been found worldwide (Cassler et al., 2016).

7.6.5

Kaposi sarcoma-associated herpesvirus

Chronic sarcoma is most prevalent in developed countries among Jews in Israel. From 1992 to 1995, serum was obtained from 1648 adults who had tested positive for HBV infection 20 years ago at blood donation; serra were also obtained from 2403 of their family members. Among family members, the seroprevalence of antibodies against KSHV was 9.9%; among the former blood donors who had tested positive for hepatitis B, it was 22%. The crude prevalence rate of KSHV among Jews in Israel is 9.9% (Davidovici et al., 2001). According to an international study based on cancer cases from about 23 studies in the United States, Europe, and Australia, the average incidence of KS in these countries decreased from 15.2 per 1000 years in 1992 to 4.9 per 1000 years between 1997 and 1999; this decrease was driven by a reduction in the number of cases of AIDS-related KS. A 2017 study, based on over 200,000 patients, reported raw KS incidence per 100,000 person-years in 42 cohorts from 57 countries, including North America (237 per 100,000 person-years), Latin America (244 per 100,000 person-years), Europe (180 per 100,000 person-years), Asia-Pacific (52 per 100,000 person-years), and South Africa (280 per 100,000 person-years). The risk of KS was nearly twice as high in men as in women (Cook-Mozaffari et al., 1998; Cottoni et al., 1996; Grulich et al., 1992; Lebbe´ et al., 2008).

7.6.6

Human immunodeficiency virus

This systematic review and global survey yielded 2203 datasets with 383,519 examples from 116 nations in 1990
2015. From 2010 to 2015, type C accounted for 46.6%of all HIV-1 contaminations worldwide. Type B has the highest pollution rate (12.1%), followed by type A (10.3%), CRF02 AG (7.7%), CRF01 AE (5.3%), subtypes G (4.6%), and subtype D (2.7%). Subtypes J, H, F, and K together accounted for just 0.9% of infections. Other CRFs represented 3.7%, making up a portion of all CRFs, 16.7% URFs make up 6.1%, representing 22.8% of the absolute HIV-1 disease worldwide

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(UNAIDS 2019; Global HIV Prevention Working Group. Behavior Change & HIV Prevention:(Re) Considerations for the 21st Century. Global HIV Prevention Working Group, 2008). The majority of HIV-positive people were living in low- and middleincome countries. In Japan and South Africa, there were a total of 20.7 million inhabitants (54%), 4.9 million (13%) and 5.8 million (15%) in Asia and the Pacific, and 2.2 million (6%) in Western, Central, and North Europe and North America by 2019. By 2019, 85% of HIV-positive pregnant ladies obtained ART to prevent HIV transmission to their toddlers during childbirth and defend their health (Iwanaga, 2020).

7.6.7

Human T-cell lymphotropic virus type 1

In Japan, there is evidence of two HTLV-I ancestral lists: the old cosmopolitan genotype, which represents about 25% of the HTLV-I present in Japan, and in found mainly in the southern islands among the inhabitants of Central Africa, especially the Pygmies. While the local subtypes vary from 2% to 8%, the HTLV-I quasi types present within a person appear to be very low, with a variation of ,0.5%. The prevalence of HTLV-1 was higher in women (80%), people between 31 and 50 years of age, heterosexual, unmarried, with low monthly income, with secondary education level or higher, occasional condom use, limited number of sexual partners, and no history of sexually transmitted diseases (Oliveira-Filho et al., 2019). The key pathways of HTLV-1 transmission in common areas include breast milk transmission from mother to infant and sexual transmission in adults; transmission from a male to a woman is almost four times high as transmission from a woman. However, in many countries, especially Japan, the spread of contaminated blood products has become a major issue. In many nations, including Japan, the United States, and Brazil, blood transfusions are also regularly screened for HTLV-1. Blood product monitoring and breastfeeding counseling were implemented following the onset of a decline in seroprevalence-based serum age of HTLV-1 in Japan (Martel-Jantin et al., 2014).

7.6.8

Merkel cell polyomavirus

According to phylogenetic research, there are five major MCPyV genotype species in these countries, [Europe/North America, Africa (Sub-Saharan), Oceania, South America, and Asia/Japan]. In 23 of the 26 cases, MCPyV DNA was used (88.5%). In enlarged components, four transformation patterns can be seen. MCPyV was common in Japanese patients with various chronic conditions. This research was conducted in the past. From January 1994 to December 2012, researchers examined the hospital’s patient index for all cases of various chronic conditions. Available skin samples from patients with various chronic conditions were analyzed using two PCR

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techniques (standard and real-time) to determine MCPyV DNA (Hattori et al., 2013; Neto et al., 2019). Analysis of regional or national cancer registration data for more than 100 people with multiple chronic conditions was detected in more than 19,000 cases from 2003 to 2007 and more than 12,000 cases in the 1990 to 2007 case analysis (Stang et al., unpublished data). In the early 1990s (1990
94), some cancer registrations showed multiple chronic conditions concentrations close to zero, suggesting inaccurate cases. Registrations for the US SEER White, Canada, Australia, Italy, and Netherlands have increased the number of multiple chronic conditions cases since the mid-1990s. Australia has the largest World Norm Population (males: 5.2 million per person per year; females: 2.2 million per person per year) from 2003 to 2007 (Abara et al., 2019a).

7.7

Oncovirus and cancer progression

Due to changes in the lifestyle and environment, they may lead changes in the growth property of cells, tumor growth, invasion, and metastasis which increases the risk of developing aggressive cancer and causes infection in oncovirus. It has been recently suggested that oncoviruses can contribute to the cancer progression (Mu¨ller-Coan et al., 2018). EBV, HBV, HPV, KSHV, and HCV are the most epidemiologically relevant human carcinogenesis oncoviruses, which plays a major role in cancer progression (Epstein et al., 1964). The three major categories in oncovirus and cancer progression are microenvironment, phenotypic modification, and genetic instability, which is illustrated in Fig. 7.4 (Banerjee et al., 2015; Mu¨ller-Coan et al., 2018).

7.7.1

Human papillomavirus on cancer progression

Herpesvirus belongs to large DNA viruses, including herpesvirus like EBV and KSHV which causes cancer in humans. These EBV and KSHV belong

FIGURE 7.4 Role of oncovirus in cancer.

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to gammaherpesvirus 4 (HHV4) and 8 (HHV8). A study found that tumorassociated macrophage with a high number expresses CCL18 associated NPC. EBV associates NPV by encoding LMP1, EBNAs, and miRNABARTs which play a major role in oncovirus cancer progression. Now research is going on how EVB affects other types of cancers (Elgui de Oliveira et al., 2016). KSHV, the herpesvirus with double strands, typically changes cell phenotype to increase the availability of growth factors and cytokine. KSHV associates with KS by allowing neoplastic spindle cells to become more angiogenic, proliferate, migratory, and invasive. KSHV uses cellular protein to interfere with the cellular pathway (Elgui de Oliveira et al., 2016).

7.7.2

Hepatitis B virus on cancer progression

HBV causes liver cancer in humans, leading to severe chronic and acute diseases. According to the research, an increase in tumorigenesis and invasion in HBV cancer progression associated with HBV-positive HCC tissues reduced the expression miRs 18a and 148a (Li et al., 2013). Research states that in HBV, the DNA in the host genome may contribute to tumor progression (Abara et al., 2019b).

7.7.3

Hepatitis C virus on cancer progression

HCV infects the liver, which tends to cause chronic hepatitis and acute hepatitis with high level of oxidative damage, causing oncogenesis. In HCV, the structural protein and nonstructural protein connect the cancer progression and liver carcinogenesis (Banerjee et al., 2010; Kasprzak & Adamek, 2008). Researchers have found several ways to contribute the cancer progression that can be direct or indirect. Such HCV can produce cell invasion, migration, and oncogenesis. HCV contributes to cancer progression to increase its tumor heterogeneity. The structural protein HCVc and nonstructural protein connect HCV infection and cause liver fibrosis, progression of HCC, and transformation of the cell (Verga-Ge´rard et al., 2013).

7.7.4

Human papillomavirus on cancer progression

HPV is genetically simple and has a limited contribution to cancer. The researchers found infected HPV malignant cells cause late progression of behavior response and are subjected to high immunological pressure, which can be seen in HPV-associated carcinomas. Due to the genomic alteration linked with HPV-associated carcinomas, the changes have been recorded in malignant behavior expression (Akagi et al., 2014; Vinokurova et al., 2008). Fig. 7.5 illustrates hallmark properties of cancer.

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Tissue activating invasion and metastasis

Resisting cell death

Evadiving growth suppressor

Change in growth property

Chronic inflammation

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Sustaining proliferation signalling

Genetic instability

FIGURE 7.5 Hallmarks of cancer.

7.8

Oncolytic virotherapy

Oncolytic virotherapy plays a promising role in oncology. Major steps in the field of cancer treatment and infectious diseases date back to the 20th century. Clinical trials involving the transport of body fluids containing viral particles to other cancer patients are performed, and the results are evaluated. Alice Moore was the first scientist who pioneered the task of testing for oncolytic viruses in animal cancer models. Southam and Moore have contributed significantly to this vast field of oncolytic bacteria by continuing to transfer this idea to paper and apply it. Directing 101 Egyptian parasites, they have attempted to study antimicrobial properties. These viruses have limited value due to the negative effects of neurotoxicity. This has prompted a continuous change in adenoviruses, paramyxoviruses, poxviruses, and so on. The main clinical preliminaries in oncolytic infection utilized as a treatment were acted in 1956 and 1972 with adenovirus, adenoidal, pharyngeal, conjunctival,and mumps infection separately (Hill, 1965; Hoster et al., 1949). Hereditary transformations have been known to us for a long time. This change is practically indistinguishable from the transformations brought about via carcinogenesis. This causes us to infer that some infections have a characteristic inclination, i.e., these oncotropic infections are normally utilized for oncolytic virotherapy. Newcastle sickness, reovirus, autonomous parvovirus, and stomatitis infection (VSV) are only a couple instances of viral contamination

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(Everts & Van Der Poel, 2005; Marchini et al., 2015; Moehler et al., 2001; Norman & Lee, 2000; Peeples et al., 1994; Stojdl et al., 2000). Oncolytic virotherapy has a brilliant future as late exploratory discoveries have brought trust in the examination of local area about the utilization of the infection as an enemy of tumor specialist that joins ordinary treatments, for example, chemotherapy and radiotherapy or substitution treatment later on. Additional reasoning work should be done in this handle; the new endorsement of tumor and oncolytic antibodies by the FDA and Chinese SFDA has significantly consolidated them into clinical practice. There is a long and developing rundown of new or improved sorts of oncolytic microorganisms. With the rise of new innovations and comprehension of OV and their part in disease improvement, recent concerns in the turn of events and full use of oncolytic infections can be addressed (Mhaske & Oncoviruses, 2015). Fig. 7.6 illustrates oncovirus effects in tumor cells. Tumor infected cell

Replication of viral cells

Release of new cells

Oncovirus

Normal cell

No viral replication

FIGURE 7.6 Oncovirus effect in normal and tumor cells.

Undamaged cell

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Conclusion

In many years of cancer research, the role of viruses in oncogenesis has been widely debated. We now agree that seven different viruses may play a major role in human cancer. A great deal of development has been made in the past 30 years. In humans, the cancer-causing virus is said to be oncovirus, which is about 12% of all cancer causing virus worldwide. Antibacterial vaccines offer rare cancer-preventing opportunities, which, more importantly, can be effective even under minimal resources. However, successful growth and distribution require a combination of several factors in the oncovirus vaccine. Current method of study tells that these cancer-causing viruses can be diagnosed and treated with less toxic antiviral compounds or diagnosed with simple blood tests. They might be easily prevented through vaccination, whereas HPV and HBV vaccines are widely available and have been used. Other nonvaccine viruses are in research to make them available. In the case of intermediate results, other tests and methods such as multiplex- PCR, real-time PCR, PCR-ELISA, quantitative-PCR, FISH, chemiluminescent immunoassay, and antibody test has been used to detect the presence of the oncogenic virus. The discovery and formation of oncoviruses have been at the forefront of biological research for decades. It provided important information based on cytology and carcinogenesis processes. In addition, these studies have created important interventions in public health that have the potential to prevent a huge number of human cancers. We hope that in the future, researchers and scientists will produce the latest exciting insights into cancer and effective ways to prevent it.

Acknowledgment The Authors thank Chettinad Academy of Research and Education for their constant support and encouragement.

References Abara, W. E., Collier, M. G., Moorman, A., Bixler, D., Jones, J., Annambhotla, P., Bowman, J., Levi, M. E., Brooks, J. T., & Basavaraju, S. V. (2019a). Characteristics of deceased solid organ donors and screening results for hepatitis B, C, and human immunodeficiency viruses
United States, 2010
2017. American Journal of Transplantation, 19(3), 939
947. Available from https://doi.org/10.1111/ajt.15284. Abara, W. E., Collier, M. G., Moorman, A., Bixler, D., Jones, J., Annambhotla, P., Bowman, J., Levi, M. E., Brooks, J. T., & Basavaraju, S. V. (2019b). Characteristics of deceased solid organ donors and screening results for hepatitis B, C, and human immunodeficiency viruses
United States, 2010
2017. MMWR. Morbidity and Mortality Weekly Report, 68(3), 61
66. Available from https://doi.org/10.15585/mmwr.mm6803a2. Akagi, K., Li, J., Broutian, T. R., Padilla-Nash, H., Xiao, W., Jiang, B., Rocco, J. W., Teknos, T. N., Kumar, B., Wangsa, D., He, D., Ried, T., Symer, D. E., & Gillison, M. L. (2014).

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Genome-wide analysis of HPV integration in human cancers reveals recurrent, focal genomic instability. Genome Research, 24(2), 185
199. Available from https://doi.org/10.1101/ gr.164806.113. Akram, N., Imran, M., Noreen, M., Ahmed, F., Atif, M., Fatima, Z., & Bilal Waqar, A. (2017). Oncogenic role of tumor viruses in humans. Viral Immunology, 30(1), 20
27. Available from https://doi.org/10.1089/vim.2016.0109. Alexander, J., & Kowdley, K. V. (2006). Epidemiology of hepatitis B
clinical implications. Medscape General Medicine, 8(2), 13. Banerjee, A., Ray, R. B., & Ray, R. (2010). Oncogenic potential of hepatitis C virus proteins. Viruses, 2(9), 2108
2133. Available from https://doi.org/10.3390/v2092108. Banerjee, S., Jha, H. C., & Robertson, E. S. (2015). Regulation of the metastasis suppressor Nm23-H1 by tumor viruses. Naunyn-Schmiedeberg’s Archives of Pharmacology, 388(2), 207
224. Available from https://doi.org/10.1007/s00210-014-1043-8. Benson, C. A., Brooks, J. T., Holmes, K. K., Kaplan, J. E., Masur, H., & Pau, A. (2009). Guidelines for prevention and treatment opportunistic infections in HIV-infected adults and adolescents. Recommendations from CDC, the National Institutes of Health, and the HIV Medicine Association/Infectious Diseases Society of America. Brummer, G. C., Bowen, A. R., & Bowen, G. M. (2016). Merkel cell carcinoma: Current issues regarding diagnosis, management, and emerging treatment strategies. American Journal of Clinical Dermatology, 17(1), 49
62. Available from https://doi.org/10.1007/s40257-0150163-3. Bu, W., Joyce, M. G., Nguyen, H., Banh, D. V., Aguilar, F., Tariq, Z., Yap, M. L., Tsujimura, Y., Gillespie, R. A., Tsybovsky, Y., Andrews, S. F., Narpala, S. R., McDermott, A. B., Rossmann, M. G., Yasutomi, Y., Nabel, G. J., Kanekiyo, M., & Cohen, J. I. (2019). Immunization with components of the viral fusion apparatus elicits antibodies that neutralize Epstein-Barr virus in B cells and epithelial cells. Immunity, 50(5), 1305
1316. Available from https://doi.org/10.1016/j.immuni.2019.03.010, e6. Callahan, G. N. (2006). Infection: The uninvited universe. Macmillan. Cassar, O., & Gessain, A. (2017). Serological and molecular methods to study epidemiological aspects of human T-cell lymphotropic virus type 1 infection. Methods in Molecular Biology, 1582, 3
24. Available from https://doi.org/10.1007/978-1-4939-6872-5_1. Cassler, N. M., Merrill, D., Bichakjian, C. K., & Brownell, I. (2016). Merkel cell carcinoma therapeutic update. Current Treatment Options in Oncology, 17(7), 36. Available from https://doi.org/10.1007/s11864-016-0409-1. Chen, C. H., Yang, P. M., Huang, G. T., Lee, H. S., Sung, J. L., & Sheu, J. C. (2007). Estimation of seroprevalence of hepatitis B virus and hepatitis C virus in Taiwan from a large-scale survey of free hepatitis screening participants. Journal of the Formosan Medical Association, 106(2), 148
155. Available from https://doi.org/10.1016/S0929-6646(09) 60231-X. UNAIDS (2019). Communities at the centre: Global AIDS update. Joint United Nations Programme on HIV/AIDS. UNAIDS. Cook-Mozaffari, P., Newton, R., Beral, V., & Burkitt, D. P. (1998). The geographical distribution of Kaposi’s sarcoma and of lymphomas in Africa before the AIDS epidemic. British Journal of Cancer, 78(11), 1521
1528. Available from https://doi.org/10.1038/ bjc.1998.717. Cottoni, F., De Marco, R., & Montesu, M. A. (1996). Classical Kaposi’s sarcoma in north-east Sardinia: An overview from 1977 to 1991. British Journal of Cancer, 73(9), 1132
1133. Available from https://doi.org/10.1038/bjc.1996.217.

Role of viral human oncogenesis: recent developments Chapter | 7

167

Dahlberg, J. E. (1988). An overview of retrovirus replication and classification. Advances in Veterinary Science and Comparative Medicine, 32(C), 1
35. Available from https://doi.org/ 10.1016/B978-0-12-039232-2.50005-0. Daneshvar, F., Haghshenas, M., Majleci, F., & Rahimi, A. (2016). The prevalence and genotype distribution of cervical human papillomavirus DNA in women with normal cytology in north of Iran. Jundishapur Journal of Microbiology, 10(1), e40823. David, F., Catherine, d M., J., L. C., Isabelle, S., Joannie, L.-T., Laia, B., Jerome, V., Jacques, F., Freddie, B., Martyn, P., & Silvia, F. (2012). Global burden of human papillomavirus and related diseases. Vaccine, 30, F12
F23. Available from https://doi.org/10.1016/j. vaccine.2012.07.055. Davidovici, B., Karakis, I., Bourboulia, D., Ariad, S., Zong, J. C., Benharroch, D., Dupin, N., Weiss, R., Hayward, G., Sarov, B., & Boshoff, C. (2001). Seroepidemiology and molecular epidemiology of Kaposi’s sarcoma-associated herpesvirus among Jewish population groups in Israel. Journal of the National Cancer Institute, 93(3), 194
202. Available from https:// doi.org/10.1093/jnci/93.3.194. Dixit, R., Bhavsar, C., & Marfatia, Y. S. (2011). Laboratory diagnosis of human papillomavirus virus infection in female genital tract. Indian Journal of Sexually Transmitted Diseases, 32 (1), 50
52. Available from https://doi.org/10.4103/0253-7184.81257. Donald, M. P. (2006). The global health burden of infection-associated cancers in the year 2002. International Journal of Cancer, 118, 3030
3044. Dowd, J., Palermo, T., Brite, J., McDade, T., & Aiello, A. (2003). Seroprevalence of EpsteinBarr virus infection in US children ages 6
19. PloS One, 8(5), e64921. Ebell, M. H., Call, M., Shinholser, J., & Gardner, J. (2016). Does this patient have infectious mononucleosis? The rational clinical examination systematic review. JAMA - Journal of the American Medical Association, 315(14), 1502
1509. Available from https://doi.org/ 10.1001/jama.2016.2111. EH, H. P., & Murphy, F. (2012). Laboratory diagnosis of infectious diseases principles and practice: Volume II viral, rickettsial, and chlamydial diseases. Springer. Eid, M., Nguyen, J., & Brownell, I. (2017). Seeking standards for the detection of merkel cell polyomavirus and its clinical significance. Journal of Investigative Dermatology, 137(4), 797
799. Available from https://doi.org/10.1016/j.jid.2016.12.024. Elgui de Oliveira, D., Mu¨ller-Coan, B. G., & Pagano, J. S. (2016). Viral carcinogenesis beyond malignant transformation: EBV in the progression of human cancers. Trends in Microbiology, 24(8), 649
664. Available from https://doi.org/10.1016/j.tim.2016.03.008. Encyclopedia of Microbiology. (2019). Engels, E. A., Biggar, R. J., Hall, H. I., Cross, H., Crutchfield, A., Finch, J. L., Grigg, R., Hylton, T., Pawlish, K. S., McNeel, T. S., & Goedert, J. J. (2008). Cancer risk in people infected with human immunodeficiency virus in the United States. International Journal of Cancer, 123(1), 187
194. Available from https://doi.org/10.1002/ijc.23487. Epstein, M. A., Achong, B. G., & Barr, Y. M. (1964). Virus particles in cultured lymphoblasts from burkitt’s lymphoma. The Lancet, 283(7335), 702
703. Available from https://doi.org/ 10.1016/S0140-6736(64)91524-7. Epstein, M. A., & Barr, Y. M. (1964). Cultivation in vitro of human lymphoblasts from burkitt’s malignant lymphoma. The Lancet, 283(7327), 252
253. Available from https://doi.org/ 10.1016/S0140-6736(64)92354-2. Everts, B., & Van Der Poel, H. G. (2005). Replication-selective oncolytic viruses in the treatment of cancer. Cancer Gene Therapy, 12(2), 141
161. Available from https://doi.org/ 10.1038/sj.cgt.7700771.

168

Oncogenic Viruses Volume 2

Lok., A. S. F., & Brain, J. M. (2007). Chronic hepatitis B. Hepatology, 45, 507
539. Available from https://doi.org/10.1002/hep.21513. Ferlay, J., Ervik, M., Lam, F., Colombet, M., Mery, L., Pin˜eros, M., Znaor, A., Soerjomataram, I., & Bray. (2018). Global cancer observatory: Cancer today. Lyon, France: International Agency for Research on Cancer. Fourati, S., Rodriguez, C., He´zode, C., Soulier, A., Ruiz, I., Poiteau, L., Chevaliez, S., & Pawlotsky, J. M. (2019). Frequent antiviral treatment failures in patients infected with hepatitis C virus genotype 4, subtype 4r. Hepatology, 69(2), 513
523. Available from https:// doi.org/10.1002/hep.30225. Gerber, M. A., Shapiro, E. D., Ryan, R. W., & Bell, G. L. (1996). Evaluations of enzyme-linked immunosorbent assay procedure for determining specific Epstein-Barr virus serology and of rapid test kits for diagnosis of infectious mononucleosis. Journal of Clinical Microbiology, 34(12), 3240
3241. Available from https://doi.org/10.1128/jcm.34.12.3240-3241.1996. Global HIV Prevention Working Group. Behavior change and HIV prevention:(re) considerations for the 21st century. Global HIV Prevention Working Group. (2008). Grulich, A. E., Swerdlow, A. J., & Beral, V. (1992). Kaposi’s sarcoma in england and wales before the aids epidemic. British Journal of Cancer, 66(6), 1135
1137. Available from https://doi.org/10.1038/bjc.1992.423. Harris, R. J., Thomas, B., Griffiths, J., Costella, A., Chapman, R., Ramsay, M., De Angelis, D., & Harris, H. E. (2014). Increased uptake and new therapies are needed to avert rising hepatitis C-related end stage liver disease in England: Modelling the predicted impact of treatment under different scenarios. Journal of Hepatology, 61(3), 530
537. Available from https:// doi.org/10.1016/j.jhep.2014.05.008. Hattori, T., Takeuchi, Y., Takenouchi, T., Hirofuji, A., Tsuchida, T., Kabumoto, T., Fujiwara, H., Ito, M., Shimizu, A., Okada, E., Motegi, S. I., Tamura, A., & Ishikawa, O. (2013). The prevalence of Merkel cell polyomavirus in Japanese patients with Merkel cell carcinoma. Journal of Dermatological Science, 70(2), 99
107. Available from https://doi.org/10.1016/j. jdermsci.2013.02.010. Hausen. (2009). The search for infectious causes of human cancers: Where and why. Virology, 392(1), 1
10. Hill, A. B. (1965). The environment and disease: Association or causation. Proceedings of the Royal Society of Medicine, 58(5), 295
300. Hino, S. (1997). Primary prevention of HTLV-1 in Japan. Leukemia, 11(3), 57
59. Available from http://www.nature.com/leu/index.html. Hoster, H. A., Zanes, R. P., & Haam, E. V. (1949). Studies in Hodgkin’s syndrome IX. The association of “viral” hepatitis and Hodgkin’s disease (A Preliminary Report). Cancer Research, 9(8), 473
480. Iwanaga, M. (2020). Epidemiology of HTLV-1 Infection and ATL in Japan: An Update. Frontiers in Microbiology, 11, 1124. Available from https://doi.org/10.3389/ fmicb.2020.01124. Kabir, M. S. (2018). Molecular methods for detection of pathogenic viruses of respiratory tract
A review. Asian Pacific Journal of Tropical Biomedicine, 8(5), 237
244. Available from https://doi.org/10.4103/2221-1691.233004. Kannagi, M., Harashima, N., Kurihara, K., Ohashi, T., Utsunomiya, A., Tanosaki, R., Masuda, M., Tomonaga, M., & Okamura, J. (2005). Tumor immunity against adult T-cell leukemia. Cancer Science, 96(5), 249
255. Available from https://doi.org/10.1111/j.13497006.2005.00050.x.

Role of viral human oncogenesis: recent developments Chapter | 7

169

Kasprzak, A., & Adamek, A. (2008). Role of hepatitis C virus proteins (C, NS3, NS5A) in hepatic oncogenesis. Hepatology Research, 38(1), 1
26. Available from https://doi.org/ 10.1111/j.1872-034X.2007.00261.x. Katze, w G., & Korth, M. (2015). Viral pathogenesis: From basics to systems biology. Elsevier. Khan., Fitzmaurice, C., Naghavi, M., & Ahmed, L. A. (1990). Global and regional incidence, mortality and disability-adjusted life-years for Epstein-Barr virus-attributable malignancies. BMJ Open, 10(8), e037505. Krings, A., Dunyo, P., Pesic, A., Tetteh, S., Hansen, B., Gedzah, I., Wormenor, C. M., Amuah, J. E., Behnke, A. L., Ho¨fler, D., Pawlita, M., & Kaufmann, A. M. (2019). Characterization of human papillomavirus prevalence and risk factors to guide cervical cancer screening in the North Tongu District, Ghana. PLoS One, 14(6), e218762. Available from https://doi.org/ 10.1371/journal.pone.0218762. Kumar, A., Rajput, M. K., Paliwal, D., Yadav, A., Chhabra, R., & Singh, S. (2018). Genotyping & diagnostic methods for hepatitis C virus: A need of low-resource countries. Indian Journal of Medical Research, 147(May), 445
455. Available from https://doi.org/10.4103/ ijmr.IJMR_1850_16. Lakshmi, V., Sudha, T., Rakhi, D., Anilkumar, G., & Dandona, L. (2011). Application of polymerase chain reaction to detect HIV-1 DNA in pools of dried blood spots. Indian Journal of Microbiology, 51(2), 147
152. Available from https://doi.org/10.1007/s12088-011-0135-0. Lebbe´, C., Legendre, C., & France`s, C. (2008). Kaposi sarcoma in transplantation. Transplantation Reviews, 22(4), 252
261. Available from https://doi.org/10.1016/j.trre.2008.05.004. Levine, A. J. (2009). The common mechanisms of transformation by the small DNA tumor viruses: The inactivation of tumor suppressor gene products: p53. Virology, 384(2), 285
293. Available from https://doi.org/10.1016/j.virol.2008.09.034. Li, C., Wang, Y., Wang, S., Wu, B., Hao, J., Fan, H., Ju, Y., Ding, Y., Chen, L., Chu, X., Liu, W., Ye, X., & Meng, S. (2013). Hepatitis B virus mRNA-mediated miR-122 inhibition upregulates PTTG1-binding protein, which promotes hepatocellular carcinoma tumor growth and cell invasion. Journal of Virology, 87(4), 2193
2205. Available from https://doi.org/ 10.1128/JVI.02831-12. Liang, T. J. (2009). Hepatitis B: The virus and disease. Hepatology, 49(5), S13
S21. Available from https://doi.org/10.1002/hep.22881. Lunn, R., Jahnke, G., & Rabkin, C. S. (2017). Tumour virus epidemiology. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1732), 20160266. Lynch, M. P., & Kaumaya, P. T. P. (2006). Advances in HTLV-1 peptide vaccines and therapeutics. Current Protein and Peptide Science, 7(2), 137
145. Available from https://doi.org/ 10.2174/138920306776359803. Malteˆz, F., Doroana, M., Branco, T., & Valente, C. (2011). Recent advances in antiretroviral treatment and prevention in HIV-infected patients. Current Opinion in HIV and AIDS, 6(1), S21
S30. Available from https://doi.org/10.1097/01.COH.0000410238.80894.81. Marchini, A., Bonifati, S., Scott, E. M., Angelova, A. L., & Rommelaere, J. (2015). Oncolytic parvoviruses: From basic virology to clinical applications. Virology Journal, 12(1), 6. Available from https://doi.org/10.1186/s12985-014-0223-y. Martel-Jantin, C., Filippone, C., Tortevoye, P., Afonso, P. V., Betsem, E., Descorps-Declere, S., Nicol, J. T. J., Touze´, A., Coursaget, P., Crouzat, M., Berthet, N., Cassar, O., & Gessaina, A. (2014). Molecular epidemiology of merkel cell polyomavirus: Evidence for geographically related variant genotypes. Journal of Clinical Microbiology, 52(5), 1687
1690. Available from https://doi.org/10.1128/JCM.02348-13.

170

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Martin, J. N., Ganem, D. E., Osmond, D. H., Page-Shafer, K. A., Macrae, D., & Kedes, D. H. (1998). Sexual transmission and the natural history of human herpesvirus 8 infection. New England Journal of Medicine, 338(14), 948
954. Available from https://doi.org/10.1056/ NEJM199804023381403. Mhaske, S., & Oncoviruses. (2015). An overview of oncogenic and oncolytic viruses. Oncobiology and targets (Vol. 2). Medknow Publications and Media Pvt. Ltd. Moehler, M., Blechacz, B., Weiskopf, N., Zeidler, M., Stremmel, W., Rommelaere, J., Galle, P. R., & Cornelis, J. J. (2001). Effective infection, apoptotic cell killing and gene transfer of human hepatoma cells but not primary hepatocytes by parvovirus H1 and derived vectors. Cancer Gene Therapy, 8(3), 158
167. Available from https://doi.org/10.1038/sj. cgt.7700288. Monie, A., Hung, C., Roden, R., & Wu, T. C. (2008). CervarixTM: A vaccine for the prevention of HPV 16, 18-associated cervical cancer. Biologics: Targets & Therapy, 2(1), 97
105. Morrison, B. J., Labo, N., Miley, W. J., & Whitby, D. (2015). Serodiagnosis for tumor virusesz. Seminars in Oncology, 42(2), 191
206. Available from https://doi.org/10.1053/j. seminoncol.2014.12.024. Mu¨ller-Coan, B. G., Caetano, B. F. R., Pagano, J. S., & Elgui de Oliveira, D. (2018). Cancer progression goes viral: The role of oncoviruses in aggressiveness of malignancies. Trends in Cancer, 4(7), 485
498. Available from https://doi.org/10.1016/j.trecan.2018.04.006. Nascimento, M. C., De Souza, V. A., Sumita, L. M., Freire, W., Munoz, F., Kim, J., Pannuti, C. S., & Mayaud, P. (2007). Comparative study of Kaposi’s sarcoma-associated herpesvirus serological assays using clinically and serologically defined reference standards and latent class analysis. Journal of Clinical Microbiology, 45(3), 715
720. Available from https:// doi.org/10.1128/JCM.01264-06. Neto, C. F., Oliveira, W. R. P., Costa, P. V. A., Cardoso, M. K., Barreto, P. G., Romano, C. M., & Urbano, P. R. (2019). The first observation of the association of Merkel cell polyomavirus and Merkel cell carcinoma in Brazil. International Journal of Dermatology, 58(6), 703
706. Available from https://doi.org/10.1111/ijd.14325. Nitayaphan, S., Pitisuttithum, P., Karnasuta, C., Eamsila, C., De Souza, M., Morgan, P., Polonis, V., Benenson, M., VanCott, T., Ratto-Kim, S., Kim, J., Thapinta, D., Garner, R., Bussaratid, V., Singharaj, P., El Habib, R., Gurunathan, S., Heyward, W., Birx, D., . . . Brown, A. E. (2004). Safety and immunogenicity of an HIV subtype B and E prime-boost vaccine combination in HIV-negative Thai adults. Journal of Infectious Diseases, 190(4), 702
706. Available from https://doi.org/10.1086/422258. Norman, K. L., & Lee, P. W. K. (2000). Reovirus as a novel oncolytic agent. Journal of Clinical Investigation, 105(8), 1035
1038. Available from https://doi.org/10.1172/JCI9871. Oliveira-Filho, A. B., Arau´jo, A. P. S., Souza, A. P. C., Gomes, C. M., Silva-Oliveira, G. C., Martins, L. C., Fischer, B., Machado, L. F. A., Vallinoto, A. C. R., Ishak, R., Lemos, J. A. R., & Kupek, E. (2019). Human T-lymphotropic virus 1 and 2 among people who used illicit drugs in the state of Par´a, northern Brazil. Scientific Reports, 9(1), 14750. Available from https://doi.org/10.1038/s41598-019-51383-7. Pan, L., Milligan, L., Michaeli, J., Cesarman, E., & Knowles, D. M. (2001). Polymerase chain reaction detection of kaposi’s sarcoma-associated herpesvirus-optimized protocols and their applicationto myeloma. Journal of Molecular Diagnostics, 3(1), 32
38. Available from https://doi.org/10.1016/S1525-1578(10)60647-2. Parekh, B., Ou, C., Fonjungo, P., Kalou, M., Rottinghaus, E., Puren, A., Alexander, H., Cox, M., & Nkengasong, J. N. (2018). Diagnosis of human immunodeficiency virus infection. Clinical Microbiology Reviews, 32, e00064-18.

Role of viral human oncogenesis: recent developments Chapter | 7

171

Parkin, D. M. (2006). The global health burden of infection-associated cancers in the year 2002. International Journal of Cancer, 118(12), 3030
3044. Available from https://doi.org/ 10.1002/ijc.21731. Peeples, M. E., Reichard, K. W., Reyes, H. M., Phuangsab, A., & Walter, R. J. (1994). Complete regression of human fibrosarcoma xenografts after local newcastle disease virus therapy. Cancer Research, 54(23), 6017
6021. Petropoulos, C. (1997). Retroviral taxonomy, protein structures, sequences, and genetic maps (pp. 757
805). Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press. Petruzziello, A., Marigliano, S., Loquercio, G., Cozzolino, A., & Cacciapuoti, C. (2016). Global epidemiology of hepatitis C virus infection: An up-date of the distribution and circulation of hepatitis C virus genotypes. World journal of gastroenterology, 22(34), 7824
7840. Available from https://doi.org/10.3748/wjg.v22.i34.7824. Ruggieri, M., Berini, C., Ducasa, N., Malkovsky, M., Fisch, P., & Biglione, M. (2019). Molecular detection of human T-lymphotropic virus type 1 infection among oncology patients in Germany: A retrospective view. PLoS One, 14(5), e0217560. Available from https://doi.org/10.1371/journal.pone.0217560. Santpere, G., Darre, F., Blanco, S., Alcami, A., Villoslada, P., Alba`, M. M., & Navarro, A. (2014). Genome-wide analysis of wild-type epstein-barr virus genomes derived from healthy individuals of the 1000 genomes project. Genome Biology and Evolution, 6(4), 846
860. Available from https://doi.org/10.1093/gbe/evu054. Schowalter, R. M., Pastrana, D. V., Pumphrey, K. A., Moyer, A. L., & Buck, C. B. (2010). Merkel cell polyomavirus and two previously unknown polyomaviruses are chronically shed from human skin. Cell Host and Microbe, 7(6), 509
515. Available from https://doi.org/ 10.1016/j.chom.2010.05.006. Schweitzer, A., Horn, J., Mikolajczyk, R. T., Krause, G., & Ott, J. J. (2015). Estimations of worldwide prevalence of chronic hepatitis B virus infection: A systematic review of data published between 1965 and 2013. The Lancet, 386(10003), 1546
1555. Available from https://doi.org/10.1016/S0140-6736(15)61412-X. ˇ Sevik, M. (2012). Oncogenic viruses and mechanisms of oncogenesis. Turkish Journal of Veterinary and Animal Sciences, 36(4), 323
329. Available from https://doi.org/10.3906/ vet-1104-2. Shimamoto, T., & Yamanishi, K. (1999). Herpesviruses 6 and 7-Human (Herpesviridae). Encyclopedia of Virology (Second Edition). Elsevier. Silverthorn, D. U. (2015). Human physiology: An integrated approach (7th Edition). Austin: University of Texas. Steinhauer, D. A., & Holland, J. J. (1987). Rapid evolution of RNA viruses. Annual Review of Microbiology, 409
431. Available from https://doi.org/10.1146/annurev. mi.41.100187.002205. Stelzle, D., Tanaka, L. F., Lee, K. K., Ibrahim Khalil, A., Baussano, I., Shah, A. S. V., McAllister, D. A., Gottlieb, S. L., Klug, S. J., Winkler, A. S., Bray, F., Baggaley, R., Clifford, G. M., Broutet, N., & Dalal, S. (2021). Estimates of the global burden of cervical cancer associated with HIV. The Lancet Global Health, 9(2), e161
e169. Available from https://doi.org/10.1016/S2214-109X(20)30459-9. Stojdl, D. F., Lichty, B., Knowles, S., Marius, R., Atkins, H., Sonenberg, N., & Bell, J. C. (2000). Exploiting tumor-specific defects in the interferon pathway with a previously unknown oncolytic virus. Nature Medicine, 6(7), 821
825. Available from https://doi.org/10.1038/77558. Sullivan, R. J., Pantanowitz, L., & Dezube, B. J. (2009). Targeted therapy for kaposi sarcoma. BioDrugs, 23(2), 69
75. Available from https://doi.org/10.2165/00063030-200923020-00001.

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Sundaram, R., Lynch, M. P., Rawale, S., Dakappagari, N., Young, D., Walker, C. M., Lemonnier, F., Jacobson, S., & Kaumaya, P. T. P. (2004). Protective efficacy of multiepitope human leukocyte antigen-A 0201 restricted cytotoxic T-lymphocyte peptide construct against challenge with human T-cell lymphotropic virus type 1 Tax recombinant vaccinia virus. Journal of Acquired Immune Deficiency Syndromes, 37(3), 1329
1339. Available from https://doi.org/10.1097/00126334-200411010-00001. Sverdlov, E. D. (2000). Retroviruses and primate evolution. BioEssays, 22(2), 161
171, https:// doi.org/10.1002/(SICI)1521-1878(200002)22:2 , 161::AID-BIES7 . 3.0.CO;2-X. Thinley KalsangBhutia Adenovirus Infections. Modified link of Web site: Merck Manuals Professional Version. (2018). Uyen, M., Christopher, H., & Stephen, T. (2017). Viral oncology: Molecular biology and pathogenesis. Journal of Clinical Medicine, 6, 111. Available from https://doi.org/10.3390/ jcm6120111. Verga-Ge´rard, A., Porcherot, M., Meyniel-Schicklin, L., Andre´, P., Lotteau, V., & Perrin-Cocon, L. (2013). Hepatitis C virus/human interactome identifies SMURF2 and the viral protease as critical elements for the control of TGF-β signaling. FASEB Journal, 27(10), 4027
4040. Available from https://doi.org/10.1096/fj.13-229187. Vinokurova, S., Wentzensen, N., Kraus, I., Klaes, R., Driesch, C., Melsheimer, P., Kisseljov, F., Du¨rst, M., Schneider, A., & Doeberitz, M. V. K. (2008). Type-dependent integration frequency of human papillomavirus genomes in cervical lesions. Cancer Research, 68(1), 307
313. Available from https://doi.org/10.1158/0008-5472.CAN-07-2754. Westmoreland, S. V., & Mansfield, K. G. (2008). Comparative pathobiology of Kaposi sarcomaassociated herpesvirus and related primate rhadinoviruses. Comparative Medicine, 58(1), 31
42. Wilson, J., & Harrison, T. R. (1991). Harrison’s principles of internal medicine. McGraw-Hill Professional. Zeng, Q., Gomez, B. P., Viscidi, R. P., Peng, S., He, L., Ma, B., Wu, T. C., & Hung, C. F. (2012). Development of a DNA vaccine targeting Merkel cell polyomavirus. Vaccine, 30(7), 1322
1329. https://doi.org/10.1016/j.vaccine.2011.12.072

Chapter 8

Strategies for the development of hepatitis B virus vaccines Fadoua El Battioui1, Fatima El Malki2 and Said Barrijal1 1

Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco, 2Institute of Nursing, Tangier, Morocco

8.1

Introduction

Hepatitis B virus (HBV) infection is a major worldwide health issue. It is considered a potentially fatal infection. Despite the effectiveness of currently available antiviral treatments against chronic hepatitis B (CHB), unfortunately, complete elimination of HBV from the liver is hard to achieve. Therefore almost 15%
40% of chronic infections are complicated by cirrhosis and/or hepatocellular carcinoma (HCC) (Lai & Yuen, 2007; Lee, 1997). According to the World Health Organization (WHO), CHB affects approximately 260 million people worldwide (WHO1, 2020), with almost 890,000 deaths worldwide linked to its complications in 2015 (Berthier et al., 2020). Thus a safe and effective vaccine that offers 98%
100% protection against hepatitis B is available. Prevention hinders the occurrence of complications, including the occurrence of disease, chronic and liver cancer, for which the development of therapeutic vaccines remains a perpetual challenge (WHO1, 2020).

8.2

Virus-like particle-based hepatitis B vaccines

Virus-like particles (VLPs) are nanoparticles that are assembled from viral structural proteins. Structurally, it is impossible to distinguish them from their corresponding viruses. The development of vaccines using VLP-based technology is the most effective method for preventing or treating certain infectious or chronic diseases like cancer. Due to their favorable size and their mimetic viral structure, the VLPs easily penetrate the lymphatic vessels and flow to the lymph Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00005-4 Copyright © 2023 Elsevier Inc. All rights reserved.

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FIGURE 8.1 A schematic diagram of virus-like particle (VLP) in triggering specific immune responses against tumor cells. Chimeric VLPs displaying tumor antigen (red oval) on their surface are administered into an animal model. Tumor antigen is taken up, processed, and presented by dendritic cells (DCs) to respective T cells. The presentation of tumor antigen by DCs converts naı¨ve cytotoxic and helper T cells into cytotoxic (Tc) and effector helper (Th) T cells, respectively. Tc cells (red triangles) kill tumor cells by releasing cytotoxic proteins, such as granzyme and perforin. Th1 cells support the activation of Tc cells by releasing interleukin-2 (IL-2) and interferon-gamma (IFNγ), whereas Th2 cells stimulate B cells to produce tumor antigen-specific antibodies, which are capable to bind and mark (red triangles) tumor cells for effective killing by natural killer (NK) cells and phagocytes. (Ong et al., 2017). https://doi.org/ 10.7717/peerj.4053.

nodes, thus inducing an efficient T-cell response by interaction with the antigen-presenting cells, especially in dendritic cells (DCs) (Fig. 8.1) (Cubas et al., 2009; Spohn & Bachmann, 2008), and inducing cytotoxic T-cell responses by cross-presentation (Chackerian, 2007; Inoue & Tanaka, 2020; Moron et al., 2003; Win et al., 2011). Genetic fusion and chemical conjugation are two classic approaches to displaying foreign epitopes on VLPs. However, gene fusion expression can induce poor VLP assembly and protein insolubility and requires different purification methods. Chemical conjugation is a postassembly way to decorate VLPs formed by coupling the epitope peptide with lysine, aspartic acid, or glutamic acid (Fig. 8.2) (Ji et al., 2020; Kushnir et al., 2012). Currently, the development of vaccines against HBV is based primarily on the VLP of the hepatitis B surface S antigen (HBsAg), which is the fundamental molecule for the entry of HBV into hepatocytes (Yan et al., 2012). The administration of a vaccine against HBV allows the generation of host immunity through the activation of B lymphocytes which release specific anti-HBs antibodies capable of neutralizing HBsAg (Chackerian, 2007).

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FIGURE 8.2 Illustration of virus-like particles (VLPs) production using different approaches. (A) Production of chimeric VLP using genetic insertion. The foreign antigen is fused in the coat protein by genetic engineering, and then chimeric VLP is expressed in a suitable host system. (B) Chimeric VLP is generated by the chemical conjugation of foreign peptides to the surface of the VLP. The VLP production can be carried out on a small scale for scientific research, while under the current GMP, it can be produced on a large scale for humans or veterinary. (Caldeira et al., 2020). https://doi.org/10.3390/v12050488.

FIGURE 8.3 Schematic representation of the overlap between the HBV polymerase and envelope open reading frames. The numbers indicate amino acid (aa) sites. Numbering is according to genotype D. The “a” determinant of HBsAg that is located between aa 124 and 149, and which includes the major antibody neutralization domain of HBV, is indicated. (Romano` et al., 2014). https://doi.org/10.4161/hv.34306.

Most of the vaccines currently licensed to prevent infection with HBV are made out of a region encompassing residues 124
147 known as the “a” determinant, which is the most immunogenic part of HBsAg, it is shared by all genotypes and serotypes of the virus (Golsaz-Shirazi et al., 2020) (Fig. 8.3). This antigen is able to induce specific humoral immune responses to HBV. It has been used worldwide in various recombinant vaccines (Keating et al., 2003). Up to date, there are 12 VLP-based HBV vaccines on the market (Table 8.1), and the one most recently produced is the HEPLISAV-B vaccine, combined with a CpG adjuvant (Del Giudice et al., 2018).

TABLE 8.1 VLP (virus-like particle)-based HBV (hepatitis B virus) vaccines available on the market. Vaccine name

Company/ institution

Route of administration (adjuvant)

Expression system

VLP platform and vaccine antigen

VLP type

References

GenHevac B

Pasteur-Merieux Aventis

IM (aluminum hydroxide)

Mammalian (CHO cells)

HBsAg

Nonenvelopeda

Soulie´ et al. (1991)

Bio-Hep-B (Sci-B-Vac)

BTG (SciGen, FDS Pharma)

IM (aluminum hydroxide)

Mammalian (CHO cells)

HBsAg

Nonenvelopeda

SciGen (2012)

DTP-Hep B

P.T. Bio Farma

IM (aluminum hydroxide)

Yeast (Pichia pastoris)

HBsAg

Nonenvelopeda

WHO2 (2001), Bio Farma (2011)

Engerix-B

GSK

IM (aluminum hydroxide)

Yeast (Saccharomyces cerevisiae)

HBsAg

Nonenvelopeda

Harford et al. (1983), Hauser et al. (1987)

Enivac HB

Panacea Biotec

IM (aluminum hydroxide)

Yeast (Pichia pastoris)

HBsAg

Nonenvelopeda

Panacea (2012)

Euvax B

LG Life Sciences

IM (aluminum hydroxide)

Yeast (Saccharomyces cerevisiae)

HBsAg

Nonenvelopeda

Sanofi Pasteur (1998)

Gene Vac-B

Serum Inst. of India

IM (aluminum hydroxide)

Yeast (Hansenula polymorpha)

HBsAg

Nonenvelopeda

Serum Institute of India Ltd. Gene Vac-Bs (2012) http://www. seruminstitute.Com

Heberbiovac HB

CIGB-Heber Biotec

IM (aluminum hydroxide)

Yeast (Pichia pastoris)

HBsAg

Nonenvelopeda

D´ıaz Gonz´alez et al. (1997)

HepavaxGene

Crucell

IM (aluminum hydroxide)

Yeast (Hansenula polymorpha)

HBsAg

Nonenvelopeda

DiethelmLtd./Berna Biotech Korea Corp (2012)

Recombivax HB

Merck

IM (aluminum hydroxide)

Yeast (Saccharomyces cerevisiae)

HBsAg

Nonenvelopeda

Recombivax (2011), McAleer et al. (1984), Ellis and Gerety (1989)

Revac-B

Bharat Biotech International

IM (aluminum hydroxide)

Yeast (Pichia pastoris)

HBsAg

Nonenvelopeda

Bharat (2008)

Shanvac-B

Shantha Biotechnics Polish Academy of Sciences/TJU ASA

IM (Aluminum hydroxide) Oral (none) Oral (none)

Yeast (Pichia pastoris) Plant (Tg lettuce) Plant (Tg potato)

HBsAg

Nonenvelopeda

Shantha Biotechnics (2012)

Up to date, there are 12 VLP-based HBV vaccines on the market.

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These vaccines have a purely prophylactic role, contributing to a decrease in the incidence of HBV infection worldwide, but are unable to eliminate the preexisting infection (Caldeira et al., 2020).

8.3

Therapeutic vaccines

Therapeutic vaccines make it possible to destroy human cancer cells via a mechanism that is essentially based on the induction of cellular immunity with a direct antiviral effect in which CD 4 1 and CD 8 1 cells have main roles in the eradication of infected hepatocytes (Fig. 8.4) (Hu et al., 2018; Ji et al., 2020; Kosinska et al., 2017; Maini et al., 2000; Shouval et al., 2015). There are currently several types of therapeutic vaccines against HBV infection which are undergoing various stages of clinical trials (Table 8.2).

FIGURE 8.4 Immune therapeutic approaches to achieve a functional cure of hepatitis B virus (HBV). (A) A schematic representation of immune responses and viral load in chronic and resolved HBV infection. Chronic HBV infection is characterized by the loss or functional exhaustion of HBV-specific CD8 1 T cells and elevated HBV load in infected hepatocytes. Resolved HBV infection is characterized by the presence of functional HBV-specific T cells, antibodies blocking new infection, and a few hepatocytes harboring HBV. (B) A schematic representation of different immunotherapeutic strategies able to boost innate or adaptive immunity to suppress HBV replication. TLR, Toll-like receptor; RIG-I, retinoic acid-inducible gene-I; NKT, natural killer T; TCR, T cell receptor; APC, antigen-presenting cell; PD-1, programmed cell death-1; HBsAg, hepatitis B surface antigen. (Bertoletti et al., 2017) https://doi.org/ 10.1016/j.jcyt.2017.07.011.

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TABLE 8.2 Overview of significant gene vaccines in hepatitis B. Drug name

Class

Phase

Company

DV-601

Therapeutic vaccine: viral antigen complex based

Ib

Dynavax

Hi-8 HBV

Therapeutic vaccine: viral antigen complex based

II

Oxxon

ePA-44

Therapeutic vaccine: viral antigen complex based

II

Chongqing Jiachen Biotechnology Ltd

HB-110

Therapeutic vaccine: DNA based

I

Genexine

HBV-DNA plasmid pdpSC18 vaccine

Therapeutic vaccine: DNA based

I

PowderMed/Pfizer

GS-4774

Engineered to express a fusion protein containing HBsAg sequences

II

Gilead Sciences, Inc

TG1050

Therapeutic vaccine: Adenovirus based

I/Ia

Transgene

8.4

DNA-based vaccines

DNA-based vaccines constitute a promising approach in the field of development of therapeutic vaccination against HCC, and has several advantages over conventional vaccines: low manufacturing cost, high efficiency, and quick development (Karimkhanilouyi & Ghorbian, 2019). Indeed, the results of a study conducted in chimpanzees, for which a DNA-based HBV vaccine was administered, demonstrated that the vaccine induced a cellular immune response, as an absence of the virus was observed in the plasma for 6 months (Fig. 8.5) (Mancini-Bourgine & Michel, 2005). Yang et al. (2006) also found in their studies that essentially repeated vaccination with the basic hepatitis B DNA antigen (HBsAg), encoding the proteins preS1/preS2/S, polymerase (Pol), interleukin-12 (IL-12) adjuvant, and HBx, can elicit potent humoral and cell-mediated anti-HBV immunities (Chuai et al., 2018; Yang et al., 2006). The immunogenic effect of the vaccine was further enhanced by using a recombinant protein on DC cells targeted by a DNA vaccine (Wang et al., 2016).

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FIGURE 8.5 Mechanism of DNA vaccines for HBV. The vaccine used was a gene sequence that coded for medium and small HBV viral proteins in the pCMV plasmid. Patients were injected intramuscularly (i.m.). After immunization, transfected muscle cells may produce antigens that stimulate B cells of the immune system, which then produce antibodies. Transfected muscle cells could transfer the antigen to the antigen-presenting cells such as dendritic cells. The processing and display of MHC-antigen complexes arise. HBV, hepatitis B virus. Emil Behring (1854–1917); Paul Ehrlich (1854–1915); Elias Metchnikoff (1845–1916) (Mancini-Bourgine et al., 2004; Kaufmann, 2019).

8.5

mRNA-based vaccines

To induce antigen-specific humoral and cellular immune responses, with a superior safety profile compared to DNA vaccines, mRNA vaccines have been used primarily for therapeutic purposes against cancer or HIV (Vanham & Van Gulck, 2012). The process of developing therapeutic mRNA vaccines relies on ex vivo manipulation of DCs with mRNA and then their reinsertion into the patient. This technique is very promising and secure, but unfortunately, there are certain constraints inherent in the high cost of production and the complexity of the procedure (Vanham & Van Gulck, 2012).

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8.6

181

Proteins/peptides vaccines

During the last few years, several types of research are directed toward the development and the use of vaccines based on chimeric VLPs with epitopic peptides expressed or conjugated to prevent infectious diseases or to treat chronic diseases such as cancer, autoimmunity, allergy, and neurodegenerative diseases (Ji et al., 2020; Li, Fierer et al., 2014; Li, Joshi et al., 2014; Ong et al., 2017; Zeltins et al., 2017). Indeed, the orientation toward the design of this type of vaccine is justified by their advantageous characteristics compared to traditional vaccines, namely simple preparation, a good level of cost-effectiveness, storage stability, and harmlessness, and solubility (Gomes et al., 2019). However, peptide vaccines exhibit low immunogenicity due to the risk of enzymatic degradation. To deal with this problem, the researchers developed a strategy based on the use of multiple epitopes (Chaudhuri et al., 2020). Ding (2009) demonstrated that the use of the multiepitope approach of HBV x displayed to the core HBV protein elicited epitope-specific CD8 1 T cells and a more energetic response than a single peptide, proving that the VLP loaded by several epitopes increases the immunogenicity for each epitope by increasing the number of CTLs which leads to a more efficient tumor response (Caldeira et al., 2020; Ding et al., 2009). Recently, a new technology for the coupling of proteins by the spontaneous formation of an isopeptide bond between SpyCatcher (protein) and SpyTag (peptide) has emerged (Li, Fierer et al., 2014; Li, Joshi et al., 2014; Zakeri et al., 2012). Using this technique, SpyCatcher and SpyTag polypeptides react easily under a variety of mild conditions, do not require chemical coupling agents, and can maintain the natural structure of epitopes (Ji et al., 2020). The integration of this technology was done for the first time with HBc VLP and it has established a platform for rapid, convenient, universal, and safe cancer vaccines (Ji et al., 2020).

8.7

Cell-based vaccines

Therapeutic cell-based vaccines are primarily based on the principle of in vitro activation of intrinsic immune cells, including NK cells, or most commonly presenting dendritic cells by viral peptides or viral genes (Karimkhanilouyi & Ghorbian, 2019; Moron et al., 2003). Because HBVspecific T cells are depleted and present in limited numbers in patients with chronic HBV infection (Boni et al., 2007; Boudreau et al., 2011; Fisicaro et al., 2010), restoration of the peripheral and intrahepatic function of HBVspecific T cells can be done in vitro using therapies with anti-PD1/PDL-1 checkpoint inhibitors (Fig. 8.6). However, at present these therapies have

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FIGURE 8.6 Strategies to boost HBV-specific T cells. Restoration of peripheral and intrahepatic function of HBV-specific T cells can be done in vitro using therapies with anti-PD1/PDL-1 checkpoint inhibitors. HBV, hepatitis B virus. (Bertoletti et al., 2017). https://doi.org/10.1016/j. jcyt.2017.07.011.

only been used successfully in certain solid malignant tumors such as lung cancer, renal carcinoma, and melanoma, and no data are available up to date on chronic HBV (Bertoletti et al., 2017).

8.8

Nanovaccines

Currently, we are witnessing the emergence of new vaccine development techniques using the principles of nanotechnology, which allows efficient delivery to target tissues (Mei et al., 2020). Thanks to their ability to induce humoral and intercellular responses, nanovaccines are generally more convincing than ordinary vaccines. The nanovaccines are generally dedicated to particular objectives, for example, an assisted discharge and improvement of the delivery of an antigen. Therefore the determination of polymers is of extreme importance (Karthick Raja Namasivayam et al., 2020; Mei et al., 2020). Results from a study using a chitosan-PEG polymer-polymer made with a hepatitis B surface antigen nanocomposite demonstrated controlled discharge for several weeks with a high rate of immunogenicity (Karthick Raja Namasivayam et al., 2020). In terms of safety, despite the fact that the nanocomposite has demonstrated a high level of safety, additional investigations will be useful to abuse the standards of nanocomposite manufactured with the HBsAg plan in the field of biomedicine as a protected or biocompatible nanovaccine against hepatitis (Karthick Raja Namasivayam et al., 2020; Mei et al., 2020).

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8.9

183

Efficacy of therapeutic vaccines

Up to date, several studies have been carried out to evaluate the therapeutic efficacy of certain vaccines against HBV (Karimkhanilouyi & Ghorbian, 2019). In this context, the first clinical trial of the HBV vaccine was carried out in France by Mancini-Bourgine; the vaccine was a gene sequence that was encoded for the medium and small HBV viral proteins in plasmid pCMV (Mancini-Bourgine et al., 2006). The results of this clinical trial, carried out on 10 patients with chronic HBV infection, showed that the designed vaccine was safe and also effective in inducing an immune response against the viral infection. Administration of three doses of the vaccine 2 months apart generates a significant increase in the response rate of specific T lymphocytes that produce interferongamma. Quantitative PCR analysis also demonstrated a marked reduction in serum virus levels in five patients, with complete absence of viral infection in serum in one patient (Mancini-Bourgine et al., 2004). Another clinical trial to assess the therapeutic efficacy of a binary DNA vaccine in combination with a drug (lamivudine) was performed in patients with chronic HBV infection. In this study, two plasmids were used. One of the plasmids contained the MHBs-encoding antigen and the other was an adjuvant plasmid that contained the interleukin-2 fusion protein and the interferon gamma gene encoding. Vaccination was also performed at 0, 4, 12, and 24 weeks. The results of this trial demonstrated an increase in the number of T cells secreting a specific viral gamma-interferon after the administration of the vaccine alone and an evident decrease in the genotoxicity of the HBV virus in the serum of subjects who received the vaccine simultaneously with the lamivudine (Yang et al., 2012). However, therapeutic vaccines do not prevent the recurrence of chronic viral infection in most treated patients (Fontaine et al., 2014).

8.10 Harmlessness Most studies carried out, up to date, to assess the safety of therapeutic HBV vaccines do not show serious side effects in the subjects studied, except for some mild side effects that were not attributed to the vaccination (Karimkhanilouyi & Ghorbian, 2019).

8.11 Immunization coverage WHO has set the elimination of both hepatitis C virus (HCV) and HBV by 2030 as an ultimate goal (WHO1, 2020). To achieve this goal, the universal

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introduction of vaccination against hepatitis B for newborns remains the most reasonable strategy, since 90% of chronic HBV infections and 65% of mortality can be reduced through vaccination (Gomes et al., 2019). Thus WHO has included HB vaccination in the Expanded Program on Immunization and recommends that all infants should receive HB vaccine as soon as possible after birth, preferably within 24 hours (Locarnini et al., 2015). As a result, in 2019, the proportion of children under 5 years of age chronically infected with HBV decreased to just under 1%, compared to around 5% in the prevaccine era from 1980 until the beginning of 2000 (WHO1, 2020). However, coverage of the birth dose of the hepatitis B vaccine remains uneven. Global coverage is 43%, while coverage in the African Region is only 6% (WHO1, 2020). The full-series (three-dose) vaccination schedule induces protective antibody levels in more than 95% of infants, children, and young adults. With a protection life of up to at least 20 years, it probably lasts a lifetime. Thus the WHO does not recommend the booster vaccination for people who have completed the three-dose vaccination schedule (WHO1, 2020). In 2019, coverage of three doses of the vaccine reached 85% worldwide compared to around 30% in 2000 (WHO1, 2020).

8.12 Conclusion Hepatitis B remains a worrying health problem for several countries. Although vaccination has clearly been shown to be very effective in the prevention and control of the disease globally, the emergence of mutant resistant liver infections is of concern. Thus the development of new common vaccines for the prevention of all types of hepatitis is warranted. Global surveillance networks should be set up to monitor the epidemiological dynamics of hepatitis and the impact of vaccines on public health.

References Berthier, L., Brass, O., Deleage, G., & Terreux, R. (2020). Construction of atomic models of full hepatitis B vaccine particles at different stages of maturation. Journal of Molecular Graphics and Modelling, 98, 107610. Available from https://doi.org/10.1016/j.jmgm.2020.107610. Bertoletti, A., Tan, A. T., & Koh, S. (2017). T-cell therapy for chronic viral hepatitis. Cytotherapy, 19(11), 1317
1324. Available from https://doi.org/10.1016/j.jcyt.2017.07.011. Bharat Biotech. Revac -B 1 TM (2008). Available from: ,http://www.bharatbiotech.com.. Bio Farma. Company Profile 2011. Available from: ,http://biofarma.co.id/tlfiles/data/compro. pdf..

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185

Boni, C., Fisicaro, P., Valdatta, C., Amadei, B., Di Vincenzo, P., Giuberti, T., & Ferrari, C. (2007). Characterization of hepatitis B virus (HBV)-specific T-cell dysfunction in chronic HBV infection. Journal of Virology, 81(8), 4215
4225. Available from https://doi.org/ 10.1128/jvi.02844-06. Boudreau, J. E., Bonehill, A., Thielemans, K., & Wan, Y. (2011). Engineering dendritic cells to enhance cancer immunotherapy. Molecular Therapy, 19(5), 841
853. Available from https://doi.org/10.1038/mt.2011.57. Caldeira, J. C., Perrine, M., Pericle, F., & Cavallo, F. (2020). Virus-like particles as an immunogenic platform for cancer vaccines. Viruses, 12(5), 488. Available from https://doi.org/ 10.3390/v12050488. Chackerian, B. (2007). Virus-like particles: Flexible platforms for vaccine development. Expert Review of Vaccines, 6(3), 381
390. Available from https://doi.org/10.1586/14760584.6.3.381. Chaudhuri, D., Datta, J., Majumder, S., & Giri, K. (2020). In silico designing of peptide-based vaccine for hepatitis viruses using reverse vaccinology approach. Infection, Genetics, and Evolution, 84, 104388. Available from https://doi.org/10.1016/j.meegid.2020.104388. Chuai, X., Xie, B., Chen, H., Tan, X., Wang, W., Huang, B., Deng, Y., Li, W., & Tan, W. (2018). The immune response of rhesus macaques to novel vaccines comprising hepatitis B virus S, PreS1, and core antigens. Vaccine, 36(26), 3740
3746. Available from https://doi. org/10.1016/j.vaccine.2018.05.061. Cubas, R., Zhang, S., Kwon, S., Sevick-Muraca, E. M., Li, M., Chen, C., & Yao, Q. (2009). Virus-like particle (VLP) lymphatic trafficking and immune response generation after immunization by different routes. Journal of Immunotherapy, 32(2), 118
128. Available from https://doi.org/10.1097/cji.0b013e31818f13c4. Del Giudice, G., Rappuoli, R., Didierlaurent, A. M., Gonz´alez., Lay, L., Gonz´alez, L. o´pezCh´avez, & Flaquet, P. (2018). Correlates of adjuvanticity: A review on adjuvants in licensed vaccines. Seminars in Immunology. Available from https://doi.org/10.1186/1742-4690-9-72. D´ıaz Gonz´alez, M., Rodr´ıguez Lay, L., Lo´pez-Ch´avez, A. U., Bravo Gonz´alez, J. R., & Pedroso Flaquet, P. (1997). Adverse reactions and immune response to heberbiovac-HB vaccine administered to infants simultaneously with DPT and VA-MENGOC-BC. Revista Cubana de Medicina Tropical, (49), 196
203. DiethelmLtd./Berna Biotech Korea Corp. Hepavax-Genes (2012). Summary of product characteristics. Available from: ,http://drug.fda.moph.go.th/zone.pdf.. Ding, F. X., Wang, F., Lu, Y. M., Li, K., Wang, K. H., He, X. W., & Sun, S. H. (2009). Multiepitope peptide-loaded virus-like particles as a vaccine against hepatitis B virus-related hepatocellular carcinoma. Hepatology, 49(5), 1492
1502. Available from https://doi.org/ 10.1002/hep.22816. Ellis, R. W., & Gerety, R. J. (1989). Plasma-derived and yeast-derived hepatitis B vaccines. American Journal of Infection Control, 17, 181
189. Fisicaro, p, Valdatta, C., Massari, M., Loggi, E., Biasini, E., Sacchelli, L., & Ferrari, C. (2010). Antiviral intrahepatic T-cell responses can be restored by blocking programmed death-1 pathway in chronic hepatitis B. Gastroenterology, 138(2), 682
693. Available from https:// doi.org/10.1053/j.gastro.2009.09.052. Fontaine, H., Kahi, S., Chazallon, C., Bourgine, M., Varaut, A., Buffet, C., Godon, O., Meritet, J. , F., Saı¨di, Y., Michel, M. , L., Scott-Algara, D., Aboulker, J. , P., & Pol, S. (2014). AntiHBV DNA vaccination does not prevent relapse after discontinuation of analogues in the treatment of chronic hepatitis B: A randomised trial—ANRS HB02 VAC-ADN. Gut, 64, 139
147. Available from https://doi.org/10.1136/gutjnl-2013-305707.

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Golsaz-Shirazi, F., Asadi-Asadabad, S., Sarvnaz, H., Mehdi Amiri, M., Hojjat-Farsangi, M., Chudy, M., Jeddi-Tehrani, M., & Shokri, F. (2020). Immunoreactivity pattern of monoclonal antibodies against hepatitis B vaccine with global Hepatitis B virus genotypes. Clinica Chimica Acta, 510, 203
210. Available from https://doi.org/10.1016/j.cca. 2020.07.026. Gomes, C., Wong, R. J., & Gish, R. G. (2019). Global perspective on hepatitis B virus infections in the era of effective vaccines. Clinics in Liver Disease, 23(3), 383
399. Available from https://doi.org/10.1016/j.cld.2019.04.001. Harford, N., Cabezon, T., Crabeel, M., Simoen, E., Rutgers, A., & DeWilde, M. (1983). Expression of hepatitis B surface antigen in yeast. Developments in Biological Standardization, 54, 125
130. Hauser, P., Voet, P., Simoen, E., Thomas, H. C., Peˆtre, J., De Wilde, M., et al. (1987). Immunological properties of recombinant HBsAg produced in yeast. Postgraduate Medical Journal, 63(Suppl 2), 83
91. Hu, Z., Ott, P. A., & Wu, C. J. (2018). Towards personalized, tumour-specific, therapeutic vaccines for cancer. Nature Reviews Immunology, 18(3), 168
182. Available from https://doi. org/10.1038/nri.2017.131. Inoue, T., & Tanaka, Y. (2020). Cross-protection of hepatitis b vaccination among different genotypes. Vaccines, 8(3), 1
21. Available from https://doi.org/10.3390/vaccines8030456. Ji, M., Zhu, J., Xie, X., Liu, D., Wang, B., Yu, Z., & Liu, R. (2020). A novel rapid modularized hepatitis B core virus-like particle-based platform for personalized cancer vaccine preparation via fixed-point coupling. Nanomedicine: Nanotechnology, Biology and Medicine, 28, 102223. Available from https://doi.org/10.1016/j.nano.2020.102223. Karimkhanilouyi, S., & Ghorbian, S. (2019). Nucleic acid vaccines for hepatitis B and C virus. Infection, Genetics, and Evolution, 75, 103968. Available from https://doi.org/10.1016/j. meegid.2019.103968. Karthick Raja Namasivayam, S., Nishanth, A. N., Arvind Bharani, R. S., Nivedh, K., Syed, N. H., & Rosario Samuel, R. (2020). Hepatitis B-surface antigen (HBsAg) vaccine fabricated chitosan-polyethylene glycol nanocomposite (HBsAg-CS-PEG- NC) preparation, immunogenicity, controlled release pattern, biocompatibility or non-target toxicity. International Journal of Biological Macromolecules, 144, 978
994. Available from https:// doi.org/10.1016/j.ijbiomac.2019.09.175. Kaufmann, S. H. E. (2019). Immunology’s coming of age. Frontiers in Immunology, 10. https:// doi.org/10.3389/fimmu.2019.00684. Keating, G. M., Noble, S., Averhoff, F. M., Belloni, C., Duval, B., Goldwater, P. N., Hall, A. J., Honorati, M. C., Kallinowski, B., Leroux-Roels, G., & Poovorawan, Y. (2003). Recombinant hepatitis B vaccine (Engerix-Bs): A review of its immunogenicity and protective efficacy against hepatitis B. Drugs, 63(10), 1021
1051. Available from https://doi. org/10.2165/00003495-200363100-00006. Kosinska, A. D., Bauer, T., & Protzer, U. (2017). Therapeutic vaccination for chronic hepatitis B. Current Opinion in Virology, 23, 75
81. Available from https://doi.org/10.1016/j. coviro.2017.03.011. Kushnir, N., Streatfield, S. J., & Yusibov, V. (2012). Virus-like particles as a highly efficient vaccine platform: Diversity of targets and production systems and advances in clinical development. Vaccine, 31(1), 58
83. Available from https://doi.org/10.1016/j.vaccine.2012.10.083. Lai, C. L., & Yuen, M. F. (2007). The natural history and treatment of chronic hepatitis B: A critical evaluation of standard treatment criteria and end points. Annals of Internal Medicine, 147 (1), 58
61. Available from https://doi.org/10.7326/0003-4819-147-1-200707030-00010.

Strategies for the development of hepatitis B virus vaccines Chapter | 8

187

Lee, W. M. (1997). Hepatitis B virus infection. New England Journal of Medicine, 337(24), 1733
1745. Available from https://doi.org/10.1056/nejm199712113372406. Li, L., Fierer, J. O., Rapoport, T. A., & Howarth, M. (2014). Structural analysis and optimization of the covalent association between spycatcher and a peptide tag. Journal of Molecular Biology, 426(2), 309
317. Available from https://doi.org/10.1016/j.jmb.2013.10.021. Li, W., Joshi, M., Singhania, S., Ramsey, K., & Murthy, A. (2014). Peptide vaccine: Progress and challenges. Vaccines, 2(3), 515
536. Available from https://doi.org/10.3390/ vaccines2030515. Locarnini, s, Hatzakis, A., Chen, D. S., & Lok, A. (2015). Strategies to control hepatitis B: Public policy, epidemiology, vaccine and drugs. Journal of Hepatology, 62(1), 76
86. Available from https://doi.org/10.1016/j.jhep.2015.01.018. Maini, M. K., Boni, C., Lee, C. K., Larrubia, J. R., Reignat, S., Ogg, G. S., & Bertoletti, A. (2000). The role of virus-specific Cd8 1 cells in liver damage and viral control during persistent hepatitis B virus infection. The Journal of Experimental Medicine, 191(8), 1269
1280. Available from https://doi.org/10.1084/jem.191.8.1269. Mancini-Bourgine, M., & Michel, M. L. (2005). Therapeutic vaccination against chronic hepatitis B virus infection. The´rapie, 60(3), 257
265. Available from https://doi.org/10.2515/ therapie:2005033. Mancini-Bourgine, M., Fontaine, H., Bre´chot, C., Pol, S., & Michel, M.-L. (2006). Immunogenicity of a hepatitis B DNA vaccine administered to chronic HBV carriers. Vaccine, 24(21), 4482
4489. Available from https://doi.org/10.1016/j.vaccine.2005.08.013. Mancini-Bourgine, M., Fontaine, H., Scott-Algara, D., Pol, S., Bre´chot, C., & Michel, M.-L. (2004). Induction or expansion of T-cell responses by a hepatitis B DNA vaccine administered to chronic HBV carriers. Hepatology, 40(4), 874
882. Available from https://doi.org/ 10.1002/hep.20408. McAleer, W. J., Buynak, E. B., Maigetter, R. Z., Wampler, D. E., Miller, W. J., & Hilleman, M. R. (1984). Human hepatitis B vaccine from recombinant yeast. Nature, 307(5947), 178
180. Available from https://doi.org/10.1038/307178a0. Mei, J., Jie, Z., Xi-xiu, X., Dong-qun, L., Bin, W., Zhuo, Y., & Rui-tian, L. (2020). A novel rapid modularized hepatitis B core virus-like particle-based platform for personalized cancer vaccine preparation via fixed-point coupling. Nanomedicine: Nanotechnology, Biology and Medicine, 28, 102223. Available from https://doi.org/10.1016/j.nano.2020.102223. Moron, V. G., Rueda, P., Sedlik, C., & Leclerc, C. (2003). In vivo, dendritic cells can cross-present virus-like particles using an endosome-to-cytosol pathway. The Journal of Immunology, 171(5), 2242
2250. Available from https://doi.org/10.4049/jimmunol.171.5.2242. Ong, H. K., Tan, W. S., & Ho, K. L. (2017). Virus like particles as a platform for cancer vaccine development. PeerJ, 5, e4053. Available from https://doi.org/10.7717/peerj.4053. Panacea Biotech. Hepatitis B Virus Vaccine IP Recombinant (Genetically Engineered) (2012): Enivac HB. Available from: ,http://www.panaceabiotec.Com/product-pdf/vaccine1.pdf.. Recombivax HBs. Prescribing information. Merck; (July 2011). Available from: ,https://pdf. hres.ca/dpd_pm/00016542.PDF.. Romano`, L., Paladini, S., Galli, C., Raimondo, G., Pollicino, T., & Zanetti, A. R. (2014). Hepatitis B vaccination. Human Vaccines & Immunotherapeutics, 11(1), 53
57. Available from https://doi.org/10.4161/hv.34306. Sanofi Pasteur Ltd. Euvax Bs (1998). Summary of product characteristics. Available from: ,http://drug.fda.moph.go.th/zonesearch/files/.pdf.. SciGen Ltd. Sci-B-VacTM (2012). Available from: ,http://www.scigenltd.com/productsscibvacs.htm..

188

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Serum Institute of India Ltd. Gene Vac-Bs (2012). Available from: ,http://www.seruminstitute.Com/content/products/productgenevac.htm.. Shantha Biotechnics Ltd. Shanvacs-B (2012). Available from: ,http://www.shanthabiotech. com/files/.pdf.. Shouval, D., Roggendorf, H., & Roggendorf, M. (2015). Enhanced immune response to hepatitis B vaccination through immunization with a Pre-S1/Pre-S2/S vaccine. Medical Microbiology and Immunology, 204(1), 57
68. Available from https://doi.org/10.1007/s00430-014-0374-x. Soulie´, J. C., Devillier, P., Santarelli, J., Goudeau, A., Vermeulen, P., Guellier, M., & Huchet, J. (1991). Immunogenicity and safety in newborns of a new recombinant hepatitis B vaccine containing the S and pre-S2 antigens. Vaccine, 9(8), 545
548. Available from https://doi. org/10.1016/0264-410x(91)90240-7. Spohn, G., & Bachmann, M. F. (2008). Exploiting viral properties for the rational design of modern vaccines. Expert Review of Vaccines, 7(1), 43
54. Available from https://doi.org/ 10.1586/14760584.7.1.43. Vanham, G., & Van Gulck, E. (2012). Can immunotherapy be useful as a “functional cure” for infection with Human Immunodeficiency Virus-1? Retrovirology, 9(1), 72. Available from https://doi.org/10.1186/1742-4690-9-72. Wang, Y., Wu, S., Wang, Z. C., Zhu, X. M., Yin, X. T., Gao, K., & Yu, J. Y. (2016). Enhanced immunity and antiviral effects of an HBV DNA vaccine delivered by a DC-targeting protein. Journal of Viral Hepatitis, 23(10), 798
804. Available from https://doi.org/10.1111/ jvh.12542. WHO1. (2020). Facts heets for Chronic Hepatitis B. ,https://www.wh/detail/hepatitis-bo.int/fr/ news-room/fact-sheets.. WHO2. (2001). Hepatitis B immunization: Introducing hepatitis B vaccine into national immunization services. Available online from ,http://whqlibdoc.who.int/hq/2001/WHOV&B01.28. pdf.. Win, S. J., Ward, V. K., Dunbar, P. R., Young, S. L., & Baird, M. A. (2011). Cross-presentation of epitopes on virus-like particles via the MHC I receptor recycling pathway. Immunology and Cell Biology, 89(6), 681
688. Available from https://doi.org/10.1038/icb.2010.161. Yan, H., Zhong, G., Xu, G., He, W., Jing, Z., Gao, Z., Huang, Y., Qi, Y., Peng, B., Wang, H., Fu, L., Song, M., Chen, P., Gao, W., Ren, B., Sun, Y., Cai, T., Feng, X., Sui, J., & Li, W. (2012). Sodium taurocholate cotransporting polypeptide is a functional receptor for human hepatitis B and D virus. eLife, 2012(1), e00049. Available from https://doi.org/10.7554/ eLife.00049. Yang, F. Q., Yu, Y. Y., Wang, G. Q., Chen, J., Li, J. H., Li, Y. Q., Rao, G. R., Mo, G. Y., Luo, X. R., & Chen, G. M. (2012). A pilot randomized controlled trial of dual-plasmid HBV DNA vaccine mediated by in vivo electroporation in chronic hepatitis B patients under lamivudine chemotherapy. Journal of Viral Hepatitis, 19(8), 581
593. Available from https:// doi.org/10.1111/j.1365-2893.2012.01589.x. Yang, S. H., Lee, C. G., Park, S. H., Im, S. J., Kim, Y. M., Son, J. M., & Soung, Y. C. (2006). Correlation of antiviral T-cell responses with suppression of viral rebound in chronic hepatitis B carriers: A proof-of-concept study. Gene Therapy, 13(14), 1110
1117. Available from https://doi.org/10.1038/sj.gt.3302751. Zakeri, B., Fierer, J. O., Celik, E., Chittock, E. C., Schwarz-Linek, U., Moy, V. T., & Howarth, M. (2012). Peptide tag forming a rapid covalent bond to a protein, through engineering a bacterial adhesin. Proceedings of the National Academy of Sciences, 109(12), E690
E697. Available from https://doi.org/10.1073/pnas.1115485109.

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Zeltins, A., West, J., Zabel, F., El Turabi, A., Balke, I., Haas, S., & Bachmann, M. F. (2017). Incorporation of tetanus-epitope into virus-like particles achieves vaccine responses even in older recipients in models of psoriasis, Alzheimer’s, and cat allergy. NPJ Vaccines, 2(1), 30. Available from https://doi.org/10.1038/s41541-017-0030-8.

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Chapter 9

MYC oncogenes as potential anticancer targets ˇ Radostina Alexandrova1 and Crtomir Podlipnik2 1

Department of Pathology, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, Sofia, Bulgaria, 2Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia

9.1

Introduction

The viral MYC (v-MYC) oncogene was originally identified as a transforming factor in the genome of the acute avian leukemia retrovirus called myelocytomatosis virus MC29, which was isolated from a Rhode Island red chicken with spontaneous myelocytomatosis in 1961 (Ivanov, 1964). Later, the virus MC29 was accepted as the prototype of the group of myelocytomatosis viruses, including viruses such as MH2, CMII, and OK10. Similarly to other acute avian oncogenic retroviruses, MC29 is also a defective one: there are defects in all three structural genes (gag, pol, env, the pol gene is completely absent). Therefore, its replication can only take place in the presence of a replication-competent helper virus. The original MC29 strain contains helpers from subgroups A and B of the avian leukemia-sarcoma retroviruses. In place of the missing parts in the genome of the MC29 strain, there is a specific nucleotide sequence with a size of 1.5 kb and its product causes malignant transformation of the targeted cells. This sequence was originally denoted by the initials MAC or MCV, and later by MYC. In vitro, the virus induces transformation of fibroblasts, epithelial cells, and macrophages. In vivo transmission of the virus causes primarily myelocytomatosis and myelocytomas in chickens, but it is also responsible for a broad spectrum of leukemias and tumor growths, including endothelioma, mesothelioma sarcoma, and erythroblastosis as well as some epithelial tumors in the kidney, pancreas, and liver. Identical or similar sequences have been found in the genomes of other myelocytomatosis viruses (Lee & Reddy, 1999; Mladenov, 1974; Payne & Nair, 2012; Payne, 1992; Weiss, 1982). Cell homologs of v-MYC have been found in the genome of both avian species and mammals, including humans. In humans, MYC family consists of three Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00011-X Copyright © 2023 Elsevier Inc. All rights reserved.

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members: c-MYC (8q24), N-MYC (2p24), and L-MYC (1p34), recognized as “super transcription factors,” which control almost all cellular processes. v-MYC is thought to have originated through a specialized form of viral transduction in which a spliced form of c-MYC (from which introns are excised) has been incorporated into the retroviral genome. Duplication of the gene in the early stages of vertebrate evolution led to the emergence of the c-MYC line and another line, which later, by repeated duplication, gave rise to the N- and L-MYC genes. The general structure and organization of the c-, N- and L-MYC genes, as well as their transcripts, are similar (Alexandrova, 2005; Weiss, 1982). Studies in avian species and mammals have shown that in many respects, v-MYC has a significantly more pronounced effect than c-MYC due to various factors: (1) There is a high level of transcriptional activity due to the presence of strong activating elements in long terminal repeats (LTR) of the retrovirus. (2) Various mutations have been observed in v-MYC proteins, due to which they differ from their cellular counterparts (c-MYC). The V-MYC gene of MC29 contains five to seven (depending on the virus isolate) amino acid substitutions compared with the c-MYC. Replacement of threonine with methionine at position 61 (regulating the phosphorylation site) leads to significantly higher transforming activity of v-MYC than its cellular counterpart. The OK10 and MH2 viruses also contain key mutations at position 61. Most of the mutations occur at the N- and C-termini of the v-MYC regions required for cell transformation. (3) v-MYC is three times more stable than c-MYC. After the reverse mutation, in which methionine is replaced with threonine, the degradation periods of c-MYC and v-MYC are equalized—their half-life is about 20
30 s (Alitalo et al., 1983; Gavine et al., 1999; Lee & Reddy, 1999; Watson et al., 1983).

9.2

Biological role of MYC genes

MYC genes are involved in a wide variety of key biological processes, including cell proliferation, growth, survival and apoptosis, (energy) metabolism, differentiation, angiogenesis, DNA repair, and immune response (Beltran, 2014; Duffy et al., 2021). MYC proteins are mainly known as transcription factors, but they also take part in DNA synthesis and protein translation and have been reported to regulate many different microRNAs (Chang et al., 2008; O’Donnell et al., 2005). c-MYC is expressed in virtually all proliferating cells during development and in dividing cells of adult tissues, but its expression is blocked in nondividing cells due to various mechanisms, including terminal differentiation (Beltran, 2014). N-MYC is expressed during embryogenesis in pre-B cells, kidney and brain, and at a significant level in adult tissues (Beltran, 2014; Yoshida, 2018). L-MYC is expressed in the developing brain and primitive neuronal cells as well as in neonatal and adult lung tissue (Beltran, 2014). Both c-MYC and N-MYC are proven to be

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essential for mammalian embryogenesis, whereas L-MYC is dispensable (Hatton et al., 1996; Hurlin, 2013).

9.3

MYC in normal tissues and cancer

MYC proteins preferentially regulate the same sets of genes and are drivers of critical cell processes such as proliferation, differentiation, metabolism, survival, and death. Their availability in excess and functioning may result in malignant cell transformation and cancer. During the evolution, normal cells adopt multiple systems to maintain MYC RNA and MYC protein levels such as cross-regulation, compensation between the three main MYC genes (c-, N-, and L-MYC), regulated degradation via the ubiquitin
proteasome system as well as MYC-trigged apoptosis when in excess (Conacci-Sorrell et al., 2014; Malynn et al., 2000; Meyer & Penn, 2008). This tight control can be disrupted as a result of various mechanisms (Table 9.1) that convert an MYC gene into an oncogene. There is data-based evidence that dysregulation and/or overexpression of MYC is involved in more than 70% of human cancers (including breast, prostate, colon and cervical cancers, myeloid leukemia, lymphoma, etc.) and is often associated with aggressive behavior, drug resistance, and poor prognosis (Dang, 2012; Fallah et al., 2017; Kalkat et al., 2017; Nesbit et al., 1999; Srutova et al., 2018). Many of these tumors are oncogene-addicted (Weinstein, 2002), which means that deregulation of the MYC gene product is required to maintain their malignant phenotype and progression (Gabay et al., 2014; Kuzyk & Mai, 2014). MYC can be activated through gene amplification, chromosome translocation, retroviral promoter insertion, mutation, enhanced cell signaling, and altered protein stability (Table 9.1) (Duffy et al., 2021; Kuzyk & Mai, 2014; Lee & Reddy, 1999). Various single-nucleotide (Ghoussaini et al., 2008) polymorphisms (SNPs) near MYC have been reported to be associated with the increased risk (by 25%
50%) of developing cancer (e.g., prostate, colon, breast, nasopharynx, and bladder) (Easton et al., 2007; Guo et al., 2019; Lancho & Herranz, 2018). MYC genes increase the total capacity for protein synthesis and are responsible for the energy (glucose) metabolism of cancer cells. Influencing activities of several microRNAs modulate cell growth, survival/ death, differentiation, and transformation (Chang et al., 2008; O’Donnell et al., 2005). The increased expression of MYC induces DNA damages (as a result of ROS-dependent and ROS-independent mechanisms), replication stress, and a number of alterations in a nuclear organization that can affect the position and function of telomeres (Kuzyk & Mai, 2014). Overexpressed MYC has also been found to play an important role in promoting escape from antitumor immune responses, which is one of the essential hallmarks of cancer (Casey et al., 2018; Vera de Jonge et al., 2020).

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TABLE 9.1 Mechanisms of MYC activation in cancer. Type of alteration

Mechanism

Gene amplification

One of the most frequent mechanisms for MYC activation (especially for c-MYC) found in 10%
60% of carcinomas (such as ovarian, oesophagal, breast, and squamous lung cancers) and sarcomas, depending on the tumor type; can be associated with the molecular subtype of cancer; more typical for c-MYC, and less frequently observed in N-MYC (neuroblastoma, medulloblastoma, retinoblastoma, neuroendocrine/ small-cell prostate cancer, etc.) and L-MYC (some small cell lung cancers). MYC is the most frequently amplified gene among cancers (Beltran, 2014; Duffy et al., 2021; Yoshida, 2018)

Chromosomal translocation

Found mainly in hematological malignancies, such as Bcell lymphomas (for example, in 100% of Burkitt’s lymphomas) and less frequently in T-cell lymphomas and multiple myeloma; documented for the first time in Burkitt’s lymphoma. Three types of MYC translocations have been reported joining the long arm of chromosome 8 to the immunoglobulin (IG) heavy locus [t(8;14)(q24; q32)] to the kappa light chain locus [t(2;8)(p11;q24)] or to the lambda light chain locus [t(8;22)(q24;q11)]. These translocations place the MYC gene under the control of powerful IG gene enhancers, which stimulate constitutive MYC expression (Duffy et al., 2021; Kuzyk & Mai, 2014)

Retroviral promoter insertion

First identified in avian chronic leukemic retroviruses (ALVs) that do not have their own oncogenes. The tumorigenic effect occurs when these viruses integrate near the cellular c-MYC because the long terminal repeats of retroviruses contain powerful enhancers and significantly increase the expression of c-MYC

Mutation

It is rarely observed. In the case of both gene amplification and chromosome translocation, the MYC gene stays intact with no change in the protein. MYC mutations are found in a subset of Burkitt’s lymphoma (with the translocated allele undergoing secondary mutations occurring in the MYC transactivation domain with hotspots in the MYC box 1 motif) and in diffuse large B cell lymphomas (showing different impacts on MYC function and clinical outcomes). In contrast to MYC gene translocations and overexpression, most MYC gene mutations seem to have no role in driving lymphomagenesis (Duffy et al., 2021) (Continued )

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TABLE 9.1 (Continued) Type of alteration

Mechanism

Increased gene expression and/or stability

It takes place through activation of signaling pathways and other oncogenes (i.e., ERK, PI3K, β-catenin, RAS, SRC, NOTCH), inactivation of tumor suppressor genes (such as APC, p53), various posttranslational modifications (phosphorylation, acetylation, sumoylation), participation of long noncoding RNAs, E3 ubiquitin ligase recruitment, and proteasomal degradation

9.4

MYC signal transduction pathway

Nearly every major signal transduction pathway regulating cell proliferation or quiescence depends on the MYC promoter and regulates MYC transcription, either directly or indirectly. These signaling pathways are activated by various signaling molecules, including mitogens, growth factors, hormones, cytokines, oncogenes, and tumor suppressors. c-MYC plays a critical role in controlling cell proliferation, cell cycle progression, differentiation, and apoptosis. Global transcriptional analyses estimate that approximately 15% of the human genome genes are under the transcriptional regulation of c-MYC (Fernandez et al., 2003; Li et al., 2003). Experimental data have shown that c-MYC directly binds to the promoters of many genes with different cellular functions and regulates their expression. For example, c-MYC directly activates the transcription of CDK4 and CDC25A genes and represses P27 and P15 genes, which play a role in cell cycle regulation. Direct targets of cMYC are also genes involved in metabolism (such as CAD, LDHA and ODC1) (Bello-Fernandez et al., 1993; Fernandez et al., 2003; Shim et al., 1997), regulation of cell apoptosis (such as BAX, Mcl-1 and Bcl-2) (Mitchell et al., 2000), and proliferation (such as MINA53 and PTMA) (Gaubatz et al., 1994; Mu¨ller & Eilers, 2008; Tsuneoka et al., 2002). Besides, c-MYC directly binds and regulates the expression of other transcription factors such as E2F1 and CEBPα (Li et al., 1994; Sears et al., 1997), thus expanding the pool of downstream targets of c-MYC. Besides, studies on microRNAs (miRNAs), a class of endogenous small noncoding RNAs that play essential regulatory roles by targeting protein-coding genes (Bartel, 2004), have identified some miRNAs (such as the miR-17
92 cluster, let-7) whose expression is transcriptionally regulated by c-MYC (Chang et al., 2008). On the other hand, c-MYC lies downstream of many critical signaling pathways in the cell (Fig. 9.1). Palomero et al. showed that NOTCH1 directly binds to the c-MYC promoter and regulates c-MYC

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FIGURE 9.1 Many critical signaling pathways regulate the expression of c-MYC, for example, Wnt/beta-catenin, Ras/MEK/MAPK, and the Ras/PI3K/AKT. c-MYC controls the expression of many genes involved in regulating cell cycle progression, cell apoptosis, proliferation, or metabolism as a transcription factor. Transcriptional regulation of miRNAs by c-MYC further increases the complexity of the c-MYC signaling network. The genes marked in red signify activation, and the genes marked in green signify repression.

mRNA and protein expression in T-ALL, thereby promoting leukemic cells’ growth (Palomero et al., 2006). It has also been shown that signaling through the Src/Rac pathway mediates c-MYC mRNA induction by PDGFR (Chiariello et al., 2001). Upon activation of the Wnt/β-catenin pathway, c-MYC transcription is also activated. The Ras/Raf/ERK and Ras/PI3K/ AKT/GSK-3β pathways regulate c-MYC protein stability (Sears et al., 2000). Considered together, the response of the c-MYC gene to a variety of signaling pathways and the emerging role of the c-MYC protein as a central transcriptional regulator suggest a critical role for c-MYC as an essential signaling node making this oncogene a promising target for cancer therapy. Fig. 9.1 shows that the major signal transduction pathway depends on the MYC promoter.

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Structure of MYC

The human MYC gene, 6 kb long, is located at locus 8q.24.21 on the eighth chromosome. The gene consists of three exons, of which exon 1 is noncoding; the other two exons encode two proteins. The MYC gene’s main product is a 62 kDa protein with a sequence of 439 amino acids and belongs to the class of transcription factor basic helix
loop
helix zipper (bHLHZip). This protein contains several highly conserved regions that are functionally important and equally organized among the three paralogs (c-MYC, N-MYC, and L-MYC) (ConacciSorrell et al., 2014). The N-terminal transactivation domain (NTD) contains the transcription activation domain (TAD) and two MYC boxes, MBI and MBII, which are highly conserved sequence elements involved in transcriptional regulation and protein stability. The central part of c-MYC contains a nuclear localization signal and two other conserved sequence elements, MBIII and MBIV. The C-terminal domain contains the bHLHZip motif, which remains partially unstructured until it dimerizes with another bHLHZip protein, MAX. It then forms an ordered alpha
helix structure, which is subject to several posttranslational modifications and protein interactions that regulate the function of c-MYC. Proteins from the MYC family are unstable with half-lives of 20
30 min in normal cells (Gregory & Hann, 2000); however, in many tumors, MYC’s stabilization contributes to its deregulation. Several ubiquitin ligases have been shown to control MYC stability. Fig. 9.2 represents conserved domains of the MYC protein and some of its binding partners. The C-region is essential for DNA binding by forming a basic helix
loop
helix leucine zipper (bHLHZ) domain complex with Max (Brownlie et al., 1997). Additional conserved sequence motifs known as MYC boxes (MB0-IV) serve as docking sites for protein
protein interactions.

FIGURE 9.2 Domain structure of MYC proteins with conserved regions are shown as blue boxes; MB, MYC boxes; bHLHZ, basic helix
loop
helix leucine zipper; functional annotation of regions and proteins interacting at these sites; structures of protein complexes containing MYC (red). PDB entries are 1MV0 (BIN1), 5G1X (AURORA-A), 4Y7R (WDR5), 1EE4 (KPNA), and 1NKP (MAX).

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The MYC transactivation domain (TAD) spans the N-terminal conserved motifs MB0, MBI, and MBII.

9.6

The MYC
Max interaction

Basic helix
loop
helix (bHLH) and leucine zipper (LZ) domains are often part of the domains of eukaryotic transcription factors. The combination of the two domains folding into multimeric helix bundles allows the formation of specific heterodimers. Modularity provides means to control function by differential expression of each member of the pair. It has been shown that cMYC forms homodimers in analogy to other bHLH- and LZ-containing transcription factors at very high concentrations of the bacterially overexpressed protein; on the other hand, c-MYC fails to form soluble dimers with an affinity for the specific DNA-binding site. Co-immunoprecipitation experiments of a GST-MYC bHLH-LZ with phage display library highlighted Max (MYC-associated factor X) as an interaction partner of MYC. The c-MYC binds to the partner protein Max via a polypeptide patch, which has a similar domain and can also be a homo-dimer. Both LZ are disordered before binding, but after binding, both chains become ordered and form a left-handed four-helix bundle (Nair & Burley, 2003). The resulting heterodimer (the functional species) binds DNA and modulates gene expression, transcriptional activation, and apoptosis (Amati et al., 1993; Ponzielli et al., 2005). Deregulation of c-MYC is often associated with aggressive tumors in various tissues (breast, lung, cervix, etc.) and regulation of genes controlled by RNA polymerase (Grandori et al., 2005). As noted in the previous section, a cMYC that belongs to the bHLHZip class of the transcriptional factor comprises three domains: N-terminal transactivation domain, central region, and a C-terminal domain. The C-terminal domain composed of 79 amino acids contains the bHLHZip motif, which plays an essential role in binding with its obligatory partner Max, also bHLZip protein. Both c-MYC and Max are rich in charged amino acids, which show a disorder-promoting propensity (Habchi et al., 2014; Kumar et al., 2017). The amino acid composition of the c-MYC
Max complex based on PDB structure 1NKP is shown in Table 9.2.

TABLE 9.2 Amino acid composition of the c-MYC
Max complex based on the PDB-ID: 1NKP. Leu

Lys

Arg

Glu

Asp

Total

cMYC

12 (13.6%)

10 (11.4%)

10 (11.4%)

11 (12.5%)

2 (2.3%)

88

Max

8 (9.6%)

7 (8.4%)

10 (12.0%)

4 (4.8%)

8 (9.6%)

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Upon binding c-MYC to Max, the C-terminal domain transforms disordered conformation to an ordered α-helical structure, which extends into a left-handed coiled coil formed by the two LZ motifs (Amati et al., 1993; Grandori et al., 2000). This stable four-helix formation binds to specific DNA sequences, for example, CACGTG E-box motifs, in promoters and enhancers of MYC-regulated genes. The dimerization is driven by the LZ and HLH motifs, while the basic regions are responsible for the interaction with DNA. The helix
loop
helix region of c-MYC targets several posttranslational modifications such as acetylation, phosphorylation, ubiquitination, and sumoylation (Mu¨ller & Eilers, 2008; Suna et al., 2018; Tu et al., 2015; Vervoorts et al., 2006). This region is also involved in protein
protein interactions (PPIs) that mediate and regulate c-MYC functions. The first structure of an MYC
Max heterodimer was determined by X-ray crystallography in 2003 (PDB ID: 1NKP) (Nair & Burley, 2003). This X-ray structure represents a c-MYC
Max bHLHZip complex bound to DNA containing an E-box motif bound by an artificial disulfide bridge created by adding a cysteine residue C-terminus of the LZ of both c-MYC and Max proteins. The structure of the complex of DNA and an MYC
Max heterodimer is shown in Fig. 9.3. Another interesting study related to c-MYC is the NMR study of the vMYC, both in free form and in complex with v-Max in the absence of DNA (Baminger et al., 2004). The extensive studies on the MYC
Max complex structure were performed by Sammak et al. (2019). They performed a detailed structural analysis of the apo-form of the c-MYC
Max complex, without an artificial linker, in solution both by nuclear magnetic resonance

FIGURE 9.3 The structure of the complex of DNA and an MYC
Max heterodimer.

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FIGURE 9.4 The X-ray structures of the c-MYC
Max apo complex (PDB-ID: 6K6K) and superposition of apo and DNA bound c-MYC
Max (PDB-ID: 1NPK).

spectroscopy (NMR) and by X-ray crystallography. They reported crystal structures in three different crystal forms with resolutions between 1.35 and ˚ , showing an extended helical structure in the basic region. 2.2 A Determination of the α-helical NMR chemical shift analysis indicates that the basic regions of c-MYC and Max adopt a helical conformation; moreover, c-MYC exhibits an intrinsic helical propensity of the basic region even without an interaction with Max. The presence of a helical structure in the basic regions in the absence of DNA suggests that molecular recognition occurs by conformational selection rather than induced alignment. The X-ray structures of the c-MYC
Max apo complex (PDB-ID: 6K6K) and superposition of apo and DNA bound c-MYC
Max (PDB-ID: 1NPK) are shown in Fig. 9.4.

9.7

MYC as a potential target for antitumor therapy

MYC has been identified as a promising target for antitumor therapy (Chen et al., 2018; Dang, 2012; Duffy et al., 2021). Unfortunately, despite research and efforts over more than 40 years, we still do not have an MYC gene inhibitor for use in clinical practice. The challenges facing the development of such MYC gene inhibitors are as follows: 1. Functioning of MYC is required for both normal and malignant cells; hence, one of the most important questions is whether its suppression will negatively affect the implementation of physiological processes.

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FIGURE 9.5 Different strategies to combat MYC. Inhibitors of CDK7, CDK9, and BRD4 inhibit MYC transcription. Inhibition of the PI3K/AKT/mTOR pathway prevents MYC translation, while USP7, AURKA, and PLK1 inhibitors destabilize MYC at the posttranslational level. 10058-F4 and Omomyc act as disruptors of the MYC
Max dimeric complex.

2. MYC proteins are localized in the cell nucleus and monoclonal antibodies cannot reach them. 3. These proteins possess an intrinsically disordered structure, and there are no drug-binding pockets into which compounds can bind with high affinity. They have no catalytic activity and cannot be blocked by traditional enzyme inhibitors. 4. There is no known natural ligand to guide us in the development of inhibitors. In recent years, several compounds that directly or indirectly inhibit MYC have been developed, and some of them have shown promising anticancer activity in preclinical models and even entered clinical trials (AllenPetersen & Sears, 2019; Duffy et al., 2021; Whitfield et al., 2017). To reduce the devastating effects of MYC, scientists have explored several strategies, some of which target MYC directly and some indirectly. Some of these strategies are shown in Fig. 9.5 and are described in more detail in this section.

9.8 Targeting the MYC
Max interaction with small molecule inhibitors Currently, there are no small-molecule inhibitors approved for commercial use that would successfully inhibit the interaction between MYC and Max directly. However, some conventional small molecules have been identified

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that inhibit c-MYC
Max dimerization or DNA binding. The IDP structure of c-MYC and Max monomers has hampered novel modulators’ discovery by traditional structure-based design, resulting in inhibitors being identified mainly through high-throughput screening of chemical libraries. In initial screens using small molecule libraries, compounds IIA6B17 (Lu et al., 2008) and NY2267 (Kiessling et al., 2006) were identified as capable of interfering with the c-MYC
Max interaction. However, both compounds were later found to be nonselective and acted on c-Jun, probably due to similarities in the LZ components (Fletcher & Prochownik, 2015). This absence of specificity is a common problem with small molecule inhibitors of c-MYC function. Two compounds, 10058-F4 (Huang et al., 2006) and 10074-G5 (Yap et al., 2013), identified from a library of 10,000 compounds using a yeast twohybrid screen, have been shown to bind to c-MYC in the IDP state and prevent it from folding into the conformation required for dimerization with Max. Both ligands have a modest affinity to the c-MYC; molecule 10058-F4 binds to the interface of Helix 2 and the c-MYC LZ (KD 5 42 μM), whereas 10074-G5 binds to Helix 1 and the basic region (KD 5 20 μM). Thioxothiazolidinone 10058-F4 is effective in vitro against acute myeloid leukemia, pancreatic ductal adenocarcinoma (PDAC), and ovarian carcinoma, inducing apoptosis (Aksoz et al., 2018; Lin et al., 2007), but fails in vivo to reduce the growth of human prostate cancer xenografts in mice due to its low metabolic stability (Guo et al., 2009). Further optimization of the inhibitor 10074-G5 leads to the formation of the compound JY-3
094, a much more potent inhibitor of c-MYC
Max dimerization than the parent compound, an esterified prodrug form of JY-3
094. The low metabolic stability and poor distribution of the mentioned inhibitors is the main reason for unsuccessful in vivo experiments despite promising in vitro potency. Mycro3 is an inhibitor of c-MYC
Max dimerization based on the pyrazole-[1,5-a]pyrimidine scaffold, the result of optimizing hits from high-throughput screening. This inhibitor has more improved pharmacokinetic properties compared to other c-MYC inhibitors, and in vivo studies resulted in prolonged survival and reduced tumor size in a KRas-driven PDAC mouse model (Stellas et al., 2014). MYCMI-6 is another small molecule inhibitor that can selectively bind to the MYC family’s bHLHZip domain at low micromolar concentrations. The obstruction of c-MYC
Max interaction by MYCMI-6 in cells leads to the suppression of tumor growth in several cell lines, especially those expressing high levels of c-MYC protein, without cytotoxicity to normal human cells. Moreover, MYCMI-6 promotes significant apoptosis and reduces tumor proliferation in a neuroblastoma xenograft model in vivo (Castell et al., 2018). MYCi361 and MYCi975 were discovered with the initial selection of hits from the library of 16 million compounds via a pharmacophore-based high-throughput virtual screen, followed by a secondary screen assessing c-MYC inhibitory activity in vitro, and later coupled with a rapid in vivo screen in mice bearing a c-MYC-dependent

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FIGURE 9.6 Inhibitors that target c-MYC directly or interaction between c-MYC and Max.

E-box luciferase reporter. These two inhibitors have significant antitumor activity in mice and good pharmacokinetic properties, such as high plasma concentration, longer half-life, and improved tumor penetration. Among the published inhibitors, KJ-Pyr-9 exhibited the highest reported binding affinity for c-MYC with a KD of 6.5 nM. KJ-Pyr-9 was isolated from a Kroehnke pyridine library in which fluorescence polarization (FP) was used to screen for c-MYC
Max dimerization inhibitors. KJ-Pyr-9 has also shown anticancer activity in vivo by inhibiting tumor growth in a human triple-negative breast cancer xenograft model without acute toxicity. Small molecules like MYCMI-6, MYCi361/975, and KJ-Pyr-9 are promising prospects for further development as potential clinical candidates. Some of the important inhibitors that directly target c-MYC or c-MYC
Max interaction are shown in Fig. 9.6.

9.9

Indirect targeting of the MYC

One of the possible strategies for treating MYC-driven cancers is the inhibition of crucial factors in MYC transcription, translation, stability, and activation. The first option is to target BRD4, CDK7, or CDK9, which are involved in MYC transcription. Then, we can prevent MYC translation by inhibition of the PI3K/AKT/mTOR pathway. The ubiquitin
proteasome system tightly controls MYC stability; thus, one potential strategy to combat MYC is selective inhibition of the kinases (AURKA, PLK1) and deubiquitinases (USP7) that stabilize MYC. Disabling MYC
Max complex formation,

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which we discussed in the previous section, is also an approach to inhibiting MYC signal transduction.

9.10 Targeting MYC transcription MYC is regulated by both transcription and protein stability. BRD4 transcriptional and epigenetic regulator activates MYC transcription. BRD4 directly destabilizes the protein MYC by phosphorylating it at a site, which leads to ubiquitination and degradation, thereby maintaining homeostatic levels of the protein MYC (Mertz et al., 2011; Zuber et al., 2011). The small-molecule inhibitor JQ1 of BRD4 showed potent anticancer effects in several cancers associated with c-MYC overexpression hematopoietic cancers and PDAC (Puissant et al., 2013). The structure of the JQ1 inhibitor and its interaction with BRD is shown in Fig. 9.7. Another strategy to combat MYC-driven cancers is inhibition of the kinases CDK7 and CDK9; these two cyclin-dependent kinases play critical roles in transcription initiation and elongation (Fisher, 2005). CDK7 is a part of the transcription factor IIH complex (TFIIH), and CDK9 is a kinase subunit of P-TEFb (Peng et al., 1998; Shiekhattar et al., 1995). Administration of specific inhibitors against CDK7 and/or CDK9 significantly reduces c-MYC overexpression. Consequently, it has an antitumor effect in cancers associated with c-MYC overexpression, such as T-cell acute lymphoblastic leukemia, mixed lineage leukemia, and neuroblast and small cell lung carcinoma (Chen et al., 2018). THZ1, a selective and potent covalent CDK7 inhibitor, has an IC50 of 3.2 nM. THZ1 also inhibits the closely related kinases CDK12 and CDK13. It downregulates the expression of MYC (Kwiatkowski et al., 2014). AZD4573 is a potent and highly selective CDK9 inhibitor (IC50 , 4 nM) that allows transient targeting for the treatment of hematologic malignancies (Huang et al., 2021). (1)-BAY-1251152 a potent

FIGURE 9.7 Structure of JQ1, small-molecule inhibitor of BRD4 (left), and complex between JQ1 and BRD4 (right).

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FIGURE 9.8 CDK inhibitors.

and selective CDK9 inhibitor with an IC50 of 3 nM has also shown antitumor activity (Diamond et al., 2018). CDK inhibitors mentioned in this paragraph are shown in Fig. 9.8.

9.11 Targeting of MYC expression Deregulation of the PI3K/AKT/mTOR pathway is typical of several cancers (Bjornsti & Houghton, 2004). mTOR serine/threonine kinase acts as the catalytic subunit of two distinct complexes called mTOR complexes 1 and 2 (mTORC1 and mTORC2), which play an essential role in protein synthesis. The mTORC1-dependent phosphorylation of 4EBP1 decreases its ability to downregulate the translation initiation factor eIF4E and, consequently, promote the translation of mRNAs, leading to the synthesis of MYC (Bjornsti & Houghton, 2004). Pharmacological inhibition of the PI3K/AKT/mTOR pathway significantly decreased MYC levels and showed remarkable therapeutic efficacy in MYC-driven cancers, including neuroblastoma, small cell lung cancer, breast cancer, and various hematopoietic cancers (Chen et al., 2018). Another target for inhibition of MYC expression are proteins from the CPEB family, which expression is frequently downregulated in human cancers (Fern´andez-Miranda & Me´ndez, 2012). The CPEB family proteins are sequence-specific RNA-binding proteins that control poly(A) tail elongation and polyadenylation-induced translation. It has been shown that the c-MYC mRNA contains CPEs that CPEB can recognize (Groisman et al., 2006). Mechanistically, CPEB recruits Caf1 deadenylase by interacting with Tob, an antiproliferative protein, and inhibits c-MYC expression by accelerating deadenylation and decay of its mRNA (Ogami et al., 2014).

9.12 Targeting MYC stability Several deubiquitinating enzymes are involved in the stabilization of MYC. USP28 has been shown to bind c-MYC and antagonize its E3 ligase activities in the nucleus by interacting with FBW7α, leading to MYC stabilization and tumor cell proliferation (Fan et al., 2013). Together with FBW7γ, USP36 deubiquitinates and stabilizes c-MYC in the nucleolus (Otto et al., 2009). USP7 binds directly to N-MYC and stabilizes it by deubiquitination

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FIGURE 9.9 Inhibitors that target MYC stability.

in neuroblastoma cells, and a small molecule inhibitor of USP7, P22077, markedly suppressed the growth of N-MYC-amplified neuroblastoma in a xenograft model (Fan et al., 2013). In principle, targeting these deubiquitinases causes MYC destabilization and tumor suppression. AURKA forms a complex with N-MYC, which protects N-MYC from FBW7-mediated proteasomal degradation (Otto et al., 2009). Two AURKA inhibitors, MLN8237 and MLN8054, disrupt the MYC
AURKA complex, leading to N-MYC degradation and tumor regression in N-MYC-amplified neuroblastomas. MLN8237 also induced c-MYC degradation in P53-mutated human hepatocellular carcinoma cells. AURKA inhibitors may be potential cures for the treatment of MYC-dependent cancers (Brockmann et al., 2013). Inhibitors of PLK1, such as BI6727 or BI2356, preferentially induce substantial apoptosis of MYC-overexpressing tumor cells and synergistically potentiate the therapeutic efficacy of BCL-2 antagonists. These findings reveal a PLK1
FBW7
MYC signaling circuit underlying tumorigenesis and validate PLK1 inhibitors, alone or with BCL-2 antagonists, as potentially effective therapeutic MYC-overexpressing cancers (Xiao et al., 2016). Inhibitors that target MYC stability are shown in Fig. 9.9.

9.13 Synthetic lethality with MYC Synthetic lethality occurs when a combination of deficits in the expression of two or more genes results in cell death, whereas a deficit in only one of these genes does not. Overexpression of MYC, which occurs in many cancers, sensitizes cells to apoptosis by allowing the synthetic lethality gene to target cancer-relevant MYC overexpression to kill only cancer cells but spare normal cells. Synthetic lethality can be exploited to indirectly target the nontargetable MYC oncoprotein and prevent tumorigenesis in MYC-driven tumors (Thng et al., 2021).

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FIGURE 9.10 Compounds that interact with synthetic lethality targets of MYC.

Purvalanol B, an inhibitor of CDK1, selectively induced apoptosis in cells overexpressing MYC and significantly reduced tumor growth in MYCdependent lymphoma and hepatoblastoma mouse models. The selective induction of apoptosis after CDK1 inhibition is connected with upregulation of the pro-apoptotic molecule BIM and/or downregulation of the antiapoptotic molecule (Goga et al., 2007; Kang et al., 2014). The basic idea of using CHK1 (checkpoint kinase 1) inhibitors to treat cancer arose from the observation that tumor cells turn off DNA damage checkpoints during tumorigenesis or therapy, making them highly susceptible to additional genomic instability. MYC deregulation is sufficient to induce genome instability. MYC induces replication stresses and DNA damages through excessive replication-fork firing, making MYC-overexpressing tumors substantially more sensitive to CHK1 inhibition. CHK1 inhibition leads to cell death in MYC-overexpressing neuroblastomas, lymphomas, and breast and lung cancers (Ferrao et al., 2012; Murga et al., 2011). Glutamine metabolism is an essential energy source for the sustained growth and proliferation of some types of tumor cells (Xiang et al., 2015). Oncogenic MYC alters mitochondrial metabolism by increasing the surface expression of glutamine transporters involved in the transport of endogenous glutamine, which is also used as a bioenergetic substrate in cancer cells (Yuneva et al., 2007). Glutamine is converted into glutamate by GLS, an enzyme that is highly expressed in tumor cells. Inhibition of glutamine metabolism by GLS inhibitors selectively induces apoptosis in MYCoverexpressing tumor cells. The glutaminase inhibitor telagenastat (CB-839) is currently in use in clinical trials and can be an important new agent for the treatment of a broad spectrum of cancers associated with MYC deregulation (Duffy et al., 2021; Gross et al., 2014). Structures of two compounds that interact with synthetic lethality targets of MYC are shown in Fig. 9.10.

9.14 G-quadruplexes and expression of c-MYC This section discusses the mechanism for controlling MYC expression, which involves four-stranded tetrahelical DNA G-quadruplex (G4) structures overexpressed in gene promoters with small molecule inhibitors.

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As mentioned above, it is challenging to design small molecule inhibitors for the intrinsically disordered c-MYC protein because of the lack of a unique binding site and also because it has a very short half-life (Gregory & Hann, 2000). Instead of inhibiting the protein, we can also control the c-MYC protein expression by ligands that stabilize the G-quadruplex (G4) structures located at the beginning of the promoter region c-MYC gene. G-quadruplex, considered to repress transcription, is a unique four-stranded conformation of DNA formed from single-stranded guanine (G)-rich sequences, in which each guanine is hydrogen-bonded to the two adjacent guanines, forming a square planar shape known as the G-tetrad. Two or more such G-tetrads stack on top of one another to form the G-quadruplex’s four-stranded helical structure. The stability of a G4 largely depends on the monovalent cations, specifically K1 and Na1, where the positively charged cations occupy the stacked G-tetrads’ central channel and neutralize the electrostatic repulsion of guanine O6 oxygens (Dingley et al., 2005). The G4 structure of the c-MYC promoter, which adopts a parallel conformation of single-stranded DNA, consists of three G tetrads. The thermodynamically stable conformation has a significantly high melting temperature. The 30 and 50 regions of c-MYC form a capping structure at the corresponding terminal tetrads, providing a nice binding pocket for the small molecule ligands (Dai et al., 2011). Simonsson et al. first reported a parallel G-quadruplex structure in the proximal promoter region of the c-MYC proto-oncogene in 1998 (Simonsson et al., 1998). Siddiqui-Jain et al. subsequently showed that a small-molecule stabilization of G4 in the c-MYC promoter region could repress c-MYC transcription (Siddiqui-Jain et al., 2002). Interest in small-molecule development for promoter G-quadruplexes intensified when a synthetic ligand TMPyP4 successfully downregulated c-MYC proto-oncogene expression (Grand et al., 2002). The TMPyP4 ligand bound to G4-tetrade is shown in Fig. 9.11.

FIGURE 9.11 1H NMR solution of TmPyP4 in complex with c-MYC G4 revealed the stacking interaction of TmPyP4 on the top of the tetrad and electrostatic interaction of the positive charges of the ligand with the negatively charged phosphate backbone.

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The human genome contains more than 376,000 putative G4-forming sequences (PQS), and it is challenging to assign G4-related biological effects in human cells; apart from telomeric G4, many genes (50% or more) contain a G4-responsive motif in their promoters and are therefore potentially responsive to G4 ligands (Huppert & Balasubramanian, 2005). Targeting the G4-responsive motifs of the human genome by small molecules is nowadays one of the most accepted approaches to find anticancer agents, and numerous G4 ligands are listed in the database G4LDB (G4 ligands database; http:// www.g4ldb.org). Below, we describe some small molecules that inhibit the c-MYC protein expression by stabilizing the G-quadruplex at the beginning of its gene’s promoter region. (Structures are shown in Fig. 9.12). The earliest reported G4 interacting ligands are 2,6-diamido anthraquinones (Sun et al., 1997), TmPyP4 (Wheelhouse et al., 1998), and PIPER (Rangan et al., 2001), which have been used as a starting point for designing multiple closely related compounds. 2,6-Diamido anthraquinone has inspired the synthesis of acridines such as BRACO-19, the first reported telomerase inhibitor with an IC50 value of 115 nM (Gowan et al., 2002; Mitrasinovic,

FIGURE 9.12 Small molecules that inhibit the c-MYC protein expression by stabilizing the G-quadruplex.

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2020). Telomestatin, a macrocyclic natural product, is the benchmark compound for G4 recognition; it is a nanomolar in vitro inhibitor for G4s with high selectivity over duplex DNA ( . 70-fold) (Kim et al., 2003). Pyridostatin is a small synthetic molecule rationally designed as a G4 binder; it is composed of a flat electron-rich aromatic surface with the ability to form hydrogen bonds with G4; the molecules have shown an unprecedented stabilization of the human telomeric G4 (Rodriguez et al., 2008). Quarfloxin CX-3543 is a fluoroquinolone derivative and the first G-quadruplexinteracting agent to be tested in human clinical trials. It binds to Gquadruplex DNA and has been shown to interfere with rDNA G-quadruplexes’ interaction with nucleolin protein selectively, thereby inhibiting Pol I transcription and inducing apoptotic death in cancer cells (Drygin et al., 2009). For those interested in the field of targeting MYC with quadruplexes, more details on current inhibitors can be found in the recent review published in J. Med. Chem. (Chaudhuri et al., 2021).

9.15 Conclusions and perspective The targeting of MYC is an attractive opportunity for inhibiting a key player of cancer formation and progression and a powerful weapon to combat cancer. In recent years, considerable progress has been made in the development of MYC inhibitors, although this gene had been considered “undruggable” for decades; however, even after 40 years of intensive research, there is no MYC gene inhibitor available that can be used in clinical practice. The reason it is so difficult to develop a targeted drug for MYC is MYC is essential for the functioning of both normal and cancer cells, and the protein expressed by MYC is a very short-lived, unstructured protein that has no specific binding site in the nucleus, where the administration of drug is difficult; also, there is no known natural ligand to guide us in the development of inhibitors. This chapter describes some methods by which researchers have been able to circumvent the above problems and develop small molecules that indirectly inhibit c-MYC protein overexpression by inhibiting transcription and translation of the gene MYC and destabilizing the expressed protein. In recent years, significant progress has been made in the development of therapeutics targeting c-MYC expression, and we believe that further development will continue in the future, leading to the development of effective and safe target drugs for the gene MYC, which is overexpressed in more than 70% of cancers.

Acknowledgments This work is supported by the National Science Fund, Bulgarian Ministry of Education and Science—Grant No KL-06-KOCT/16 from 16.12.2020 and by the Slovenian Research Agency (ARRS) program and project grant P1
0201.

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References Aksoz, M., Albayrak, E., Aslan, G. S., Turan, R. D., Alyazici, L. Y., Siyah, P., Tuysuz, E. C., Canikyan, S., Yucel, D., Meric, N., Gulbas, Z., Sahin, F., & Kocabas, F. (2018). c-Myc inhibitor 10074-G5 induces murine and human hematopoietic stem and progenitor cell expansion and HDR modulator Rad51 expression. Current Cancer Drug Targets, 19(6), 479
494. Available from https://doi.org/10.2174/1568009618666180905100608. Alexandrova, R. (2005). MYC genes
A short review. Experimental Pathology and Parasitology, 8, 76
82. Alitalo, K., Bishop, J. M., Smith, D. H., Chen, E. Y., Colby, W. W., & Levinson, A. D. (1983). Nucleotide sequence of the v-myc oncogene of avian retrovirus MC29. Proceedings of the National Academy of Sciences of the United States of America, 80(1), 100
104. Available from https://doi.org/10.1073/pnas.80.1.100. Allen-Petersen, B. L., & Sears, R. C. (2019). Mission possible: Advances in MYC therapeutic targeting in cancer. BioDrugs, 33(5), 539
553. Available from https://doi.org/10.1007/ s40259-019-00370-5. Amati, B., Littlewood, T. D., Evan, G. I., & Land, H. (1993). The c-Myc protein induces cell cycle progression and apoptosis through dimerization with Max. EMBO Journal, 12(13), 5083
5087. Available from https://doi.org/10.1002/j.1460-2075.1993.tb06202.x. Baminger, B., Ludwiczek, M. L., Hoffmann, B., Kontaxis, G., Bister, K., & Konrat, R. (2004). Backbone assignment of the dimerization and DNA-binding domain of the oncogenic transcription factor v-Myc in complex with its authentic binding partner Max. Journal of biomolecular NMR, 30((3), 361
362. Available from https://doi.org/10.1007/s10858-005-1058-z. Bartel, D. P. (2004). MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell, 116(2), 281
297. Available from https://doi.org/10.1016/S0092-8674(04)00045-5. Bello-Fernandez, C., Packham, G., & Cleveland, J. L. (1993). The ornithine decarboxylase gene is a transcriptional target of c-Myc. Proceedings of the National Academy of Sciences of the United States of America, 90(16), 7804. Available from https://doi.org/10.1073/ pnas.90.16.7804. Beltran, H. (2014). The N-myc oncogene: Maximizing its targets, regulation, and therapeutic potential. Molecular Cancer Research, 12(6), 536. Available from https://doi.org/10.1158/ 1541-7786.MCR-13-0536. Bjornsti, M. A., & Houghton, P. J. (2004). The TOR pathway: A target for cancer therapy. Nature Reviews Cancer, 4(5), 1362. Available from https://doi.org/10.1038/nrc1362. Brockmann, M., Poon, E., Berry, T., Carstensen, A., Deubzer, H. E., Rycak, L., Jamin, Y., Thway, K., Robinson, S. P., Roels, F., Witt, O., Fischer, M., Chesler, L., & Eilers, M. (2013). Small molecule inhibitors of aurora-A induce proteasomal degradation of N-Myc in childhood neuroblastoma. Cancer Cell, 24(1), 75
89. Available from https://doi.org/ 10.1016/j.ccr.2013.05.005. Brownlie, P., Ceska, T. A., Lamers, M., Romier, C., Stier, G., Teo, H., & Suck, D. (1997). The crystal structure of an intact human Max-DNA complex: New insights into mechanisms of transcriptional control. Structure, 5(4), 509
520. Available from https://doi.org/10.1016/ S0969-2126(97)00207-4. Casey, S. C., Baylot, V., & Felsher, D. W. (2018). The MYC oncogene is a global regulator of the immune response. Blood, 131(18), 2007
2015. Available from https://doi.org/10.1182/ blood-2017-11-742577. Castell, A., Yan, Q., Fawkner, K., Hydbring, P., Zhang, F., Verschut, V., Franco, M., Zakaria, S. M., Bazzar, W., Goodwin, J., Zinzalla, G., & Larsson, L. G. (2018). A selective high

212

Oncogenic Viruses Volume 2

affinity MYC-binding compound inhibits MYC:MAX interaction and MYC-dependent tumor cell proliferation. Scientific Reports, 8(1), 10064. Available from https://doi.org/ 10.1038/s41598-018-28107-4. Chang, T. C., Yu, D., Lee, Y. S., Wentzel, E. A., Arking, D. E., West, K. M., Dang, C. V., Thomas-Tikhonenko, A., & Mendell, J. T. (2008). Widespread microRNA repression by Myc contributes to tumorigenesis. Nature Genetics, 40(1), 43
50. Available from https:// doi.org/10.1038/ng.2007.30. Chaudhuri, R., Bhattacharya, S., Dash, J., & Bhattacharya, S. (2021). Recent update on targeting c-MYC G-quadruplexes by small molecules for anticancer therapeutics. Journal of Medicinal Chemistry, 64(1), 42
70. Available from https://doi.org/10.1021/acs. jmedchem.0c01145. Chen, H., Liu, H., & Qing, G. (2018). Targeting oncogenic Myc as a strategy for cancer treatment. Signal Transduction and Targeted Therapy, 3(1), 5. Available from https://doi.org/ 10.1038/s41392-018-0008-7. Chiariello, M., Marinissen, M. J., & Gutkind, J. S. (2001). Regulation of c-myc expression by PDGF through Rho GTPases. Nature Cell Biology, 3(6), 580
586. Available from https:// doi.org/10.1038/35078555. Conacci-Sorrell, M., McFerrin, L., & Eisenman, R. N. (2014). An overview of MYC and its interactome. Cold Spring Harbor Perspectives in Medicine, 4(1), a014357. Available from https://doi.org/10.1101/cshperspect.a014357. Dai, J., Carver, M., Hurley, L. H., & Yang, D. (2011). Solution structure of a 2:1 quindoline-cMYC G-quadruplex: Insights into G-quadruplex-interactive small molecule drug design. Journal of the American Chemical Society, 133(44), 17673
17680. Available from https:// doi.org/10.1021/ja205646q. Dang, C. V. (2012). MYC on the path to cancer. Cell, 149(1), 22
35. Available from https:// doi.org/10.1016/j.cell.2012.03.003. Diamond, J. R., Moreno, V., Lim, E. A., Cordoba, R., Cai, C., Ince, S. J., Lettieri, J. T., Merz, C., Valencia, R., & Boni, V. (2018). Phase I dose escalation study of the first-in-class selective PTEFb inhibitor BAY 1251152 in patients with advanced cancer: Novel target validation and early evidence of clinical activity. Journal of Clinical Oncology, 36(15_suppl), 2507. Available from https://doi.org/10.1200/jco.2018.36.15_suppl.2507. Dingley, A. J., Peterson, R. D., Grzesiek, S., & Feigon, J. (2005). Characterization of the cation and temperature dependence of DNA quadruplex hydrogen bond properties using highresolution NMR. Journal of the American Chemical Society, 127(41), 14466
14472. Available from https://doi.org/10.1021/ja0540369. Drygin, D., Siddiqui-Jain, A., O’Brien, S., Schwaebe, M., Lin, A., Bliesath, J., Ho, C. B., Proffitt, C., Trent, K., Whitten, J. P., Lim, J. K. C., Von Hoff, D., Anderes, K., & Rice, W. G. (2009). Anticancer activity of CX-3543: A direct inhibitor of rRNA biogenesis. Cancer Research, 69(19), 7653
7661. Available from https://doi.org/10.1158/0008-5472. CAN-09-1304. Duffy, M. J., O’Grady, S., Tang, M., & Crown, J. (2021). MYC as a target for cancer treatment. Cancer Treatment Reviews, 94, 102154. Available from https://doi.org/10.1016/j. ctrv.2021.102154. Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D. P., Thompson, D., Ballinger, D. G., Struewing, J. P., Morrison, J., Field, H., Luben, R., Wareham, N., Ahmed, S., Healey, C. S., Bowman, R., Meyer, K. B., Haiman, C. A., Kolonel, L. K., Henderson, B. E., Le Marchand, L., . . . Webb, P. (2007). Genome-wide association study identifies novel breast

MYC oncogenes as potential anticancer targets Chapter | 9

213

cancer susceptibility loci. Nature, 447(7148), 1087
1093. Available from https://doi.org/ 10.1038/nature05887. Fallah, Y., Brundage, J., Allegakoen, P., & Shajahan-Haq, A. N. (2017). MYC-driven pathways in breast cancer subtypes. Biomolecules, 7(3), 53. Available from https://doi.org/10.3390/ biom7030053. Fan, Y. H., Cheng, J., Vasudevan, S. A., Dou, J., Zhang, H., Patel, R. H., Ma, I. T., Rojas, Y., Zhao, Y., Yu, Y., Zhang, H., Shohet, J. M., Nuchtern, J. G., Kim, E. S., & Yang, J. (2013). USP7 inhibitor P22077 inhibits neuroblastoma growth via inducing p53-mediated apoptosis. Cell Death and Disease, 4(10), e867. Available from https://doi.org/10.1038/cddis.2013.400. Fernandez, P. C., Frank, S. R., Wang, L., Schroeder, M., Liu, S., Greene, J., Cocito, A., & Amati, B. (2003). Genomic targets of the human c-Myc protein. Genes and Development, 17(9), 1115
1129. Available from https://doi.org/10.1101/gad.1067003. Fern´andez-Miranda, G., & Me´ndez, R. (2012). The CPEB-family of proteins, translational control in senescence and cancer. Ageing Research Reviews, 11(4), 460
472. Available from https://doi.org/10.1016/j.arr.2012.03.004. Ferrao, P. T., Bukczynska, E. P., Johnstone, R. W., & McArthur, G. A. (2012). Efficacy of CHK inhibitors as single agents in MYC-driven lymphoma cells. Oncogene, 31(13). Available from https://doi.org/10.1038/onc.2011.358. Fisher, R. P. (2005). Secrets of a double agent: CDK7 in cell-cycle control and transcription. Journal of Cell Science, 118(22). Available from https://doi.org/10.1242/jcs.02718. Fletcher, S., & Prochownik, E. V. (2015). Small-molecule inhibitors of the Myc oncoprotein. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1849(5). Available from https://doi.org/10.1016/j.bbagrm.2014.03.005. Gabay, M., Li, Y., & Felsher, D. W. (2014). MYC activation is a hallmark of cancer initiation and maintenance. Cold Spring Harbor Perspectives in Medicine, 4(6). Available from https://doi.org/10.1101/cshperspect.a014241. Gaubatz, S., Meichle, A., & Eilers, M. (1994). An E-box element localized in the first intron mediates regulation of the prothymosin alpha gene by c-myc. Molecular and Cellular Biology, 14(6). Available from https://doi.org/10.1128/mcb.14.6.3853. Gavine, P. R., Neil, J. C., & Crouch, D. H. (1999). Protein stabilization: A common consequence of mutations in independently derived v-Myc alleles. Oncogene, 18(52). Available from https://doi.org/10.1038/sj.onc.1203102. Ghoussaini, M., Song, H., Koessler, T., Al Olama, A. A., Kote-Jarai, Z., Driver, K. E., Pooley, K. A., Ramus, S. J., Kjaer, S. K., Hogdall, E., DiCioccio, R. A., Whittemore, A. S., Gayther, S. A., Giles, G. G., Guy, M., Edwards, S. M., Morrison, J., Donovan, J. L., Hamdy, F. C., . . . Dunning, A. M. (2008). Multiple loci with different cancer specificities within the 8q24 gene desert. Journal of the National Cancer Institute, 100(13). Available from https://doi.org/10.1093/jnci/djn190. Goga, A., Yang, D., Tward, A. D., Morgan, D. O., & Bishop, J. M. (2007). Inhibition of CDK1 as a potential therapy for tumors over-expressing MYC. Nature Medicine, 13(7). Available from https://doi.org/10.1038/nm1606. Gowan, S. M., Harrison, J. R., Patterson, L., Valenti, M., Read, M. A., Neidle, S., & Kelland, L. R. (2002). A G-quadruplex-interactive potent small-molecule inhibitor of telomerase exhibiting in vitro and in vivo antitumor activity. Molecular Pharmacology, 61(5). Available from https://doi.org/10.1124/mol.61.5.1154. Grand, C. L., Han, H., M˜unoz, R. M., Weitman, S., Von Hoff, D. D., Hurley, L. H., & Bearss, D. J. (2002). The cationic porphyrin TMPyP4 down-regulates c-MYC and human telomerase

214

Oncogenic Viruses Volume 2

reverse transcriptase expression and inhibits tumor growth in vivo. Molecular Cancer Therapeutics, 1(8). Grandori, C., Cowley, S. M., James, L. P., & Eisenman, R. N. (2000). The Myc/Max/Mad network and the transcriptional control of cell behavior. Annual Review of Cell and Developmental Biology, 16. Available from https://doi.org/10.1146/annurev.cellbio.16.1.653. Grandori, C., Gomez-Roman, N., Felton-Edkins, Z. A., Ngouenet, C., Galloway, D. A., Eisenman, R. N., & White, R. J. (2005). c-Myc binds to human ribosomal DNA and stimulates transcription of rRNA genes by RNA polymerase I. Nature Cell Biology, 7(3). Available from https://doi.org/10.1038/ncb1224. Gregory, M. A., & Hann, S. R. (2000). c-Myc proteolysis by the ubiquitin-proteasome pathway: Stabilization of c-Myc in Burkitt’s lymphoma cells. Molecular and Cellular Biology, 20(7). Available from https://doi.org/10.1128/mcb.20.7.2423-2435.2000. Groisman, I., Ivshina, M., Marin, V., Kennedy, N. J., Davis, R. J., & Richter, J. D. (2006). Control of cellular senescence by CPEB. Genes and Development, 20(19). Available from https://doi.org/10.1101/gad.1438906. Gross, M. I., Demo, S. D., Dennison, J. B., Chen, L., Chernov-Rogan, T., Goyal, B., Janes, J. R., Laidig, G. J., Lewis, E. R., Li, J., MacKinnon, A. L., Parlati, F., Rodriguez, M. L. M., Shwonek, P. J., Sjogren, E. B., Stanton, T. F., Wang, T., Yang, J., Zhao, F., & Bennett, M. K. (2014). Antitumor activity of the glutaminase inhibitor CB-839 in triple-negative breast cancer. Mol Cancer Ther, 13(4), 890. Available from https://doi.org/10.1158/15357163.MCT-13-0870. Guo, J., Parise, R. A., Joseph, E., Egorin, M. J., Lazo, J. S., Prochownik, E. V., & Eiseman, J. L. (2009). Efficacy, pharmacokinetics, tisssue distribution, and metabolism of the Myc-Max disruptor, 10058-F4 [Z,E]-5-[4-ethylbenzylidine]-2-thioxothiazolidin-4- one, in mice. Cancer Chemotherapy and Pharmacology, 63(4). Available from https://doi.org/10.1007/ s00280-008-0774-y. Guo, Z., Wang, Y., Zhao, Y., Jin, Y., An, L., Xu, H., Liu, Z., Chen, X., Zhou, H., Wang, H., & Zhang, W. (2019). A functional 5’-UTR polymorphism of MYC contributes to nasopharyngeal carcinoma susceptibility and chemoradiotherapy induced toxicities. Journal of Cancer, 10(1). Available from https://doi.org/10.7150/jca.28534. Habchi, J., Tompa, P., Longhi, S., & Uversky, V. N. (2014). Introducing protein intrinsic disorder. Chemical Reviews, 114(13). Available from https://doi.org/10.1021/cr400514h. Hatton, K. S., Mahon, K., Chin, L., Chiu, F. C., Lee, H. W., Peng, D., Morgenbesser, S. D., Horner, J., & DePinho, R. A. (1996). Expression and activity of L-Myc in normal mouse development. Molecular and Cellular Biology, 16(4). Available from https://doi.org/ 10.1128/mcb.16.4.1794. Huang, M. J., Cheng, Y. C., Liu, C. R., Lin, S., & Liu, H. E. (2006). A small-molecule c-Myc inhibitor, 10058-F4, induces cell-cycle arrest, apoptosis, and myeloid differentiation of human acute myeloid leukemia. Experimental Hematology, 34(11). Available from https:// doi.org/10.1016/j.exphem.2006.06.019. Huang, W. L., Abudureheman, T., Xia, J., Chu, L., Zhou, H., Zheng, W. W., Zhou, N., Shi, R. Y., Li, M. H., Zhu, J. M., Qing, K., Ji, C., Liang, K. W., Guo, S., Yin, G., & Duan, C. W. (2021). CDK9 inhibitor induces the apoptosis of B-cell acute lymphocytic leukemia by inhibiting c-Myc-mediated glycolytic metabolism. Frontiers in Cell and Developmental Biology, 9. Available from https://doi.org/10.3389/fcell.2021.641271. Huppert, J. L., & Balasubramanian, S. (2005). Prevalence of quadruplexes in the human genome. Nucleic Acids Research, 33(9). Available from https://doi.org/10.1093/nar/gki609.

MYC oncogenes as potential anticancer targets Chapter | 9

215

Hurlin, P. J. (2013). Control of vertebrate development by MYC. Cold Spring Harbor Perspectives in Medicine, 3(9). Available from https://doi.org/10.1101/cshperspect.a014332. Ivanov, X. (1964). Experimental investigations into avian leucoses. V. Transmission, haematology and morphology of avian myelocytomatosis. Bull. Inst. Pathol. Comp. Anim. Acad. Bulg. Sci, 10, 5
38. Kalkat, M., De Melo, J., Hickman, K. A., Lourenco, C., Redel, C., Resetca, D., Tamachi, A., Tu, W. B., & Penn, L. Z. (2017). MYC deregulation in primary human cancers. Genes, 8. Available from https://doi.org/10.3390/genes8060151. Kang, J., Sergio, C. M., Sutherland, R. L., & Musgrove, E. A. (2014). Targeting cyclindependent kinase 1 (CDK1) but not CDK4/6 or CDK2 is selectively lethal to MYCdependent human breast cancer cells. BMC Cancer, 14(1). Available from https://doi.org/ 10.1186/1471-2407-14-32. Kiessling, A., Sperl, B., Hollis, A., Eick, D., & Berg, T. (2006). Selective inhibition of c-Myc/ Max dimerization and DNA binding by small molecules. Chemistry and Biology, 13(7). Available from https://doi.org/10.1016/j.chembiol.2006.05.011. Kim, M. Y., Gleason-Guzman, M., Izbicka, E., Nishioka, D., & Hurley, L. H. (2003). The different biological effects of Telomestatin and TMPyP4 can be attributed to their selectivity for interaction with intramolecular or intermolecular G-quadruplex structures. Cancer Research, 63(12). Kumar, D., Sharma, N., & Giri, R. (2017). Therapeutic interventions of cancers using intrinsically disordered proteins as drug targets: C-myc as model system. Cancer Informatics, 16. Available from https://doi.org/10.1177/1176935117699408. Kuzyk, A., & Mai, S. (2014). c-MYC-induced genomic instability. Cold Spring Harbor Perspectives in Medicine, 4(4). Available from https://doi.org/10.1101/cshperspect.a014373. Kwiatkowski, N., Zhang, T., Rahl, P. B., Abraham, B. J., Reddy, J., Ficarro, S. B., Dastur, A., Amzallag, A., Ramaswamy, S., Tesar, B., Jenkins, C. E., Hannett, N. M., McMillin, D., Sanda, T., Sim, T., Kim, N. D., Look, T., Mitsiades, C. S., Weng, A. P., . . . Gray, N. S. (2014). Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature, 511(7511). Available from https://doi.org/10.1038/nature13393. Lancho, O., & Herranz, D. (2018). The MYC Enhancer-ome: Long-range transcriptional regulation of MYC in cancer. Trends in Cancer, 4(12). Available from https://doi.org/10.1016/j. trecan.2018.10.003. Lee, C. M., & Reddy, E. P. (1999). The v-myc oncogene. Oncogene, 18(19), 2997
3003. Available from https://doi.org/10.1038/sj.onc.1202786. Li, L. H., Nerlov, C., Prendergast, G., MacGregor, D., & Ziff, E. B. (1994). C-Myc represses transcription in vivo by a novel mechanism dependent on the initiator element and Myc box II. EMBO Journal, 13(17). Available from https://doi.org/10.1002/j.1460-2075.1994. tb06724.x. Li, Z., Van Calcar, S., Qu, C., Cavenee, W. K., Zhang, M. Q., & Ren, B. (2003). A global transcriptional regulatory role for c-Myc in Burkitt’s lymphoma cells. Proceedings of the National Academy of Sciences of the United States of America, 100(14). Available from https://doi.org/10.1073/pnas.1332764100. Lin, C. P., Liu, J. D., Chow, J. M., Liu, C. R., & Eugene Liu, H. (2007). Small-molecule c-Myc inhibitor, 10058-F4, inhibits proliferation, downregulates human telomerase reverse transcriptase and enhances chemosensitivity in human hepatocellular carcinoma cells. AntiCancer Drugs, 18(2). Available from https://doi.org/10.1097/CAD.0b013e3280109424. Lu, X., Vogt, P. K., Boger, D. L., & Lunec, J. (2008). Disruption of the MYC transcriptional function by a small-molecule antagonist of MYC/MAX dimerization. Oncology Reports, 19 (3). Available from https://doi.org/10.3892/or.19.3.825.

216

Oncogenic Viruses Volume 2

Malynn, B. A., De Alboran, I. M., O’Hagan, R. C., Bronson, R., Davidson, L., DePinho, R. A., & Alt, F. W. (2000). N-myc can functionally replace c-myc in murine development, cellular growth, and differentiation. Genes and Development, 14(11). Available from https://doi.org/ 10.1101/gad.14.11.1390. Mertz, J. A., Conery, A. R., Bryant, B. M., Sandy, P., Balasubramanian, S., Mele, D. A., Bergeron, L., & Sims, R. J. (2011). Targeting MYC dependence in cancer by inhibiting BET bromodomains. Proceedings of the National Academy of Sciences of the United States of America, 108(40). Available from https://doi.org/10.1073/pnas.1108190108. Meyer, N., & Penn, L. Z. (2008). Reflecting on 25 years with MYC. Nature Reviews Cancer, 8 (12). Available from https://doi.org/10.1038/nrc2231. Mitchell, K. O., Ricci, M. S., Miyashita, T., Dicker, D. T., Jin, Z., Reed, J. C., & El-Deiry, W. S. (2000). Bax is a transcriptional target and mediator of c-Myc-induced apoptosis. Cancer Research, 60(22). Mitrasinovic, P. M. (2020). Prediction of hit-to-lead ligand molecule interaction with GQuadruplex DNA from c-Myc oncogene promoter region. Acta Chimica Slovenica, 67(2). Available from https://doi.org/10.17344/ACSI.2019.5105. Mladenov, Z. (1974). Comparative Pathology of Avian Leukoses. Publishing House of the Bulgarian academy of Sciences. Mu¨ller, J., & Eilers, M. (2008). Ubiquitination of Myc: proteasomal degradation and beyond. Ernst Schering Foundation Symposium Proceedings, 1. Available from https://doi.org/ 10.1007/2789_2008_103. Murga, M., Campaner, S., Lopez-Contreras, A. J., Toledo, L. I., Soria, R., Montan˜a, M. F., D’Artista, L., Schleker, T., Guerra, C., Garcia, E., Barbacid, M., Hidalgo, M., Amati, B., & Fernandez-Capetillo, O. (2011). Exploiting oncogene-induced replicative stress for the selective killing of Myc-driven tumors. Nature Structural and Molecular Biology, 18(12). Available from https://doi.org/10.1038/nsmb.2189. Nair, S. K., & Burley, S. K. (2003). X-ray structures of Myc-Max and Mad-Max recognizing DNA: Molecular bases of regulation by proto-oncogenic transcription factors. Cell, 112(2). Available from https://doi.org/10.1016/S0092-8674(02)01284-9. Nesbit, C. E., Tersak, J. M., & Prochownik, E. V. (1999). MYC oncogenes and human neoplastic disease. Oncogene, 18(19). Available from https://doi.org/10.1038/sj.onc.1202746. O’Donnell, K. A., Wentzel, E. A., Zeller, K. I., Dang, C. V., & Mendell, J. T. (2005). c-Mycregulated microRNAs modulate E2F1 expression. Nature, 435(7043). Available from https:// doi.org/10.1038/nature03677. Ogami, K., Hosoda, N., Funakoshi, Y., & Hoshino, S. (2014). Antiproliferative protein Tob directly regulates c-myc proto-oncogene expression through cytoplasmic polyadenylation element-binding protein CPEB. Oncogene, 33(1). Available from https://doi.org/10.1038/ onc.2012.548. Otto, T., Horn, S., Brockmann, M., Eilers, U., Schu¨ttrumpf, L., Popov, N., Kenney, A. M., Schulte, J. H., Beijersbergen, R., Christiansen, H., Berwanger, B., & Eilers, M. (2009). Stabilization of N-Myc Is a critical function of aurora A in human neuroblastoma. Cancer Cell, 15(1). Available from https://doi.org/10.1016/j.ccr.2008.12.005. Palomero, T., Wei, K. L., Odom, D. T., Sulis, M. L., Real, P. J., Margolin, A., Barnes, K. C., O’Neil, J., Neuberg, D., Weng, A. P., Aster, J. C., Sigaux, F., Soulier, J., Look, A. T., Young, R. A., Califano, A., & Ferrando, A. A. (2006). NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proceedings of the National Academy of Sciences of the United States of America, 103(48). Available from https://doi.org/10.1073/pnas.0606108103.

MYC oncogenes as potential anticancer targets Chapter | 9

217

Payne, L. N. (1992). Biology of avian retroviruses. The Retroviridae. Available from https://doi. org/10.1007/978-1-4615-3372-6_6. Payne, L. N., & Nair, V. (2012). The long view: 40 years of avian leukosis research. Avian Pathology, 41(1). Available from https://doi.org/10.1080/03079457.2011.646237. Peng, J., Marshall, N. F., & Price, D. H. (1998). Identification of a cyclin subunit required for the function of Drosophila P-TEFb. Journal of Biological Chemistry, 273(22). Available from https://doi.org/10.1074/jbc.273.22.13855. Ponzielli, R., Katz, S., Barsyte-Lovejoy, D., & Penn, L. Z. (2005). Cancer therapeutics: Targeting the dark side of Myc. European Journal of Cancer, 41(16). Available from https://doi.org/10.1016/j.ejca.2005.08.017. Puissant, A., Frumm, S. M., Alexe, G., Bassil, C. F., Qi, J., Chanthery, Y. H., Nekritz, E. A., Zeid, R., Gustafson, W. C., Greninger, P., Garnett, M. J., Mcdermott, U., Benes, C. H., Kung, A. L., Weiss, W. A., Bradner, J. E., & Stegmaier, K. (2013). Targeting MYCN in neuroblastoma by BET bromodomain inhibition. Cancer Discovery, 3(3). Available from https://doi.org/10.1158/2159-8290.CD-12-0418. Rangan, A., Fedoroff, O. Y., & Hurley, L. H. (2001). Induction of duplex to G-quadruplex transition in the c-myc promoter region by a small molecule. Journal of Biological Chemistry, 276(7). Available from https://doi.org/10.1074/jbc.M005962200. Rodriguez, R., Mu¨ller, S., Yeoman, J. A., Trentesaux, C., Riou, J. F., & Balasubramanian, S. (2008). A novel small molecule that alters shelterin integrity and triggers a DNA-damage response at telomeres. Journal of the American Chemical Society, 130(47). Available from https://doi.org/10.1021/ja805615w. Sammak, S., Hamdani, N., Gorrec, F., Allen, M. D., Freund, S. M. V., Bycroft, M., & Zinzalla, G. (2019). Crystal structures and nuclear magnetic resonance studies of the Apo form of the c-MYC:MAX bHLHZip complex reveal a helical basic region in the absence of DNA. Biochemistry, 58(29). Available from https://doi.org/10.1021/acs.biochem.9b00296. Sears, R., Ohtani, K., & Nevins, J. R. (1997). Identification of positively and negatively acting elements regulating expression of the E2F2 gene in response to cell growth signals. Molecular and Cellular Biology, 17(9). Available from https://doi.org/10.1128/ mcb.17.9.5227. Sears, R., Nuckolls, F., Haura, E., Taya, Y., Tamai, K., & Nevins, J. R. (2000). Multiple Rasdependent phosphorylation pathways regulate Myc protein stability. Genes and Development, 14(19). Available from https://doi.org/10.1101/gad.836800. Shiekhattar, R., Mermelstein, F., Fisher, R. P., Drapkin, R., Brian, D., Wessling, H. C., Morgan, D. O., & Reinberg, D. (1995). Cdk-activating kinase complex is a component of human transcription factor TFIIH. Nature, 374(6519). Available from https://doi.org/10.1038/ 374283a0. Shim, H., Dolde, C., Lewis, B. C., Wu, C. S., Dang, G., Jungmann, R. A., Dalla-Favera, R., & Dang, C. V. (1997). c-Myc transactivation of LDH-A: Implications for tumor metabolism and growth. Proceedings of the National Academy of Sciences of the United States of America, 94(13). Available from https://doi.org/10.1073/pnas.94.13.6658. Siddiqui-Jain, A., Grand, C. L., Bearss, D. J., & Hurley, L. H. (2002). Direct evidence for a Gquadruplex in a promoter region and its targeting with a small molecule to repress c-MYC transcription. Proceedings of the National Academy of Sciences of the United States of America, 99(18). Available from https://doi.org/10.1073/pnas.182256799. Simonsson, T., Pecinka, P., & Kubista, M. (1998). DNA tetraplex formation in the control region of c-myc. Nucleic Acids Research, 26(5). Available from https://doi.org/10.1093/nar/ 26.5.1167.

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Srutova, K., Curik, N., Burda, P., Savvulidi, F., Silvestri, G., Trotta, R., Klamova, H., Pecherkova, P., Sovova, Z., Koblihova, J., Stopka, T., Perrotti, D., & Polakova, K. M. (2018). BCR-ABL1 mediated miR-150 downregulation through MYC contributed to myeloid differentiation block and drug resistance in chronic myeloid leukemia. Haematologica, 103(12). Available from https://doi.org/10.3324/haematol.2018.193086. Stellas, D., Szabolcs, M., Koul, S., Li, Z., Polyzos, A., Anagnostopoulos, C., Cournia, Z., Tamvakopoulos, C., Klinakis, A., & Efstratiadis, A. (2014). Therapeutic effects of an antiMyc drug on mouse pancreatic cancer. Journal of the National Cancer Institute, 106(12). Available from https://doi.org/10.1093/jnci/dju320. Sun, D., Thompson, B., Cathers, B. E., Salazar, M., Kerwin, S. M., Trent, J. O., Jenkins, T. C., Neidle, S., & Hurley, L. H. (1997). Inhibition of human telomerase by a G-QuadruplexInteractive compound. Journal of Medicinal Chemistry, 40((14). Available from https://doi. org/10.1021/jm970199z. Suna, X. X., Chena, Y., Sua, Y., Wanga, X., Chauhana, K. M., Lianga, J., Daniela, C. J., Searsa, R. C., & Daia, M. S. (2018). SUMO protease SENP1 deSUMOylates and stabilizes c-Myc. Proceedings of the National Academy of Sciences of the United States of America, 115(43). Available from https://doi.org/10.1073/pnas.1802932115. Thng, D. K. H., Toh, T. B., & Chow, E. K. H. (2021). Capitalizing on synthetic lethality of MYC to treat cancer in the digital age. Trends in Pharmacological Sciences, 42((3). Available from https://doi.org/10.1016/j.tips.2020.11.014. Tsuneoka, M., Koda, Y., Soejima, M., Teye, K., & Kimura, H. (2002). A novel Myc target gene, mina53, that is involved in cell proliferation. Journal of Biological Chemistry, 277(38). Available from https://doi.org/10.1074/jbc.M204458200. Tu, W. B., Helander, S., Pilsta˚l, R., Hickman, K. A., Lourenco, C., Jurisica, I., Raught, B., Wallner, B., Sunnerhagen, M., & Penn, L. Z. (2015). Myc and its interactors take shape. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1849((5). Available from https://doi.org/10.1016/j.bbagrm.2014.06.002. Vera de Jonge, A., Mutis, T., Roemer, M. G. M., Scheijen, B., & Chamuleau, M. E. D. (2020). Impact of myc on anti-tumor immune responses in aggressive b cell non-hodgkin lymphomas: Consequences for cancer immunotherapy. Cancers, 12((10). Available from https://doi. org/10.3390/cancers12103052. Vervoorts, J., Lu¨scher-Firzlaff, J., & Lu¨scher, B. (2006). The ins and outs of MYC regulation by posttranslational mechanisms. Journal of Biological Chemistry, 281((46). Available from https://doi.org/10.1074/jbc.R600017200. Watson, D. K., Reddy, E. P., Duesberg, P. H., & Papas, T. S. (1983). Nucleotide sequence analysis of the chicken c-myc gene reveals homologous and unique coding regions by comparison with the transforming gene of avian myelocytomatosis virus MC29, Δgag-myc. Proceedings of the National Academy of Sciences of the United States of America, 80(8 I). Available from https://doi.org/10.1073/pnas.80.8.2146. Weinstein, I. B. (2002). Cancer: Addiction to oncogenes - The Achilles heal of cancer. Science, 297((5578). Available from https://doi.org/10.1126/science.1073096. Weiss, R. (1982). Experimental biology and assay of retroviruses. Molecular biology of tumor viruses. RNA tumor viruses (2nd ed., pp. 209
260). Cold Spring Harbor Laboratory. Wheelhouse, R. T., Sun, D., Han, H., Han, F. X., & Hurley, L. H. (1998). Cationic porphyrins as telomerase inhibitors: The interaction of tetra- (N-methyl-4-pyridyl)porphine with quadruplex DNA [1]. Journal of the American Chemical Society, 120(13). Available from https:// doi.org/10.1021/ja973792e.

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Whitfield, J. R., Beaulieu, M. E., & Soucek, L. (2017). Strategies to inhibit Myc and their clinical applicability. Frontiers in Cell and Developmental Biology, 5. Available from https:// doi.org/10.3389/fcell.2017.00010. Xiang, Y., Stine, Z. E., Xia, J., Lu, Y., O’Connor, R. S., Altman, B. J., Hsieh, A. L., Gouw, A. M., Thomas, A. G., Gao, P., Sun, L., Song, L., Yan, B., Slusher, B. S., Zhuo, J., Ooi, L. L., Lee, C. G. L., Mancuso, A., McCallion, A. S., . . . Dang, C. V. (2015). Targeted inhibition of tumor-specific glutaminase diminishes cell-autonomous tumorigenesis. The Journal of Clinical Investigation, 125(6), 2293
2306. Available from https://doi.org/10.1172/ JCI75836. Xiao, D., Yue, M., Su, H., Ren, P., Jiang, J., Li, F., Hu, Y., Du, H., Liu, H., & Qing, G. (2016). Polo-like Kinase-1 regulates Myc stabilization and activates a feedforward circuit promoting tumor cell survival. Molecular Cell, 64(3). Available from https://doi.org/10.1016/j. molcel.2016.09.016. Yap, J. L., Wang, H., Hu, A., Chauhan, J., Jung, K. Y., Gharavi, R. B., Prochownik, E. V., & Fletcher, S. (2013). Pharmacophore identification of c-Myc inhibitor 10074-G5. Bioorganic and Medicinal Chemistry Letters, 23(1). Available from https://doi.org/10.1016/j. bmcl.2012.10.013. Yoshida, G. J. (2018). Emerging roles of Myc in stem cell biology and novel tumor therapies. Journal of Experimental and Clinical Cancer Research, 37(1). Available from https://doi. org/10.1186/s13046-018-0835-y. Yuneva, M., Zamboni, N., Oefner, P., Sachidanandam, R., & Lazebnik, Y. (2007). Deficiency in glutamine but not glucose induces MYC-dependent apoptosis in human cells. Journal of Cell Biology, 178(1). Available from https://doi.org/10.1083/jcb.200703099. Zuber, J., Shi, J., Wang, E., Rappaport, A. R., Herrmann, H., Sison, E. A., Magoon, D., Qi, J., Blatt, K., Wunderlich, M., Taylor, M. J., Johns, C., Chicas, A., Mulloy, J. C., Kogan, S. C., Brown, P., Valent, P., Bradner, J. E., Lowe, S. W., & Vakoc, C. R. (2011). RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature, 478(7370). Available from https://doi.org/10.1038/nature10334.

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Chapter 10

Current status of viral biomarkers for oncogenic viruses Kazim Yalcin Arga1,2 and Medi Kori1 1

Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey, Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey 2

10.1 Introduction In 1911, Peyton Rous introduced the idea that viruses might play a role in the etiology of malignancies, based on his observations about Rous sarcoma virus (RSV) (Rous, 1911). The reporting of RSV pointed to the idea that some viruses could cause malignancies, and the presence of oncogenes should be further investigated. In the 1930s, two tumor viruses were identified in mammals, suggesting that viruses might also be involved in human cancers. In light of this information, the first human tumor viruses were discovered in the 1960s and 1970s (White et al., 2014). Today, according to the International Agency for Research on Cancer (IARC), six oncogenic viruses are classified as Group 1 human carcinogens, meaning that there is sufficient evidence for these viruses to cause carcinogenicity in humans. Group 1 carcinogens include Epstein
Barr virus (EBV), hepatitis B virus (HBV), hepatitis C virus (HCV), human T-cell lymphotropic virus-1 (HTLV-1), human herpesvirus-8 (HHV-8), also known as Kaposi’s sarcoma herpesvirus (KSHV), and highly oncogenic human papillomaviruses (HPVs) (IARC, 2012) (Table 10.1). Although the different types of viruses cause different types of cancer, they share some common features, such as the following: (1) oncogenic viral infection is essential for the development of cancer but is not sufficient on its own; (2) viral cancers occur in persistent infections and emerge years to decades after acute infection; (3) the immune system can play a harmful or protective role, for example, immunosuppression increases in several viral cancers (Mesri et al., 2014).

Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00009-1 Copyright © 2023 Elsevier Inc. All rights reserved.

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TABLE 10.1 Human oncogenic viruses. Oncogenic virus

Discovery

Type

Oncogenic proteins

Infection prevalence

Associated cancer(s)

Cancer causing infection prevalence

EBV

1964

DNA (linear doublestranded)

EBNA-1 and LMP-1

Positive in approximately 90% of the world population

Gastric cancer, nasopharyngeal cancer, Burkitt’s lymphoma, and Hodgkin’s lymphoma

Causes 1.5% of all cancers in the world

HBV

1965

DNA (enveloped, partially doublestranded, circular)

HBx

Approximately 3.4% individuals worldwide have chronic HBV infection

Hepatocellular carcinoma

Induces 80% of the total virusassociated hepatocellular cancer cases

HCV

1989

RNA (enveloped, positive-sense single-stranded)

core and NS3

Approximately 0.6% individuals worldwide have chronic HCV infection

Hepatocellular carcinoma

Induces up to 20% of the total virusassociated hepatocellular cancer cases

HTLV-1

1979

RNA (enveloped, single-stranded)

tax and HBz

Approximately 0.13% people in the world have infection

Adult T-cell leukemia/ lymphoma (ATLL)

About 2%
5% infected individuals develop ATLL

HHV-8

1994

DNA (enveloped, doublestranded)

LANA

90% of the adult population in subSaharan Africa have infection

Kaposi’s sarcoma

Kaposi’s sarcoma incidence in infected individuals is 0.001

HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, and -59

1977

DNA (nonenveloped, circular, doublestranded)

E6 and E7

Positive in approximately 12% of the world population

Cervical, anal, vaginal, penile, oropharyngeal, and vulvar cancers

About 5% of all cancers in the world

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It has been estimated that 13% of all human malignancies worldwide are due to infections (de Martel et al., 2020). Although just half a century has passed since the first discovery of oncoviruses, there is still a considerable gap to be filled to reduce much of the global human cancer burden. For example, today, specific treatment strategies for most virus-related cancers are inadequate. Therefore developments in detecting and treating these cancers caused by oncogenic viruses will transform and advance life science research and the life science industry. Biomarkers are the indicators of pharmacological response to normal biological processes, pathological processes, or therapeutic processes that can be objectively measured and tested. Biomarkers enable early detection of malignancy and guide the choice of targeted therapy based on the specific molecular features of cancer. According to their recognized applications, they are divided into subtypes such as diagnostic, prognostic, or predictive biomarkers (Califf, 2018). In this chapter, we aim to provide a global perspective on the biomarkers of the above-mentioned oncogenic viruses by clarifying the current state of knowledge regarding biomarkers that promote the search for detecting diseases and therapeutic strategies.

10.2 Epstein-Barr virus EBV is the first virus associated with human cancers (1964) and the first human virus to be sequenced (Baer et al., 1984). EBV infection is the most common infection in humans, positive in approximately 90% of the world’s population (Fugl & Andersen, 2019). EBV belongs to the gammaherpesvirus group and has a linear double-stranded DNA genome (Sugimoto et al., 2019). The EBV life cycle can be divided into two phases: (1) lytic phase (EBV replication) and (2) latent phase (EBV blocks most of its proteincoding genes). In the latent phase, the EBV genome encodes five nuclear proteins (the EBNAs), two latent membrane proteins (LMPs), two untranslated RNAs (EBERs), and nontranscribed BART. EBNA-1 and LMP-1 are the prominent oncoproteins that induce EBV-related cancer development. EBNA-1 is crucial for the amplification of the EBV genome, and it minimizes the activation of tumor suppressor p53 and cell death. LMP-1 is one of the LMPs that play an important role in EBV persistence and latency. It induces telomerase activity and activates important signaling pathways such as the JNK, NF-κB, and p38 pathways. In addition, the LMP-1 oncogene modulates migration, differentiation, and tumorigenesis processes (Kang et al., 2016).

10.2.1 Epstein-Barr virus-associated cancers EBV is responsible for developing approximately 1.5% of all cancers in the world (Farrell, 2019). EBV can infect B cells and epithelial cells and affect

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both B cells and malignancies of epithelial origin. It induces gastric cancer, nasopharyngeal cancer, Burkitt’s lymphoma, and Hodgkin’s lymphoma (Chesnokova & Hutt-Fletcher, 2014; Young et al., 2016). Gastric cancer is the third leading cause of cancer-related deaths, and 10% of gastric cancers have been associated with EBV infection (Ayee et al., 2020). One of the head and neck cancers, nasopharyngeal carcinoma, is an epithelial carcinoma and accounts for 7% of all cancers diagnosed worldwide in 2018 (Chen et al., 2019). LMP-1 was found in 65% of nasopharyngeal carcinomas, indicating an association between EBV infection and nasopharyngeal carcinoma (Ayee et al., 2020). The most common pediatric cancer worldwide, Burkitt’s lymphoma, is an aggressive B-cell lymphoma linked to EBV. Especially in the high-risk regions, such as Africa, more than 80% of Burkitt’s lymphoma cases have been linked to EBV (Ha¨mmerl et al., 2019). Hodgkin’s lymphoma is a B-cell lymphoma, and in 2020, a total of 8480 individuals were diagnosed with Hodgkin’s lymphoma in the United States (Siegel et al., 2020). The prevalence of EBV association with Hodgkin lymphoma varies by region. Around 30%
40% of Hodgkin’s lymphoma cases in North America and Europe were reported to be EBV positive, while in other parts of the world such as Latin America, Asia, and Africa, EBV was found in almost 100% (Ayee et al., 2020).

10.2.2 Epstein Bar virus-associated cancer biomarkers The LMP-1 oncogene is active in almost all EBV-associated cancers and modulates migration, differentiation, and tumorigenesis processes (Kang et al., 2016). A study based on a literature-based meta-analysis concluded that LMP1 expression could be used as a prognostic biomarker in all EBVassociated cancers (Chen, Zhang et al., 2015). As part of The Cancer Genome Atlas (TCGA) project, researchers comprehensively analyzed the molecular and clinical characteristics of 295 primary gastric cancers and investigated EBV-associated gastric cancers (EBVaGCs) in this study. Accordingly, they found PIK3CA mutations in 80% of EBVaGC cases, ARID1A mutations in 55%, BCOR mutations in 23%, and overexpression of PD-L1/PD-L2, CDKN2A promoter hypermethylation in 100% (2014). Similarly, another group of researchers evaluated EBVaGC cases as a part of the TCGA project. In contrast to the previous work, they concluded that the expressions of JAK2, ERBB2, and CD44 were amplified, while WWOX, IMMP2L, PTPRD, FAM190A, PTEN, and MACROD2 were deleted in EBVaGCs (Gulley, 2015). These aberrations and features can be presented as markers for EBVaGC and should be further evaluated by clinical studies. For example, the expression of PD-L1 was investigated in gastric cancer patients, and in accordance with the above TCGA study, PD-L1 was found to be overexpressed in EBVaGCs. Therefore the study proposed PD-L1 as a prognostic biomarker for EBVaGC cases (Bo¨ger et al., 2016).

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Among the above aberrations, Kang et al. (2016) also assessed the major molecular alterations in 234 EBVaGC patients at the protein level and suggested that PTEN may serve as a prognostic biomarker. The researchers reported that patients with PTEN loss had a higher risk of lymph node metastasis and a worse 5-year survival rate than cases without PTEN inactivation (Kang et al., 2016). In another study investigating the clinical relevance and prognostic role of PIK3CA mutations in EBVaGC patients, Seo et al. (2019) conducted a study and found that exon 9 mutation of PIK3CA is a prognostic biomarker to estimate the survival of patients with EBVaGCs (Seo et al., 2019). Researchers investigated the potential role of p16 as part of EBVassociated nasopharyngeal carcinoma (EBVaNC) biomarkers. They found that upregulation of p16 was associated with improved progression-free survival. As a result of the study, the researchers identified p16 as a potential complementary marker for the prognosis of EBVaNCs (Jiang et al., 2016). In another study by Zuo et al. (2017), activation of CDH6 and RUNX2 were found to be novel mesenchymal markers in EBVaNC (Zuo et al., 2017). The long noncoding RNAs MALAT1, AFAP1-AS1, and AL359062 were found to have higher expression in EBVaNC than in EBV-negative cancers. Moreover, the expression of these long noncoding RNAs decreased after the individuals received therapy. Therefore this study suggested that MALAT1, AFAP1-AS1, and AL359062 serve as diagnostic and prognostic biomarkers for nasopharyngeal carcinomas (He et al., 2017). In EBV-associated Hodgkin’s lymphoma (EBVaHL), FoxO3a expression was generally downregulated, while ID1 was upregulated. As a result, researchers introduced FoxO3a and ID1 as potential cancer-initiating cell markers for EBVaHL (Jun-Ichiro et al., 2016). In another study, the viral genome of EBV was found to be present in the plasma of EBVaHL individuals, while its DNA was undetectable in patients receiving therapy. From this point of view, the researchers suggested that free plasma EBV DNA can be used as a noninvasive biomarker for EBVaHL patients. They also suggested that serial monitoring of free plasma EBV DNA can estimate the patient’s response to therapy (Gandhi et al., 2006). Moreover, the increased miR-127 expression is a key event in developing EBV-positive Burkitt’s lymphoma (Leucci et al., 2010), which may serve as a marker (Table 10.2).

10.3 Hepatitis B virus and hepatitis C virus In 1965, Baruch Blumberg discovered HBV using the serological screening method, and he was awarded the Nobel Prize in Medicine in 1976 based on his discovery (Chang et al., 2017). HBV belongs to the Hepadnaviridae family and consists of enveloped, partially double-stranded circular DNA. The HBV genome encodes seven proteins, and one of the encoding proteins, HBx, plays an important role in developing HBV-induced cancer. The HBx

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TABLE 10.2 Epstein
Barr (EBV)-associated cancer biomarkers. Cancer

Biomarker

Clinical application

References

All cancers

LMP1 expression activation

Prognostic biomarker

Chen, Zhang et al. (2015), Chen, Stephen et al. (2015)

Gastric cancer

PIK3CA, ARID1A, and BCOR mutations, overexpression of PD-L1/ PD-L2, CDKN2A promoter hypermethylation

Biomarker

The Cancer Genome Atlas Research Network (2014)

JAK2, ERBB2, and CD44 were amplified, and WWOX, IMMP2L, PTPRD, FAM190A, PTEN, and MACROD2 were deleted

Biomarker

Gulley (2015)

PD-L1 overexpressed

Prognostic biomarker

Bo¨ger et al. (2016)

PTEN deletion

Prognostic biomarker

Kang et al. (2016)

Exon 9 mutation of PIK3CA

Prognostic biomarker

Seo et al. (2019)

p16 upregulation

Prognostic biomarker

Jiang et al. (2016)

CDH6 and RUNX2 activation

Mesenchymal biomarkers

Zuo et al. (2017)

MALAT1, AFAP1-AS1, and AL359062 upregulation

Diagnostic and Prognostic biomarkers

He et al. (2017)

FoxO3a downregulation, and ID1 upregulation

Cell biomarker

Ikeda et al. (2016)

Presented free plasma EBV DNA

Noninvasive biomarker

Gandhi et al. (2006)

Increased miR-127 expression

Biomarker

Leucci et al. (2010)

Nasopharyngeal cancer

Hodgkin’s lymphoma

Burkitt’s lymphoma

protein supports cell cycle progression, deactivates negative growth regulators, and binds and inhibits p53 expression (Kew, 2011). It has been estimated that 55% of cases with HBx protein expression develop HBV-induced

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cancer (Madihi et al., 2020). One of the important roles of HBx is mediating the replication of HBV (Ringehan et al., 2017). Infection with HBV can lead to acute or chronic infections. HBV-induced cancer development has been quite common in chronically infected individuals. It has been estimated that 5%
10% of EBV infections lead to chronic infection, and approximately 257 million people worldwide have chronic HBV infection. Diseases caused by chronic HBV infection could cause 20 million deaths worldwide between 2015 and 2030 (Yang et al., 2019). The discovery of HCV was based on cDNA library screening in 1989 (Chang et al., 2017), and in 2020, Charles Rice, Harvey Alter, and Michael Houghton were awarded the Nobel Prize in Physiology or Medicine for their discovery of the virus (Nobel Prize, 2020). HCV is a blood-borne and enveloped virus that belongs to the Flaviviridae family. HCV has a positive-sense single-stranded RNA genome. Therefore HCV cannot integrate directly into the host genome and its genome functions as a direct mRNA (Bukh, 2016). The genomic RNA of HCV contains three structural and seven nonstructural proteins. One structural protein, core, is essential for the formation of the viral capsid to protect the genomic RNA. It interacts with numerous transcription factors and regulates signaling pathways that play a role in cell proliferation and apoptosis, including TGF-β, VEGF, WNT, COX-2, and PPARα signaling pathways. Accordingly, core protein is a major oncoprotein that induces HCV-associated cancer development (Mahmoudvand et al., 2019). Another HCV oncoprotein involved in HCV-associated cancer development is a nonstructural protein called NS3. It has been reported that NS3 can bind p53 (Ishido & Hotta, 1998). Like HBV infection, HCV infection also leads to acute or chronic infection. Globally, an estimated 51 million people have chronic HCV infection, and of these 51 million, up to 20% are diagnosed with HCV-related diseases (Yang et al., 2019).

10.3.1 Hepatitis B virus- and hepatitis C virus-associated cancers HBV and HCV induce the development of hepatocellular carcinoma. Hepatocellular carcinoma is a primary malignancy of the liver that originates from hepatocytes and accounts for 75%
85% of liver cancers (Ghouri et al., 2017). Liver cancer is the fifth most common cancer, causing 8.2% of deaths worldwide in 2018 (Bray et al., 2018). In 2020, approximately 42,810 individuals in the United States were diagnosed with liver cancer, of which30,160 died (Siegel et al., 2020). Hepatocellular carcinoma is the fourth leading cause of cancer death worldwide (Yang et al., 2019). HBV infection is thought to cause 80% of total virus-associated hepatocellular cancers, while HCV accounts for 20% of cases (Petruzziello, 2018). Current vaccination against HBV has the potential to reduce the prevalence of developing HBVinduced hepatocellular carcinoma, but there remains a need for

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complementary treatments in addition to vaccines. Besides, no vaccine is currently available for HCV infection (Ringehan et al., 2017).

10.3.2 Hepatitis B virus-associated cancer biomarkers Although the expression of heat shock proteins (HSPs) is frequently upregulated in hepatocellular cancer, their role in HBV-associated hepatocellular carcinoma (HBVaHCC) is unknown. Based on this viewpoint, researchers investigated the role of HSPs in HBVaHCC patients. They found that HSP expression is excessive in HBVaHCCs. Moreover, glucose-regulated proteins such as GRP78 and GRP94 might have crucial functions in the development of malignancy. Through this, they recommended GRP78, GRP94, and HSP90 as prognostic biomarkers for HBVaHCC patients (Lim et al., 2005). In addition to protein markers, potential biomarkers derived from serum miRNAs remain under investigation for HBVaHCC. Researchers analyzing the possible role of miRNAs in HBVaHCC patients found that serum miR18a was significantly higher in patients than in healthy controls. The researchers deduced from the study that serum miR-18a is a potential noninvasive biomarker for HBVaHCC screening (Li et al., 2012). Sixteen serum cytokine expressions from 105 HBVaHCC patients were measured and analyzed to determine whether the expressions were associated with various clinical factors. The analysis revealed that a deficiency in platelet count, serum albumin, and interleukin-6 (IL-6) expressions negatively affected disease-free survival. Given this information, researchers suggested that serum IL-6 deficiency is a prognostic risk factor for HBVaHCC recurrence (Cho et al., 2015). Liver transplantation is the best treatment option for patients with hepatocellular carcinoma. A study investigating the prognostic value of glypican 3 (GPC-3) in liver transplanted HBVaHCC patients found that patients with positive GPC-3 had a worse prognosis. The researchers concluded that GPC3 is a potential prognostic biomarker for liver-transplanted HBVaHCC patients (Cui et al., 2015). In a retrospective study, serum alpha-fetoprotein (AFP) was determined to have diagnostic and prognostic potential in patients with hepatocellular carcinoma depending on their pathogenic characteristics (i.e., hepatitis infection, cirrhosis). As a result of the study, researchers obtained that serum AFP levels were significantly overexpressed in HBVaHCC patients, and they concluded that serum AFP is a diagnostic and prognostic biomarker for HBVaHCC patients (Yao et al., 2016). A recent study sought to evaluate PTX3 expression by comparing: (1) HBV-infected patients with healthy donors and (2) HBVaHCC patients with chronic hepatitis or cirrhosis. After the analysis, the researchers found that PTX3 expression levels may be associated with HBVaHCC. They specified PTX3 as an independent risk factor

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TABLE 10.3 Hepatitis B virus (HBV)-associated cancer biomarkers. Cancer

Biomarker

Clinical application

Reference

Hepatocellular carcinoma

GRP78, GRP94, and HSP90 overexpression

Prognostic biomarkers

Lim et al. (2005)

Serum miR-18a upregulation

Noninvasive biomarker

Li et al. (2012)

Serum IL-6 deficiency

Prognostic biomarker

Cho et al. (2015)

Positive GPC-3 cause worse prognosis

Prognostic biomarker

Cui et al. (2015)

Serum alpha-fetoprotein (AFP) overexpression

Diagnostic and prognostic biomarker

Yao et al. (2016)

Low miR-150 and high miR-101 levels

Noninvasive biomarkers

Safaa et al. (2017)

miR-22 and miR-122 downregulation

Biomarker

Qiao et al. (2017)

PTX3 expression levels

Diagnostic biomarker

Deng et al. (2020)

involved in developing HBVaHCC. They identified PTX3 as a diagnostic marker for HBVHCC patients (Deng et al., 2020). Safaa et al. (2017) conducted a research study in a cohort of Egyptian patients to investigate miRNAs in HBVaHCC. The study concluded that both miR-150 (low expression) and miR-101 (high expression) could be used as noninvasive biomarkers for the early detection of HBVaHC (Safaa et al., 2017). Another study that investigated the expression levels of miR-22 and miR-122 and their correlation with the clinical characteristics of HBVaHCC patients found that the expression of both miRNAs decreased depending on lymph node metastasis, differentiation grade, and tumor size. Therefore researchers interpreted the results that miR-22 and miR-122 have the potential to serve as markers for HBVaHCC (Qiao et al., 2017) (Table 10.3).

10.3.3 Hepatitis C virus-associated cancer biomarkers In a study utilizing cDNA microarrays on 20 HCV-positive patients, several markers comprising 50 genes were highly overexpressed in cancerous samples (Smith et al., 2003). These markers include genes associated with cell proliferation such as STK15 and secreted or plasma proteins such as PGCP,

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PLA2G13, and PLA2G7. The researchers pointed out that the 50 marker genes can serve as diagnostic and prognostic markers and provide insights into the mechanism of HCV-associated hepatocellular carcinoma (HCVaHCC) (Smith et al., 2003). In another study, where patients were stratified according to their clinical characteristics (chronic hepatitis infection, liver cirrhosis, and hepatocellular carcinoma), 328 serum samples were collected from patients chronically infected with HCV, and the serum level KL-6 (one of the MUC1 antigens) was measured by enzyme immunoassay. Accordingly, the highest serum KL-6 expression was shown in patients with HCVaHCC, and a high KL-6 serum level was associated with multiple tumor nodules. Moreover, a high KL-6 serum level was associated with later cancer development in chronic hepatitis and liver cirrhosis. As a result of all these findings, the study concluded that serum KL-6 is a novel diagnostic and prognostic tumor marker in HCVaHCC (Kurosaki et al., 2005). To understand the clinical significance of oxidative stress markers in HCVaHCC patients, the levels of manganese superoxide dismutase (MnSOD) and thioredoxin (TRX) were measured from serum samples, and the correlation between the marker levels and patient outcome was investigated. The analysis results showed that both oxidative stress markers studied were significantly overexpressed in HCVaHCC patients. Besides, these patients had a worse prognosis. Taken together, the researchers suggested that serum MnSOD and TRX levels can predict patients’ prognosis and, thus have the potential to be clinical biomarkers (Tamai et al., 2011). Similar to HBVaHCC, AFP and GPC-3 markers also correlated with HCVaHCCs. Seven hundred and seven chronically infected HCV patients were included in the study to assess the value of noninvasive risk factors for the development of HCVaHCC. As a result of the study, the researchers indicated that in addition to elevated AFP levels ($20 ng/mL), preliminarily high AFP levels (6
20 ng/mL) also contribute to HCVaHCC progression. Thus researchers indicated that in addition to the fibrosis stage, the AFP level is also a reliable risk factor for HCVaHCC (Tateyama et al., 2011). Another study examined plasma GPC-3 levels by immunoassay and expression levels by immunohistochemical staining in 56 patients and 60 controls. After analysis, the researchers found that HCV infection strongly affects the secretion and expression of GPC-3 in cancer samples and suggested that GPC-3 may serve as a predictive biomarker for HCVaHCC (Shimizu et al., 2020). Serum levels of DKK-1, GP73, and MDK were examined in HCVinfected patient samples diagnosed with hepatocellular carcinoma or liver cirrhosis. In addition, two groups with chronic HCV infection or no history of liver disease were included in the study. As a result of the expression evaluation of the studied proteins individually or in combination, the researchers showed that the expressions of DKK-1, GP73, and MDK were higher in HCVaHCC patients than in other groups. According to the results of sensitivity (100%), accuracy (82.8%), and specificity (74.4%), the study

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found that GP73 and DKK-1 are the best dual markers for HCVaHCC (N Zekri et al., 2020). A study that analyzed the miRNA expression profiles in the serum of HCVaHCC patients found that miR-125a-5p is overexpressed in HCVaHCC patients at both early and advanced stages, while the applied treatment reduces the same miRNA expression. From this point of view, the researchers offered that miR-125a-5p is a noninvasive biomarker that allows early diagnosis of HCVaHCC (Oura et al., 2019). In addition to experimental methods, researchers implemented a bioinformatics approach based on transcriptome profiling and protein-protein interaction network reconstruction. They uncovered that altered HMMR, CCNB1, and KIF20A lead to worse clinical outcomes, and they are potential targets for the diagnosis and treatment of HCVaHCC (Liu et al., 2019) (Table 10.4).

TABLE 10.4 Hepatitis C virus-associated cancer biomarkers. Cancer

Biomarker

Clinical application

Reference

Hepatocellular carcinoma

Overexpression of 50 genes (cell proliferation-related genes and secreted or plasma proteins)

Diagnostics and prognostics biomarker

Smith et al. (2003)

High serum KL-6 expression

Diagnostic and prognostic biomarker

Kurosaki et al. (2005)

MnSOD and TRX overexpression

Clinical biomarkers

Tamai et al. (2011)

Elevated AFP levels

Biomarker

Tateyama et al. (2011)

miR-125a-5p overexpression

Noninvasive biomarker

Oura et al. (2019)

Altered HMMR, CCNB1, and KIF20A cause worse clinical outcome

Diagnostic biomarker

Liu et al. (2019)

GPC-3 upregulation

Predictive biomarker

Shimizu et al. (2020)

DKK-1 and GP73 upregulation

Biomarker

Zekri et al. (2020)

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10.4 Human T-cell lymphotropic virus-1 HTLV-1 was discovered in 1979 and first mentioned in an article published in 1980 (Poiesz et al., 1980). HTLV-1 belongs to the retrovirus family and is the first human retrovirus to possess oncogenic potential (Coffin, 2015). The retrovirus family includes RNA viruses that use retroviral reverse transcriptase to amplify their genomic RNA. The discovery of retroviral reverse transcriptase dates back to 1970, and its discovery paved the way for the discovery of human cancer retroviruses (Balvay et al., 2007). HTLV-1 is an enveloped RNA virus that encodes the structural gag polyprotein, an accessory gene called HBz, and two regulatory proteins, Tax and Rex. Among the HTLV-1-encoded proteins, tax and HBz have been reported as key factors in the development of HTLV-1-associated pathologies (Yamada et al., 2021). Tax has been reported to ruin the cell cycle by disabling cell cycle checkpoints, promote cellular proliferation by mediating interleukin expression, and activate various transcriptional pathways (i.e., canonical and noncanonical NF-κB pathways) (Currer et al., 2012). T cells are one of the primary host cells for HTLV and HBz. HTLV and HBz support T-cell proliferation, induce regulatory T cell differentiation, and inactivate cell death and senescence (Zhao, 2016). HTLV-1 causes lifelong infection in approximately 10 million people worldwide (Hirons et al., 2021), and the virus is thought to be transmitted between people, mostly through blood transfusions (Kannian & Green, 2010). HTLV1 infection is endemic in southwestern Japan, Africa, Caribbean Islands, and among Australian aborigines (Hoshino, 2012).

10.4.1 HTLV-1-associated cancers Human infection with retroviruses can cause a variety of pathologies, including sarcomas, breast carcinomas, and leukemias (Balvay et al., 2007). Adult T-cell leukemia/lymphoma (ATLL) is a neoplasm that occurs due toHTLV-1 infection. ATLL is an immensely aggressive, rare T-cell malignancy that is difficult to diagnose and prognosticate. Moreover, there is currently no fulfilling therapy for ATLL (Hermine et al., 2018). The neoplasm most commonly occurs after the age of 50 in individuals who were infected in childhood. ATLL is classified into the following subtypes: acute, lymphocytic, chronic, and smoldering, in terms of lymphocyte quantity, biochemical criteria, the intensity of symptoms, and solid organ involvement (Bangham, 2018). The acute ATLL subtype accounts for 55%
60%, the lymphocytic ATLL type accounts for 20%
25%, the chronic ATLL type accounts for 10%
20%, and the smoldering ATLL type accounts for 5%
10% of ATLL patients (El Hajj et al., 2020). Approximately 2%
5% of individuals infected with HTLV-1 during their lifetime develop ATLL (Shah et al., 2020).

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10.4.2 HTLV-1-associated cancer biomarkers To uncover biomarkers for ATLL, a study used microarray to examine about 12,000 genes. After analysis, the researchers found that three genes, including TSLC1, CAV1, and PGDS, were significantly upregulated (more than 30-fold). Among these three genes, the researchers underlined TSLC1 expression and decided to confirm TSLC1 upregulation on ATLL cells and cell lines due to the role of TSLC1 in lung cancer as a tumor suppressor. After confirming the high ectopic expression of TSLC1 also in ATLL- and HTLV-1-infected cell lines, the researchers introduced TSLC1 as a new marker for acute-type ATLL (Sasaki et al., 2005). Nishioka et al. (2005) conducted a study to determine the expression levels of both the membrane type and soluble form of CD30 (also known as TNFRSF8) in ATLL patients and cell lines (MT-2, L540, and Karpas 299). The analysis results showed that the soluble form of CD30 expression was correlated with disease progression. Namely, patients with high CD30 expression had more aggressive ATLL. Moreover, despite the treatment regimen applied, the patients who died of ATLL had high soluble CD30 serum levels, while their soluble CD30 levels decreased compared to those whose treatment resulted in success. Therefore the study associated CD30 expression with ATLL cell proliferation and survival, and they suggested that soluble CD30 may be a useful marker for patient follow-up (Nishioka et al., 2005). Recently, another study suggested that soluble CD30 is a marker for ATLL. In this study, researchers compared the amount of soluble CD30 in ATLL patients with plasma from noncancerous HTLV-1-positive individuals and found that soluble CD30 expression was overexpressed in ATLL patients. Besides, researchers reported that the amount of soluble CD30 in noncancerous HTLV-1-positive individuals may help predict the risk of developing ATLL (Takemoto et al., 2016). To determine the expression patterns of soluble OX40 (also known as CD134, a member of the tumor necrosis factor receptor superfamily), the quantitative enzyme-linked immunosorbent assay was applied in another study to blood samples from ATLL patients, nonsymptomatic HTLV-1positive individuals, and healthy groups. The results of the analysis showed that the level of soluble OX40 was remarkably high in ATLL patients compared to other groups. As a result, researchers suggested that abnormally high soluble OX40 plasma levels serve as a diagnostic biomarker for ATLL (Tanaka et al., 2019). Another study intended to discover potential protein biomarkers for ATLL disease by analyzing a total of 85 plasma samples using a Slow Offrate Modified Aptamer (SOMAmer)-based high-throughput proteomic assay. In this study, the researchers compared proteomic profiles of (1) nonsymptomatic HTLV-1-positive individuals with ATLL patients, (2) acute, lymphatic, smoldering, and chronic ATLL types, and (3) pre-remission and proremission states of ATLL patients. The analysis revealed that soluble

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TABLE 10.5 Human T-cell lymphotropic virus-1 (HTLV-1) associated cancer biomarkers. Cancer

Biomarker

Clinical application

Reference

Adult T-cell leukemia/ lymphoma

TSLC1 upregulation

Biomarker

Sasaki et al. (2005)

High soluble CD30 expression

Biomarker

Nishioka et al. (2005)

High soluble CD30 expression

Biomarker

Takemoto et al. (2016)

Soluble OX40 upregulation

Diagnostic biomarker

Tanaka et al. (2019)

Soluble TNFR2 upregulation

Diagnostic biomarker

Guerrero et al. (2020)

Soluble CADM1 upregulation

Clinical biomarker

Nakahata et al. (2021)

TNFR2 expression is significantly high (10-fold) in acute ATLL patients compared with nonsymptomatic HTLV-1 carriers. TNFR2 levels also dropped in nonsymptomatic HTLV-1 carriers after the successful remission of the disease. Considering all these consequences, the researchers concluded that soluble TNFR2 is a diagnostic biomarker for acute ATLL (Guerrero et al., 2020). Soluble CADM1 levels were also measured using AlphaLISA technology by comparing HTLV-1 carriers, ATLL patients, and healthy groups with peripheral blood samples. The results of the analysis showed that soluble CADM1 was remarkably upregulated in ATLL patients. Besides, the researchers found that soluble CADM1 expression increased in response to the disease state. The aggressive ATLL patients have higher expression of soluble CADM1. In addition, soluble CADM1 expression varied depending on the treatment. Overall, the level of soluble CADM1 in plasma is reported as a clinical biomarker for ATLL patients (Nakahata et al., 2021) (Table 10.5).

10.5 Human Herpesvirus-8 Human Herpesvirus-8 (HHV-8), also known as KSHV, was discovered in 1994 by using representational difference analysis (a PCR-based method) and is the last human oncovirus discovered to date (Mesri et al., 2010). HHV-8 is an enveloped, large, double-stranded DNA virus that belongs to the gammaherpesvirus family like EBV. HHV-8 is the only rhadinovirus

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(second genus in the gammaherpesvirus family) discovered in humans (Edelman, 2005). HHV-8 remains in the host for life and exists in a latent form in infected cells. Although the HHV-8 genome encodes about 100 genes, several viral genes are expressed during the latent form (Juillard et al., 2016). Among them, latency-associated nuclear antigen (LANA) plays an important role in the development of HHV-8-associated diseases. LANA is critical for replication and persistence. It enhances the expression of genes regulated by various transcription factors, promotes the expression of latency genes, contributes to genomic reprogramming in infected cells, and stabilizes and activates the c-Myc oncogene (Liu et al., 2007). HHV-8 infection prevalence in the world is unstable and changes according to geographic region and ethnicity. The highest HHV-8 infection prevalence is found in subSaharan Africa, representing up to 90% of the adult population. It is also estimated that the incidence of infection is 20%
30% in the Mediterranean region and less than 10% in Northern Europe, Asia, and the United States (Cesarman et al., 2019).

10.5.1 HHV-8-associated cancers HHV-8 is associated with an aggressive and lethal malignancy called Kaposi’s sarcoma. The incidence of Kaposi’s sarcoma in infected individuals is 1 in 100,000 (Facciola` et al., 2017). Although the relationship between HHV-8 and Kaposi’s sarcoma is not very old, the first cases of Kaposi’s sarcoma lesions were described in 1872 by a dermatologist named Moritz Kaposi (Antman & Chang, 2000). In 2018, approximately 42,000 new cases and 20,000 deaths were associated with Kaposi’s sarcoma worldwide (Bray et al., 2018). Kaposi’s sarcoma is a multifocal neoplasm that usually appears as dark purple lesions on the skin. The cancerous lesions contain more than one cell type, and the dominant cell is a spindle cell derived from the endothelial origin (Verma & Robertson, 2003). Kaposi’s sarcoma can occur in several forms, such as (1) classic Kaposi’s sarcoma (rare and seen in the elderly), (2) endemic Kaposi’s sarcoma (common and seen in children in the Saharan Africa subgroup), (3) AIDS-associated Kaposi’s sarcoma (seen in HIV-infected individuals), and (4) iatrogenic Kaposi’s sarcoma (immunosuppression- or transplantation-associated) (Dittmer & Damania, 2013). Currently, one of the most common causes of morbidity and mortality in HIV-positive patients is Kaposi’s sarcoma (Schneider & Dittmer, 2017), and individuals who have undergone solid organ transplantation are 200 times more likely to be diagnosed with Kaposi’s sarcoma than the general population (Cesarman et al., 2019).

10.5.2 HHV-8-associated cancer biomarkers The protein expression of endogenous cyclin D1 and retinoblastoma tumor suppressor protein (pRb) was investigated in Kaposi’s sarcoma and AIDS-

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positive tissues from Kaposi’s sarcoma patients by immunohistochemical analysis. The results of the analysis showed that the expression of endogenous cyclin D1 was barely perceptible in the early stages of the disease, while it increased in advanced stages. Therefore the researchers associated endogenous cyclin D1 expression with tumor progression. Besides, the researchers found pRb expression in all Kaposi’s sarcoma lesions (Horenstein et al., 1997). Another study examined the expression of IL-8 (a pro-inflammatory cytokine) by comparing HIV-positive Kaposi’s sarcoma cell lines (KS Y-1, KSC10, KSC29, KSC59, KSC13, and KS-38) with control cells such as human umbilical vein endothelial cells (HUVECs), human aortic smooth muscle cells (AoSM), or fibroblast cells. Following the analyses, the researchers pointed out that both IL-8 and its receptors are expressed in Kaposi’s sarcoma cell lines, and preventing IL-8 expression leads to Kaposi’s sarcoma cell growth inhibition. Moreover, serum levels of IL-8 were remarkably upregulated in Kaposi’s sarcoma cell lines compared with control groups. Given these results, the researchers posited IL-8 as a potential surrogate biomarker for Kaposi’s sarcoma (Masood et al., 2001). Although studies have claimed that a chemokine CXCL12 and its receptors, CXCR4 and CXCR7, may play a role in Kaposi’s sarcoma etiology, researchers have not used their expression correlation with disease or biomarker feasibility. In a study by Desnoyer et al. (2016), the expression of CXCL12/CXCR4-CXCR7, LANA, Ki67, and VEGF was measured in Kaposi’s sarcoma, HIV-infected Kaposi’s sarcoma, angioma patients, and healthy donors using immunohistochemistry and quantitative imaging. After analysis, researchers showed that the expression levels of CXCL12/CXCR4CXCR7 proteins were significantly higher in Kaposi’s sarcomaand HIVinfected Kaposi’s sarcomawhen compared with other samples. Moreover, CXCL12/CXCR4-CXCR7 expressions were associated with the severity of Kaposi’s sarcomalesions. This study recommended the evaluation of the CXCL12/CXCR4-CXCR7 trio as potential biomarkers for Kaposi’s sarcomapatients (Desnoyer et al., 2016). As previously mentioned, PD-L1 was overexpressed in EBVaGCs, and its expression was also overexpressed according to HHV-8 infection. Indeed, one study reported that HHV-8 infection on primary human monocytes caused overexpression of PD-L1 at either the transcriptional or protein level. Besides, researchers investigated the effect of viral dose on PDL-L1 expression and indicated that increased doses of HHV-8 cause an increase in PDL1 transcription and protein production (Host et al., 2017). To discover circulating miRNA biomarkers to predict the prognosis of Kaposi’s sarcoma patients, the researchers compared HIV-infected Kaposi’s sarcoma patients with nonsymptomatic HHV-8- or HIV-infected individuals in their study. To do this, the researchers took plasma samples from the patients before and after treatment (which was successful). Then, the researchers examined 377 miRNAs from these samples and found that miR-

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TABLE 10.6 Human herpesvirus-8 (HHV-8)-associated cancer biomarkers. Cancer

Biomarker

Clinical application

Reference

Kaposi’s sarcoma

Increased endogenous cyclin D1 expression

Biomarker

Horenstein et al. (1997)

Serum IL-8 levels upregulation

Surrogate biomarker

Masood et al. (2001)

High expression of CXCL12/ CXCR4-CXCR7 proteins

Biomarker

Desnoyer et al. (2016)

Overexpression of PD-L1

Marker

Host et al. (2017)

miR-375 upregulation

Prognostic biomarker

Piano et al. (2019)

375 levels were upregulated in HIV-infected Kaposi’s sarcoma patients, and its level decreased after treatment. As a result, the researchers reported that miR-375 levels serve as a prognostic biomarker in HIV-infected Kaposi’s sarcoma patients, especially in treatment-naive patients (Piano et al., 2019) (Table 10.6).

10.6 Human papillomavirus HPV was discovered in the early 1980s by the DNA hybridization method (Chang et al., 2017). HPV is a small, unsheathed, circular, double-stranded DNA that belongs to the Papillomaviridae family. More than a hundred HPV types have been characterized by whole genome sequencing. The identified HPV types differ in their oncogenic potential. HPVs that cause benign lesions are defined as low-risk types, while HPVs that cause malignant tumors are classified as high-risk types (Graham, 2010). IARC has defined HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, and -59 as high-risk, meaning that they are carcinogenic to humans (Group 1) (IARC, 2012). Over 12 high-risk HPV types, HPV-16 and -18 are the most prevalent worldwide. Most HPVs encode eight major proteins and have different roles in the viral life cycle. Among these, the E6 and E7 oncoproteins come to the fore due to their important contributions to carcinogenesis, particularly through their interactions with various cellular targets (Pappa et al., 2018). The most important function of the E6 oncoprotein is to inhibit apoptosis and cause cell immortalization by binding p53. Besides p53 binding, E6 can increase telomerase activity and initiate genomic instability. E7 can bind pRB. pRB and its signaling pathway play a critical role in monitoring cell proliferation

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and apoptosis by coordinating the family of E2F transcription factors, and it is known that inactive pRB can promote cell proliferation. E7 can inhibit the function of pRB; thus pRB cannot suppress aberrant cell proliferation in cancer cells (Boulet et al., 2007; Kori & Arga, 2020). HPV is the most common sexually transmitted viral infection worldwide, according to WHO (Ferris et al., 2020).

10.6.1 Human papillomaviruses-associated cancers Approximately 5% of all cancers worldwide are caused by oncogenic HPV infection (Gilbert et al., 2019). Highly oncogenic HPV infection is found in almost all cases of cervical cancer (Ferris et al., 2020). Cervical cancer was the fourth leading cause of death worldwide in 2018 (311,000 deaths) and the fourth most commonly diagnosed cancer (570,000 cases) (Bray et al., 2018). Besides, it caused 13,800 new cases and 4290 deaths in the United States in 2020 (Siegel et al., 2020). HPV-16 and -18 are responsible for up to 70% of cervical cancer cases (WHO, 2019). The scientist who had discovered the relationship between HPV-16, -18, and cervical cancer won the Nobel Prize in Physiology and Medicine in 2008 (Bogolyubova, 2019). HPV is positive in about 88% of anal cancer, 78% of vaginal cancer, 51% of penile cancer, 30%
70% of oropharyngeal cancer, and less than 25% of vulvar cancer (Ferris et al., 2020). Anal cancer accounts for about 2% of cancers affecting the gastrointestinal system. Approximately 48,000 new anal cancers are detected around the world each year (Silva Dalla Libera et al., 2019). Vaginal and vulvar cancers are rare gynecological cancers. Vaginal cancer led to 17,600 new cases and 8062 mortality in 2018 in the world (Bray et al., 2018). Vulvar cancer encompassed 6120 new cases and 1350 deaths in the United States in 2020 (Siegel et al., 2020). Oropharyngeal cancer is one type of head and neck cancer, and it consists of tonsil and base of the tongue region malignancies. HPV-16 genome is encountered in about 90% of HPVassociated oropharyngeal cancer cases (Pan et al., 2018). In 2018, 92,887 individuals were diagnosed with oropharyngeal cancer, whichled to 51,005 deaths in the world (Bray et al., 2018).

10.6.2 Human papillomaviruses-associated cancer biomarkers Syndecans have notable roles in a variety of solid and blood malignancies. From this point of view, researchers have studied the expression of syndecan-1 (SDC1) in cervical cancer for the first time. The SDC1 expression profile was measured on 121 cervical cancer tissues by immunohistochemistry. The relationship of SDC1 expression with clinical parameters of the patients was also specified. Researchers stated that SDC1 is expressed in 83.5% of the patients; also, there is a correlation between SDC1 intense expression and patient’s survival (Kim et al., 2011).

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In another study, the expression patterns of the SUMO conjugating enzyme, UBC9, in cervical biopsies were examined. Researchers performed immunohistochemistry analysis in 130 patients’ cervical biopsies in a lesiondependent manner. Following the results, researchers demonstrated that UBC9 is significantly overexpressed in cervical lesions, and its expression increases in patients with high-grade cancer. Researchers suggested that UBC9 can serve as a diagnostic or monitoring biomarker for cervical cancer (Mattoscio et al., 2015). The MCM-2 upregulation in cervical cancer has been shown in former studies via DNA microarray and transcriptional profiling. Based on this aspect, a study conducted by Zheng (2015) attempts to indicate HPV type and MCM-2 association in the diagnosis of cervical cancer. Similar to previous studies, MCM-2 was found to be upregulated. Besides, it was revealed that MCM-2 upregulation positively correlates with high oncogenic subtypes of HPV in cancerous cells. In the light of this information, a researcher stated that instead of genotyping HPV, MCM-2 detection in cancerous cells would be much better. Therefore the study suggested MCM-2 as a screening biomarker that can support diagnosis (Zheng, 2015). It is known that GSK3β involves in various cancer developments. Also, one of the target proteins of GSK3β is cyclin D1. In this respect, the researchers conducted a study to evaluate both GSK3β and cyclin D1 expression in different types and grades of cervical cancer to determine the proteins’ correlation with the disease. Thereby, immunohistochemical analyses were performed for cancerous and control groups, and following the analyses, it was found that GSK3β expression is upregulated according to grade, whereas cyclin D1 decreases. Also, GSK3β expression varied from squamous cell carcinoma to adenocarcinoma (Park et al., 2016). Although the two interleukins, IL-1α and IL-6, were associated with the prognosis of many types of malignancies, the association of these interleukins with cervical cancer has not been reported yet. To this end, researchers performed an immunohistochemistry study to determine the two interleukins’ expression and the expression association with patients’ clinical parameters with respect to controls. According to the analyses, researchers indicated that IL-1α and IL-6 significantly increase in cervical cancer tissues compared with controls. Moreover, the two interleukins’ expression remarkably correlated with tumor size, FIGO histology grade tumor differentiation, and worse prognosis. Consequently, the study recommended IL-1α and IL-6 as prognostic markers, which can specify possible therapeutic targets for cervical cancer (Song et al., 2016). In order to assign stem cell markers in cervical cancer, researchers first performed RT-qPCR and investigated the mRNA expressions of ALDH1A1, OCT4, NANOG, SOX2, and Twist1. As a result of the analysis, researchers found notable upregulation in ALDH1A1 and OCT4 compared with cervical squamous cell carcinoma and controls. For this response, the researchers

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investigated the protein expression of ALDH1A1 and OCT4 by immunohistochemical staining and ELISA and redemonstrated the overexpression of the two proteins. In consequence of the study, researchers considered ALDH1A1 and OCT4 as potential biomarkers that can be used for the early detection of cervical carcinoma (Tulake et al., 2018). A meta-analysis was performed on five cervical cancer-associated gene expression datasets to reveal biomarker candidates and potential therapeutic targets. For this purpose, the gene expression data were analyzed, and obtained differentially expressed genes were integrated with genome-scale biomolecular networks such as protein
protein interaction, metabolic, and posttranscriptional regulatory networks. This study notified already-known cervical cancer biomarkers as well as novel biomarker candidates (i.e., EPHA4, EPHA5, NCOA3, NR2C1, E2F4, ETS1, KAT2B, PARP1, CDK1, miR-192
5p, miR-193b-3p, and miR-215
5p) (Kori & Arga, 2018). Also, the researchers identified genomic biomarker candidates for cervical cancer by performing differential coexpression network analysis for the same five transcriptome datasets. Researchers identified coexpressed modules and performed Kaplan
Meier survival and principle component analyses to identify diagnostic and prognostic capabilities of the modules. As a result, seven distinct coexpressed gene modules were identified, which represent potential biomarker candidates for diagnosis and prognosis for cervical cancer (Kori et al., 2019). Another study conducted by Roslind et al. (2020) assessed the association of high YKL-40 serum levels with a worse prognosis in cervical carcinoma. For these, researchers included 116 cervical cancer patients, 152 cervical intraepithelial neoplasia patients, and control groups and measured their YKL40 serum concentration by ELISA. Following the analysis, researchers showed that YKL-40 serum concentrations are high when they are compared with the other two groups. Also, with univariate cox analysis, researchers determined that YKL-40 is correlated with recurrence-free survival and overall survival. Analysis results demonstrated serum YKL-40 as a biomarker related to overall survival in cervical cancer patients (Roslind et al., 2020). Researchers conducted a study that aims to determine the marker potential of ANXA2 by applying both bioinformatics and experimental analysis. Researchers first pointed out that ANXA2 is upregulated in cervical cancer tissues when compared with controls by bioinformatics analysis. Moreover, researchers performed Kaplan
Meier survival analysis and found that expression of ANXA2 correlates with cervical cancer prognosis. In order to validate these results experimentally, researchers did immunohistochemical staining on 121 cervical carcinoma tissues and 29 normal tissues. While the researchers detected ANXA2 expression in the cytoplasm and cell membrane of malignant tissues, ANXA2 expression is found only in cell membranes in controls. Taken together with these results, researchers provide that ANXA2 is a potential marker for the diagnosis of human cervical cancer (Wang et al., 2021).

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The study about oropharyngeal squamous cell carcinoma and HPV relation pointed out that IGSF4 methylation was an independent biomarker of HPV-positive individuals (Chen, Stephen et al., 2015). Based on the reporting of MCM2 importance in cervical cancer, another study investigated the biomarker potential of MCM2 and TOP2A in anal cancer. Accordingly, the

TABLE 10.7 Human papillomavirus-associated cancer biomarkers. Cancer

Biomarker

Clinical application

Reference

Cervical cancer

SDC1 expression

Biomarker

Kim et al. (2011)

UBC9 over-expression

Diagnostic or monitoring biomarker

Mattoscio et al. (2015)

MCM-2 upregulation

Screening biomarker

Zheng (2015)

GSK3β upregulation and cyclin D1 downregulation

Biomarker

Park et al. (2016)

IL-1α and IL-6 upregulation

Prognostic biomarker

Song et al. (2016)

Upregulation in ALDH1A1 and OCT4

Biomarker

Tulake et al. (2018)

EPHA4, EPHA5, NCOA3, NR2C1, E2F4, ETS1, KAT2B, PARP1, CDK1, miR192
5p, miR-193b-3p, and miR-215
5p

Biomarker candidates

Kori and Arga (2018)

Seven distinct coexpressed gene modules

Biomarker candidates

Kori et al. (2019)

High YKL-40 serum concentrations

Biomarker

Roslind et al. (2020)

ANXA2 upregulation

diagnostic Biomarker

Wang et al. (2021)

Oropharyngeal cancer

IGSF4 methylation

Biomarker

Chen, Zhang et al. (2015), Chen, Stephen et al. (2015)

Anal cancer

MCM2 and TOP2A expression

Prognostic biomarker

ScapulatempoNeto et al. (2017)

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expressions of MCM2 and TOP2A were screened immunohistochemically in 75 anal carcinoma lesions. The analyses’ results indicated that MCM2 and TOP2A are constantly and notably expressed in anal carcinoma lesions. The study concluded that both proteins could be used to utilize the prognosis of anal cancer (Scapulatempo-Neto et al., 2017) (Table 10.7).

10.7 Conclusions Oncogenic viruses, present in about one in ten people, are also one of the apparent causes of cancer-related deaths worldwide. Therefore supporting existing screening or treatment approaches with new strategies will help reduce some of the global human cancer burdens. Biomarkers that enable early diagnosis, prognosis, and appropriate or complementary therapies will help reduce the incidence of disease, including viral cancers. Today, a new virus called COVID-19 has entered the scene, causing an ongoing global pandemic. Studies that investigated the link between COVID-19 and cancer indicated that cancer patients are more affected by COVID-19 than healthy people, and especially in lung cancer, the viral symptoms are more virulent. However, there are still too many unknowns about the virus. For example, will people infected with COVID-19 have an increased risk of cancer in the future? Possibly. Although there are still many questions, there is growing interest in COVID-19 in virology and viral cancers. We believe that in the foreseeable future, efforts to prevent COVID19 (such as mRNA vaccine technology) will also lead to significant advances in the treatment of viral cancers. Considering all these aspects, we have reviewed the six human oncogenic viruses in this book chapter and highlighted the biomarkers recommended for them. In this book chapter, we believe that we have provided information to set new goals for the treatment, prognosis, and diagnosis of oncogenic viruses and guide the scientific community to achieve this goal.

References Antman, K., & Chang, Y. (2000). Kaposi’s sarcoma. New England Journal of Medicine, 342 (14), 1027
1038. Available from https://doi.org/10.1056/NEJM200004063421407. Ayee, R., Ofori, M. E. O., Wright, E., & Quaye, O. (2020). Epstein Barr virus associated lymphomas and epithelia cancers in humans. Journal of Cancer, 11(7), 1737
1750. Available from https://doi.org/10.7150/jca.37282. Baer, R., Bankier, A. T., Biggin, M. D., Deininger, P. L., Farrell, P. J., Gibson, T. J., Hatfull, G., Hudson, G. S., Satchwell, S. C., Se´guin, C., Tuffnell, P. S., & Barrell, B. G. (1984). DNA sequence and expression of the B95-8 Epstein-Barr virus genome. Nature, 310(5974), 207
211. Available from https://doi.org/10.1038/310207a0. Balvay, L., Lastra, M. L., Sargueil, B., Darlix, J. L., & Ohlmann, T. (2007). Translational control of retroviruses. Nature Reviews Microbiology, 5(2), 128
140. Available from https://doi. org/10.1038/nrmicro1599.

244

Oncogenic Viruses Volume 2

Bangham, C. R. M. (2018). Human T cell leukemia virus type 1: Persistence and pathogenesis. Annual Review of Immunology, 36, 43
71. Available from https://doi.org/10.1146/annurevimmunol-042617-053222. Bo¨ger, C., Behrens, H. M., Mathiak, M., Kru¨ger, S., Kalthoff, H., & Ro¨cken, C. (2016). PD-L1 is an independent prognostic predictor in gastric cancer of Western patients. Oncotarget, 7 (17), 24269
24283. Available from https://doi.org/10.18632/oncotarget.8169. Bogolyubova, A. V. (2019). Human oncogenic viruses: Old facts and new hypotheses. Molekuliarnaia Biologiia, 53(5), 871
880. Available from https://doi.org/10.1134/ S0026898419050033. Boulet, G., Horvath, C., Vanden Broeck, D., Sahebali, S., & Bogers, J. (2007). Human papillomavirus: E6 and E7 oncogenes. The International Journal of Biochemistry & Cell Biology, 39, 2006
2011. Available from https://doi.org/10.1016/j.biocel.2007.07.004. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer Journal for Clinicians, 68(6), 394
424. Available from https://doi.org/10.3322/caac.21492. Bukh, J. (2016). The history of hepatitis C virus (HCV): Basic research reveals unique features in phylogeny, evolution and the viral life cycle with new perspectives for epidemic control. Journal of Hepatology, 65(1), S2
S21. Available from https://doi.org/10.1016/j. jhep.2016.07.035. Califf, R. M. (2018). Biomarker definitions and their applications. Experimental Biology and Medicine, 243(3), 213
221. Available from https://doi.org/10.1177/1535370217750088. Cancer Genome Atlas Research Network. (2014). Comprehensive molecular characterization of gastric adenocarcinoma. Nature, 513(7517), 202
209. Available from https://doi.org/ 10.1038/nature13480. Cesarman, E., Damania, B., Krown, S. E., Martin, J., Bower, M., & Whitby, D. (2019). Kaposi sarcoma. Nature Reviews Disease Primers, 5(1), 9. Available from https://doi.org/10.1038/ s41572-019-0060-9. Chang, Y., Moore, P. S., & Weiss, R. A. (2017). Human oncogenic viruses: Nature and discovery. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1732), 20160264. Available from https://doi.org/10.1098/rstb.2016.0264. Chen, K. M., Stephen, J. K., Havard, S., Mahan, M., Divine, G., & Worsham, M. J. (2015). IGSF4 methylation as an independent marker of human papillomavirus-positive oropharyngeal squamous cell carcinoma. JAMA Otolaryngology - Head and Neck Surgery, 141(3), 257
263. Available from https://doi.org/10.1001/jamaoto.2014.3393. Chen, Y. P., Chan, A. T. C., Le, Q. T., Blanchard, P., Sun, Y., & Ma, J. (2019). Nasopharyngeal carcinoma. The Lancet, 394(10192), 64
80. Available from https://doi.org/10.1016/S01406736(19)30956-0. Chen, Y. P., Zhang, W. N., Chen, L., Tang, L. L., Mao, Y. P., Li, W. F., Liu, X., Zhou, G. Q., Sun, Y., Kang, T. B., Zeng, M. S., Liu, N., & Ma, J. (2015). Effect of latent membrane protein 1 expression on overall survival in Epstein-Barr virus-associated cancers: A literaturebased meta-analysis. Oncotarget, 6(30), 29311
29323. Available from https://doi.org/ 10.18632/oncotarget.4906. Chesnokova, L. S., & Hutt-Fletcher, L. M. (2014). Epstein-Barr virus infection mechanisms. Chinese Journal of Cancer, 33(11), 545
548. Available from https://doi.org/10.5732/ cjc.014.10168. Cho, H. J., Kim, S. S., Ahn, S. J., Park, S. Y., Park, J. H., Kim, J. K., Wang, H. J., Cheong, J. Y., & Cho, S. W. (2015). Low serum interleukin-6 levels as a predictive marker of

Current status of viral biomarkers for oncogenic viruses Chapter | 10

245

recurrence in patients with hepatitis B virus related hepatocellular carcinoma who underwent curative treatment. Cytokine, 73(2), 245
252. Available from https://doi.org/10.1016/j. cyto.2015.02.027. Coffin, J. M. (2015). The discovery of HTLV-1, the first pathogenic human retrovirus. Proceedings of the National Academy of Sciences of the United States of America, 112(51), 15525
15529. Available from https://doi.org/10.1073/pnas.1521629112. Cui, X., Li, Z., Gao, P. J., Gao, J., & Zhu, J. Y. (2015). Prognostic value of glypican-3 in patients with HBV-associated hepatocellular carcinoma after liver transplantation. Hepatobiliary and Pancreatic Diseases International, 14(2), 157
163. Available from https://doi.org/10.1016/S1499-3872(15)60349-6. Currer, R., Van Duyne, R., Jaworski, E., Guendel, I., Sampey, G., Das, R., Narayanan, A., & Kashanchi, F. (2012). HTLVTax: A fascinating multifunctional co-regulator of viral and cellular pathways. Frontiers in Microbiology, 3, 406. Available from https://doi.org/10.3389/ fmicb.2012.00406. de Martel, C., Georges, D., Bray, F., Ferlay, J., & Clifford, G. M. (2020). Global burden of cancer attributable to infections in 2018: A worldwide incidence analysis. The Lancet Global Health, 8(2), e180
e190. Available from https://doi.org/10.1016/S2214-109X(19)30488-7. Deng, H., Fan, X., Wang, X., Zeng, L., Zhang, K., Zhang, X., Li, N., Han, Q., Lv, Y., & Liu, Z. (2020). Serum pentraxin 3 as a biomarker of hepatocellular carcinoma in chronic hepatitis B virus infection. Scientific Reports, 10(1), 20276. Available from https://doi.org/10.1038/ s41598-020-77332-3. Desnoyer, A., Dupin, N., Assoumou, L., Carlotti, A., Gaudin, F., Deback, C., Peytavin, G., Marcelin, A. G., Boue´, F., Balabanian, K., & Pourcher, V. (2016). Expression pattern of the CXCL12/CXCR4
CXCR7 trio in Kaposi sarcoma skin lesions. British Journal of Dermatology, 175(6), 1251
1262. Available from https://doi.org/10.1111/bjd.14748. Dittmer, D. P., & Damania, B. (2013). Kaposi sarcoma associated herpesvirus pathogenesis (KSHV)
An update. Current Opinion in Virology, 3(3), 238
244. Available from https:// doi.org/10.1016/j.coviro.2013.05.012. Edelman, D. C. (2005). Human herpesvirus 8
A novel human pathogen. Virology Journal, 2, 78. Available from https://doi.org/10.1186/1743-422X-2-78. El Hajj, H., Tsukasaki, K., Cheminant, M., Bazarbachi, A., Watanabe, T., & Hermine, O. (2020). Novel Treatments of adult T cell leukemia lymphoma. Frontiers in Microbiology, 11, 1062. Available from https://doi.org/10.3389/fmicb.2020.01062. Facciola`, A., Rullo, E. V., Ceccarelli, M., D’aleo, F., Di Rosa, M., Pinzone, M. R., Condorelli, F., Visalli, G., Picerno, I., Fisichella, R., Nunnari, G., & Pellicano`, G. F. (2017). Kaposi’s sarcoma in HIV-infected patients in the era of new antiretrovirals. European Review for Medical and Pharmacological Sciences, 21(24), 5869
5879. Available from http://www. europeanreview.org/. Farrell, P. J. (2019). Epstein
Barr virus and cancer. Annual Review of Pathology: Mechanisms of Disease, 14, 29
53. Available from https://doi.org/10.1146/annurev-pathmechdis012418-013023. Ferris, D. G., Brown, D. R., Giuliano, A. R., Myers, E., Joura, E. A., Garland, S. M., Kjaer, S. K., Perez, G., Saah, A., Luxembourg, A., & Velicer, C. (2020). Prevalence, incidence, and natural history of HPV infection in adult women ages 24 to 45 participating in a vaccine trial. Papillomavirus Research, 10, 100202. Available from https://doi.org/10.1016/j.pvr.2020.100202. Fugl, A., & Andersen, C. L. (2019). Epstein-Barr virus and its association with disease
A review of relevance to general practice. BMC Family Practice, 20(1), 62. Available from https://doi.org/10.1186/s12875-019-0954-3.

246

Oncogenic Viruses Volume 2

Gandhi, M. K., Lambley, E., Burrows, J., Dua, U., Elliott, S., Shaw, P. J., Prince, H. M., Wolf, M., Clarke, K., Underhill, C., Mills, T., Mollee, P., Gill, D., Marlton, P., Seymour, J. F., & Khanna, R. (2006). Plasma Epstein-Barr virus (EBV) DNA is a biomarker for EBV-positive Hodgkin’s lymphoma. Clinical Cancer Research, 12(2), 460
464. Available from https:// doi.org/10.1158/1078-0432.CCR-05-2008. Ghouri, Y. A., Mian, I., & Rowe, J. H. (2017). Review of hepatocellular carcinoma: Epidemiology, etiology, and carcinogenesis. Journal of Carcinogenesis, 16(1), 1
8. Available from https://doi.org/10.4103/jcar.JCar_9_16. Gilbert, D. C., Wakeham, K., Langley, R. E., & Vale, C. L. (2019). Increased risk of second cancers at sites associated with HPV after a prior HPV-associated malignancy, a systematic review and meta-analysis. British Journal of Cancer, 120(2), 256
268. Available from https://doi.org/10.1038/s41416-018-0273-9. Graham, S. V. (2010). Human papillomavirus: Gene expression, regulation and prospects for novel diagnostic methods and antiviral therapies. Future Microbiology, 5(10), 1493
1506. Available from https://doi.org/10.2217/fmb.10.107. Guerrero, C. L. H., Yamashita, Y., Miyara, M., Imaizumi, N., Kato, M., Sakihama, S., Hayashi, M., Miyagi, T., Karimata, K., Uchihara, J., Ohshiro, K., Todoroki, J., Nakachi, S., Morishima, S., Karube, K., Tanaka, Y., Masuzaki, H., & Fukushima, T. (2020). Proteomic profiling of HTLV-1 carriers and ATL patients reveals sTNFR2 as a novel diagnostic biomarker for acute ATL. Blood Advances, 4(6), 1062
1071. Available from https://doi.org/ 10.1182/bloodadvances.2019001429. Gulley, M. L. (2015). Genomic assays for Epstein-Barr virus-positive gastric adenocarcinoma. Experimental and Molecular Medicine, 47(1), e134. Available from https://doi.org/10.1038/ emm.2014.93. Ha¨mmerl, L., Colombet, M., Rochford, R., Ogwang, D. M., & Parkin, D. M. (2019). The burden of Burkitt lymphoma in Africa. Infectious Agents and Cancer, 14(1), 17. Available from https://doi.org/10.1186/s13027-019-0236-7. He, B., Zeng, J., Chao, W., Chen, X., Huang, Y., Deng, K., Huang, Z., Li, J., Dai, M., Chen, S., Huang, H., & Dai, S. (2017). Serum long non-coding RNAs MALAT1, AFAP1-AS1 and AL359062 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma. Oncotarget, 8(25), 41166
41177. Available from https://doi.org/10.18632/oncotarget.17083. Hermine, O., Ramos, J. C., & Tobinai, K. (2018). A review of new findings in adult T-cell leukemia
lymphoma: A focus on current and emerging treatment strategies. Advances in Therapy, 35(2), 135
152. Available from https://doi.org/10.1007/s12325-018-0658-4. Hirons, A., Khoury, G., & Purcell, D. F. J. (2021). Human T-cell lymphotropic virus type-1: A lifelong persistent infection, yet never truly silent. The Lancet Infectious Diseases, 21(1), e2
e10. Available from https://doi.org/10.1016/S1473-3099(20)30328-5. Horenstein, M. G., Cesarman, E., Wang, X., Linkov, I., Prieto, V. G., & Louie, D. C. (1997). Cyclin D1 and retinoblastoma protein expression in Kaposi’s sarcoma. Journal of Cutaneous Pathology, 24(10), 585
589. Available from https://doi.org/10.1111/j.1600-0560.1997. tb01088.x. Hoshino, H. (2012). Cellular factors involved in HTLV-1 entry and pathogenicity. Frontiers in Microbiology, 3, 222. Available from https://doi.org/10.3389/fmicb.2012.00222. Host, K. M., Jacobs, S. R., West, J. A., Zhang, Z., Costantini, L. M., Stopford, C. M., Dittmer, D. P., & Damania, B. (2017). Kaposi’s sarcoma-associated herpesvirus increases PD-L1 and proinflammatory cytokine expression in human monocytes. mBio, 8(5), e00917-17. Available from https://doi.org/10.1128/mBio.00917-17.

Current status of viral biomarkers for oncogenic viruses Chapter | 10

247

IARC. (2012). Biological agents. Volume 100 B. A review of human carcinogens. IARC Monogr Eval Carcinog Risks Hum, 100(PtB), 1
441. Ikeda, J. I., Wada, N., Nojima, S., Tahara, S., Tsuruta, Y., Oya, K., & Morii, E. (2016). ID1 upregulation and FoxO3a downregulation by Epstein-Barr virus-encoded LMP1 in Hodgkin’s lymphoma. Molecular and clinical oncology, 5(5), 562
566. Available from https://doi.org/10.3892/mco.2016.1012. Ishido, S., & Hotta, H. (1998). Complex formation of the nonstructural protein 3 of hepatitis C virus with the p53 tumor suppressor. FEBS Letters, 438(3), 258
262. Available from https://doi.org/10.1016/S0014-5793(98)01312-X. Jiang, W., Chamberlain, P. D., Garden, A. S., Kim, B. Y. S., Ma, D., Lo, E. J., Bell, D., Gunn, G. B., Fuller, C. D., Rosenthal, D. I., Beadle, B. M., Frank, S. J., Morrison, W. H., ElNaggar, A. K., Glisson, B. S., Sturgis, E. M., & Phan, J. (2016). Prognostic value of p16 expression in Epstein-Barr virus-positive nasopharyngeal carcinomas. Head and Neck, 38, E1459
E1466. Available from https://doi.org/10.1002/hed.24258. Juillard, F., Tan, M., Li, S., & Kaye, K. M. (2016). Kaposi’s sarcoma herpesvirus genome persistence. Frontiers in Microbiology, 7, 1149. Available from https://doi.org/10.3389/ fmicb.2016.01149. Jun-Ichiro, I., Naoki, W., Satoshi, N., Shinichiro, T., Yoko, T., Kaori, O., & Eiichi, M. (2016). ID1 upregulation and FoxO3a downregulation by Epstein-Barr virus-encoded LMP1 in Hodgkin’s lymphoma. Molecular and Clinical Oncology, 5, 562
566. Available from https://doi.org/10.3892/mco.2016.1012. Kang, H. J., Lee, I. S., Park, Y. S., Ho, W. J., Sohn, D. H., Ahn, J. Y., Yook, J. H., & Kim, B. S. (2016). Biomarkers of EBV-positive gastric cancers: Loss of PTEN expression is associated with poor prognosis and nodal metastasis. Annals of Surgical Oncology, 23(11), 3684
3692. Available from https://doi.org/10.1245/s10434-016-5284-2. Kannian, P., & Green, P. L. (2010). Human T lymphotropic virus type 1 (HTLV-1): Molecular biology and oncogenesis. Viruses, 2(9), 2037
2077. Available from https://doi.org/10.3390/ v2092037. Kew, M. C. (2011). Hepatitis B virus x protein in the pathogenesis of hepatitis B virus-induced hepatocellular carcinoma. Journal of Gastroenterology and Hepatology, 26, 144
152. Available from https://doi.org/10.1111/j.1440-1746.2010.06546.x. Kim, Y. I., Lee, A., Lee, B. H., & Kim, S. Y. (2011). Prognostic significance of syndecan-1 expression in cervical cancers. Journal of Gynecologic Oncology, 22(3), 161
167. Available from https://doi.org/10.3802/jgo.2011.22.3.161. Kori, M., & Arga, K. Y. (2018). Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective. PLoS One, 13(7), e0200717. Available from https://doi.org/10.1371/journal. pone.0200717. Kori, M., & Arga, K. Y. (2020). Pathways involved in viral oncogenesis: New perspectives from virus-host protein interactomics. Biochimica et Biophysica Acta - Molecular Basis of Disease, 1866(10), 165885. Available from https://doi.org/10.1016/j.bbadis.2020.165885. Kori, M., Gov, E., & Arga, K. Y. (2019). Novel genomic biomarker candidates for cervical cancer as identified by differential co-expression network analysis. OMICS A Journal of Integrative Biology, 23(5), 261
273. Available from https://doi.org/10.1089/omi.2019.0025. Kurosaki, M., Izumi, N., Onuki, Y., Nishimura, Y., Ueda, K., Tsuchiya, K., Nakanishi, H., Kitamura, T., Asahina, Y., Uchihara, M., & Miyake, S. (2005). Serum KL-6 as a novel tumor marker for hepatocellular carcinoma in hepatitis C virus infected patients. Hepatology Research, 33(3), 250
257. Available from https://doi.org/10.1016/j.hepres.2005.06.011.

248

Oncogenic Viruses Volume 2

Leucci, E., Onnis, A., Cocco, M., De Falco, G., Imperatore, F., Giuseppina, A., Costanzo, V., Cerino, G., Mannucci, S., Cantisani, R., Nyagol, J., Mwanda, W., Iriso, R., Owang, M., Schurfeld, K., Bellan, C., Lazzi, S., & Leoncini, L. (2010). B-cell differentiation in EBVpositive Burkitt lymphoma is impaired at posttranscriptional level by miRNA-altered expression. International Journal of Cancer, 126(6), 1316
1326. Available from https://doi.org/ 10.1002/ijc.24655. Li, L., Guo, Z., Wang, J., Mao, Y., & Gao, Q. (2012). Serum miR-18a: A potential marker for hepatitis B virus-related hepatocellular carcinoma screening. Digestive Diseases and Sciences, 57(11), 2910
2916. Available from https://doi.org/10.1007/s10620-012-2317-y. Lim, S. O., Park, S. G., Yoo, J. H., Park, Y. M., Kim, H. J., Jang, K. T., Cho, J. W., Yoo, B. C., Jung, G. H., & Park, C. K. (2005). Expression of heat shock proteins (HSP27, HSP60, HSP70, HSP90, GRP78, GRP94) in hepatitis B virus-related hepatocellular carcinomas and dysplastic nodules. World Journal of Gastroenterology, 11(14), 2072
2079. Available from https://doi.org/10.3748/wjg.v11.i14.2072. Liu, J., Martin, H. J., Liao, G., & Hayward, S. D. (2007). The Kaposi’s sarcoma-associated herpesvirus LANA protein stabilizes and activates c-Myc. Journal of Virology, 81(19), 10451
10459. Available from https://doi.org/10.1128/JVI.00804-07. Liu, J., Ma, Z., Liu, Y., Wu, L., Hou, Z., & Li, W. (2019). Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis. Oncology Letters, 18(3), 2500
2508. Available from https://doi.org/10.3892/ol.2019.10578. Madihi, S., Madihi, S., Syed, H., Lazar, F., Zyad, A., & Benani, A. (2020). A Systematic review of the current hepatitis B viral infection and hepatocellular carcinoma situation in mediterranean countries. BioMed Research International, 2020, 7027169. Available from https://doi. org/10.1155/2020/7027169. Mahmoudvand, S., Shokri, S., Taherkhani, R., & Farshadpour, F. (2019). Hepatitis c virus core protein modulates several signaling pathways involved in Hepatocellular carcinoma. World Journal of Gastroenterology, 25(1), 42
58. Available from https://doi.org/10.3748/wjg.v25.i1.42. Masood, R., Cai, J., Tulpule, A., Zheng, T., Hamilton, A., Sharma, S., Espina, B. M., Smith, D. L., & Gill, P. S. (2001). Interleukin 8 is an autocrine growth factor and a surrogate marker for Kaposi’s sarcoma. Clinical Cancer Research, 7(9), 2693
2702. Mattoscio, D., Casadio, C., Fumagalli, M., Sideri, M., & Chiocca, S. (2015). The SUMO conjugating enzyme UBC9 as a biomarker for cervical HPV infections. Ecancermedicalscience, 9, 534. Available from https://doi.org/10.3332/ecancer.2015.534. Mesri, E. A., Cesarman, E., & Boshoff, C. (2010). Kaposi’s sarcoma and its associated herpesvirus. Nature Reviews Cancer, 10(10), 707
719. Available from https://doi.org/10.1038/nrc2888. Mesri, E. A., Feitelson, M. A., & Munger, K. (2014). Human viral oncogenesis: A cancer hallmarks analysis. Cell Host and Microbe, 15(3), 266
282. Available from https://doi.org/ 10.1016/j.chom.2014.02.011. Nakahata, S., Chilmi, S., Nakatake, A., Sakamoto, K., Yoshihama, M., Nishikata, I., Ukai, Y., Matsuura, T., Kameda, T., Shide, K., Kubuki, Y., Hidaka, T., Kitanaka, A., Ito, A., Takemoto, S., Nakano, N., Saito, M., Iwanaga, M., Sagara, Y., & Morishita, K. (2021). Clinical significance of soluble CADM1 as a novel marker for adult T-cell leukemia/lymphoma. Haematologica, 106(2), 532
542. Available from https://doi.org/10.3324/ haematol.2019.234096. Nishioka, C., Takemoto, S., Kataoka, S., Yamanaka, S., Moriki, T., Shoda, M., Watanabe, T., & Taguchi, H. (2005). Serum level of soluble CD30 correlates with the aggressiveness of adult T-cell leukemia/lymphoma. Cancer Science, 96(11), 810
815. Available from https://doi. org/10.1111/j.1349-7006.2005.00106.x.

Current status of viral biomarkers for oncogenic viruses Chapter | 10

249

Nobel Prize. (2020). Available from: ,http://www.nobelprize.org.. Oura, K., Fujita, K., Morishita, A., Iwama, H., Nakahara, M. A. I., Tadokoro, T., Sakamoto, T., Nomura, T., Yoneyama, H., Mimura, S., Tani, J., Kobara, H., Okano, K., Suzuki, Y., & Masaki, T. (2019). Serum microRNA-125a-5p as a potential biomarker of HCV-associated hepatocellular carcinoma. Oncology Letters, 18(1), 882
890. Available from https://doi.org/ 10.3892/ol.2019.10385. Pan, C., Issaeva, N., & Yarbrough, W. G. (2018). HPV-driven oropharyngeal cancer: Current knowledge of molecular biology and mechanisms of carcinogenesis. Cancers of the Head & Neck, 3, 12. Available from https://doi.org/10.1186/s41199-018-0039-3. Pappa, K. I., Kontostathi, G., Lygirou, V., Zoidakis, J., & Anagnou, N. P. (2018). Novel structural approaches concerning HPV proteins: Insight into targeted therapies for cervical cancer (Review). Oncology Reports, 39(4), 1547
1554. Available from https://doi.org/10.3892/or.2018.6257. Park, H., Lee, M., Kim, W. D., Hong, Y. S., & Lee, H. (2016). Glycogen synthase kinase 3β and cyclin D1 expression in cervical carcinogenesis. Obstetrics & Gynecology Science, 59, 470
478. Available from https://doi.org/10.5468/ogs.2016.59.6.470. Petruzziello, A. (2018). Epidemiology of hepatitis B virus (HBV) and hepatitis C virus (HCV) related hepatocellular carcinoma. The Open Virology Journal, 12, 26
32. Available from https://doi.org/10.2174/1874357901812010026. Piano, M. A., Gianesello, L., Grassi, A., Del Bianco, P., Mattiolo, A., Cattelan, A. M., Sasset, L., Zanovello, P., & Calabro`, M. L. (2019). Circulating miRNA-375 as a potential novel biomarker for active Kaposi’s sarcoma in AIDS patients. Journal of Cellular and Molecular Medicine, 23(2), 1486
1494. Available from https://doi.org/10.1111/jcmm.14054. Poiesz, B. J., Ruscetti, F. W., Gazdar, A. F., Bunn, P. A., Minna, J. D., & Gallo, R. C. (1980). Detection and isolation of type C retrovirus particles from fresh and cultured lymphocytes of a patient with cutaneous T-cell lymphoma. Proceedings of the National Academy of Sciences of the United States of America, 77(12), 7415
7419. Available from https://doi. org/10.1073/pnas.77.12.7415. Qiao, D. D., Yang, J., Lei, X. F., Mi, G. L., Li, S. L., Li, K., Xu, C. Q., & Yang, H. L. (2017). Expression of microRNA-122 and microRNA-22 in HBV-related liver cancer and the correlation with clinical features. European Review for Medical and Pharmacological Sciences, 21(4), 742
747. Ringehan, M., McKeating, J. A., & Ulrike, P. (2017). Viral hepatitis and liver cancer. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1732), 20160274. Available from https://doi.org/10.1098/rstb.2016.0274. Roslind, A., Palle, C., Johansen, J. S., Christensen, I. J., Nielsen, H. J., & Mosgaard, B. J. (2020). Prognostic utility of serum YKL-40 in patients with cervical cancer. Scandinavian Journal of Clinical and Laboratory Investigation, 80(8), 687
693. Available from https:// doi.org/10.1080/00365513.2020.1846209. Rous, P. (1911). A sarcoma of the fowl transmissible by an agent separable from the tumor cells. Journal of Experimental Medicine, 13(4), 397
411. Available from https://doi.org/10.1084/ jem.13.4.397. Safaa, M., Marwa, M., & Noha, E. (2017). Microrna-150 and microrna-101 as diagnostic markers for hepatitis B virus related hepatocellular carcinoma in Egypt. Journal of Liver: Disease & Transplantation, 6, 156. Available from https://doi.org/10.4172/23259612.1000156. Sasaki, H., Nishikata, I., Shiraga, T., Akamatsu, E., Fukami, T., Hidaka, T., Kubuki, Y., Okayama, A., Hamada, K., Okabe, H., Murakami, Y., Tsubouchi, H., & Morishita, K. (2005). Overexpression of a cell adhesion molecule, TSLC1, as a possible molecular marker

250

Oncogenic Viruses Volume 2

for acute-type adult T-cell leukemia. Blood, 105(3), 1204
1213. Available from https://doi. org/10.1182/blood-2004-03-1222. Scapulatempo-Neto, C., Veo, C., Fregnani, J. H. T. G., Lorenzi, A., Mafra, A., Melani, A. G. F., Loaiza, E. A. A., Rosa, L. A. R., DE Oliveira, C. M., Levi, J. E., & Longatto-Filho, A. (2017). Characterization of topoisomerase II α and minichromosome maintenance protein 2 expression in anal carcinoma. Oncology Letters, 13(3), 1891
1898. Available from https:// doi.org/10.3892/ol.2017.5650. Schneider, J. W., & Dittmer, D. P. (2017). Diagnosis and treatment of Kaposi Sarcoma. American Journal of Clinical Dermatology, 18(4), 529
539. Available from https://doi.org/ 10.1007/s40257-017-0270-4. Seo, A. N., Kang, B. W., Bae, H. I., Kwon, O. K., Park, K. B., Lee, S. S., Chung, H. Y., Yu, W., Jeon, S. W., Kang, H., & Kim, J. G. (2019). Exon 9 mutation of PIK3CA associated with poor survival in patients with Epstein-Barr virus-associated gastric cancer. Anticancer Research, 39(4), 2145
2154. Available from https://doi.org/10.21873/anticanres.13328. Shah, U. A., Shah, N., Qiao, B., Acuna-Villaorduna, A., Pradhan, K., Adrianzen Herrera, D., Sica, R. A., Shastri, A., Mantzaris, I., Derman, O., Kornblum, N., Braunschweig, I., Ye, B. H., Verma, A., & Janakiram, M. (2020). Epidemiology and survival trend of adult T-cell leukemia/lymphoma in the United States. Cancer, 126(3), 567
574. Available from https:// doi.org/10.1002/cncr.32556. Shimizu, Y., Mizuno, S., Fujinami, N., Suzuki, T., Saito, K., Konishi, M., Takahashi, S., Gotohda, N., Tada, T., Toyoda, H., Kumada, T., Miura, M., Suto, K., Yamaji, T., Matsuda, T., Endo, I., & Nakatsura, T. (2020). Plasma and tumoral glypican-3 levels are correlated in patients with hepatitis C virus-related hepatocellular carcinoma. Cancer Science, 111(2), 334
342. Available from https://doi.org/10.1111/cas.14251. Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA Cancer Journal for Clinicians, 70(1), 7
30. Available from https://doi.org/10.3322/caac.21590. Silva Dalla Libera, L., Almeida de Carvalho, K. P., Enocencio Porto Ramos, J., Oliveira Cabral, L. A., de Cassia Goncalves de Alencar, R., Villa, L. L., Alves, R. R. F., Rabelo Santos, S. H., Aparecida dos Santos Carneiro, M., & Saddi, V. A. (2019). Human papillomavirus and anal cancer: Prevalence, genotype distribution, and prognosis aspects from midwestern region of Brazil. Journal of Oncology, 2019, 1
10. Available from https://doi.org/10.1155/ 2019/6018269. Smith, M. W., Yue, Z. N., Geiss, G. K., Sadovnikova, N. Y., Carter, V. S., Boix, L., Lazaro, C. A., Rosenberg, G. B., Bumgarner, R. E., Fausto, N., Bruix, J., & Katze, M. G. (2003). Identification of novel tumor markers in hepatitis C virus-associated hepatocellular carcinoma. Cancer Research, 63(4), 859
864. Song, Z., Lin, Y., Ye, X., Feng, C., Lu, Y., Yang, G., & Dong, C. (2016). Expression of IL-1α and IL-6 is associated with progression and prognosis of human cervical cancer. Medical Science Monitor, 22, 4475
4481. Available from https://doi.org/10.12659/MSM.898569. Sugimoto, A., Yamashita, Y., Kanda, T., Murata, T., & Tsurumi, T. (2019). Epstein-Barr virus genome packaging factors accumulate in BMRF1-cores within viral replication compartments. PLoS One, 14(9), e0222519. Available from https://doi.org/10.1371/journal. pone.0222519. Takemoto, S., Iwanaga, M., Sagara, Y., & Watanabe, T. (2016). Plasma soluble CD30 as a possible marker of adult T-cell leukemia in HTLV-1 carriers: A nested case-control study. Asian Pacific Journal of Cancer Prevention, 16(18), 8253
8258. Available from https://doi. org/10.7314/APJCP.2015.16.18.8253.

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Tamai, T., Uto, H., Takami, Y., Oda, K., Saishoji, A., Hashiguchi, M., Kumagai, K., Kure, T., Mawatari, S., Moriuchi, A., Oketani, M., Ido, A., & Tsubouchi, H. (2011). Serum manganese superoxide dismutase and thioredoxin are potential prognostic markers for hepatitis C virus-related hepatocellular carcinoma. World Journal of Gastroenterology, 17(44), 4890
4898. Available from https://doi.org/10.3748/wjg.v17.i44.4890. Tanaka, Y., Takahashi, Y., Tanaka, R., Miyagi, T., Saito, M., & Fukushima, T. (2019). Association of high levels of plasma OX40 with acute adult T-cell leukemia. International Journal of Hematology, 109(3), 319
327. Available from https://doi.org/10.1007/s12185018-02580-z. Tateyama, M., Yatsuhashi, H., Taura, N., Motoyoshi, Y., Nagaoka, S., Yanagi, K., Abiru, S., Yano, K., Komori, A., Migita, K., Nakamura, M., Nagahama, H., Sasaki, Y., Miyakawa, Y., & Ishibashi, H. (2011). Alpha-fetoprotein above normal levels as a risk factor for the development of hepatocellular carcinoma in patients infected with hepatitis C virus. Journal of Gastroenterology, 46(1), 92
100. Available from https://doi.org/10.1007/s00535-010-0293-6. Tulake, W., Yuemaier, R., Sheng, L., Ru, M., Lidifu, D., & Abudula, A. (2018). Upregulation of stem cell markers ALDH1A1 and OCT4 as potential biomarkers for the early detection of cervical carcinoma. Oncology Letters, 16(5), 5525
5534. Available from https://doi.org/ 10.3892/ol.2018.9381. Verma, S. C., & Robertson, E. S. (2003). Molecular biology and pathogenesis of Kaposi sarcoma-associated herpesvirus. FEMS Microbiology Letters, 222(2), 155
163. Available from https://doi.org/10.1016/S0378-1097(03)00261-1. Wang, Z., Jiang, C., Pang, L., Jia, W., Wang, C., Gao, X., Zhang, X., Dang, H., & Ren, Y. (2021). ANXA2 is a potential marker for the diagnosis of human cervical cancer. Biomarkers in Medicine, 15(1), 57
67. Available from https://doi.org/10.2217/bmm-2020-0629. White, M. K., Pagano, J. S., & Khalili, K. (2014). Viruses and human cancers: A long road of discovery of molecular paradigms. Clinical Microbiology Reviews, 27(3), 463
481. Available from https://doi.org/10.1128/CMR.00124-13. WHO. (2019). Available from: ,https://www.who.int/news-room/fact-sheets/detail/human-papillomavirus-(hpv)-and-cervical-cancer.. Yamada, K., Miyoshi, H., Yoshida, N., Shimono, J., Sato, K., Nakashima, K., Takeuchi, M., Arakawa, F., Asano, N., Yanagida, E., Seto, M., & Ohshima, K. (2021). Human T-cell lymphotropic virus HBZ and tax mRNA expression are associated with specific clinicopathological features in adult T-cell leukemia/lymphoma. Modern Pathology, 34(2), 314
326. Available from https://doi.org/10.1038/s41379-020-00654-0. Yang, J. D., Hainaut, P., Gores, G. J., Amadou, A., Plymoth, A., & Roberts, L. R. (2019). A global view of hepatocellular carcinoma: Trends, risk, prevention and management. Nature Reviews Gastroenterology and Hepatology, 16(10), 589
604. Available from https://doi. org/10.1038/s41575-019-0186-y. Yao, M., Zhao, J., & Lu, F. (2016). Alpha-fetoprotein still is a valuable diagnostic and prognosis predicting biomarker in hepatitis B virus infection-related hepatocellular carcinoma. Oncotarget, 7(4), 3702
3708. Available from https://doi.org/10.18632/oncotarget.6913. Young, L. S., Yap, L. F., & Murray, P. G. (2016). Epstein-Barr virus: More than 50 years old and still providing surprises. Nature Reviews Cancer, 16(12), 789
802. Available from https://doi.org/10.1038/nrc.2016.92. Zekri, A.-R. N., Kassas, M. E. L., Salam, E. S. T. A. E., Hassan, R. M., Mohanad, M., Gabr, R. M., Lotfy, M. M., Abdel-zaher, R. A. T., Bahnassy, A. A., & Ahmed, O. S. (2020). The possible role of Dickkopf-1, Golgi protein-73 and Midkine as predictors of

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hepatocarcinogenesis: A review and an Egyptian study. Scientific Reports, 10(1), 6. Available from https://doi.org/10.1038/s41598-020-62051-6. Zhao, T. (2016). The role of HBZ in HTLV-1-induced oncogenesis. Viruses, 8(2), 34. Available from https://doi.org/10.3390/v8020034. Zheng, J. (2015). Diagnostic value of MCM2 immunocytochemical staining in cervical lesions and its relationship with HPV infection. International Journal of Clinical and Experimental Pathology, 8(1), 875
880. Available from http://www.ijcep.com/files/ijcep0003901.pdf. Zuo, L. L., Zhang, J., Liu, L. Z., Zhou, Q., Du, S. J., Xin, S. Y., Ning, Z. P., Yang, J., Yu, H. B., Yue, W. X., Wang, J., Zhu, F. X., Li, G. Y., & Lu, J. H. (2017). Cadherin 6 is activated by Epstein-Barr virus LMP1 to mediate EMT and metastasis as an interplay node of multiple pathways in nasopharyngeal carcinoma. Oncogenesis, 6(12), 402. Available from https://doi.org/10.1038/s41389-017-0005-7.

Chapter 11

Bioinformatics serving oncoviral studies Virupaksha Ajit Bastikar1, Pramodkumar Pyarelal Gupta2, Alpana Bastikar3, Santosh Subhash Chhajed4 and Santosh Ajabrao Bothe5 1

Amity Institute of Biotechnology, Amity University Mumbai, Maharashtra, India, 2School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Navi Mumbai, Maharashtra, India, 3Department of Computer-Aided Drug Design, Navin Saxena Research and Technology Pvt. Ltd, Gandhidham, Gujarat, India, 4Department of Pharmaceutical Chemistry, METs Institute of Pharmacy, Nashik, Maharashtra, India, 5Saraswati College, Gaulkhed Road, Shegaon, Dist Buldhana, Maharashtra, India

Oncovirus cancer is a group of more than 100 different diseases. It can develop almost anywhere in the body (Cancer.net, 2020; What Is Cancer?, 2020a, 2020b). Initiation of cancer largely due to genetic changes interferes with the normal process, leading to uncontrolled cell growth. This huge cell mass transforms into a tumor, which can be benign or cancerous and can be malignant, which can grow and spread to other parts of the body. Whereas some types of cancers are nontumorous like leukemia, cancers like lymphoma and myeloma (What Is Cancer?, 2020a, 2020b) are tumorous. Apart from genetic mutations and other environmental factors such as chemical changes and change in lifestyle, approximately 12% of human cancers occur due to viruses and they are known as oncoviruses. A huge group of people harbors at least one of these oncoviruses, but few go on to develop into cancer. The path from oncovirus infection to the onset of cancer in humans involves a complex process. The viral factors and host interaction create a favorable microenvironment for oncogenesis. At present, seven human oncoviruses are known: Epstein
Barr virus (EBV), human papillomavirus (HPV), Hepatitis B and C viruses (HBV and HCV), human T-cell lymphotropic virus-1 (HTLV-1), human herpesvirus-8 (HHV-8), and Merkel cell polyomavirus (MCPyV) (Mui et al., 2017). As the first human oncovirus, EBV was first detected in Burkitt lymphoma cells by electron microscopy in 1964 (Epstein et al., 1964). Since then work in oncovirology has been carried out on a large scale to understand the host
virus pathogenesis. Globally, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00002-9 Copyright © 2023 Elsevier Inc. All rights reserved.

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around 20% of all cancers are caused by infectious agents and 12% are caused due to the oncoviruses, and of these, approximately 80% of viral cancers occur in the developing nations (Bouvard et al., 2009; Boyle & Levin, 2008; de Martel et al., 2012; Mui et al., 2017; zur Hausen, 2009). With the help of the existing biochemical techniques such as genomics and proteomics, the identification of virus and their role in the cancer pathogenesis could be investigated, but still knowledge about their microenvironment and interactions is lacking. Current computational techniques support the deep mining of information, and knowledge modeling with preexisting data helps in understanding the sequence-to-sequence mapping followed by structure elucidation, which brings more curated data into the picture. Bioinformatics, cheminformatics, and amalgamation of artificial intelligence play an important role in research in the field of oncovirology. The large datasets obtained from multiple biochemical experiments contain gigabytes of data related to the sequence, structure, and interactions of oncoviruses. Techniques such as computational genomics, computational proteomics, interactomics, sequence analysis, structure modeling, systems biology, protein modeling, and computer-aided drug design have significantly contributed to making research in this field cost-effective. Genomics and proteomics of oncoviruses and bioinformatics are significantly contributing to our understanding of the onset of disease and the mutation or pathogenic interactions underlying these diseases. A series of investigations into disease outbreaks and pathogenesis, genomic variation in hosts and pathogens, and host immune circumvention mechanisms in the identification of potential diagnostic markers and vaccine targets have been carried out. Currently, big data generated from high-throughput genomic experimental technologies applied on animal models and pathogens can be combined with the host genomics and patient health record systems to understand the trend and guide the treatment selection as well as potential drug and vaccine interactions (Bah et al., 2018). Merkel cells are mainly found in the hair follicles, certain mucosal tissues, and are also present in areas of the skin giving the sensation of touch (Spurgeon & Lambert, 2013). Merkel cell carcinoma (MCC) is an extremely contentious neuroendocrine carcinoma of the skin induced by either the integration of Merkel cell polyomavirus (MCPyV) and expression of viral T antigens or by ultraviolet-induced damage to the tumor genome from excessive sunlight exposure. Using the advancements in the area of genome science and sequencing techniques, the authors Starrett et al. have carried out a detailed study with a cohort of 71 MCC patients using deep sequencing with OncoPanel, a clinically designed next-generation sequencing assay targeting over 400 cancer-associated genes. PCR and IHC methods were implemented to validate the accuracy and sensitivity of the study and developed a hybrid capture bait set against the compete MCPyV genome, and indigenous software was design to detect the integration sites and structure. Recurrent somatic alterations common across MCC and alterations specific to each class of tumor were reported,

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associated with differences in overall survival (Starrett et al., 2020). Marc Wilkins in 1996 first ever used the term “Proteomics,” which means “PROTein complement of a genOME” (Wilkins et al., 1996). The proteome is a well-known concept now and can be defined as the global protein content of a cell represented in terms of localization, interactions, posttranslational modifications, and turnover, at a particular time. Proteomics consists of numerous applications of technologies for the identification and quantification of comprehensive proteins present in a cell, tissue, or whole organism. Proteomics-based technologies are employed in a wide range of research settings such as the identification and mining of diagnostic markers, vaccine production, understanding pathogenicity mechanisms, alteration of expression patterns in response to different signals, and interpretation of functional protein pathways in different diseases (Aslam et al., 2017). Virion proteomics is a powerful, dedicated, and unbiased methodology to achieve insights into the process of virus assembly and the host factors, including their intra- and interinteractions, important for understanding the infection and onset of disease. In hepatitis C virus (HCV), the infectious virions are assembled alongside endogenous lipoproteins and give rise to a chimeric lipoviral particle. These host-derived apolipoproteins coat the exterior of circulating particles, leading to the formation of a veiled pathogen, but the protein composition of HCV still remains unidentified. Lussignol et al. using MS-based analysis revealed and identified the novel viral and cellular proteins specifically associated with HCV virions and discovered an important interaction between the capsid protein and the nucleoporin Nup98, which is required for virion biogenesis (Lussignol et al., 2016).

11.1 Biological database With the advancement of tools and technologies for the investigation of biological organisms (including oncoviruses), there has been an exponential growth in the raw data generated by these technologies. Biological databases hosted by NCBI, EMBL, ExPASy, RCSB, and others are fueled by various research groups and organizations carrying out the research work on oncovirus. Some of the well-established databases include NCBI and EMBL (nucleotide, gene, and genome), Uniprot, NextProt, and PIR (protein sequence database), RCSB-Protein Data Bank (protein structural database), and PubChem, DrugBank, and ZINC (small chemical structure compounds and drugs). HPV strains cause 70% of cervical cancers and 90% of genital warts. The two types of HPV, i.e., HPV type 16 and 18, account for approximately 70% of the total cases (Braaten & Laufer, 2008; “Human papillomavirus (HPV) and cervical cancer,” 2020). With more biological data available, it will be easier for biologists and chemists to come up with dedicated solutions to diseases. As of March 2021, there had been a total of 9 reviewed and 8282 unreviewed protein sequences added to the UniProtKB

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and UniProt TrEMBL databases (Apweiler, 2004). KEGG Pathway database consists of information related to Epstein
Barr virus infection—Homo sapiens (human), pathway entry for hsa05169, pathways for Huan T-cell leukemia virus1 infection, hepatitis B and C—Homo sapiens (human), Kaposi sarcomaassociated herpesvirus infection—Homo sapiens (human), and human papillomavirus infection—Homo sapiens (human) (Ogata et al., 1999).

11.2 Sequence analysis Sequence analysis is the process of analyzing the biological data, DNA, RNA, or a protein sequence by comparing them pairwise or in multiples. The sequence analysis methods help us to understand the similarity and identity among the biological sequences and correlate their features, structures, functions, or evolution. The methodologies include using either one sequence as a target and comparing it with multifold high biological sequences present in the databases or identifying relationships among two or more biological sequences. Various tools such as BLAST and FASTA are widely used to compare an individual oncovirus biological sequence with other sequences from the database, the process also known as homology search. Tang et al. described the de novo genome assembly of viruses and missing strains, and novel virus strains were matched using the BLAST tool of NCBI with high sensitivity (Tang et al., 2013). Gene prediction and high-end sequencing techniques can provide us with the complete genome sequence of a virus within days. The next step toward understanding the details of a virus life cycle is to identify the whole viral genes and proteins. This information can provide critical insights on many occasions. For instance, for a team working on an antiviral drug design, promising drug targets would be those viral proteins that have identical major strains and are significantly different from the proteins in the host, e.g., humans (Mills, 2003). Numerous gene prediction online servers are available such as GenMark, Glimmer, FGENESH, and GenScan. Increasing population and deteriorating health condition is a major problem worldwide. In the process of disease diagnosis and therapy design, we often come across a situation where functional characterization of a protein is needed. Currently, few protein structures have been solved using experimental techniques, and a large dataset is not yet solved due to various experimental challenges. Computational modeling in structure biology enables scientists overcome such difficult biological challenges. Predicting the 3D structure of a protein from its amino acid sequence is one of the most challenging aspects of structural bioinformatics. Various techniques like homology modeling, fold recognition, and threading and ab-initio / de-novo based modeling are available to solve the protein 3D structure (Deng et al., 2018). Homology modeling, fold recognition, and threading are templated-based structure prediction methods, whereas ab-initio-based methods build the

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structure based on the first principles of physics, which does not rely on any previously solved structure (Deng et al., 2018). SwissModel, Phyre2, ITasser, and Robetta are well-known online protein structure prediction servers. Aarthy et al. modeled the E7 oncoprotein of human papillomavirus using the homology-based method and studied its conformation using molecular dynamics simulation (Aarthy et al., 2018). Implementing the immunoinformatics approach, Purnama and Kharisma carried out epitope mapping of capsid protein L1 from human papillomavirus, and the work supported in the development of a cervical cancer vaccine (Purnama & Kharisma, 2018).

11.3 Molecular dynamics simulations Molecular dynamics (MD) is an investigative approach in the identification of the location of atom, position, and its dynamic behavior within a given environment. The MD-based analysis also provides insights into the thermodynamic properties of the molecules. The MD study is widely used in the determination of protein and other biological structures and in the refinement of experimentally solved structures such as a 3D structure solved using X-ray crystallography and an NMR method (Polanski, 2009). Multifold protein sequences lead to the generation of a huge amount of data in comparison to structure, and the difference is huge, and structural bioinformatics help model the protein 3D structure using a computational modeling method to fulfil the biological research objectives. MD-based analysis helps refine and understand the protein conformational space for the approximate activity. The wide applications of MD not only explore the 3D structure of biological macromolecules but also play a wide role in drug design and discovery projects. A common strategy while determining conformational space is to perform the simulation at a very high temperature as this enhances the ability of the system to overcome energy barriers. Structures are then selected at regular intervals from the trajectory for subsequent energy minimization (Leach, 2007). MD can be carried out by using numerous programs or packages built for project-specific objectives (Han et al., 2019). AMBER (Assisted Model Building with Energy Refinement) was created for refining NMR structures. The name AMBER refers to both forcefield and MD program packages. The most widely used AMBER forcefield versions are AMBER94, AMBER99SB, and AMBER03, in addition to other AMBER forcefields such as AMBER-96, AMBER-GS, AMBER99φ, and AMBER99SB (Han et al., 2019; Sorin, 2021). CHARMM (Chemistry at HARvard Macromolecular Mechanics) refers to both program packages and forcefield, developed by Martin Karplus at Harvard University, USA. CHARMM molecular simulation program package has a broad application for many-particle systems, with energy function. There are a variety of enhanced sampling methods and multiscale techniques, including QM/MM, MM/CG, and a range of implicit solvent systems. CHARMM primarily targets biological systems, including peptides, proteins, prosthetic groups, small

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molecule ligands, nucleic acids, lipids, and carbohydrates in solution, crystals, and membrane environments. CHARMM also finds broad applications for inorganic materials with applications in material design (“CHARMM,” 2021; Han et al., 2019). GROMACS (GROningen Machine for Chemical Simulations) is a molecular dynamics package and is designed for MD simulations of proteins, nucleic acids, and lipids. Unlike AMBER and CHARMM, GROMACS does not have its own forcefield, and uses the forcefield of AMBER, CHARMM, GROMOS, and OPLS to run the MD simulations for the selected input (Han et al., 2019; Pronk et al., 2013). NAMD (NANnoscale Molecular Dynamics) is developed by the Theoretical and Computational Biophysics Group at the University of Illinois, Urbana-Champaign, USA, It acts as an interface and is compatible with other MD programs such as CHARMM (Han et al., 2019; Phillips et al., 2005). Desmond is developed by the D. E, Shaw Research group, which has its own novel algorithm to achieve high computing performance. Desmond also has the ability to import the AMBER, CHARMM, and OPLS forcefields to run MD simulations (Bowers et al., 2006; Han et al., 2019). One of the major fatal cancers in women is cervical cancer (CC) with a high rate of mortality worldwide. Typically, human papilloma virus (HPV) is one of the prime factors for cervical cancer. HPV 16 and HPV 18 were identified to be more virulent for cancer among others. E6 oncoprotein from HPV 16 of sequence length 158 aa and HPV 18 of sequence length 159 aa of Indian origin (NCBI-id: AMS04044, APO85550) share a sequence identity up to 52% (Fig. 11.1). Ramakrishnan et al. have explored the structural association between E6 oncoprotein from HPV 16 and that from HPV 18 in the cause of cancer. The protein 3D structure was modeled using the I-Tasser server, and MD simulation is carried out using the GROMACS package with the help of the AMBER03 forcefield for 50 ns to understand the dynamics. Due to the high degree of conformational fluctuation between the E6 oncoprotein of HPV-16 and that of PV-18, it is important to differentiate both the structures at various residual positions. However, the radius of gyration depicted the continuous fluctuation between 35 and 50 ns in the E6

FIGURE 11.1 Comparison between HPV 16 sequence length of 158 aa and HPV 18 sequence length of 159 aa from Indian origin (NCBI-id: AMS04044, APO85550).

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oncoprotein of HPV18 as compared to in the E6 oncoprotein of HPV18HPV16. This fluctuation shows the high compactness of E6 oncoproteins of HPV16 as compared to HPV18 (Akram Husain et al., 2018). The Epstein
Barr virus (EBV) is associated with a variety of cancers, and chronic active EBV infection is a severe systematic disease associated with a high rate of mortality and morbidity. Chung and Ishida performed the interaction and MD simulation based study of EBV-expressed latent membrane protein 1 (LMP1) with cellular signaling intermediate tumor necrosis factor receptor (TNFR)-associated factor 3 (TRAF3) using standard classical MD protocols. In a comparative analysis, the authors performed the MD simulation of TRAF3 with CD40, and a TNFR mimicked by LMP1 to effect EBV infection is also calculated under similar conditions. The MD simulation is carried out using the AMBER 9 suite. The outcome from both the MD of TRAF3 with CD40 and LMP1 revealed a stable conformation, which was not revealed in earlier X-ray structures. The stability and detailed interaction data from both the MD studies revealed a binding pattern, which might help in further new drug design or vaccine development (Chung & Ishida, 2011).

11.4 Computer-aided drug discovery Computer-based drug design (CADD) uses a computational approach in the design and development of compounds suitable for the desired activity as per the requirement. The advent of new computational techniques supported the drug discovery projects and drastically reduced the timeline, helping in the early detection of error and preventing late failures. Structure-based drug design (SBDD) and Ligand-based drug design (LBDD) are widely used approaches of CADD. In SBDD, the modeling starts with the presence of the 3D structure of target protein, and molecular docking and structure-based virtual screening are the major tools of SBDD. In case the 3D structure of the target protein could not be determined, we generally apply the LBDD approach and modeling, and QSAR and QSPR are the major tools for the LBDD. Numerous academic freeware and commercial expensive tools are available in the market that facilitate CADD. Singh et al. carried out a work involving the CADD-SBDD and LBDD approaches to identify the compounds analogous to (-)-Epigallocatechin-3-gallate (EGCG), a green tea component that inhibits the development of cervical cancer through apoptosis generation, cell cycle arrest, and regulation of gene expression. A shape-based screening was employed in Maestro 10.4 and the given template structure was screened against the ZINC and NCI databases. Further, the SBDD-based molecular docking was employed using Induced fit and GLIDE XP in Schrodinger. Post-docking, binding free energy calculation was carried out using the Prime MMGBSA method. The selective compounds with acceptable ADME properties, high binding affinity, and similar pharmacophoric interactions were studied for their dynamic behavior using MD simulations in GROMACS 5.16, and post-

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dynamics, binding free energy calculations were done using MMPBSA. Finally, molecules analogous to EGCG from the natural small molecule library were identified, and then a docking strategy was applied on the identified small molecules, which showed that the compounds ZINC49069570 and ZINC49115270 possess better docking score and binding energy in comparison with the EGCG and other identified molecules. The mean RMSD of the compound ZINC49069570 exhibits higher stability when compared to the apoprotein and the other selective molecules, including EGCG. A principal component analysis has been performed, which depicts that the compound ZINC49069570 has collective motions when compared with the EGCG. The compound ZINC49069570 possesses a minimal conformational phase space depicting that the compound can be a better inhibitor of the protein. The 100 ns MD simulation of ZINC49069570 and protein E7 complex exhibited a stable post dynamics binding free energy. Insights gained from the study could assist in the identification of novel inhibitors analogous to EGCG, which could shed light on the conformation of the protein, which, in turn, inhibits the oncogenic property of the protein E7 (Aarthy et al., 2020). Fig. 11.2 and Table 11.1 enlist few tools used in CADD. In a similar study, Talele et al. implemented LBBD methods, i.e., comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), to develop a QSAR model using 67 benzimidazole derivatives HCV NS5B polymerase inhibitors. The LBDD-based 3D QSAR model was initially performed based on the lowest energy conformations employing the atom fit alignment method. Secondly, the SBDD, receptorbased 3D QSAR model, was generated from the predicted binding conformations obtained by docking all NS5B inhibitors at the allosteric binding site of NS5B (PDB ID: 2DXS). The outcomes of the ligand-based model were superior as compared to the receptor-based model. Based on the ligand-based model, CoMFA r2cv values 5 0.630 and CoMSIA r2cv values 5 0.668. Based on the receptor-based model, CoMFA r2cv values 5 0.536 and for CoMSIA r2cv values 5 0.561. r2 values for ligand-based CoMFA and CoMSIA models were 0.734 and 0.800, whereas the r2 values for the receptor-based CoMFA and CoMSIA models were 0.538 and 0.639.

FIGURE 11.2 Screening of EGCG structural analogs. EGCG, Epigallocatechin-3-gallate.

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TABLE 11.1 Tools used in CADD (computer-based drug design). Sr no

Tool

Application

Freeware/commercial

1

BioVia Discovery studio

CADD package

Commercial

2

Schrodinger

CADD package

Commercial

3

Sybyl

CADD package

Commercial

4

MOE

CADD package

Commercial

5

VLife MDS

CADD package

Commercial

6

FlexX

CADD package

Commercial

7

Dock

Molecular docking

Free ware (Academic)

8

AutoDock Packages

Molecular docking

Free ware

9

Chimera

Visualization

Free ware

10

Pymol

Visualization

Free ware

11.5 Systems biology approach Systems biology is an integrative approach that connects the bits and pieces to accommodate the whole information in modeling, biochemical reactions, metabolic pathway, cells, tissue, organ, or a complete organism (Wanjek, 2011). It widely covers the paradigm of genomic, transcriptomic, proteomic, interactomics, molecular network modeling, pharmacokinetic, pharmacodynamic, and agent-based modeling in the design and development of the conceptual framework of metabolic pathways and analysis to view the diagrammatic representation of the healthy versus diseased models. Successful modeling of disease, the infection spread, and implementation of concepts are greatly assisted by the data standards (Gupta, 2018). Numerous approaches have been devised to model the metabolomics data in a mechanistic model such as kinetic or constraint-based models. These computational tools and developed models allow the researchers to explore new avenues in metabolism regulation. Considering the problem from the mathematical point of view, we can perform data integration in two main ways: variable constraints and parameter fitting. In variable constraints, the upper and lower limits of a metabolic reaction flux are fixed and the changes in the phenotype are calculated. In parameter fitting, we estimate or find the kinetic parameters such as the turnover rate or the Michaelis
Menten constant to predict or describe another metabolic state (Volkova et al., 2020). Tables 11.2 and 11.3 list the systems biology databases and tools. Despite significant advancements in cancerology, surgery, chemotherapy, or radiotherapy remains ineffective under certain conditions. Oncolytic virotherapy

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TABLE 11.2 Systems biology databases. Sr no

Name

Link

1

BioModel Database

https://www.ebi.ac.uk/biomodels/

2

BRENDA Database

https://www.brenda-enzymes.org/

3

IntAct

https://www.ebi.ac.uk/intact/

4

KEGG Pathway Database

https://www.genome.jp/kegg/pathway.html

5

MetaCyc

https://metacyc.org/

TABLE 11.3 System biology tools. Sr no

Name

Link

1

BioTapestry

http://www.biotapestry.org/

2

Cell Designer

http://www.celldesigner.org/

3

Copasi

http://copasi.org/

4

Cytoscape

https://cytoscape.org/

5

Systems Biology Workbench (SBW)

http://sbw.sourceforge.net/

6

Tinker Cell

http://www.tinkercell.com/

7

Simbiology Package— MATLABs

https://www.mathworks.com/products/ simbiology.html

opens a new perspective in the management of cancer disease. Laaroussi et al. have carried out an extensive mathematical modeling “Analysis of Multiple Delays Model for Treatment of Cancer with Oncolytic Virotherapy.” All simulations were carried out using the following parameters: growth rate constant (r), maximal tumor size (K), infection rate (βi), cell-to-cell fusion rate constant (ρ), infected cells death rate (δ), burst size of a virus (b), elimination rate of free virus particles (γ), and time to complete lytic cycle variables (τi). The authors concluded that if (reproductive ratio) R0 , 1, the virotherapy fails as the population of tumor cells increases and the population of infected tumor cells decreases, and if R0 . 1, the virotherapy success and treatment will reach the equilibrium point (El Alami laaroussi et al., 2019).

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11.6 Artificial intelligence approaches Artificial intelligence (AI) helps mimic human cognitive functions. Exponential growth in data in the healthcare sector has opened new avenues of learning and evaluation of data via a self-learning algorithm. The AI techniques can be applied to various types of healthcare data (structured and nonstructured). Before deploying AI in the biological and healthcare systems, the systems are needed to be trained through the data generated from the clinical and biological activities such as high-throughput experiments of genomics, proteomics, interactomics, metabolomics, drug interactions, images, screening, diagnosis, and treatment (Jiang et al., 2017). Hepatitis B or hepatitis C is one of the most common diseases worldwide and the cause of liver cancer. Diagnosis and treatment of hepatitis B and C are guided by liver biopsies (Keltch et al., 2014). The most accepted type of liver biopsy or hepatectomy is percutaneous liver biopsy, which involves the insertion of a thin needle through the abdomen into the liver and removing a small piece of tissue by a surgeon. The tissue is examined by a pathologist to determine the fibrosis stage in the range F0 (no damage) to F4 (cirrhosis) (Keltch et al., 2014; “Liver Resection,” 2021). As AI and data mining tools are widely accepted in clinical decision support systems (CDSS), the commonly applied techniques are Bayesian classifiers, decision trees, genetic algorithm, fuzzy logic, neural networks, support vector machines, and hybrid systems. Keltch et al. have explored a dataset of 424 hepatitis B and hepatitis C patients from publicly available datasets, considering the demographic and standard serum marker data (albumin, alkaline phosphate, cholinesterase, biluribin direct, gamma glutamyl transpeptidase, and gamma globulin) to predict the fibrosis stage and then compare these predictions with known biopsy results. The authors have implemented the Neuro3 NN method in an AI-CDSS based system and compared it with four other methods based on AI, Neural Network (NN), Decision Tree (DT), Naı¨ve Bayesian (NB), and Logistics Regression (LR), using the Weka 6.2 tool, to evaluate the best outcome. The comparative analysis resulted in a good prediction ranging from the F0 to F4 level of disease, i.e., insignificant fibrosis (F0 and F1) and significant fibrosis (F2, F3, and F4), to evaluate the accuracy of models in order to distinguish diseased cases from normal cases (Keltch et al., 2014).

11.7 Conclusion Overcoming the challenges facing the identification and diagnosis of oncoviruses and disease conditions is a global need. Numerous scientist groups and laboratories worldwide are involved in the design and development of new techniques, algorithms, and tools. With the amalgamation of high-throughput experimental outcomes, bioinformatics, and AI, accurate detection of these oncoviruses, oncovirus components, and their infection might be possible.

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References Aarthy, M., Kumar, D., Giri, R., & Singh, S. K. (2018). E7 oncoprotein of human papillomavirus: Structural dynamics and inhibitor screening study. Gene, 658, 159
177. Available from https://doi.org/10.1016/j.gene.2018.03.026. Aarthy, M., Panwar, U., & Singh, S. K. (2020). Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7. Scientific Reports, 10(1), 8661. Available from https://doi.org/10.1038/s41598-020-65446-7. Akram Husain, R. S., Rajakeerthana, R., Sreevalsan, A., Prema Jayaprasad, P., Ahmed, S. S. S. J., & Ramakrishnan, V. (2018). Prevalence of human papilloma virus with risk of cervical cancer among south Indian women: A genotypic study with meta-analysis and molecular dynamics of HPV E6 oncoprotein. Infection, Genetics and Evolution, 62, 130
140. Available from https://doi.org/10.1016/j.meegid.2018.04.029. Apweiler, R. (2004). UniProt: The Universal Protein knowledgebase. Nucleic Acids Research, 32(90001), 115D
119D. Available from https://doi.org/10.1093/nar/gkh131. Aslam, B., Basit, M., Nisar, M. A., Khurshid, M., & Rasool, M. H. (2017). Proteomics: Technologies and their applications. Journal of Chromatographic Science, 55(2), 182
196. Available from https://doi.org/10.1093/chromsci/bmw167. Bah, S. Y., Morang’a, C. M., Kengne-Ouafo, J. A., Amenga
Etego, L., & Awandare, G. A. (2018). Highlights on the application of genomics and bioinformatics in the fight against infectious diseases: Challenges and opportunities in Africa. Frontiers in Genetics, 9, 575. Available from https://doi.org/10.3389/fgene.2018.00575. Bouvard, V., Baan, R., Straif, K., Grosse, Y., Secretan, B., Ghissassi, F. El, & Cogliano, V. (2009). A review of human carcinogens—Part B: Biological agents. The Lancet Oncology, 10(4), 321
322. Available from https://doi.org/10.1016/S1470-2045(09)70096-8. Bowers, K. J., Chow, D. E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., . . . Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. In ACM/IEEE SC 2006 Conference (SC’06) (pp. 43
43). IEEE. https://doi.org/ 10.1109/SC.2006.54 Boyle, P., & Levin, B. (Eds.), (2008). World Cancer Report 2008. IARC Press. Available from https://www.cabdirect.org/cabdirect/abstract/20103010665. Braaten, K. P., & Laufer, M. R. (2008). Human papillomavirus (HPV), HPV-related disease, and the HPV vaccine. Reviews in Obstetrics & Gynecology, 1(1), 2
10. Available from http:// www.ncbi.nlm.nih.gov/pubmed/18701931. Cancer.net. (2020). Retrieved March 2, 2021, from https://www.cancer.net/cancer-types CHARMM. (2021). Retrieved February 22, 2021, from https://www.charmm.org/ Chung, W. C., & Ishida, T. (2011). An MD simulation of the decoy action of Epstein
Barr virus LMP1 protein mimicking the CD40 interaction with TRAF3. Theoretical Chemistry Accounts, 130(2
3), 401
410. Available from https://doi.org/10.1007/s00214-011-1006-9. de Martel, C., Ferlay, J., Franceschi, S., Vignat, J., Bray, F., Forman, D., & Plummer, M. (2012). Global burden of cancers attributable to infections in 2008: A review and synthetic analysis. The Lancet Oncology, 13(6), 607
615. Available from https://doi.org/10.1016/ S1470-2045(12)70137-7. Deng, H., Jia, Y., & Zhang, Y. (2018). Protein structure prediction. International Journal of Modern Physics B, 32(18), 1840009. Available from https://doi.org/10.1142/S021797921840009X. El Alami laaroussi, A., Hia, M. El, Rachik, M., & Ghazzali, R. (2019). Analysis of a multiple delays model for treatment of cancer with oncolytic virotherapy. Computational and Mathematical Methods in Medicine, 2019, 1
12. Available from https://doi.org/10.1155/2019/1732815.

Bioinformatics serving oncoviral studies Chapter | 11

265

Epstein, M., Achong, B., & Barr, Y. (1964). Virus particles in cultured lymphoblasts from Burkitt’s lymphoma. The Lancet, 283(7335), 702
703. Available from https://doi.org/ 10.1016/S0140-6736(64)91524-7. Gupta, P. P. (2018). Biological systems and pathway modeling approaches. Austin J Biotechnol Bioeng., 5(3), 1099. Han, X., Shin, W.-H., Christoffer, C. W., Terashi, G., Monroe, L., & Kihara, D. (2019). Study of the variability of the native protein structure. Encyclopedia of bioinformatics and computational biology (pp. 606
619). Elsevier. Available from https://doi.org/10.1016/B978-012
809633-8.20148
9. Human papillomavirus (HPV) and cervical cancer. (2020). Retrieved December 5, 2020, from https:// www.who.int/news-room/fact-sheets/detail/human-papillomavirus-(hpv)-and-cervical-cancer Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230
243. Available from https://doi.org/10.1136/svn-2017-000101. Keltch, B., Lin, Y., & Bayrak, C. (2014). Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients. Journal of Medical Systems, 38(8), 60. Available from https:// doi.org/10.1007/s10916-014-0060-y. Leach, A. R. (2007). Ligand-based approaches: Core molecular modeling. Comprehensive medicinal chemistry II (pp. 87
118). Elsevier. Available from https://doi.org/10.1016/ B0
08-045044-X/00246-7. Liver Resection. (2021). Retrieved April 21, 2021, from https://surgery.ucsf.edu/conditions
procedures/liver-resection.aspx#:B:text 5 A liver resection is the,a deceased donor (cadaver). Lussignol, M., Kopp, M., Molloy, K., Vizcay-Barrena, G., Fleck, R. A., Dorner, M., & Catanese, M. T. (2016). Proteomics of HCV virions reveals an essential role for the nucleoporin Nup98 in virus morphogenesis. Proceedings of the National Academy of Sciences, 113 (9), 2484
2489. Available from https://doi.org/10.1073/pnas.1518934113. Mills, R. (2003). Improving gene annotation of complete viral genomes. Nucleic Acids Research, 31(23), 7041
7055. Available from https://doi.org/10.1093/nar/gkg878. Mui, U., Haley, C., & Tyring, S. (2017). Viral oncology: Molecular biology and pathogenesis. Journal of Clinical Medicine, 6(12), 111. Available from https://doi.org/10.3390/ jcm6120111. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 27(1), 29
34. Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., & Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26 (16), 1781
1802. Available from https://doi.org/10.1002/jcc.20289. Polanski, J. (2009). Chemoinformatics. Comprehensive chemometrics (pp. 459
506). Elsevier. Available from https://doi.org/10.1016/B978-044452701-1.00006-5. Pronk, S., P´all, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., & Lindahl, E. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845
854. Available from https://doi.org/10.1093/bioinformatics/btt055. Purnama, E. R., & Kharisma, V. D. (2018). Epitope mapping of capsid protein L1 from human papillomavirus to development cervical cancer vaccine through computational study. Journal of Physics: Conference Series, 1108, 012096. Available from https://doi.org/ 10.1088/1742-6596/1108/1/012096. Sorin, E. J. (2021). FFAMBER. Retrieved January 5, 2021, from http://ffamber.cnsm.csulb.edu/ ffamber.php

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Spurgeon, M. E., & Lambert, P. F. (2013). Merkel cell polyomavirus: A newly discovered human virus with oncogenic potential. Virology, 435(1), 118
130. Available from https:// doi.org/10.1016/j.virol.2012.09.029. Starrett, G. J., Thakuria, M., Chen, T., Marcelus, C., Cheng, J., Nomburg, J., & DeCaprio, J. A. (2020). Clinical and molecular characterization of virus-positive and virus-negative Merkel cell carcinoma. Genome Medicine, 12(1), 30. Available from https://doi.org/10.1186/s13073020-00727-4. Tang, K.-W., Alaei-Mahabadi, B., Samuelsson, T., Lindh, M., & Larsson, E. (2013). The landscape of viral expression and host gene fusion and adaptation in human cancer. Nature Communications, 4(1), 2513. Available from https://doi.org/10.1038/ncomms3513. Volkova, S., Matos, M. R. A., Mattanovich, M., & Mar´ın de Mas, I. (2020). Metabolic modelling as a framework for metabolomics data integration and analysis. Metabolites, 10(8), 303. Available from https://doi.org/10.3390/metabo10080303. Wanjek, C. (2011). Systems biology as defined by NIH an intellectual resource for integrative biology. The NIH Catalyst, 19(6), 1
20. Available from https://irp.nih.gov/sites/default/ files/catalyst/catalyst_v19i6.pdf. What Is Cancer? (2020a). Retrieved December 15, 2020, from https://www.cancer.net/navigating-cancer-care/cancer-basics/what-cancer What Is Cancer? (2020b). Retrieved December 3, 2020, from https://www.cancer.org/cancer/cancer-basics/what-is-cancer.html Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19
50. Available from https://doi.org/10.1080/ 02648725.1996.10647923. zur Hausen, H. (2009). The search for infectious causes of human cancers: Where and why. Virology, 392(1), 1
10. Available from https://doi.org/10.1016/j.virol.2009.06.001.

Chapter 12

QSAR approach for combating cancer cells ˇ Said Byadi1, Aziz Aboulmouhajir1 and Crtomir Podlipnik2 1

Organic Synthesis, Extraction and Valorization Laboratory, Team of Extraction, Spectroscopy and Valorization, Faculty of Sciences Ain Chock, Hassan II University, Morocco, North Africa, 2 Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia

12.1 Introduction A molecule’s structural features determine its behavior in physical, chemical, biological, or environmental processes and are thus essential for understanding and modeling compound action. The quantitative structure
activity relationship (QSAR) methodology attempts to create a quantitative connection between a compound’s molecular structure and its biological activity. Similarly, the quantitative structure
property relationship aims to model the relationship between a chemical structure and a wide range of physical or chemical properties (Kubinyi, Folkers, et al., 1998). This chapter introduces readers to the QSAR approach to modeling, especially the anticancer activity of the small molecules. Although the amount of experimental data is constantly increasing, the number of newly synthesized or in silico engineered compounds increases much faster. Moreover, compounds for which no experimental evidence is available can be built in silico, thanks to virtual library screening. Indeed, their biological activities can also be modeled using a reliable model, correlating biological processes and structural features, as expressed (Muratov et al., 2020) in Eq. (12.1). Biological activity 5 fðdescriptorsÞ

ð12:1Þ

Hansch and Fujita (1964) should be credited with popularizing physicochemical properties and statistical methods in structure
activity relationship (SAR) studies. Hansch’s initial study involved the linear combination of many properties using multiple linear regression (MLR) to produce a quantitative model. Typically, the modeled properties were equilibrium constants, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00014-5 Copyright © 2023 Elsevier Inc. All rights reserved.

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K, in log units. Since log K is linearly related to free energy, this method is also known as the linear free energy relationship. The first QSAR models were developed based on the observation that partition coefficients (e.g., log P) are associated with biological results. In some instances, the interaction did not seem to be linear. Nonlinear models such as the parabolic and bilinear models can be used in this case to obtain a better fit between the model and the experimental data (Kubinyi, 1976a, 1976b). The Free
Wilson method, which was established in the early days of QSAR, is another technique in which biological behavior is associated with the inclusion of unique structural features in molecules. The reader is guided to the literature (Free & Wilson, 1964) for additional detail. Typically, a QSAR model is developed using a series of previously characterized compounds, thus constituting a training set. To correlate the measured molecular descriptors, which reflect the structural features that determine the property or behavior of a group of molecules, with biological activities, mathematical models are created. There are two types of models to build: classification models that categorize an entity (often referred to as SAR) and regression or correlation models that simulate an activity quantitatively (QSAR). The last method is known as modeling or, in chemometrics, calibration. Several scientists are using the QSAR approach as a powerful chemoinformatic tool for anticancer activity modeling, allowing them to discover new anticancer drugs, for chemotherapy (Alam & Khan, 2017) or immunotherapy (Qianhong, 2010). The generation of QSAR models is based on the following steps (Roy, Kar & Das, 2015), schematized in the flowchart presented in Fig. 12.1.

FIGURE 12.1 QSAR steps in anticancer activity modeling. QSAR, quantitative structure
activity relationship.

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Chemical database collection with biological activities. Structures drawing and database construction. Molecular descriptors calculation. Multivariate analysis. Model generation and correlation. Model examination and validation. Applicability domain. Model application to predict the activities of new compounds. This is the subject of the following section.

12.2 Handling and curation of chemical and biological data The primary goal of any QSAR research is to discover a relationship between biological data and compound molecular structures. The molecular data of all compounds used in the research are needed to set up a QSAR sample. In the last decade, several campaigns have been launched to gather chemical and biological evidence for the general public. These databases are precious for validating novel QSAR models and methods. ChEMBL (Gaulton et al., 2012) and PubChem (Kim et al., 2016) are the two major publicly accessible databases. ChEMBL includes chemical structural data as well as bioactivity data for thousands of drug targets. The latest edition (ChEMBL22) incorporates over 14.4 million bioassay data for about 2.0 million compounds and 11,200 targets. The PubChem database (Chen et al., 2005), administered by the National Institutes of Health in the United States, is another accessible repository for chemical and biological data. PubChem contains bioactivity reports from 1.1 million high-throughput screening systems with millions of values and about 94 million compounds compiled from over 70 depositing organizations. Furthermore, the accuracy of the input data in a dataset is a necessary prerequisite of every modeling analysis. Thus, data curation is vital for any QSAR research, particularly in recent years when the availability of compound databases in the public domain has increased drastically. Several recent studies (Tetko et al., 2008) have clearly shown that the nature of chemical descriptors has a much more significant impact on the prediction efficiency of QSAR models than the design of model optimization techniques. These findings demonstrate that having false constructs in a dataset has a significant impact on model efficiency. Indeed, models built with inaccurate data would produce incorrect models that are unstable for prediction. Therefore, it is critical to validate the consistency of primary data wherever possible before developing any model. On the other hand, biological evidence is often articulated in terms that cannot be explicitly used for QSAR modeling. Since QSAR is based on the relationship between free energy and equilibrium constants, the data for a QSAR model must be associated with biological signal-free energy

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variations. The logarithmic transformation converts asymmetrically distributed biological data to a nearly regular distribution. Thus, log [C] or log 1/ [C] are often used to express concentrations resulting from biological experiments. The chemical structures should be cleaned and standardized (duplicates removed, salts stripped, neutral formed, and canonical tautomer standardized) to enable rigorous model development (Fourches et al., 2010).

12.3 Structures drawing and database building Creating chemical structures by standard drawing software programs is highly time-consuming and virtually impossible. We now have drawing applications with built-in models, bonds limited to set lengths and angles, and other features that make drawing easier. Molecule editors can manipulate chemical structure representations in either a two-dimensional (2D) space or a three-dimensional (3D) space, using 2D or 3D computer graphics. 2D production is used to construct diagrams or to scan chemical databases. The drawing software applications run on the Windows, Mac operating systems, and Linux in some cases. Windows itself handles most functions (printing, screen and printer fonts, resolution, or some other device-wide parameters), and its setup is the deciding factor. Another significant factor is the program’s usability. The best-known software applications for chemical drawing publication content on Microsoft’s Windows platform are presented further (Kaushik, 2014): G G G G

G G G G

Accelrys (Symyx) Draw 4.1 Academic Edition. ChemBioDraw Ultra 17.0.0.206 (ChemDraw). DrawIt-KnowIt All Academic Edition 2020.1. ACD/ChemSketch 2020.2.9 Free and Commercial (Advanced Chemistry Development). Chemistry 4-D Draw 9.0.0 Pro (ChemInnovation Software). ChemDoodle 9.0.0 (iChemLabs LLC). Mestrenova NMR predict desktop 14.1.2 (Mesterlab Research). MarvinSketch 6.3.0 (ChemAxon Ltd).

12.4 Molecular descriptors In the mathematical model, molecular descriptors reflect structure properties. More than 5000 molecular descriptors have been established (Todeschini & Viviana, 2009), and they are classified depending on their nature (physicochemical, quantum, topological, etc.) (Todeschini & Viviana, 2009; Todeschini & Consonni, 2010) dimensionality, or structural representation.

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In addition to theoretical descriptors presented further, experimental descriptors such as log P are essential. The 0-D descriptors are derived solely from the counting of atoms and bonds (e.g., number of nitrogen atoms) and their composition (e.g., bond orders hybridization states). We may also apply the total or average of atomic properties such as weight, length, electronegativity, and others; these descriptors are simple to compute, but they do not distinguish enough when used alone (Toropov & Benfenati, 2007). The general formula of the molecular structure can be thought of as a one-dimensional (1D) description of compounds and provides 1D descriptors, as it defines the composition and structural features of a molecule in a way that can be conveniently measured and treated. Where a compound contains a fragment or a fundamental feature, we refer to fingerprint descriptors. If a particular bit of the string is set to 1 (true), the rest of the string is set to 0 (false). Each bit in this sequence represents a distinct fragment. A hash-coding algorithm assigns a particular collection of bits along with the fingerprint to each pattern of a molecule (Leszczynski et al., 2012). Topological indices are single-valued descriptors that can be determined from a molecule’s 2D graph representation. In contrast, fingerprints are usually encoded as binary bit strings, the configurations of which yield different effects. Biological behavior is often the product of a small molecule’s shape and electrostatic complementarity with a protein target structure. 3D descriptors are typically associated with a structure or a conformation, which is more informative than topological descriptors. Geometric descriptors, on the other hand, have several disadvantages, such as they are very hard to interpret. It is commonly unclear which conformation of a molecule is bioactive. As a result, a multiconformation method with statistical ratios for model construction must be used to derive the bioactive conformation for all compounds under analysis (Chandrasekaran et al., 2018).

12.5 Multivariate analysis A chemical database provides knowledge about different characteristics and properties of molecules, and a wide variety of methods for obtaining useful information from databases is available. Pattern detection, machine learning, data mining, and chemometrics are some of the names assigned to these techniques (Pirhadi et al., 2015). The learning process typically starts with discovering a database, which is then split into two subsets: a testing array used to produce the model (training set) and a test set used to validate the outcome. The used methods, which can be linear or nonlinear, employ the training set and attempt to learn from it. In other words, a mechanism that maps molecular descriptors into biological behavior must be discovered (Luco & Ferretti, 1997). The model’s

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ability to forecast the outcomes of test sets is used to approximate learning efficiency.

12.6 Multiple linear regression analysis MLR is the primary approach for correlating binding affinities with molecular descriptors. It is the first mathematical technique used in classical QSAR studies to generate model coefficients. It provides a model to a set of features using a straight line. Via least-squares fitting, the expected values are correlated to the empirical values, and the model that better reproduces the experimental values is used to predict the bioactivities of new compounds against the same limit (Kubinyi, 1976a, 1976b; Kubinyi, Folkers, et al., 1998). ˆ and χ i In MLR, the relationship between the dependent variable Y the independent variable demonstrates the explanatory variables equation: ˆ Y

5

b0

1

b1χ1

1

b2χ2

1

::: 1

bnχn

1

ei ;

ð12:2Þ

where b0 and bi are fixed parameters (y-intercept and grade, respectively), and ei are errors. The sum of squares of the variations between the observable and predicted values for each measurement in the unit can be minimized P 2using the least-squares criterion to estimate the equations. That is, ei will be minimized.

12.7 Principal component regression Many implementations of principal component regression (PCR) and similar procedures for estimating a dependent variable from many strongly clustered predictors have been made in QSAR. Xiao Li et al. related toxicity to physical and chemical descriptors QSAR by the PCR method (Kubinyi, 1976b). There are descriptions of the application of PCR in various fields of QSAR (Adisyahputra et al., 2014). In PCR, the solution vector is regressed on the principal components (PCs) rather than the original variables (Hemmateenejad et al., 2012). In general, a PCA is performed on X, with only a subset of all PCs kept as necessary. A multiple regression analysis of the response variable versus the reduced PCs collection is constructed using traditional least-squares estimation. Thus, the main advantage of PCR over multiple regression is that the number of variables is reduced to a few uncorrelated variables.

12.8 Partial least squares Wold et al. created partial least square (PLS), which is a generalization of MLR (Lorber et al., 1987; Wold et al., 2001), and it straddles the line of

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MLR and PCR, and both are rare cases of continuum regression. PCR factors catch the greatest amount of variation in the predictor (x) variables, while MLR seeks an individual feature that better compares predictor (x) variables with expected (Y) variables. The principle behind PLS regression is that both the independent matrix X and the dependent matrix Y can be generalized into a low-dimensional factor space and that the rates of the two blocks have a linear relationship. PLS seeks variables that include variation but still achieve correlation. Furthermore, the matrix Y’s columns are linear combinations of the fundamental variables. The following is the relation in matrix form: Y

5

XW;

ð12:3Þ

where W(m k) is the matrix of coefficients representing linear combinations and Y is the new matrix whose columns form “artificial variables,” obtained by the linear combination of the fundamental variables; after this transformation, MLR is applied to table Y in place of X.

12.9 Kernel partial least squares Kernel partial least squares (KPLS) has been used for nonlinear multivariate consistency estimation (Chandrasekaran et al., 2018), and process monitoring (Zhang & Hu, 2011) over the last decade. KPLS (Rosipal & Trejo, 2001) is a new nonlinear PLS technique used to solve nonlinear problems. Through the use of a kernel function, the original input data are nonlinearly translated into linear ones in high-dimensional space. As a result, KPLS preserves the benefits of PLS and has a strong nonlinear mapping capability. In contrast to other nonlinear PLS methods, such as spline PLS (Wold, 1992), quadratic PLS (Abdel-Rahman & Lim, 2009), and neural network PLS (Xing et al., 2014), KPLS requires only linear algebra in high-dimensional space, making it as simple to perform as linear PLS. Furthermore, by using various types of kernel functions, KPLS can accommodate a wide variety of nonlinearities.

12.10 Artificial neural network As a modeling system, the artificial neural network (ANN) has become a familiar and effective chemoinformatic tool (Habibi-Yangjeh et al., 2006; Su & Zhou, 2006). Unlike traditional statistical approaches, ANN-based techniques do not need prior knowledge of the numerical form of the relationship connecting the variables (Aoyama et al., 1990), making the ANN ideal for extrapolating the complex and unstable relationships between the biological occurrence and the 2D or 3D configuration of the compounds. ANN is one of the most effective approaches for determining the relationship between nonlinear descriptors. ANN is made up of nodes or neurons and the connections (weights) that link them. The neurons in an ANN are arranged in layers

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(input, covered, and output), with monodirectional connections. The neurons in the neighboring layers are fully related, but there are no connections between the neurons in the same layer. The architecture calculates a numerical output value for a given mathematical input vector. A formal neuron adds up incoming signals compounded by relation weights, subtracts a threshold value, and uses a transfer function to measure an output signal. A hidden layer of neurons has the following sigmoid transport functions: tf 5

1 ð1 1 e2 inputt Þ

ð12:4Þ

where tf is the transport function that restricts the neuron’s output signal to values between 0 and 1.

12.11 Other methods Generative topographic mapping (GTM) (Jaworska et al., 2005) is a nonlinear mapping approach used successfully in various data processing domains. Each point in the low-dimensional (typically 2D) latent space is mapped onto the manifold embedded in the initial descriptor space in GTM (Sidorov et al., 2017). Ferreira invented gene expression programming (GEP) in 1999 based on genetic algorithms and genetic programming (GP). GEP employs the same kind of diagram representation as GP, but the entities evolved by GEP (translation trees) are gene expression. Instead of considering the whole dataset, Local Lazy Regression (LLR) extracts a prediction by locally interpolating the nearby examples of the query that are considered significant based on a distance metric.

12.12 Classification-based QSAR approaches The term “K-means” refers to a kind of partitioning (k-means) and is a vector quantization method derived from signal processing that aims to divide observations into k clusters, with each observation belonging to the cluster with the closest mean (cluster centers or cluster centroid). James MacQueen used K-means partitioning (k-means) for the first time in 1967 (Macqueen, 1967)—a nonhierarchical sorting method that can be used when the number of relations present in the subjects or cases is recognized. Overall, the kmeans process yields precisely k distinct clusters. The following sentences highlight the algorithm’s benefits: 1. The k-means algorithm is standard because it is easy to understand and apply. 2. It has intellectual simplicity and quickness. 3. It is applicable to vast volumes of data by picking a good notion of distance.

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It should be remembered that the data were separated into a “training package” for model generation and an “external evaluation collection” for external validation (Burden et al., 2000).

12.13 QSAR model generation Using statistical methods, firstly, we remove the duplicates and clean the data; then, we divided our database into a training set (70% of the compounds) and a validation set (30% of the compounds) using k-means partitioning as the classification method (R´acz et al., 2015). We select the most relevant descriptors that carry the maximum amount of information to avoid redundancy and collinearity between the descriptors. We construct the predictive models that connect structure-based descriptors with experimentally measured affinity using the statistical method whatever are linear or nonlinear. Then, we estimate the correlation coefficient (R2) and standard deviation (SD) by modeling the training (internal validation).

12.14 Model examination and validation Evaluate the predictiveness of the model by evaluating the validation coefficient and root mean square error (RMSE) base based on the prediction set (external validation). There are two main kinds of QSAR model validation: internal and external validation.

12.15 Internal validation Internal methods of validating a model include least-squares fit (R2), crossvalidation (Q2), modified R2 (R2 adj), root-mean-squared error (RMSE), and bootstrapping (Bajorath, 2004). 2 32 P P P N XY 2ð XÞð YÞ 6 7 R2 5 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð12:5Þ P 2 P 2 P 2 P 2 5 ð½N X 2ð XÞ ½N Y 2ð YÞ Þ Least-squares fitting is the most widely used internal model validation process. This validation approach is close to linear regression in that the fitting coefficient is R2 adj for evaluating predicted and observational behaviors. The RMSE approach can be used to assess the fit of QSAR models. This approach is used to assess whether or not the model preserves the predictive efficiency shown in R2. The RMSE method calculates the difference between the mean of empirical values and estimated biological activities: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n X ðy^ 2ym Þ2 RMSE 5 & i ; ð12:6Þ n21 i51

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ˆ denote the experimental and expected behavior for various where y and Y compounds in the training sample, ym denotes the mean of the experimental operations, and n denotes the number of compounds in the dataset. Even though the model has a good R2 value (0.7), large RMSE values (around 1) represent the model’s poor ability to predict events correctly. The RMSE values for a good statistical model should be lower than 0.4 (Byadi et al., 2020). However, better R2 and RMSE values are not indicative of model validity. As a result, alternative parameters must be produced to demonstrate the predictiveness of models. Cross-validation (Q2), a common approach for internal validation of a QSAR model, replicates the regression on subsets of the database several times.

12.16 External validation Internal evaluation is suitable for judging the consistency and goodness of the model. The only drawback of this approach is the model’s lack of predictability when extended to an entirely new dataset. We use external validation to ensure the predictability and applicability of the generated QSAR model to predict untested molecules. The external validation estimates the predictive ability of a QSAR model to analyze the expected and experimental activities of an external test set of compounds not used in model production (Guha & Jurs, 2005; Zefirov & Palyulin, 2001). The predictive potential is also dependent on the determination coefficient Q2 test between the observed and predicted activities of the test set; the higher the Q2 measure ( . 0.5), the better the model’s efficiency. Golbraikh and Tropsha suggested R2 as a correlation coefficient between expected and actual events (Kubinyi, Hamprecht, et al., 1998).

12.17 Applicability domain The applicability domain (AD) of a QSAR has been defined (Netzeva et al., 2005) as the response and the chemical structure space in which the model makes predictions with given reliability. Netzeva et al. (2005) and Jaworska et al. (2005) have reviewed methods for defining an AD and made several recommendations for their determination and use. They suggested that the concept of an AD is the task of the model maker rather than the model consumer and that the starting point should be the publishing of all training set compounds, including all structures and descriptors. A QSAR model cannot be called a general model since it is based on a small number of compounds that do not cover the entire chemical space. There are many approaches for evaluating a QSAR model’s applicability domain; the leverage approach is the most widely used. The one mentioned above is based on the dependent variable’s uniform residuals variance, with the distance between the descriptor values and their means being referred to

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as leverage (Roy, Kar & Ambure, 2015). Whether a compound’s residual and leverage surpass the h 5 3 p/n threshold (where p is the number of descriptors plus one and n is the number of observations included in the training set), the compound is assumed to be beyond the applicability scope of the elaborated model (Netzeva et al., 2005). The hi-value is estimated for each compound in the initial space of the independent variables (xi) using the following rule: L5X

iT

ðX T XÞ21

Xi

½i 5 1; 2; 3::k:

ð12:7Þ

The Williams map, a scatter plot of the uniform cross-validated residuals versus leverages (or hat values) hi, was used to approximate and visualize the final model’s applicability area: hi 5 mi ðMt MÞ21 mi t :

ð12:8Þ

Another validation method is to use the model to estimate the affinity from the decoy and the active datasets, and use binary metrics to generate the receiver operating characteristic curves (ROC curves) (Byadi et al., 2020). Gaspar et al. took a different approach, applying a likelihood-dependent applicability domain to GTM models and RF, kNN, M5P, and PLS models based on the original or GTM descriptors (Gaspar et al., 2015).

12.18 Model application for the prediction of compounds activity After building the QSAR models using different statistical methods and validating them with statistical parameters, we also assess their predictability. Usually, the developed and validated QSAR models were used to examine chemical structure sets, such as the Pubchem, PubMed, or Zinc database. After screening the library, we found active compounds against the same target; we evaluated the activities of these compounds by an anchoring approach stimulating the interaction of the compounds with the site binding of the protein; finally, we examined the stability of the complex formed by molecular dynamics.

References Abdel-Rahman, A. I., & Lim, G. J. (2009). A nonlinear partial least squares algorithm using quadratic fuzzy inference system. Journal of Chemometrics, 23(10), 530
537. Available from https://doi.org/10.1002/cem.1249. Adisyahputra, A., Mudasir, M., Nuryono, N., Azis, A., & Tahir, I. (2014). QSAR study of insecticides of phthalamide derivatives using multiple linear regression and artificial neural network methods. Indonesian Journal of Chemistry, 2014, 94
101. Available from https://doi. org/10.22146/ijc.21273.

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Alam, S., & Khan, F. (2017). 3D-QSAR studies on Maslinic acid analogs for anticancer activity against breast cancer cell line MCF-7. Scientific Reports, 7(1), 1
13. Available from https:// doi.org/10.1038/s41598-017-06131-0. Aoyama, T., Suzuki, Y., & Ichkawa, H. (1990). Neural networks applied to quantitative structure-activity relationship analysis. American Chemical Society, 1, 2583
2590. Available from https://doi.org/10.1021/jm00171a037. Bajorath, J. (2004). Chemoinformatics concepts, methods, and tools for drug discovery (275). American Standards Institute, 524
524. Burden, F. R., Ford, M. G., Whitley, D. C., & Winkler, D. A. (2000). Use of automatic relevance determination in QSAR studies using Bayesian neural networks. Journal of Chemical Information and Computer Sciences, 40(6), 1423
1430. Available from https://doi.org/ 10.1021/ci000450a. ˇ & Aboulmouhajir, A. (2020). Fingerprint-based 2DByadi, S., Hachim, E., Sadik, K., Podlipnik, C., QSAR models for predicting Bcl-2 inhibitors affinity. Letters in Drug Design & Discovery, 17, 1206
1215. Available from https://doi.org/10.2174/1570180817999200414155403. Chandrasekaran, B., Tekade, R. K., & Design, F. (2018). Computer-aided prediction of pharmacokinetic (ADMET) properties. Dosage form design parameters. Academic Press. Chen, J., Swamidass, S. J., Dou, Y., Bruand, J., & Baldi, P. (2005). ChemDB: A public database of small molecules and related chemoinformatics resources. Bioinformatics (Oxford, England), 21(22), 4133
4139. Available from https://doi.org/10.1093/bioinformatics/bti683. Fourches, D., Muratov, E., & Tropsha, A. (2010). Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling, 50(7), 1189
1204. Available from https://doi.org/ 10.1021/ci100176x. Free, S. M., & Wilson, J. W. (1964). A mathematical contribution to structure-activity studies. Journal of Medicinal Chemistry, 7(4), 395
399. Available from https://doi.org/10.1021/ jm00334a001. Gaspar, H. A., Baskin, I. I., Marcou, G., Horvath, D., & Varnek, A. (2015). GTM-based QSAR models and their applicability domains. Molecular Informatics, 34(6
7), 348
356. Available from https://doi.org/10.1002/minf.201400153. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P. (2012). ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(D1), 1100
1107. Available from https://doi.org/10.1093/nar/gkr777. Guha, R., & Jurs, P. C. (2005). Interpreting computational neural network QSAR models: A measure of descriptor importance. Journal of Chemical Information and Modeling, 45(3), 800
806. Available from https://doi.org/10.1021/ci050022a. Habibi-Yangjeh, A., Danandeh-Jenagharad, M., & Nooshyar, M. (2006). Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR. Journal of Molecular Modeling, 12(3), 338
347. Available from https://doi.org/10.1007/s00894005-0050-6. Hansch, C., & Fujita, T. (1964). ρ-σ-π analysis. A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 1616
1626. Available from https://doi.org/10.1021/ja01062a035. Hemmateenejad, B., Miri, R., & Elyasi, M. (2012). A segmented principal component analysisregression approach to QSAR study of peptides. Journal of Theoretical Biology, 305, 37
44. Available from https://doi.org/10.1016/j.jtbi.2012.03.028.

QSAR approach for combating cancer cells Chapter | 12

279

Jaworska, J., Nikolova-Jeliazkova, N., & Aldenberg, T. (2005). QSAR applicability domain estimation by projection of the training set in descriptor space: A review. ATLA Alternatives to Laboratory Animals, 33(5), 445
459. Available from https://doi.org/10.1177/ 026119290503300508. Kaushik, M. (2014). A review of innovative chemical drawing and spectra prediction computer software. Mediterranean Journal of Chemistry, 3(1), 759
766. Available from https://doi. org/10.13171/mjc.3.1.2014.04.04.16. Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., & Bryant, S. H. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44(D1), D1202
D1213. Available from https://doi.org/10.1093/nar/gkv951. Kubinyi, H. (1976a). Quantitative structure-activity relationships. IV. Non-linear dependence of biological activity on hydrophobic character: A new model. Arzneimittel-Forschung, 26(11), 1991
1997. Available from http://europepmc.org/abstract/MED/1037231. Kubinyi, Hugo (1976b). Quantitative structure-activity relationships. 2. A mixed approach, based on hansch and free-Wilson analysis. Journal of Medicinal Chemistry, 19(5), 587
587. Kubinyi, Hugo, Folkers, G., & Martin, Y. C. (1998). 3D QSAR in drug design. Kluwer Academic. Available from https://doi.org/10.1007/0-306-46857-3. Kubinyi, Hugo, Hamprecht, F. A., & Mietzner, T. (1998). Three-dimensional quantitative similarityactivity relationships (3D QSiAR) from SEAL similarity matrices. Journal of Medicinal Chemistry, 41(14), 2553
2564. Available from https://doi.org/10.1021/jm970732a. Leszczynski, J., Kaczmarek-kedziera, A., Puzyn, T., Reis, M. G. P. H., & Shukla, M. K. (2012). Handbook of computational chemistry (p. 2365) Springer. Available from https://doi.org/ 10.1007/978-3-319-27282-5. Lorber, A., Wangen, L. E., & Kowalski, B. R. (1987). A theoretical foundation for the PLS algorithm. Journal of Chemometrics, 1(1), 19
31. Available from https://doi.org/10.1002/ cem.1180010105. Luco, J. M., & Ferretti, F. H. (1997). QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. Journal of Chemical Information and Computer Sciences, 37(2), 392
401. Available from https://doi.org/ 10.1021/ci960487o. Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. Mathematics, 233, 281
297. Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., Isayev, O., Curtalolo, S., Fourches, D., Cohen, Y., Aspuru-Guzik, A., Winkler, D. A., Agrafiotis, D., Cherkasov, A., & Tropsha, A. (2020). QSAR without borders. Chemical Society Reviews, 49(11), 3525
3564. Available from https://doi.org/10.1039/d0cs00098a. Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. T., Van De Sandt, J. J. M., Tong, W., & Yang, C. (2005). Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA Alternatives to Laboratory Animals, 33(2), 155
173, https://doi.org/DOI 10.1016/j.ympev.2010.04.030. Pirhadi, S., Shiri, F., & Ghasemi, J. B. (2015). Multivariate statistical analysis methods in QSAR. RSC Advances, 5(127), 104635
104665. Available from https://doi.org/10.1039/ c5ra10729f.

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Qianhong, Z. (2010). QSAR for anticancer activity by using mathematical descriptors (Issue July). http://purl.umn.edu/93639 Rosipal, R., & Trejo, L. J. (2001). Kernel partial least squares regression in reproducing Kernel Hilbert space. Journal of Machine Learning Research, 2, 97
123. Roy, K., Kar, S., & Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 145, 22
29. Available from https://doi.org/10.1016/j.chemolab.2015.04.013. Roy, K., Kar, S., & Das, R. N. (2015). A primer on QSAR/QSPR modeling. Springer International Publishing. Available from https://doi.org/10.1007/978-3-319-17281-1. R´acz, A., Bajusz, D., & He´berger, K. (2015). Consistency of QSAR models : Correct split of training and test sets, ranking of models and performance parameters. SAR and QSAR in Environmental Research, 26(7), 21
25. Available from https://doi.org/10.1080/ 1062936X.2015.1084647. Sidorov, P., Viira, B., Davioud-Charvet, E., Maran, U., Marcou, G., Horvath, D., & Varnek, A. (2017). QSAR modeling and chemical space analysis of antimalarial compounds. Journal of Computer-Aided Molecular Design, 31(5), 441
451. Available from https://doi.org/10.1007/ s10822-017-0019-4. Su, Q., & Zhou, L. (2006). QSAR modeling of AT1 receptor antagonists using ANN. Journal of Molecular Modeling, 12(6), 869
875. Available from https://doi.org/10.1007/s00894-006-0105-3. ¨ berg, T., Todeschini, Tetko, I. V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., O R., Fourches, D., & Varnek, A. (2008). Critical assessment of QSAR models of environmental toxicity against tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling, 48(9), 1733
1746. Available from https://doi.org/10.1021/ci800151m. Todeschini, R., & Consonni, V. (2010). Molecular descriptors for chemoinformatics (2, pp. 1
252). Deutsche Nationalbibliografi. Available from https://doi.org/10.1002/9783527628766. Todeschini, R., & Viviana, C. (2009). Molecular Descriptors for Volumes I & II. Wiley, 1265
1265. Toropov, A. A., & Benfenati, E. (2007). SMILES as an alternative to the graph in QSAR modelling of bee toxicity. Computational Biology and Chemistry, 31(1), 57
60. Available from https://doi.org/10.1016/j.compbiolchem.2007.01.003. Wold, S. (1992). Nonlinear partial least squares modelling II. Spline inner relation. Chemometrics and Intelligent Laboratory Systems, 14, 71
84. Wold, S., Sjo¨stro¨m, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109
130. Available from https:// doi.org/10.1016/S0169-7439(01)00155-1. Xing, J., Luo, R., Guo, H., Li, Y., Fu, H., Yang, T., & Zhou, Y. (2014). Chemometrics and intelligent laboratory systems radial basis function network-based transformation for nonlinear partial least-squares as optimized by particle swarm optimization : Application to QSAR studies. Chemometrics and Intelligent Laboratory Systems, 130, 37
44. Available from https://doi.org/10.1016/j.chemolab.2013.10.006. Zefirov, N. S., & Palyulin, V. A. (2001). QSAR for boiling points of “small” sulfides. are the “high-quality structure-property-activity regressions” the real high quality QSAR models? Journal of Chemical Information and Computer Sciences, 41(4), 1022
1027. Available from https://doi.org/10.1021/ci0001637. Zhang, Y., & Hu, Z. (2011). Multivariate process monitoring and analysis based on multi-scale KPLS. Chemical Engineering Research and Design, 89(12), 2667
2678. Available from https://doi.org/10.1016/j.cherd.2011.05.005.

Chapter 13

Human papillomaviruses and their carcinogens effect Elamrani Elhassani Salma and Bahia Bennani Laboratory of Human Pathology, Biomedicine and Environment, University Sidi Mohammed Ben Abdellah of Fez, Fez, Morocco

13.1 Introduction Papillomaviruses represent a large family of viruses that infect mucosal and cutaneous epithelia and which are implicated in several cancers. The virus particles are nonenveloped icosahedrons (T 5 7) with a diameter of 50
55 nm. The encapsidated genome is circular double-stranded DNA (Day & Schelhaas, 2014; Louie et al., 2008). The large group of human papillomaviruses (HPVs) including more than 220 genotypes is classified into five genera (α,β, γ, μ and ν) (Chiantore et al., 2020). From identified genotypes, those with mucosal tropism were grouped in the genus Alphapapillomavirus, and those with cutaneous tropism were included in the three genera: Alphapapillomavirus, Betapapillomavirus, and Gammapapillomavirus (De Villiers et al., 2004; Tadlaoui et al., 2019). The HPV classification is based on the nucleotide sequence of the capsid protein L1 gene. The mucosal types are also classified according to their carcinogenic potential (Molet et al., 2019). However, the molecular biology of HPV is very complex. Three oncogenes (E5, E6, and E7) modulate the transformation process, two regulatory proteins (E1 and E2) modulate transcription and replication, and two structural proteins (L1 and L2) constitute the viral capsid (De Villiers et al., 2004). The E6 and E7 products interfere with the p53 and pRB functions, respectively, and deregulate the cell cycle (Furumoto & Irahara, 2002).

13.2 Epidemiology of human papillomaviruse HPV is one of the most common causes of sexually transmitted diseases worldwide. An estimated 6.2 million people are infected every year (Larkin, 2006). HPV 16 and 18 are the most common high-risk (HR)-HPV types Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00008-X Copyright © 2023 Elsevier Inc. All rights reserved.

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found worldwide, which are currently preventable by vaccination (Aubin et al., 2007). According to the World Health Organization (WHO), the highest prevalence of HPV was observed among young people (,25 years old), with an adjusted value of 24.0%, while a second peak of prevalence was observed among middle-aged groups (45
50 years) (WHO, 2017; Tadlaoui et al., 2019). In human cancer etiology, a major discovery has been the recognition that cervical cancer is a rare consequence of infection by some mucosatropic types of HPV (Jenkins & Xavier Bosch, 2019). HPV infection and its association with cervical cancer were first recognized by Meisels & Fortin (1976), who demonstrated that HPV DNA was present in approximately 70%
80% of cervical carcinomas (Furumoto & Irahara, 2002). In fact, HPV DNA is associated with several different anogenital cancers other than cervicals like the vulva, vagina, anus, and penis. It has also been identified in head and neck cancers, in the oral cavity, the oropharynx, and the larynx in both sexes. In men, 80%
85% of anal cancers and approximately 50% of penile cancers are associated with HPV infection. In women, HPV DNA is prevalent in 36%
40% of vulvar cancer cases and close to about 90% of vaginal cancers (Giuliano et al., 2008). Mainly HPV types 16 and 18 are identified as causative agents of at least 90% of cervical cancer and are also etiologically linked to more than 50% of other anogenital cancers (Finzer et al., 2002). The etiologic role of HPV in nonmelanoma skin cancer is also discussed. Its role was first demonstrated in patients with the rare hereditary disease epidermodysplasia verruciformis (EV). Those patients are often found to have flat warts and macular lesions. In 2014, a study on the involvement of HPVs in skin carcinogenesis proved the immortalization and transformation properties of some HPVs, and this was related to the E6 and E7 proteins (viral oncoproteins) that can support the promotion and the progression of the epidermal tumor in the presence of UVR (Aubin, 2014). Different types belonging to β-HPV species 2 are detected in cutaneous squamous cell carcinoma (CSCs) with a higher frequency than in healthy skin and other skin cancers. In fact, study showed that 12.0% of healthy skin samples were positive for HPV DNA, compared with 22.5% of actinic keratoses (AKs), 17.5% of basal cell carcinomas, and 25.6% of CSCs (Forslund et al., 2007).

13.3 Human papillomaviruse classification As mentioned above, the HPVs classification is based on the nucleotide sequence of the capsid protein L1 gene. The mucosal types belong to the Alphapapillomavirus genus and were also classified into three categories according to their carcinogenic potential: low risk (LR), intermediate risk, and HR (Table 13.1) (De Villiers et al., 2004). Thus, genotypes 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, and 82 were classified as HR

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283

TABLE 13.1 Classification of HPVs (human papillomaviruses) according to their carcinogenic potential. Classification

Types

High risk

16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, 82

Intermediate risk

26, 53, 66

Low risk

6, 11, 13, 40, 42, 43, 44, 54, 61, 70, 72, 81, 89

HPVs not shown in the table are considered as indeterminate risk (Aubin et al., 2007; World Health Organization, 2017).

HPVs, the types 26, 53 and 66 as probably HR HPVs and the types 6, 11, 13, 40, 42, 43, 44, 54, 61, 70, 72, 81, and 89 were classified as LR HPVs. Other types were considered indeterminate risks (Table 13.1) (Molet et al., 2019). Cutaneous HPVs belong to different genera, of which the Betapapillomavirus genus contains five distinct species (ß1: HPV 5, 8, 12, 14, 19, 20, 21, 24, 25, 36, 47, and 93; ß2: HPV 9, 15, 17, 22, 23, 37, 38, and 80; ß3: HPV 49, 75, and 76; ß4: HPV 92; and ß5: HPV 96), the genus Gammapapillomavirus contains seven HPV types (HPV 4, 48, 50, 60, 65, 88, and 95), the genus Mu-papillomavirus contains two HPV types (HPV 1 and 63), and the genus Nu-papillomavirus contains type 41 (Andersson et al., 2008). Even if most of Alpha-papillomavirus are mucosal types, some types are cutaneous as α2 (HPV 3, 10, 28, 29, 77, 78, and 94), α4 (HPV 2, 27, and 57), and α8 (HPV 7, 40, 43, and 91) species (De Villiers et al., 2004). Several studies have shown that the different types of cutaneous HPV, mainly belonging to beta and gamma genera, are widely present on the surface of the skin in the general population. In fact, some sequencing studies in Sweden reported that the β-HPV genus is the most frequently detected genus in keratinocytes of infected skin (Bzhalava et al., 2015). The HPV types found in macular lesions are called EV-HPV types and include HPV types 5, 8, 9, 12, 14, 15, 17, and 19
25 (Pfister, 1992). HPV types 5 and 8 are associated with EV, and this infection can progress to CSCs (Louie et al., 2008). Other β-HPV species 2 are also detected in CSCs with a higher frequency than in healthy skin and other skin cancers (12.0% of healthy skin samples were positive for HPV DNA, compared with 22.5% of AKs, 17.5% of basal cell carcinomas, and 25.6% of CSCs) (Forslund et al., 2007).

13.4 Human papillomavirus transmission HPVs are responsible for skin and mucosal epithelial infections and their transmission can be direct or indirect since they retain their infectious capacity in the external environment (Doorbar et al., 2015).

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13.4.1 Vertical transmission HPV vertical transmission can occur from the father or mother to the child. Thus, the virus can be transmitted to the embryo, fetus, or baby during pregnancy or childbirth (perinatal and intrauterine transmission). It can also occur at the time of fertilization via infected oocyte or spermatozoon (Sabeena et al., 2017; Syrja¨nen, 2010).

13.4.2 Horizontal transmission The most frequent mode of HPV horizontal transmission is sexual activity. There is currently strong epidemiological evidence of the sexual activity role, with or without penetration, in the HPV transmission (Liu et al., 2016). Horizontal transmission is also possible through indirect contact with soiled objects (clothes and underwear, etc.) or contaminated contact surfaces (toilets, bath, pool floor, etc.) (Tadlaoui et al., 2019). Nevertheless, the virus can also be transmitted, but rarely, between sexual partners through the hands (Liu et al., 2016).

13.5 Structure, genomic organization, and viral proteins All papillomavirus genomes had a common structure and genomic organization. They are nonenveloped, icosahedron capsid particles with a doublestranded circular DNA of approximately 8 kbp of size (de Sanjose´ et al., 2018). This small genome can be divided into three major regions: early, late, and a long control region (LCR or noncoding region [NCR]) which regulates the expression of the open reading frames (ORFs) (Fig. 13.1) (Zheng & Baker, 2006). The three regions are separated by two polyadenylation sites: early and late ones (Tadlaoui et al., 2019).

FIGURE 13.1 HPV 16 structure and viral proteins. HPV, human papillomaviruse. From de Sanjose´, S., Brotons, M. & Pavo´n, M. A. (2018). The natural history of human papillomavirus infection. Best Practice and Research: Clinical Obstetrics and Gynaecology, 47, 2
13. https:// doi.org/10.1016/j.bpobgyn.2017.08.015.

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13.5.1 The long control region The LCR region is a segment of about 850 bp (10% of the HPV genome), located between L1 and E6/E7. This region has no protein-coding function but bears the origin of replication as well as multiple transcription factors binding sites that are important in the regulation of RNA polymerase IIinitiated transcription from viral early as well as late promoters (Zheng & Baker, 2006).

13.5.2 The early region The early region contains ORFs encoding proteins that are necessary for viral DNA replication and oncogenesis, namely ORFs E1, E2, E4, E5, E6, and E7 (Table 13.2) (Dillner, 1990). The E6 and the E7 ORFs play a key role in cellular neoplastic transformation processes. The E6 protein binds p53 and promotes its degradation, whereas the E7 protein binds and inactivates pRb. These viral oncoproteins determine cell cycle entry and inhibition of p53-mediated apoptosis (Suh et al., 2014).

13.5.2.1 E1 The E1 gene encodes E1 viral protein of 73-kDa. It is a phosphoprotein that composed of a long amino acid (Aa) sequence varying from 593 to 681 depending on the HPV type (Chiang et al., 1992). This protein binds to a specific DNA sequence called E1BS (E1 binding sites) in the viral origin of replication and assembles into hexameric complexes involving a second viral protein called E2 (Humans et al., 2007). TABLE 13.2 Main functions of the proteins of HPV. Protein

Function

E1

Assists episomal replication DNA helicase

E2

Transcription factor

E4

Assists packaging of virus

E5

Prevents cell differentiation

E6

Prevents cell differentiation promotes p53 degradation

E7

Prevents cell-growth arrest/differentiation-inhibits inhibitors of E2F transcription factor

L1

Major capsid protein (structural)

L2

Minor capsid protein assists packaging of DNA

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13.5.2.2 E2 The E2 gene encodes an E2 protein of 40
45 kDa divided into three functional domains. The C-terminal domain that recognizes and bind 12-bp palindromic DNA sequences, defined as E2-BSs; the middle region called “the hinge region” that regulate the stability of some E2 proteins and determine their nuclear localization in others; and the A-terminal domain essential to regulate the transcription and the viral DNA replication through the interaction with E1 protein (Kim et al., 2000). Moreover, E2 interacts with L2 and leads to the amplification of viral DNA to facilitate the production of new viral progeny (Cobo, 2012). 13.5.2.3 E4 The E4 gene encodes 17 kDa E4 protein located in the E region, overlapping with E2. It is a fusion product with a 5-amino acid sequence from the Nterminus of E1 expressed as E1^E4. E4 is the first protein expressed primarily at later stages followed by late proteins L1 and L2 and is the most abundant viral protein in the virus life cycle (Humans et al., 2007). E4 plays a role in facilitating and supporting viral genome amplification, regulation of late gene expression, control of virus maturation, and the meditation of virus release (Cobo, 2012). 13.5.2.4 E5 The E5 encodes 7
10 kDa E5 protein primarily localized in the intracellular endoplasmic reticulum and Golgi apparatus membranes. This protein plays a role in carcinogenesis that results in stimulating cells proliferation by interacting with the E7 protein and the formation of activating complexes with growth factor receptors (Esteˆva˜o et al., 2019). It also plays a vital role in cell signaling modulation by its association with the vacuolar proton ATPase (Straight et al., 1995). E5 plays a role in decreasing cell death and then promoting the accumulation of cells with abnormal DNA genetic mutations and consequently promoting the malignancy process (Oh et al., 2010). This protein plays role in virus protection against cellular immunity (Suprynowicz et al., 2008; Tadlaoui et al., 2019). Furthermore, E5 inhibits gap junctions by decreasing connexin 43 expression. This phenomenon is often found in transformed cells (Mougin et al., 2008). 13.5.2.5 E6 The E6 gene encodes 16
18 kDa E6 protein. It is one of the three wellestablished oncoproteins that are associated with the malignant progression of HPV-infected cells (Howie et al., 2009). Its main function is the ability to

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bind and degrade the tumor suppressor protein p53 through the protein ligase E6 associated protein (Cobo, 2012). The binding of E6 protein to p53 leads to its rapid degradation, and the eclipse in the G1 phase, DNA repair mechanisms, and apoptosis are done (Alp Avcı, 2012). E6 protein binds to other several cellular proteins such as the proteins involved in cell polarity and motility, tumor suppressors and inducers of apoptosis, as well as the replication proteins and DNA repair factors. It also induces the expression and activity of telomerase and further cell immortalization (Cobo, 2012).

13.5.2.6 E7 The E7 gene encodes 15 kDa protein E7. It is an accessory protein that is not encoded by all papillomaviruses. It plays a central role in the human papillomavirus life cycle, reprogramming the cellular environment to be conducive to viral replication (Roman & Munger, 2013). E7 protein interacts with pRb and mitotically interactive cellular proteins such as cyclin-E, causing stimulation of cellular DNA synthesis and cell proliferation (Alp Avcı, 2012). In addition to binding pRb, this protein can bind to p107 and p130. The interaction with these pocket proteins underlies the ability of E7 to immortalize cells and to abrogate normal responses of DNA damage (Humans et al., 2007). E6 and E7 proteins inhibit p53 and pRb proteins functions and cause uncontrolled proliferation and immortalization of the cells (Perrard, 2019). 13.5.2.7 E3 and E8 E3 and E8 proteins were recently identified in the early gene region and found only in a few papillomavirus types (HPV 1, 11, 16, 31, 33). A fusion protein, E8^E2C, may play a role in the control of viral copy number as well as in the stable maintenance of HPV episomes (Alp Avcı, 2012). 13.5.3 The late region: L1 and L2 The late region of HPV consists of about 2500 base pairs that codes for the viral capsid proteins: L1 and L2 (Table 13.2) (Beutner & Tyring, 1997). L1 codes for the major structural protein of papillomavirus and has a weight of about 55
60 kDa. It is highly immunogenic, presents conformational virus-neutralizing epitopes, and could be used to detect HPV antibodies in the sera of patients with high specificity (Cobo, 2012; Humans et al., 2007). L1 has the ability to self-assemble into virus-like particles (VLPs) which is widely used in HPV prophylaxis vaccine production (Rautava & Syrja¨nen, 2012).

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L2 is the minor capsid protein of papillomavirus with a size of 70 kDa. It contributes to the interaction of the virion with the cell surface (Cobo, 2012; Humans et al., 2007).

13.6 Human papillomavirus replication cycle Papillomaviruses and their natural host tissue are perfectly adapted to each other. They exploit the cellular machinery for their own purposes (Mun˜oz et al., 2006). HPV penetrates cutaneous or mucosal epithelium through microlesions and infects the basal layer, where they bind to and enter into cells. For infection maintenance, the virus has to infect an epithelial stem cell (Doorbar, 2005). The HPV genome is replicated in an episomal form in the nucleus of epithelial cells. The replication cycle is completed on two times. First, the viral genome is replicated into a copy number and carried on for different periods of time at this low copy number within the initially infected competent cells (Doorbar, 2005). Second, after the basal cells are pushed to the suprabasal compartment, they lose their ability to divide and initiate the terminal differentiation program. In this compartment, papillomaviruses replicate and take advantage of the disintegration of epithelial cells that occurs following their natural renewal in the superficial layers to be released into the environment (Mun˜oz et al., 2006). The mechanisms of HPV entry are not fully explained. It has been shown that some HPV types use heparan sulfate for attachment to the cell surface and an integrin would be necessary for the virus entry (Giroglou et al., 2001). Recently, it has been reported that LN5 (Laminine 5), uniquely epithelial laminin secreted as a heterotrimeric complex by migrating and basal keratinocytes, may function in vitro as an extracellular transreceptor for infection (Culp et al., 2006). The successful transfer of virus particles from LN5 to membrane-associated receptors on adjacent cells appears to be mediated by the expression of α6 integrin, which binds LN5 (Culp et al., 2006). The caveola-mediated endocytosis pathway is reported to be part of the entry process (Bousarghin et al., 2003). Nonenveloped DNA viruses, HPV-16 and HPV-58, are usually internalized by clathrin-coated vesicles. The virions are then transported to the nucleus via the microtubules and actin microfilaments of the cytoskeleton and decapsidation occurs just before the viral DNA enters the nucleus to begin replication (Coursaget et al., 2007). The viral genome replication in stem cells is under the control of early proteins E1 and E2. This is known as the establishment phase, which allows the production of 50
100 copies of viral DNA per cell. This phase takes place during the S phase of the cell cycle and is called nonproductive because there is no production of virions. In maintenance phase, viral genomes occurs and consists to maintain a constant number of HPV genomes during cell divisions (Lim et al., 1998). Then, the rolling circle replication of the viral DNA occurs (Flores & Lambert, 1997). After that, the L1 and L2

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proteins are expressed in the most superficial layers of the epithelium, and the genome is encapsulated leading to the production of new infectious virions. The virions are released into the environment with the desquamating cells and the mucosa becomes very infecting that increases the risk of HPV transmission (Monsonego, 2007).

13.7 Infection evolution Most HPV infections are nonpersistent and are known as acute. According to Virgin et al. (2009), acute infection is defined as a nonequilibrium process that results in either clearance of the infection, host death, or chronic infection. However, the importance of viral persistence in HPV is related to its ability to induce some cancers. This has led most researchers to focus on chronic infections and to relatively neglect acute ones because cancers usually occur after several years of infection (Alizon et al., 2017). In fact, most infections progress to viral clearance which results in spontaneous healing. When the virus remains latent in the cells, the genome remains either in an extrachromosomal form (episomal) or integrated into the cellular genome (Denis et al., 2008). Thus, detection of HPV in many specimens may result not only from recent acquisition or re-infection, but also from recurrent detection of latent infection, self-inoculation from other epithelial sites (e.g., anus), or transient deposition of viral nucleic acid (Baay et al., 2011; Gravitt & Winer, 2017). In fact, HPV infection is related to various cutaneous and mucosal lesions, benign or malignant, affecting both children and adults. Cutaneous warts, vulvar warts, and mosaic plantar warts are some of the lesions related to HPV infection. Vulvar and mosaic plantar warts are mainly due to different HPV genotypes notably (HPV2, 27, and 57 (genus α), HPV4 (genus γ), and HPV1 (genus μ)). These warts can be resorbed on 24 months or persist for several additional months (Zaidane, 2017). Persistent infection with beta-HPV types seems to play a potential role in the initiation of nonmelanoma skin cancers. This association is still widely discussed (McBride & Mu¨nger, 2018). Infections with mucous HPV-HR types can also be cleared or persistent. In this late case, the virus can cause deregulation of the cell cycle, which promotes the proliferation of infected cells and their immortalization. This can lead to precancerous lesions and cancers of the cervical, vulva, vagina, penis, and many other regions (Lepiller et al., 2021). The progression from simple infection to cancer is a multistep process passing through precancerous lesions notably “low-grade squamous intraepithelial lesion” (LSIL) and “high-grade squamous intraepithelial lesion” (HSIL) successively. Sometimes, direct progression from infection to HSIL is noted (Denis et al., 2008). Usually, the time required for the development of severe dysplasia is one to several decades, but this period is sometimes shortened to 1
2 years (Hantz et al., 2005). The prevalence of HR genital HPV infections is age-dependent with two peaks of high rates, one at 25 years of age and the other at 50 years

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of age. In contrast, the incidence of LR types decreases with age (Denis et al., 2008; Mun˜oz et al., 2004).

13.8 Molecular mechanisms of HPV-induced carcinogenesis Carcinogenesis is a complex and multistep process requiring the acquisition of several genetic and/or epigenetic alterations (Lehoux et al., 2009). In fact, the inability to develop effective cell-mediated immunity to clear or control infection results in persistent infection. This infection is associated with cancer progression and can lead to several modifications such as excessive cell proliferation, deficient DNA repair, and accumulation of genetic damage in the infected cell (Doorbar et al., 2012). The oncoprotein E6, in association with its associated protein E6-AP protein, activates telomerase which allows the maintenance of telomere length during successive divisions and represents a primordial step in immortalization which leads to persistent infection and cell transformation (Mougin et al., 2008). This association leads to rapid degradation of tumor suppressor p53 via the ubiquitin-proteasome pathway (Fig. 13.2), which causes inhibition of the

HPV E6

P53

HPV

RB

P16

P53 RB HPV E7

P21 RB

CykD1 CDK4/6

E2F CykE

CDK2 P RB

P P

S G2

E2F G1

M

FIGURE 13.2 Mechanism of action of the human papillomavirus (HPV) on cell cycle regulation. From Suh, Y., Amelio, I., Guerrero Urbano, T. & Tavassoli, M. (2014). Clinical update on cancer: Molecular oncology of head and neck cancer. Cell Death & Disease, 5, e1018
e1018. https://doi.org/10.1038/cddis.2013.548.

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proapoptotic functions of p53 and bypass of the p53-mediated checkpoints (Scheffner et al., 1990; Suh et al., 2014). The E7 oncoprotein enters into competition with the E2F transcription factor for binding to the pRb tumor suppressor, displacing E2F. E2F activates genes implicated in cell cycle progression through the G1 to S phases, including cyclin A, E, and DNA polymerase, causing inactivation of checkpoints and regulatory pathways, and ultimately promoting cellular proliferation and transformation (Fig. 13.2) (Dyson et al., 1989; Suh et al., 2014). Retinoblastoma protein (pRb) is a negative regulator of the cyclin-dependent kinase inhibitor p16, and thus inactivation of pRb results in p16 upregulation. This can be identified by immunohistochemistry in HPV-associated tumor samples and represents a biologically significant surrogate marker for HPV oncoprotein expression (Suh et al., 2014; Weinberger et al., 2006). To progress from G1 to S cell cycle phase, cells have to pass the G1 restriction point that is under the control of the retinoblastoma protein (pRb). pRb binds and represses E2F transcriptional factors. Mitogenic signaling through CyclinD1/CDK4 or CyclinD1/CDK6 phosphorylates pRb, promoting E2F release. CyclinE/CDK2 completes pRb phosphorylation, allowing S-phase entry. HPV affects the cell cycle by using the two viral oncoproteins: E6 and E7 (Lehoux et al., 2009; Suh et al., 2014).

13.9 Mechanisms of cell transformation HPV, as mentioned before, targets stem cells of the squamous epithelium. The complete life cycle of the virus involves three phases, with a sequential expression of viral genes leading to viral DNA replication and the production of highly infectious virions. Viral DNA integration occurs and leads to the overexpression of two viral oncoproteins: E6 and E7. These proteins, in combination with E5, promote the immortalization and transformation of infected cells (Pre´tet et al., 2007). Two scenarios explain the progression from immortalization to malignant cells. The first one focuses on that alteration in host cell DNA might interact with viral oncoproteins to allow progression from immortalization to transformation (Hausen, 2000). The second hypothesis is that a distinct signaling cascade within or between cells stops the progression of immortalized cells to malignant tissue. Either oncogene transcription or viral oncoprotein expression may be controlled by the retinoic acid receptor or by cytokines such as transforming growth factor, interferon, or tumor necrosis factor α (Zur Hausen, 2002). These data suggest that cytokine-mediated intercellular control has an important role in suppressing malignant transformation, even in immortalized cells. Progression to malignant cells requires a genetic change in the pathways controlling intracellular or intercellular signaling. The chromosomal

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instability found in HPV infection could lead to such genetic changes (Hausen, 2000; Zur Hausen, 2002).

13.10 Conclusion HPV is one of the few viruses definitively linked to human malignancies and especially to carcinoma. It has revealed much about the pathways leading to cellular transformation. Further elucidation of the viral and cellular events that lead to cancer will help to find ways to treat HPV infections or prevent malignant transformation of benign growths. Otherwise, HPV cancers can be prevented if diagnosis and treatment are done at early stages.

Acknowledgments Our gratitude goes to the Team of Human Pathologies, Biomedicine, and Environment laboratory for providing their support and encouragement.

References Alizon, S., Murall, C. L., & Bravo, I. G. (2017). Why human papillomavirus acute infections matter. Viruses, 9(10), 293. Available from https://doi.org/10.3390/v9100293. Alp Avcı, G. (2012). Genomic organization and proteins of human papillomavirus. Mikrobiyoloji Bulteni, 46(3), 507
515. Available from http://europepmc.org/abstract/MED/ 22951665. Andersson, K., Waterboer, T., Kirnbauer, R., Slupetzky, K., Iftner, T., De Villiers, E. M., Forslund, O., Pawlita, M., & Dillner, J. (2008). Seroreactivity to cutaneous human papillomaviruses among patients with nonmelanoma skin cancer or benign skin lesions. Cancer Epidemiology Biomarkers and Prevention, 17(1), 189
195. Available from https://doi.org/ 10.1158/1055-9965.EPI-07-0405. Aubin, F. (2014). Implication des HPV dans la carcinogene`se cutane´e. Revue Francophone Des Laboratoires, 2014, 16
17. Available from https://doi.org/10.1016/s1773-035x(14)72680-2. Aubin, F., Pretet, J.-L., Mougin, C., & Riethmuller, D. (2007). Infection a` Papillomavirus humains. Annales de Dermatologie et de Ve´ne´re´ologie, 134, 94
99. Available from https:// doi.org/10.1016/s0151-9638(07)89005-3. Baay, M. F. D., Francois, K., Lardon, F., Van Royen, P., Pauwels, P., Vermorken, J. B., Peeters, M., & Verhoeven, V. (2011). The presence of Y chromosomal deoxyribonucleic acid in the female vaginal swab: Possible implications for human papillomavirus testing. Cancer Epidemiology, 35(1), 101
103. Available from https://doi.org/10.1016/j.canep.2010.10.005. Beutner, M. P. K. R., & Tyring, M. P. S. (1997). Human papillomavirus and human disease. The American Journal of Medicine, 102(5A), 9
15. Available from https://doi.org/10.1016/ s0002-9343(97)00178-2. Bousarghin, L., Touze´, A., Sizaret, P. Y., & Coursaget, P. (2003). Human papillomavirus types 16, 31, and 58 use different endocytosis pathways to enter cells. Journal of Virology, 77(6), 3846
3850. Available from https://doi.org/10.1128/JVI.77.6.3846-3850.2003. Bzhalava, D., Mu¨hr, L. S. A., Lagheden, C., Ekstro¨m, J., Forslund, O., Dillner, J., & Hultin, E. (2015). Deep sequencing extends the diversity of human papillomaviruses in human skin. Scientific Reports, 4, 5807. Available from https://doi.org/10.1038/srep05807.

Human papillomaviruses and their carcinogens effect Chapter | 13

293

Chiang, C. M., Ustav, M., Stenlund, A., Ho, T. F., Broker, T. R., & Chow, L. T. (1992). Viral E1 and E2 proteins support replication of homologous and heterologous papillomaviral origins. Proceedings of the National Academy of Sciences, 89(13), 5799
5803. Available from https://doi.org/10.1073/pnas.89.13.5799. Chiantore, M. V., Mangino, G., Iuliano, M., Capriotti, L., Di Bonito, P., Fiorucci, G., & Romeo, G. (2020). Human papillomavirus and carcinogenesis: Novel mechanisms of cell communication involving extracellular vesicles. Cytokine and Growth Factor Reviews, 51, 92
98. Available from https://doi.org/10.1016/j.cytogfr.2019.12.009. Cobo, F. (2012). Human papillomavirus infections: From the laboratory to clinical practice. Human papillomavirus infections: From the laboratory to clinical practice (pp. 1
149). Elsevier Ltd. Available from https://doi.org/10.1533/9781908818171. Coursaget, P., Touze´, A., Bousarghin, L., Fleury, M., & Monsonego, J. (2007). Immunoge´nicite´ et tole´rance (pp. 469
473). Paris: Springer. Available from https://doi.org/10.1007/978-2287-72066-6_54. Culp, T. D., Budgeon, L. R., Peter Marinkovich, M., Meneguzzi, G., & Christensen, N. D. (2006). Keratinocyte-secreted laminin 5 can function as a transient receptor for human papillomaviruses by binding virions and transferring them to adjacent cells. Journal of Virology, 80(18), 8940
8950. Available from https://doi.org/10.1128/JVI.00724-06. Day, P. M., & Schelhaas, M. (2014). Concepts of papillomavirus entry into host cells. Current Opinion in Virology, 4, 24
31. Available from https://doi.org/10.1016/j.coviro. 2013.11.002. Denis, F., Hanz, S., & Alain, S. (2008). Clairance, persistance et re´cidive de l’infection a` Papillomavirus. Gyne´cologie Obste´trique & Fertilite´, 36(4), 430
440. Available from https://doi.org/10.1016/j.gyobfe.2008.02.008. de Sanjose´, S., Brotons, M., & Pavo´n, M. A. (2018). The natural history of human papillomavirus infection. Best Practice and Research: Clinical Obstetrics and Gynaecology, 47, 2
13. Available from https://doi.org/10.1016/j.bpobgyn.2017.08.015. De Villiers, E. M., Fauquet, C., Broker, T. R., Bernard, H. U., & Zur Hausen, H. (2004). Classification of papillomaviruses. Virology, 324(1), 17
27. Available from https://doi.org/ 10.1016/j.virol.2004.03.033. Dillner, J. (1990). Mapping of linear epitopes of human papillomavirus type 16: The E1, E2, E4, E5, E6 and E7 open reading frames. International Journal of Cancer, 46(4), 703
711. Available from https://doi.org/10.1002/ijc.2910460426. Doorbar, J. (2005). The papillomavirus life cycle. Journal of Clinical Virology, 32, 7. Available from https://doi.org/10.1016/j.jcv.2004.12.006. Doorbar, J., Egawa, N., Griffin, H., Kranjec, C., & Murakami, I. (2015). Human papillomavirus molecular biology and disease association. Reviews in Medical Virology, 25(S1), 2
23. Available from https://doi.org/10.1002/rmv.1822. Doorbar, J., Quint, W., Banks, L., Bravo, I. G., Stoler, M., Broker, T. R., & Stanley, M. A. (2012). The biology and life-cycle of human papillomaviruses. Vaccine, 30(5), F55
F70. Available from https://doi.org/10.1016/j.vaccine.2012.06.083. Dyson, N., Howley, P. M., Mu¨nger, K., & Harlow, E. (1989). The human papilloma virus-16 E7 oncoprotein is able to bind to the retinoblastoma gene product. Science (New York, N.Y.), 243(4893), 934
937. Available from https://doi.org/10.1126/science.2537532. Esteˆva˜o, D., Costa, N. R., Gil da Costa, R. M., & Medeiros, R. (2019). Hallmarks of HPV carcinogenesis: The role of E6, E7 and E5 oncoproteins in cellular malignancy. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1862(2), 153
162. Available from https:// doi.org/10.1016/j.bbagrm.2019.01.001.

294

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Finzer, P., Aguilar-Lemarroy, A., & Ro¨sl, F. (2002). The role of human papillomavirus oncoproteins E6 and E7 in apoptosis. Cancer Letters, 188(1
2), 15
24. Available from https://doi. org/10.1016/S0304-3835(02)00431-7. Flores, E. R., & Lambert, P. F. (1997). Evidence for a switch in the mode of human papillomavirus type 16 DNA replication during the viral life cycle. Journal of Virology, 71(10), 7167
7179. Available from https://doi.org/10.1128/jvi.71.10.7167-7179.1997. Forslund, O., Iftner, T., Andersson, K., Lindelo¨f, B., Hradil, E., Nordin, P., Stenquist, B., Kirnbauer, R., Dillner, J., & De Villiers, E. M. (2007). Cutaneous human papillomaviruses found in sunexposed skin: Beta-papillomavirus species 2 predominates in squamous cell carcinoma. Journal of Infectious Diseases, 196(6), 876
883. Available from https://doi.org/10.1086/521031. Furumoto, H., & Irahara, M. (2002). Human papilloma virus (HPV) and cervical cancer. Journal of Medical Investigation, 49(3
4), 124
133. Giroglou, T., Florin, L., Scha¨fer, F., Streeck, R. E., & Sapp, M. (2001). Human papillomavirus infection requires cell surface heparan sulfate. Journal of Virology, 75(3), 1565
1570. Available from https://doi.org/10.1128/JVI.75.3.1565-1570.2001. Giuliano, A. R., Tortolero-Luna, G., Ferrer, E., Burchell, A. N., de Sanjose, S., Kjaer, S. K., Mun˜oz, N., Schiffman, M., & Bosch, F. X. (2008). Epidemiology of human papillomavirus infection in men, cancers other than cervical and benign conditions. Vaccine, 26(10), K17
K28. Available from https://doi.org/10.1016/j.vaccine.2008.06.021. Gravitt, P. E., & Winer, R. L. (2017). Natural history of HPV infection across the lifespan: Role of viral latency. Viruses, 9(10), 267. Available from https://doi.org/10.3390/v9100267. Hantz, S., Alain, S., & Denis, F. (2005). Vaccins anti-papillomavirus et pre´vention du cancer du col de l’ute´rus: Avance´es et perspectives. Presse Medicale, 34(10), 745
753. Available from https://doi.org/10.1016/s0755-4982(05)84030-8. Hausen, H. z. (2000). Papillomaviruses causing cancer: Evasion from host-cell control in early events in carcinogenesis. Journal of the National Cancer Institute, 92(9), 690
698. Available from https://doi.org/10.1093/jnci/92.9.690. Howie, H. L., Katzenellenbogen, R. A., & Galloway, D. A. (2009). Papillomavirus E6 proteins. Virology, 384(2), 324
334. Available from https://doi.org/10.1016/j.virol.2008.11.017. Humans, I. W. G., of., & to. (2007). Human papillomaviruses. World Health Organization. Jenkins, D., & Xavier Bosch, F. (2019). Human papillomavirus: Proving and using a viral cause for cancer. Human papillomavirus: Proving and using a viral cause for cancer (pp. 1
414). Elsevier. Available from https://doi.org/10.1016/C2016-0-02398-7. Kim, S. S., Tam, J. K., Wang, A. F., & Hegde, R. S. (2000). The structural basis of DNA target discrimination by papillomavirus E2 proteins. Journal of Biological Chemistry, 275(40), 31245
31254. Available from https://doi.org/10.1074/jbc.M004541200. Larkin, M. (2006). Buffett gift will benefit global health and disease research. The Lancet Infectious Diseases, 6, 468. Available from https://doi.org/10.1016/s1473-3099(06)70534-5. Lehoux, M., D’Abramo, C. M., & Archambault, J. (2009). Molecular mechanisms of human papillomavirus-induced carcinogenesis. Public Health Genomics, 12(5
6), 268
280. Available from https://doi.org/10.1159/000214918. Lepiller, Q., Puget, L., Debernardi, A., & Pre´tet, J. L. (2021). Infections a` papillomavirus humains et le´sions associe´es. Journal de Pe´diatrie et de Pue´riculture, 34(3), 122
129. Available from https://doi.org/10.1016/j.jpp.2021.02.003. Lim, D. A., Gossen, M., Lehman, C. W., & Botchan, M. R. (1998). Competition for DNA binding sites between the short and long forms of E2 dimers underlies repression in bovine papillomavirus type 1 DNA replication control. Journal of Virology, 72(3), 1931
1940. Available from https://doi.org/10.1128/jvi.72.3.1931-1940.1998.

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295

Liu, Z., Rashid, T., & Nyitray, A. G. (2016). Penises not required: A systematic review of the potential for human papillomavirus horizontal transmission that is non-sexual or does not include penile penetration. Sexual Health, 13(1), 10
21. Available from https://doi.org/10.1071/SH15089. Louie, K., Didelot, M.-N., Damay, A., Nagot, N., Mayaud, P., & Segondy, M. (2008). Papillomavirus humains (HPV) et cancers associe´s: Aspects e´pide´miologiques. Revue Francophone Des Laboratoires, 405, 27
34. Available from https://doi.org/10.1016/s1773035x(08)74275-8. McBride, A. A., & Mu¨nger, K. (2018). Expert views on HPV infection. Viruses, 10(2), 94. Available from https://doi.org/10.3390/v10020094. Meisels, A., & Fortin, R. (1976). Condylomatous lesions of the cervix and vagina. I. Cytologic patterns. Acta Cytologica, 20(6), 505
509. Molet, L., Girlich, D., Bonnin, R. A., Proust, A., Bouligand, J., Bachelerie, F., Hantz, S., & Deback, C. (2019). Identification by high-throughput sequencing of HPV variants and quasispecies that are untypeable by linear reverse blotting assay in cervical specimens. Papillomavirus Research, 8, 100169. Available from https://doi.org/10.1016/j.pvr.2019.100169. Monsonego, J. (2007). Pre´vention du cancer du col ute´rin (I): apport du de´pistage, re´cents progre`s et perspectives. Presse Medicale, 36(1), 92
111. Available from https://doi.org/ 10.1016/j.lpm.2006.10.023. Mougin, C., Nicolier, M., & Decrion-Barthod, A. Z. (2008). HPV et cancers: Me´canismes de l’oncogene`se. Revue Francophone des Laboratoires, 38(405), 35
42. Available from https://doi.org/10.1016/s1773-035x(08)74276-x. Mun˜oz, N., Castellsague´, X., de Gonz´alez, A. B., & Gissmann, L. (2006). Chapter 1: HPV in the etiology of human cancer. Vaccine, 24(3), S1
S10. Available from https://doi.org/10.1016/j. vaccine.2006.05.115. Mun˜oz, N., Me´ndez, F., Posso, H., Molano, M., Van Den Brule, A. J. C., Ronderos, M., Meijer, C., & Mun˜oz, A. (2004). Incidence, duration, and determinants of cervical human papillomavirus infection in a cohort of Colombian women with normal cytological results. Journal of Infectious Diseases, 190(12), 2077
2087. Available from https://doi.org/10.1086/425907. Oh, J. M., Kim, S. H., Cho, E. A., Song, Y. S., Kim, W. H., & Juhnn, Y. S. (2010). Human papillomavirus type 16 E5 protein inhibits hydrogen peroxide-induced apoptosis by stimulating ubiquitin-proteasome-mediated degradation of Bax in human cervical cancer cells. Carcinogenesis, 31(3), 402
410. Available from https://doi.org/10.1093/carcin/bgp318. Perrard, J. (2019). Re´gulation de l’expression des oncoge`nes du papillomavirus humain de type 16: E´tude dans des ligne´es cellulaires de cancers du col de l’ute´rus traite´es avec un agent de´me´thylant. (Ph.D. thesis), Universite´ Bourgogne Franche-Comte´. Pfister, H. (1992). Human papillomaviruses and skin cancer. Seminars in Cancer Biology, 3(5), 263
271. Pre´tet, J.-L., Charlot, J.-F., & Mougin, C. (2007). Aspects virologiques et carcinologiques des papillomavirus humains HPV. Bulletin de l’Acade´mie Nationale de Me´decine, 191(3), 611
623. Available from https://doi.org/10.1016/s0001-4079(19)33048-1. Rautava, J., & Syrja¨nen, S. (2012). Biology of human papillomavirus infections in head and neck carcinogenesis. Head and Neck Pathology, 6(1), 3
15. Available from https://doi.org/ 10.1007/s12105-012-0367-2. Roman, A., & Munger, K. (2013). The papillomavirus E7 proteins. Virology, 445(1
2), 138
168. Available from https://doi.org/10.1016/j.virol.2013.04.013. Sabeena, S., Bhat, P., Kamath, V., & Arunkumar, G. (2017). Possible non-sexual modes of transmission of human papilloma virus. Journal of Obstetrics and Gynaecology Research, 43(3), 429
435. Available from https://doi.org/10.1111/jog.13248.

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Scheffner, M., Werness, B. A., Huibregtse, J. M., Levine, A. J., & Howley, P. M. (1990). The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. Cell, 63(6), 1129
1136. Available from https://doi.org/10.1016/0092-8674(90)90409-8. Straight, S. W., Herman, B., & McCance, D. J. (1995). The E5 oncoprotein of human papillomavirus type 16 inhibits the acidification of endosomes in human keratinocytes. Journal of Virology, 69(5), 3185
3192. Available from https://doi.org/10.1128/jvi.69.5.3185-3192.1995. Suh, Y., Amelio, I., Guerrero Urbano, T., & Tavassoli, M. (2014). Clinical update on cancer: Molecular oncology of head and neck cancer. Cell Death & Disease, 5, e1018. Available from https://doi.org/10.1038/cddis.2013.548. Suprynowicz, F. A., Disbrow, G. L., Krawczyk, E., Simic, V., Lantzky, K., & Schlegel, R. (2008). HPV-16 E5 oncoprotein upregulates lipid raft components caveolin-1 and ganglioside GM1 at the plasma membrane of cervical cells. Oncogene, 27(8), 1071
1078. Available from https://doi.org/10.1038/sj.onc.1210725. Syrja¨nen, S. (2010). Current concepts on human papillomavirus infections in children. APMIS: Acta Pathologica, Microbiologica, et Immunologica Scandinavica, 118(6
7), 494
509. Available from https://doi.org/10.1111/j.1600-0463.2010.02620.x. Tadlaoui, K. A., Hassou, N., Bennani, B., & Ennaji, M. M. (2019). Emergence of oncogenic high-risk human papillomavirus types and cervical cancer. Emerging and reemerging viral pathogens: Volume 1: Fundamental and basic virology aspects of human, animal and plant pathogens (pp. 539
570). Elsevier. Available from https://doi.org/10.1016/B978-0-12819400-3.00024-7. Virgin, H. W., Wherry, E. J., & Ahmed, R. (2009). Redefining chronic viral infection. Cell, 138 (1), 30
50. Available from https://doi.org/10.1016/j.cell.2009.06.036. Weinberger, P. M., Yu, Z., Haffty, B. G., Kowalski, D., Harigopal, M., Brandsma, J., Sasaki, C., Joe, J., Camp, R. L., Rimm, D. L., & Psyrri, A. (2006). Molecular classification identifies a subset of human papillomavirus
Associated oropharyngeal cancers with favorable prognosis. Journal of Clinical Oncology, 24(5), 736
747. Available from https://doi.org/10.1200/ JCO.2004.00.3335. WHO. (2017). Human papillomavirus vaccines: WHO position paper, May 2017. Releve Epidemiologique Hebdomadaire, 92(19), 241
268. Zaidane, I. (2017). Les formes cliniques et diagnostic diffe´rentiel des verrues cutane´es et anoge´nitales chez l’enfant. (Medical thesis), Universite´ Mohammed V de Rabat. Zheng, Z. M., & Baker, C. C. (2006). Papillomavirus genome structure, expression, and posttranscriptional regulation. Frontiers in Bioscience, 11(1), 2286
2302. Available from https:// doi.org/10.2741/1971. Zur Hausen, H. (2002). Papillomaviruses and cancer: From basic studies to clinical application. Nature Reviews. Cancer, 2(5), 342
350. Available from https://doi.org/10.1038/nrc798.

Chapter 14

Progress in the development of vaccines against human papillomavirus Fadoua El Battioui1, Fatima El Malki2, Hassan Ghazal3 and Said Barrijal1 1

Laboratory of Biotechnology, Genomic and Bioinformatics, Faculty of Science and Techniques, Tangier, Abdelmalek Essaaˆdi University, Tetouan, Morocco, 2Institute of Nursing, Tangier, Morocco, 3National Center for Scientific and Technical Research (CNRST), Rabat, Morocco

14.1 Introduction Human papillomavirus (HPV) is the most common viral infection that affects the reproductive system of both women and men (WHO, 2017). Up to now, more than 200 types of HPV have been identified (Cheng et al., 2020). Most HPV infections may vanish spontaneously thanks to the natural defense of the immune system. However, in some cases of immunosuppression, the infection may persist and give rise to precancerous lesions and cancers of the cervix, vagina, vulva, anus, penis, head, and neck (Bergman et al., 2019). According to the World Health Organization (WHO), HPV constituted approximately 4.5% of all kinds of cancer worldwide in 2012, 8.6% of women’s cancer, and 0.8% of male’s cancer (De Martel et al., 2017). Therefore the efforts have been directed toward reinforcing the preventive measures through vaccination as the best means to hinder the spread of this virus. In 2006, the prophylactic HPV vaccine was licensed in more than 100 countries (Markowitz et al., 2012; Zhou et al., 2020). Vaccine plays an essential role in reducing the incidence of HPV-related disease, especially cancer of the cervix. However, the introduction of HPV vaccination into national immunization programs remains low and varies from one state to another. Only 82 countries have adopted this vaccine in 2017, with a wide gap in coverage rates ranged between 8% and 98% (Brotherton & Bloem, 2018). Adopting a vaccination strategy with the most important impact and an optimal vaccine for the strategic objective of HPV eradication are presently the main issues. Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00004-2 Copyright © 2023 Elsevier Inc. All rights reserved.

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FIGURE 14.1 A schematic of HPV (human papillomavirus virion). A double-stranded DNA genome (red) is surrounded by the capsid, which is composed of two proteins: the major capsid protein (L1, shown in light green) and the minor capsid protein (L2, shown in brown color). The L1 protein forms pentamers, and L2 protein is inserted on vertices of the pentamers. Seventytwo copies of the pentamers and about12
72 copies of L2 protein assemble to form a virion (Yadav et al., 2019). https://doi.org/10.3390/v12010018.

14.2 Virus-like particle vaccination strategy The virus-like particles (VLPs) are proteins devoid of the viral genome and do not produce any infectious effect or oncogenes, but are similar to the natural virus particles. They have first been highlighted by Romanowski et al. in 1990. VLPs can be produced in bacteria, yeast, or insect cells (Kirnbauer et al., 1992). These particles have become an essential means for the development and manufacture of vaccines, either for prophylactic or therapeutic purposes, for human beings as well as animals. However, most of the available manuscripts dealt with the development of prophylactic vaccines for humans. Currently, anti-HPV vaccines based on VLP have assembled from the main papillomavirus capsid L1 protein (Fig. 14.1) (Zhai et al., 2019). Vaccination with VLPs can induce different types of specific antibodies which permits the annihilation of the virus by preventing its absorption by the target cell (Wang et al., 2019). The second-generation VLPs, such as L2-VLPs (Fig. 14.1), are drawing a lot of attention for their wider genotypic coverage (Huber et al., 2017; Schellenbacher et al., 2009).

14.3 Vaccines prophylactic against human papillomavirus 14.3.1 Types of vaccines The main role of prophylactic vaccines is the development of humoral immunity against the late proteins L1 or L2 of HPV (Wang et al., 2019). The prophylactic HPV vaccine is the first that has been clinically proven to prevent cervical cancer and other types of cancers (Zhou et al., 2020). Three types of prophylactic HPV vaccines are currently available: Cervix (GlaxoSmithKline Biologicals, Belgium), Gardasil (Merck & Co., United

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States), and Gardasil 9 (Merck & Co., United States). These three vaccines have different immune components (Bogani et al., 2018). Cervix is a bivalent vaccine against HPV (2vHPV); it consists of two VLPs, including HPV 16 and 18, and protects against these two types of HPV which are responsible for nearly 70% of cervical malignancies (Khan et al., 2005). Gardasil is an HPV quadrivalent vaccine (4vHPV) containing the VLPs of HPV types 16, 18, 6, and 11. It protects from these four types of HPV, which are responsible for 90% of genital warts (Winer et al., 2008). Gardasil 9 is a nonavalent vaccine against HPV (9vHPV) 6/11/16/18/31/ 33/45/52/58, and protects from all these nine types of HPV. The administration of three doses of the bivalent or quadrivalent HPV vaccine is enough to protect a woman against precancer of the cervix caused by the HPV types included in the vaccine (Zhou et al., 2020). Similar protection from cervical precancerous lesions, vaginal and vulvar, is ensured by nonavalent vaccines (Bergman et al., 2019). In addition, vaccine-mediated protection against HPV is not type-specific, and cross-protection exists after vaccination against a closely related type of the same species (Cameron et al., 2016; Kavanagh et al., 2017; Zhou et al., 2020). To ensure a better protection against HPV, the vaccine should normally be administered before any exposure to the virus, that is before the beginning of a sexual intercourse. Thus all national HPV vaccination programs target preadolescent girls. Some countries have opted for vaccinating girls only, considering that the immunization of women against HPV offers indirect protection to men. Other countries, however, have expanded their program to males, given the fact that vaccinating females only do not protect homosexuals against anal cancer and warts anogenital (Drolet et al., 2019).

14.4 Immunization procedures and doses To ensure easy access and broad adherence to the HPV vaccine, simplified vaccination schedules with fewer doses are required. Several studies have been carried out to determine if less than three doses in 6 months would not be less effective than three doses for girls aged 15
18 years (Bergman et al., 2019). Schiller (2018) in his review, explained the important role of antibodies synthesized after administration of an HPV L1 VLP vaccine as the main mediators of protection against HPV, he also noted that the immunogenicity after two doses of vaccination against HPV was as effective as with three doses. Therefore two doses are not less effective than three doses for the prevention of HPV-related illnesses for girls aged 15
18 years (Fig. 14.2) (Zhou et al., 2020). It has also been reported that the vaccine has similar efficacy, even when given in a single dose. This is probably due to the structural characteristics of the VLPs, which permits efficient production of long-lived plasma cells with reduced dosage regimens (Schiller & Lowy, 2018).

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FIGURE 14.2 Timeline of trials and licensure/registration of the HPV vaccines. 4vHPV, quadrivalent HPV vaccine; 2vHPV, bivalent HPV vaccine; 9vHPV, nonvalent HPV vaccine; FDA, The US Food and Drug Administration; HPV, human papillomavirus; EU, European Union; VLP, virus-like particle.1.1 HPV and HPV infection. From (Zhou et al., 2020). https://doi.org/ 10.3389/fimmu.2020.01434.

However, it has been shown that the immune response differs depending on age. In fact, preadolescents and adolescents (aged 9
15) produce stronger antibody responses, using HPV type protein virus vaccines, than older teens and adult (Block et al., 2006; Sankaranarayanan et al., 2018). For this reason, WHO has advocated two doses of 2vHPV and 4vHPV vaccines for people under 15 years old and three doses for women over 15 years old (Zhou et al., 2020). To date, several tests have tempted to demonstrate the efficacity of a single dose of vaccine. Accordingly, a large case-control study in India was carried out among women who received a single dose of 4 HPV vaccines. After a 7year follow-up, the results demonstrated durable protection of the vaccine against HPV16 and related disease HPV18 (Sankaranarayanan et al., 2018). Single-dose vaccination is more affordable and beneficial for wider implementation in low-income countries. However, data on the efficacity of a single dose of vaccine do not exceed 7 years (Zhou et al., 2020).

14.5 Efficacy and safety of human papillomavirus vaccines 14.5.1 Efficacy According to the results of a systematic meta-analysis carried out recently on nearly 60 million people from 14 high-income countries, vaccines against HPV significantly reduced the prevalence of parameters related to HPV (genital HPV infections, anogenital wart diagnoses, or histologically confirmed CIN2 1 ) among girls, women, and boys (Cheng et al., 2020; Drolet et al., 2019). The most current E-type HPV (HPV 16 and 18) significantly decreased by 83%, and HPV 31%, 33%, and 45% decreased by 54%, among girls aged

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13
19 years. The prevalence of anogenital warts decreased by 67% and CIN2 1 by 51% (Cheng et al., 2020; Drolet et al., 2019). The main results in relation to the degree of immunogenicity of each type of vaccine are summarized in Table 14.1 and Fig. 14.3. Overall, all three vaccines demonstrated sufficiently high and lasting antibody response levels to protect against infection and subsequent disease (Phillips et al., 2018).

TABLE 14.1 Comparison of the three prophylactic vaccines against HPV (human papillomavirus) available in the market. HPV vaccines

Cervarix

Gardasil

Gardasil 9

Time of FDA Approval

2009

2006

2014

Antigen

L1 VLP of HPV 16 and 18

L1 VLP of HPV 6, 11, 16, and 18

L1 VLP of HPV 6, 11, 16, 18, 31, 33, 45, 52, and 58

Expression system

Baculovirus— insect cell

Yeast

Yeast

Adjuvant

50 μg MPL absorbed on 500 μg aluminum hydroxide (AS04)

225 μg aluminum hydroxyphosphate sulfate

500 μg aluminum hydroxyphosphate sulfate

Indications



Females: cervical precancer and cancer Males: not approved for use in males



Females: cervical precancer and cancer; genital warts Males: anal precancer and cancer; genital warts



Females: cervical precancer and cancer; genital warts Males: anal precancer and cancer; genital warts

Cervical cancer Protection rate

70%

70
75%

90%

Immunogenicity

High anti-HPV16 and 18 immunogenicity, which can prevent the impact of the infection for at least 10 years 1 high protection ( . 90%) against HPV-related precancerous anomalies and lesions, including cervical

Significant decrease in HPV infections at the level of the anus, vulva, and penis as well as in the oral cavity 1 a high prevention rate .90%, (injection before exposure to HPV) against CIN 2 or worse (CIN 2 1 ), CIN 3 1 , and

High immunogenicity following vaccination up to 6 years 1 inhibition of about 90% and 80%
85% of vulvar and vaginal diseases, respectively 1 effective transfer of antibodies across the placenta, which potentially (Continued )

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TABLE 14.1 (Continued) HPV vaccines

Cervarix

Gardasil

Gardasil 9

intraepithelial neoplasia 2 (CIN2), CIN3, and adenocarcinoma in situ (AIS) 1 93% decrease in the prevalence of HPV16 and 18 oral infections

vulvar / vaginal intraepithelial neoplasia of grade 2 or worse (VIN/ VaIN 2 1 )

protects the infant from HPV 6 and 11 infections

Cross protection

Long-term crossprotection against the HPV 31 and 45 at

High efficacy of protection against the HPV 31, 33, 45, 52, and 58 (46%, 29%, 7%, 18%, and 6%, respectively) at

Low crossprotection efficacy at

Vaccination schedule

0, 1, and 6 months

0, 2, and 6 months

0, 2, and 6 months

FIGURE 14.3 HPV VLP types in VLP vaccines. VLPs in the 2vHPV, 4vHPV, and 9vHPV are shown with the proportion of neoplastic disease attributed to each group. HPV, human papillomavirus; VLP, virus-like particle (Zhou et al., 2020).

14.5.2 Safety and security The vaccine safety is the most shared concern of all stakeholders in the implementation of public vaccination.

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TABLE 14.2 AEs (Adverse effects) of three vaccines against HPV (human papillomavirus). Type of vaccine

Cervarix

Gardasil

Gardasil9

AEs

Reactions at injection site as pain and swelling 1 systemic symptoms, such as fever, nausea, vomiting, dizziness, diarrhea, and myalgia 1 headache and fatigue

Reactions at injection site as pain and swelling 1 small fiber neuropathy and dysautonomia 1 syndrome chronic regional pain

General symptoms, but no increased risk of systemic symptoms

References

Gonc¸alves et al. (2014), Cheng et al., 2020, Paavonen et al. (2007), Van Klooster et al. (2011)

Mart´ınez-Lav´ın (2015), Ozawa et al. (2017), Gonc¸alves et al. (2014), Cheng et al. (2020)

Gonc¸alves et al. (2014), Cheng et al. (2020)

No Permission Required.

The HPV vaccine is considered to be one of the most studied vaccines up to now, given the active monitoring of safety signals since its preapproval (Angelo et al., 2014). The results of several studies have shown that the three HPV vaccines are excellently safe and tolerant within different age groups (Phillips et al., 2018; Cheng et al., 2020). Most side effects are reactions caused by the vaccination process and not by the vaccine itself (Centers for Disease Control Prevention, 2008; Zhu FC et al., 2019; Zhou et al., 2020). The most-reported undesirable effects are listed in Table 14.2. In 2014, the safety of the 9vHPV vaccine was evaluated in seven studies before being licensed by FDA. These prelicensing studies indicated that the 9vHPV vaccine has similar safety to that of the 4vHPV vaccine. In addition, approximately 92% of reports about the 4vHPV vaccine were classified as non serious (Wang et al., 2019). Numerous experimental data show that the vaccine does not cause any serious or unexpected adverse effects. Therefore we should restore public confidence in the safety of HPV vaccines by expanding the scope of vaccination and creating awareness about its benefits and safety (Cheng et al., 2020).

14.6 L2-based human papillomavirus prophylactic vaccines Vaccination with L2 using adjuvants such as aluminum provides long-lasting immunity among animal models, and passive transfer studies have shown

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that low titers of neutralizing antibodies (nAb) but sufficient for protection (Kalnin et al., 2017; Tumban et al., 2013). The L2-based vaccines have the potential to provide protection against various HPV at a low cost of vaccination due to their simple form (Huber et al., 2017; Wang et al., 2015).

14.7 Human papillomavirus vaccine coverage In general, national HPV programs cover about 30% of the global target population, with low “full-dose” coverage in many regions (Bruni et al., 2016). In fact, up to December 13, 2017, 65 countries worldwide have adopted the immunization schedules against HPV in two doses for girls. However, only a few countries with high income (namely Australia, Switzerland, and the United States) recommended HPV vaccination for boys (Bergman et al., 2019). An estimated 118 million women have received a dose of HPV vaccine worldwide (Bruni et al., 2016; Wang et al., 2019). Otherwise, there is a great disparity in vaccine coverage against HPV between regions of the world (Fig. 14.4). The HPV vaccine coverage is significantly higher in high-income countries, where about 32% of females aged 10
20 years received full-dose vaccination in 2014 (De Martel et al., 2017). However, most low- and middle-income countries (LMIC) remain unprotected, as only about 1% of adolescent girls have received a full cycle of HPV vaccines (Bruni et al., 2016). The HPV vaccination gap also exists between urban and rural areas for LMIC countries. Several factors maintain this situation which requires more effort for the remedy (Wang et al., 2019).

FIGURE 14.4 Worldwide HPV vaccination rates. HPV, human papillomavirus. From (Zhou et al., 2020).

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14.8 Factors influencing vaccination coverage There are many determinants that influence vaccination coverage: 1. Geographic areas: Studies have shown that Gardasil 9 protects against a cervix cancers associated with HPV types with an efficiency of 92% in Africa and North America, 91% in Europe, 90% in Latin America and the Caribbean, 88% in Asia and 87% in Australia (Ogembo et al., 2015; Zhai & Tumban, 2016). 2. Ethnic disparities: A meta-analysis has assumed that ethnic minorities in the United States are more likely to start but less likely to follow the full series of HPV vaccination (Spencer et al., 2019). 3. Vaccines’ high price: The expensive price of most of these vaccines hinders meeting the needs of low-income populations (Wang et al., 2019). 4. Cold chain: The transport and storage condition for most of the vaccines also limit the large-scale deployment in developing countries. This can be solved by lyophilized formulations or thermostable capsomer preparations (Hassett et al., 2015). 5. Women’s knowledge and educational interventions: Educational programs prevent the transmission of HPV, increase the HPV vaccine acceptability, and help to improve HPV vaccination coverage (Dixon et al., 2018; Larasati et al., 2018). 6. Interventions of communication training: Training on communication strategies and tools significantly improved the launching of the series of HPV vaccines (10%) (Dempsey et al., 2018).

14.9 Therapeutic vaccines Unlike the neutralizing role of prophylactic HPV vaccines, therapeutic vaccines aim at eliminating preexisting damage caused by HPV, by stimulating cell-mediated immunity that kills infected cells (Hancock et al., 2018). In fact, Bellone et al. (2009) demonstrated that CD8 1 T and specific CD4 1 lymphocytes L1 gene have therapeutic effects among patients with cervical cancer (Bellone et al., 2009; Karimi et al., 2020). Most currently available or under clinical testing HPV therapeutic vaccines are vaccines that target proteins E6 and E7, as these are essential to the induction and the growth of cancerous cells (Karimi et al., 2020). Various mechanisms have been adopted for the development of these vaccines: those which are based on vectors, acid/protein/peptide, and others based on cells, and combined therapies (Maciag et al., 2009; Alvarez et al., 2016; Cheng et al., 2020). The advantages, disadvantages, efficacy, and safety of all types of therapeutic vaccines used against HPV are summarized in Table 14.3.

TABLE 14.3 List of therapeutic vaccines used against HPV (human papillomavirus). Vaccine type

Advantages

Disadvantages

Bacterial vector vaccines

Highly immunogenic 1 introducing innate and adaptive immune response to pathogens 1 reducing purification difficulties of the target antigen 1 lowcost manufacturing and a simple inoculation

Generation of neutralizing antibodies restricts the efficacy of repeated therapy 1 potential immunodominance to the vectors 1 potentially toxic / harmful to patients

Viral vector vaccines

Highly rates of nAbs against adenovirus type 5 (Ad5) 1 inducing a solid innate and adaptative immune response associated with pro-inflammatory cytokines 1 wide range of selection for available vectors 1 can be engineered to include and express multiple genes such as costimulatory molecules/cytokines

Potentially toxic / harmful to patients 1 potential immunodominance to the vectors 1 generation of neutralizing antibodies restrict the efficacy of repeated therapy

Vaccinia virus

High transgenic insertion potential 1 easy manufacturing 1 administration through various routes (intranasal, intravaginal, intra rectal, intradermal) 1 thermostability

Uncertain efficacy

DNA vaccines

Easy fabrication and purification 1 high stability 1 reliable efficacy 1 ability to induce both humoral and cell-mediated immune responses 1 long-term protection through repeated vaccination 1 capacity to include and express multiple genes such as costimulatory molecules / cytokines 1 multiple delivery methods available

Lack of intrinsic ability to self-amplify and spread to surrounding cells 1 potential risk of chromosomal integration 1 low immunogenicity

References

Hancock et al. (2018), Rosales et al. (2014)

RNA-based vaccines

Inducing innate immune responses 1 ensuring safety without risk of chromosomal 1 integration or cell transformation 1 replicating rapidly 1 producing high expression in the cytoplasm 1 combining with polymer or liposome-based encapsulation approaches

Poor stabilit y 1 inability to spread intercellularly 1 preparation/production is labor intensive 1 difficult to prepare in large quantity

Peptide- based vaccines

Great stability and security 1 ease of production and stockage Can be modified for better MHC binding

Low immunogenicity 1 MHC specific that need to match the patient’s human leukocyte antigen (HLA) haplotypes

Protein vaccines

Stable, safe, easy to produce 1 no limitation of MHC restriction

Low immunogenicity 1 inducing more humoral responses than cell-mediated responses

Coleman et al. (2016)

Cellular vaccines (Dendritic cell vaccines)

Highly immunogenic 1 serve as natural adjuvants 1 multiple methods of antigen loading 1 inducing both potent innate and adaptive immune responses

Difficult for large-scale production 1 lack of a consensus on the optimal methods of DC preparation 1 different culture techniques may result in inconsistent vaccine quality 1 limited life span due to the T-cell-mediated apoptosis

Santin et al. (2005)

No Permission Required.

Lundstrom (2018, 2016)

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14.9.1 Bacterial vector vaccines Therapeutic vaccines using bacterial vector to deliver antigens constitutes a promising approach. This is due to their potential to generate the cellular and humoral immune responses against pathogen agents while protecting the host. Recently, several bacterial vectors have been tested in clinical essays, such as Listeria monocytogenes (Lm), Lactobacillus casei, Lactobacillus lactis, and Salmonella (Wang et al., 2019).

14.9.2 Viral vector vaccines Thanks to their strong ability to express antigens and their natural potential to transduce their genetic information in the host, viral vectors are widely used to design therapeutic vaccines. Recently, several viral vectors have been tested, such as adenovirus (Ad), adeno-associated virus, alphavirus and vaccinia virus. Adenovirus constitutes a promising vaccine platform used for vaccine vectors and gene therapy (Wang et al., 2019). 1. The results of studies carried out by Khan et al. (2017) have demonstrated a high therapeutic efficacy of the TC-1 vaccine administered in the mouse model against carcinoma related to HPV16 and HPV18. The concerned vaccine was designed from an Ad26 replication deficient and Ad35-based vector vaccines made up of fusion proteins of HPV E2, E6 (Khan et al., 2017). 2. Data from the study carried out by C ¸ uburu et al. (2017) to test the immunogenicity of TC-1 vaccine via heterologous methods combining intramuscular and intravaginal immunization in mice, and found this combination to be very promising to induce considerable responses of CD8 1 T lymphocytes (C ¸ uburu et al., 2017).

14.9.3 Vaccinia virus It is a double-stranded DNA virus, which belongs to the Poxviridae family. The poxvirus has a stable and large genome that can express large amounts of foreign antigen. Modified vaccinia virus Ankara (MVA) is licensed as a third-generation smallpox vaccine and serves as an effective vector platform for the development of new vaccines (Wang et al., 2019).

Efficacy and safety A phase III study has been carried out by Rosales et al. (2014) to evaluate the therapeutic efficacy of a viral vector vaccine (MVA) targeting HPV16

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E2 (MVA-E2). The study included 1176 women and 180 men with intraepithelial lesions associated to HPV infection. The vaccine was injected directly into the uterus, urethra, vulva, or anus. The results obtained demonstrate that all male patients and 89.3% of female patients experienced complete lesion removal and no apparent adverse events were observed (Rosales et al., 2014).

14.9.4 DNA vaccines DNA vaccines are a valuable form of immunotherapy which permits the activation of CTLs and induce a long-term immune response (Karimi et al., 2020; Saade & Petrovsky, 2012). This type of vaccine has become a safe alternative to live and inactivated vaccines for human and animal infections (Delany et al., 2014). In a clinical trial of a DNA vaccine GX-188E co-expressed HPV-16 and -18 antigens E6 and E7 and the Fms-liketyrosine kinase-3 ligand (Flt3L), which could activate dendritic cells (DCs), performed in patients with HPV16/18 1 infection.The results of this test showed that the vaccine was able to elicit E6/E7 IFN-γ-secreting T cell responses in nine patients with HPV16/18 1 CIN 3 , seven of whom showed complete regression of lesions within 36 weeks. No serious side effects associated with the vaccine were identified in female patients (Kim et al., 2014). The outcomes of another randomized, double-blind, placebo-controlled phase IIb study, to test the efficacy of a VGX-3100 vaccine among women with CIN2/3, demonstrated a histopathological regression in 49.5% of vaccines versus 30.6% of placebo recipients (Trimble et al., 2015). Nowadays, the VGX-3100 is the first therapeutic DNA vaccine which has been shown to be effective against CIN2/3 (Wang et al., 2019). DNA vaccines are an interesting approach to vaccination, but their potency in clinical essays has been insufficient to generate effective immunity, which may be related to DNA degradation via nucleases, poor delivery to antigen presenting cells (APC), and insufficient uptake of DNA plasmids by cells upon injection (Karimi et al., 2020).

14.9.5 RNA-based vaccines Due to its very high potential of autonomous replication, the administration of an RNA virus as a viral vector for vaccines could elicit strong immune responses and generate protection against infectious agents and tumor cells. Recently, several RNA viruses are used as vectors for the expression of antigens, such as retrovirus, flavivirus, alphavirus, rhabdovirus, lentivirus, measles virus, Newcastle disease, and picornavirus (Lundstrom, 2019). Most of the studies conducted so far have shown that viral RNA vectors against diseases associated with HPV can generate a high-level expression of recombinant heterologous antigens.

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14.9.6 Peptide-based vaccines Peptide-based vaccines can be divided into synthetic long peptides and specific epitope peptides (short). Short peptides are specific to the HLA type of each patient and can only be administered after the establishment of a preliminary HLA typing (Vici et al., 2016). In 2011, Solares et al., evaluated the efficacy of a therapeutic vaccine against HPV in seven women with a high grade HPV16 CIN. This vaccine was based on short peptides (CIGB-228) adjuvanted with a proteoliposome. The results reported showed that the vaccine induced a T lymphocyte response associated with IFN- γ and that five patients had a regression of the lesions and a clearance of HPV (Solares et al., 2011). After studying the immunogenicity of a synthetic long-peptide vaccine among 20 women with high-grade vulvar intraepithelial-related HPV16 neoplasia (VIN), the same results were demonstrated by Kenter et al, where 47% of patients had presented a response of T lymphocytes CD4 1 and CD8 1 associated with IFN- γ, to 12 months of follow-up, and maintained at 24 months (Kenter et al., 2009). The Phase I clinical study of PepCan also found encouraging results. This test was conducted among 24 patients with CIN2/3 administering them by a vaccine composed of four synthetic peptides HPV16 E6 and a new adjuvant Candin. The study showed that 45% of patients had histologic regression of lesions and a high immune response with a significant decrease in viral load (Coleman et al., 2016). Another study was carried out in 2013 to test the efficacy of a vaccine composed of 13 long peptides overlapping HPV16 E6 and HPV16 E7, in combination with the adjuvant Montanide ISA-51. This essay was done on 20 women with advanced cervical cancer. Unfortunately, the results were disappointing, as the participants experienced no tumor regressions or prevention of disease progression, and only nine women developed an HPV16specific T-cell response associated with IFN-γ., TNFα, IL-5, and/or IL-10.

14.9.7 Protein vaccines Compared with peptide-based vaccines, protein-based vaccines can avoid the limitation of MHC restriction due to numerous CD4 1 and CD8 1 T epitopes (Coleman et al., 2016). The results of several clinical trials testing a vaccine based on a fusion protein HPV16 E6E7L2 (TA- CIN) have shown strong immunogenicity (De Jong et al., 2002; Davidson et al., 2004). Indeed, the study conducted by Daayana et al. (2010), administering a vaccine TA-CIN after an immunomodulator (imiquimod) to treat 19 women affected by a VIN grades 2 and 3, has shown that 63% of patients developed

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complete histological regression of VIN and increased local infiltration of CD4 1 and CD8 1 T cells (Daayana et al., 2010).

14.9.8 Cellular vaccines (Dendritic cell-based vaccines) Dendritic cells (DCs) are recognized to be the most potent APCs in vivo, due to their strong ability to mediate and induce both the humoral immune and the adaptive immune response (Wang et al., 2019). Thus DCs constitute a promising approach to therapeutic vaccine design, since they can be loaded with specific peptide or antigens. They can serve as natural adjuvants to increase the capacity of the specific antigen immunotherapy against cancer (Santin et al., 2005). A clinical test carried out by Ramos et al. (2013) in phase I/II to treat 12 patients with metastatic epithelial cancers associated with HPV 16 E6 has proven remarkable regression of metastatic epithelial cancer.

14.10 Conclusion HPV vaccination has played a primary role in reducing the incidence of HPV-related cancers worldwide, given that prophylactic HPV vaccines can prevent approximately 95% of vaccinated individuals from the persistent infections and precancerous lesion. However, there are still obstacles facing their generalization, especially in low-income countries, where the burden of cervical cancer is very high. The high manufacturing cost remains the primary challenge to improve immunization coverage and reach the most vulnerable populations. To do this, affordable vaccine manufacturing processes and sustainable delivery must be developed. The second-generation HPV L2-based vaccines are currently under clinical tests, and may solve this problem of cost. Unlike prophylactic HPV vaccines, progress in the development of therapeutic strategies has so far been very slow; thus intensified efforts must be deployed to develop therapeutic HPV vaccines to ensure a simple and effective treatment for sick people.

References Alvarez, R. D., Huh, W. K., Bae, S., Lamb, L. S., Conner, M. G., Boyer, J., & Trimble, C. L. (2016). A pilot study of pNGVL4a-CRT/E7(detox) for the treatment of patients with HPV16 1 cervical intraepithelial neoplasia 2/3 (CIN2/3). Gynecologic Oncology, 140(2), 245
252. Available from https://doi.org/10.1016/j.ygyno.2015.11.026. Angelo, M.-G., David, M.-P., Zima, J., Baril, L., Dubin, G., Arellano, F., & Struyf, F. (2014). Pooled analysis of large and long-term safety data from the human papillomavirus- 16/18AS04-adjuvanted vaccine clinical trial programme. Pharmacoepidemiology and Drug Safety, 23(5), 466
479. Available from https://doi.org/10.1002/pds.3554.

312

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Bellone, S., El-Sahwi, K., Cocco, E., Casagrande, F., Cargnelutti, M., Palmieri, M., & Santin, A. D. (2009). Human papillomavirus type 16 (HPV-16) virus-like particle L1-specific CD8 1 cytotoxic T lymphocytes (CTLs) are equally effective as E7-specific CD8 1 CTLs in killing autologous HPV-16-positive tumor cells in cervical cancer patients: Implications for L1 dendritic cell-based therapeutic vaccines. Journal of Virology, 83(13), 6779
6789. Available from https://doi.org/10.1128/jvi.02443-08. Bergman, H., Buckley, B. S., Villanueva, G., Petkovic, J., Garritty, C., Lutje, V., & Henschke, N. (2019). Comparison of different human papillomavirus (HPV) vaccine types and dose schedules for prevention of HPV-related disease in females and males. Cochrane Database of Systematic Reviews, 2019, CD013479. Available from https://doi.org/10.1002/14651858. cd013479. Block, S. L., Nolan, T., Sattler, C., Barr, E., Giacoletti, K. E. D., Marchant, C. D., Castellsague´, X., Rusche, S. A., Lukac, S., Bryan, J. T., Cavanaugh, P. F., & Reisinger, K. S. (2006). Comparison of the immunogenicity and reactogenicity of a prophylactic quadrivalent human papillomavirus (types 6, 11, 16, and 18) L1 virus-like particle vaccine in male and female adolescents and young adult women. Pediatrics, 118(5), 2135
2145. Available from https://doi.org/10.1542/peds.2006-0461. Bogani, G., Leone Roberti Maggiore, U., Signorelli, M., Martinelli, F., Ditto, A., Sabatucci, I., & Raspagliesi, F. (2018). The role of human papillomavirus vaccines in cervical cancer: Prevention and treatment. Critical Reviews in Oncology/Hematology, 122, 92
97. Available from https://doi.org/10.1016/j.critrevonc.2017.12.017. Brotherton, J. M. L., & Bloem, P. N. (2018). Population-based HPV vaccination programmes are safe and effective: 2017 update and the impetus for achieving better global coverage. Best Practice & Research. Clinical Obstetrics & Gynaecology, 47, 42
58. Available from https://doi.org/10.1016/j.bpobgyn.2017.08.010. Bruni, L., Diaz, M., Barrionuevo-Rosas, L., Herrero, R., Bray, F., Bosch, F. X., & Castellsague´, X. (2016). Global estimates of human papillomavirus vaccination coverage by region and income level: A pooled analysis. The Lancet Global Health, 4(7), e453
e463. Available from https://doi.org/10.1016/s2214-109x(16)30099-7. Cameron, R. L., Kavanagh, K., Pan, J., Love, J., Cuschieri, K., Robertson, C., Ahmed, S., Palmer, T., & Pollock, K. G. J. (2016). Human papillomavirus prevalence and herd immunity after introduction of vaccination program, Scotland, 2009
2013. Emerging Infectious Diseases, 22(1), 56
64. Available from https://doi.org/10.3201/eid2201.150736. Centers for Disease Control Prevention. (2008). Syncope after vaccination—United States, January 2005
July 2007. JAMA: The Journal of the American Medical Association, 299 (21), 2502. Available from https://doi.org/10.1001/jama.299.21.2502. Cheng, L., Wang, Y., & Du, J. (2020). Human papillomavirus vaccines: An updated review. Vaccines, 8(3), 1
15. Available from https://doi.org/10.3390/vaccines8030391. Coleman, H. N., Greenfield, W. W., Stratton, S. L., Vaughn, R., Kieber, A., Moerman-Herzog, A. M., & Nakagawa, M. (2016). Human papillomavirus type 16 viral load is decreased following a therapeutic vaccination. Cancer Immunology, Immunotherapy, 65(5), 563
573. Available from https://doi.org/10.1007/s00262-016-1821-x. C¸uburu, N., Khan, S., Thompson, C. D., Kim, R., Vellinga, J., Zahn, R., & Schiller, J. T. (2017). Adenovirus vector-based prime-boost vaccination via heterologous routes induces cervicovaginal CD8 1 T cell responses against HPV16 oncoproteins. International Journal of Cancer, 142(7), 1467
1479. Available from https://doi.org/10.1002/ijc.31166. Daayana, S., Elkord, E., Winters, U., Pawlita, M., Roden, R., Stern, P. L., & Kitchener, H. C. (2010). Phase II trial of imiquimod and HPV therapeutic vaccination in patients with vulval

Vaccine development against HPV Chapter | 14

313

intraepithelial neoplasia. British Journal of Cancer, 102(7), 1129
1136. Available from https://doi.org/10.1038/sj.bjc.6605611. Davidson, E., Faulkner, R., Sehr, P., Pawlita, M., Smyth, L. J., Burt, D., & Stern, P. (2004). Effect of TA-CIN (HPV 16 L2E6E7) booster immunisation in vulval intraepithelial neoplasia patients previously vaccinated with TA-HPV (vaccinia virus encoding HPV 16/18 E6E7). Vaccine, 22 (21
22), 2722
2729. Available from https://doi.org/10.1016/j.vaccine.2004.01.049. Delany, I., Rappuoli, R., & De Gregorio, E. (2014). Vaccines for the 21st century. EMBO Molecular Medicine, 6, 708
720. Available from https://doi.org/10.1002/emmm.201403876. Dempsey, A. F., Pyrznawoski, J., Lockhart, S., Barnard, J., Campagna, E. J., Garrett, K., & O’Leary, S. T. (2018). Effect of a health care professional communication training intervention on adolescent human papillomavirus vaccination. JAMA Pediatrics, 172(5), e180016. Available from https://doi.org/10.1001/jamapediatrics.2018.0016. De Martel, C., Plummer, M., Vignat, J., & Franceschi, S. (2017). Worldwide burden of cancer attributable to HPV by site, country and HPV type. International Journal of Cancer, 141(4), 664
670. Available from https://doi.org/10.1002/ijc.30716. De Jong, A., O’Neill, T., Khan, A., Kwappenberg, K. M., Chisholm, S., Whittle, N., & Hickling, J. (2002). Enhancement of human papillomavirus (HPV) type 16 E6 and E7-specific T-cell immunity in healthy volunteers through vaccination with TA-CIN, an HPV16 L2E7E6 fusion protein vaccine. Vaccine, 20(29
30), 3456
3464. Available from https://doi.org/ 10.1016/s0264-410x(02)00350-x. Dixon, B. E., Zimet, G. D., Xiao, S., Tu, W., Lindsay, B., Church, A., & Downs, S. M. (2018). An educational intervention to improve HPV vaccination: A cluster randomized trial. Pediatrics, 143(e20181457). Available from https://doi.org/10.1542/peds.2018-1457. Drolet, M., Be´nard, E´., Pe´rez, N., Brisson, M., Ali, H., Boily, M. C., Baldo, V., Brassard, P., Brotherton, J. M. L., Callander, D., Checchi, M., Chow, E. P. F., Cocchio, S., Dalianis, T., Deeks, S. L., Dehlendorff, C., Donovan, B., Fairley, C. K., Flagg, E. W., & Yu, B. N. (2019). Population-level impact and herd effects following the introduction of human papillomavirus vaccination programmes: Updated systematic review and meta-analysis. The Lancet, 394(10197), 497
509. Available from https://doi.org/10.1016/S0140-6736(19)30298-3. Gonc¸alves, A. K., Cobucci, R. N., Rodrigues, H. M., de Melo, A. G., & Giraldo, P. C. (2014). Safety, tolerability and side effects of human papillomavirus vaccines: A systematic quantitative review. The Brazilian Journal of Infectious Diseases, 18(6), 651
659. Available from https://doi.org/10.1016/j.bjid.2014.02.005. Hancock, G., Hellner, K., & Dorrell, L. (2018). Therapeutic HPV vaccines. Best Practice and Research: Clinical Obstetrics and Gynaecology, 47, 59
72. Available from https://doi.org/ 10.1016/j.bpobgyn.2017.09.008. Hassett, K. J., Meinerz, N. M., Semmelmann, F., Cousins, M. C., Garcea, R. L., & Randolph, T. W. (2015). Development of a highly thermostable, adjuvanted human papillomavirus vaccine. European Journal of Pharmaceutics and Biopharmaceutics, 94, 220
228. Available from https://doi.org/10.1016/j.ejpb.2015.05.009. Huber, B., Schellenbacher, C., Shafti-Keramat, S., Jindra, C., Christensen, N., & Kirnbauer, R. (2017). Chimeric L2-based virus-like particle (VLP) vaccines targeting cutaneous human papillomaviruses (HPV). PLoS One, 12(1), e0169533. Available from https://doi.org/10.1371/journal.pone.0169533. Kalnin, K., Chivukula, S., Tibbitts, T., Yan, Y., Stegalkina, S., Shen, L., & Kleanthous, H. (2017). Incorporation of RG1 epitope concatemers into a self-adjuvanting Flagellin-L2 vaccine broaden durable protection against cutaneous challenge with diverse human papillomavirus genotypes. Vaccine, 35(37), 4942
4951. Available from https://doi.org/10.1016/j. vaccine.2017.07.086.

314

Oncogenic Viruses Volume 2

Karimi, H., Soleimanjahi, H., Abdoli, A., & Banijamali, R. S. (2020). Combination therapy using human papillomavirus L1/E6/E7 genes and archaeosome: A nanovaccine confer immuneadjuvanting effects to fight cervical cancer. Scientific Reports, 10(1), 1
15. Available from https://doi.org/10.1038/s41598-020-62448-3. Kavanagh, K., Pollock, K. G., Cuschieri, K., Palmer, T., Cameron, R. L., Watt, C., & Robertson, C. (2017). Changes in the prevalence of human papillomavirus following a national bivalent human papillomavirus vaccination programme in Scotland: A 7-year cross-sectional study. The Lancet Infectious Diseases, 17(12), 1293
1302. Available from https://doi.org/10.1016/ s1473-3099(17)30468-1. Kenter, G. G., Welters, M. J. P., Valentijn, A. R. P. M., Lowik, M. J. G., Berends-van de Vloon, r Meer, D. M. A, A. P. G., & Melief, C. J. M. (2009). Vaccination against HPV-16 oncoproteins for vulvar intraepithelial neoplasia. New England Journal of Medicine, 361(19), 1838
1847. Available from https://doi.org/10.1056/nejmoa0810097. Khan, M. J., Castle, P. E., Lorincz, A. T., Wacholder, S., Sherman, M., Scott, D. R., & Schiffman, M. (2005). The elevated 10-year risk of cervical precancer and cancer in women with human papillomavirus (HPV) Type 16 or 18 and the possible utility of type-specific HPV testing in clinical practice. JNCI: Journal of the National Cancer Institute, 97(14), 1072
1079. Available from https://doi.org/10.1093/jnci/dji187. Khan, S., Oosterhuis, K., Wunderlich, K., Bunnik, E. M., Bhaggoe, M., Boedhoe, S., & Custers, J. (2017). Development of a replication-deficient adenoviral vector-based vaccine candidate for the interception of HPV16- and HPV18-induced infections and disease. International Journal of Cancer, 141(2), 393
404. Available from https://doi.org/10.1002/ijc.30679. Kim, T. J., Jin, H.-T., Hur, S.-Y., Yang, H. G., Seo, Y. B., Hong, S. R., & Sung, Y. C. (2014). Clearance of persistent HPV infection and cervical lesion by therapeutic DNA vaccine in CIN3 patients. Nature Communications, 5(1), 5317. Available from https://doi.org/10.1038/ncomms6317. Kirnbauer, R., Booy, F., Cheng, N., Lowy, D. R., & Schiller, J. T. (1992). Papillomavirus L1 major capsid protein self-assembles into virus-like particles that are highly immunogenic. Proceedings of the National Academy of Sciences, 89(24), 12180
12184. Available from https://doi.org/10.1073/pnas.89.24.12180. Larasati, L., Afiyanti, Y., Rahmah, H., & Milanti, A. (2018). Women’s knowledge, beliefs, and behaviors toward the prevention of human papillomavirus transmission. Enfermer´ıa Cl´ınica, 28, 191
194. Available from https://doi.org/10.1016/s1130-8621(18)30065-2. Lundstrom, K. (2016). Replicon RNA viral vectors as vaccines. Vaccines, 4(4), 39. Available from https://doi.org/10.3390/vaccines4040039. Lundstrom, K. (2018). Self-replicating RNA Viruses for RNA therapeutics. Molecules, 23(12), 3310. Available from https://doi.org/10.3390/molecules23123310. Lundstrom, K. (2019). RNA viruses as tools in gene therapy and vaccine development. Genes (Basel), 10, 189. Available from https://doi.org/10.3390/genes10030189. Maciag, P. C., Radulovic, S., & Rothman, J. (2009). The first clinical use of a live-attenuated Listeria monocytogenes vaccine: A Phase I safety study of Lm-LLO-E7 in patients with advanced carcinoma of the cervix. Vaccine, 27(30), 3975
3983. Available from https://doi. org/10.1016/j.vaccine.2009.04.041. Markowitz, L. E., Tsu, V., Deeks, S. L., Cubie, H., Wang, S. A., Vicari, A. S., & Brotherton, J. M. L. (2012). Human papillomavirus vaccine introduction
The first five years. Vaccine, 30, F139
F148. Available from https://doi.org/10.1016/j.vaccine.2012.05.039. Mart´ınez-Lav´ın, M. (2015). Hypothesis: Human papillomavirus vaccination syndrome small fiber neuropathy and dysautonomia could be its underlying pathogenesis. Clinical Rheumatology, 34(7), 1165
1169. Available from https://doi.org/10.1007/s10067-015-2969-z.

Vaccine development against HPV Chapter | 14

315

Ogembo, R. K., Gona, P. N., Seymour, A. J., Park, H. S.-M., Bain, P. A., Maranda, L., & Ogembo, J. G. (2015). Prevalence of human papillomavirus genotypes among African women with normal cervical cytology and neoplasia: A systematic review and meta-analysis. PLoS One, 10(4), e0122488. Available from https://doi.org/10.1371/journal.pone.0122488. Ozawa, K., Hineno, A., Kinoshita, T., Ishihara, S., & Ikeda, S. I. (2017). Suspected adverse effects after human papillomavirus vaccination: A temporal relationship between vaccine administration and the appearance of symptoms in Japan. Drug Safety: An International Journal of Medical Toxicology and Drug Experience, 40, 1219
1229. Paavonen, J., Jenkins, D., Bosch, F. X., Naud, P., Salmero´n, J., Wheeler, C. M., Chow, S. N., Apter, D. L., Kitchener, H. C., Castellsague, X., et al.HPV PATRICIA study group. (2007). Efficacy of a prophylactic adjuvanted bivalent L1 virus-like-particle vaccine against infection with human papillomavirus types 16 and 18 in young women: An interim analysis of a phase III double-blind, randomised controlled trial. Lancet, 369, 2161
2170. Phillips, A., Patel, C., Pillsbury, A., Brotherton, J., & Macartney, K. (2018). Safety of human papillomavirus vaccines: An updated review. Drug Safety: An International Journal of Medical Toxicology and Drug Experience, 41, 329
346. Ramos, N., Narala, G. M., Vyas, A. M., Leen, U., Gerdemann, E. M., Sturgis, M. L., Anderson, B., Savoldo, H. E., Heslop, M. K., & Brenner, C. M. (2013). Rooney, human papillomavirus type 16 E6/E7 specific cytotoxic T lymphocytes for adoptive immunotherapy of HPVassociated malignancies. Journal of Immunotherapy (Hagerstown, Md.: 1997), 36, 66
76. Rosales, R., Lo´pez-Contreras, M., Rosales, C., Magallanes-Molina, J.-R., Gonzalez-Vergara, R., Arroyo-Cazarez, J. M., & Villarreal, F. (2014). Regression of human papillomavirus intraepithelial lesions is induced by MVA E2 therapeutic vaccine. Human Gene Therapy, 25(12), 1035
1049. Available from https://doi.org/10.1089/hum.2014.024. Saade, F., & Petrovsky, N. (2012). Technologies for enhanced efficacy of DNA vaccines. Expert Review of Vaccines, 11(2), 189
209. Available from https://doi.org/10.1586/erv.11.188. Sankaranarayanan, R., Joshi, S., Muwonge, R., Esmy, P. O., Basu, P., Prabhu, P., et al. (2018). Can a single dose of human papillomavirus (HPV) vaccine prevent cervical cancer? Early findings froman Indian study. Vaccine, 36, 4783
4791. Available from https://doi.org/ 10.1016/j.vaccine.2018.02.087. Santin, S., Bellone, J. J., Roman, A., Burnett, M. J., & Cannon, S. (2005). Pecorelli, Therapeutic vaccines for cervical cancer: Dendritic cell-based immunotherapy. Current Pharmaceutical Design, 11, 3485
3500. Schellenbacher, C., Roden, R., & Kirnbauer, R. (2009). Chimeric L1-L2 virus-like particles as potential broad-spectrum human papillomavirus vaccines. Journal of Virology, 83(19), 10085
10095. Available from https://doi.org/10.1128/jvi.01088-09. Schiller, J., & Lowy, D. (2018). Explanations for the high potency of HPV prophylactic vaccines. Vaccine, 36(32 Pt A), 4768
4773. Solares, I., Baladron, T., Ramos, C., Valenzuela, Z., Borbon, S., Fanjull, L., Gonzalez, D., Castillo, J., Esmir, M., Granadillo, A., Batte, A., Cintado, M., Ale, M. E., Fernandez de Cossio, A., Ferrer, I., & Torrens, P. (2011). Lopez-Saura, Safety and immunogenicity of a human papillomavirus peptide vaccine (CIGB-228) in women with high-grade cervical intraepithelial neoplasia: First-in-human, proof-of-concept trial. ISRN Obstetrics and Gynecology, 2011, 292951. Spencer, J. C., Calo, W., & Brewer, N. T. (2019). Disparities and reverse disparities in HPV vaccination: A systematic review and meta-analysis. Preventive Medicine, 123, 197
203. Available from https://doi.org/10.1016/j.ypmed.2019.03.037. Trimble, M. P., Morrow, K. A., Kraynyak, X., Shen, M., Dallas, J., Yan, L., Edwards, R. L., Parker, L., Denny, M., Giffear, A. S., Brown, K., Marcozzi-Pierce, D., Shah, A. M., Slager,

316

Oncogenic Viruses Volume 2

A. J., Sylvester, A., Khan, K. E., Broderick, R. J., Juba, T. A., Herring, J., . . . Weiner, M. L. (2015). Bagarazzi, safety, efficacy, and immunogenicity of VGX-3100, a therapeutic synthetic DNA vaccine targeting human papillomavirus 16 and 18 E6 and E7 proteins for cervical intraepithelial neoplasia 2/3: A randomised, double-blind, placebo-controlled phase 2btrial. Lancet (London, England), 386. Tumban, J., Peabody, D. S., & Peabody, B. (2013). Chackerian, A universal virus-like particlebased vaccine for human papillomavirus: Longevity of protection and role of endogenous and exogenous adjuvants. Vaccine, 31, 4647
4654. Van Klooster TM, Kemmeren JM, van der Maas NA, de Melker HE. (2011). Reported adverse events in girls aged 13
16 years after vaccination with the human papillomavirus (HPV)16/18 vaccine in the Netherlands. Vaccine. 29:4601
4607. Available from https://doi.org/ 10.1016/j.vaccine.2011.04.050. review of vaccines, 15 (2016) 1327
1336. Vici, P., Pizzuti, L., Mariani, L., Zampa, G., Santini, D., Di Lauro, L., & Venuti, A. (2016). Targeting immune response with therapeutic vaccines in premalignant lesions and cervical cancer: Hope or reality from clinical studies. Expert Review of Vaccines, 15(10), 1327
1336. Available from https://doi.org/10.1080/14760584.2016.1176533. Wang, D., Li, Z., Xiao, J., Wang, J., Zhang, L., Liu, Y., & Xia, N. (2015). Identification of broad-genotype HPV L2 neutralization site for pan-HPV vaccine development by a crossneutralizing antibody. PLoS One, 10(4), e0123944. Available from https://doi.org/10.1371/ journal.pone.0123944. WHO.(2017). Human papillomavirus vaccines: WHO position paper, May 2017. Weekly Epidemiological Record 2017; 92:241-268. Available from: https://www.who.int/wer/2017/ wer9219/en/ Wang, R., Pan, W., Jin, L., Huang, W., Li, Y., Wu, D., Gao, C., Ma, D., & Liao, S. (2019). Human papillomavirus vaccine against cervical cancer: Opportunity and challenge. Cancer Letters, 471, 88
102. Available from https://doi.org/10.1016/j.canlet.2019.11.039. Winer, R. L., Feng, Q., Hughes, J. P., O’Reilly, S., Kiviat, N. B., & Koutsky, L. A. (2008). Risk of female human papillomavirus acquisition associated with first male sex partner. The Journal of Infectious Diseases, 197(2), 279
282. Available from https://doi.org/10.1086/ 524875. Yadav, R., Zhai, L., & Tumban, E. (2019). Virus-like particle-based L2 vaccines against HPVs: Where are we today? Viruses, 12(1), 18. Available from https://doi.org/10.3390/v12010018. Zhai, L., & Tumban, E. (2016). Gardasil-9: A global survey of projected efficacy. Antiviral Research, 130, 101
109. Available from https://doi.org/10.1016/j.antiviral.2016.03.016. Zhai, L., Yadav, R., Kunda, N. K., Anderson, D., Bruckner, E., Miller, E. K., Basu, R., Muttil, P., & Tumban, E. (2019). Oral immunization with bacteriophage MS2-L2 VLPs protects against oral and genital infection with multiple HPV types associated with head & neck cancers and cervical cancer. Antiviral Research, 166(February), 56
65. Available from https://doi.org/10.1016/j.antiviral.2019.03.012. Zhou, X., Sun, L., Yao, X., Li, G., Wang, Y., & Lin, Y. (2020). Progress in vaccination of prophylactic human papillomavirus vaccine. Frontiers in Immunology, 11, 1434. Available from https://doi.org/10.3389/fimmu.2020.01434. Zhu, F. C., Hu, S. Y., Hong, Y., Hu, Y. M., Zhang, X., Zhang, Y. J., et al. (2019). Efficacy, immunogenicity and safety of the AS04-HPV-16/18 vaccine in Chinese women aged 18
25 years: End-of-study results from a phase II/III, randomised, controlled trial. Cancer Medicine, 8, 6195
6211. Available from https://doi.org/10.1002/cam4.2399.

Chapter 15

Development and characterization of an electrochemical sensor using molecularly imprinted polymer based on a gold screen-printed electrode for the detection of creatinine and glucose in human urine and saliva Benachir Bouchikhi1, Alassane Diouf2, Moulay Mustapha Ennaji3 and Nezha El Bari2 1

Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Moulay Ismaı¨l University, Meknes, Morocco, 2Biotechnology Agroalimentary and Biomedical Analysis Group, Faculty of Sciences, Department of Biology, Moulay Ismaı¨l University, Meknes, Morocco, 3Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco

15.1 Introduction Due to its high sensitivity, specificity, efficiency, and cost-effectiveness, electrochemical techniques are now widely used for the detection and evaluation of biological substances in body fluids. However, invasive sampling of these body species is becoming increasingly controversial due to the suffering that patient’s experience from skin irritation and the risks of injection. Like blood, urine and saliva provide a lot of metabolic information, and can therefore reflect the state of health of the human body. They seem to be more practical for a truly noninvasive diagnosis of diseases. As an example, Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00016-9 Copyright © 2023 Elsevier Inc. All rights reserved.

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a number of diseases have been recognized and successfully diagnosed from the analysis of urine and saliva. These fluids can therefore be used to detect and monitor certain diseases such as kidney disease. On the one hand, before discussing the use of urine and saliva as diagnostic tools in medicine, the term biomarker should be defined. It is “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (Biomarkers Definitions Working Group et al., 2001). Indeed, biomarkers could be used for risk prediction or screening, monitoring, and diagnosis (Mayeux, 2004). Recently, the use of biomarkers in biomedicine has become an indispensable tool due to their ability to serve as a quantitative and individualized reflection of an individual’s health status. On the other hand, a procedure is defined as noninvasive technique when no break in the skin is created and there is no contact with the mucosa, or a break in the internal body cavity beyond a natural or artificial body orifice (Mosby, 2009). It can therefore be said that the collection of urine and saliva is noninvasive. Therefore, the aim of this study is first to present a brief state of the art of urine and saliva analysis. Secondly, their use as a means of noninvasive diagnosis of diseases through biochemical substances will be discussed. Finally, the development of noninvasive electrochemical sensors for the detection of creatinine and glucose will be discussed.

15.2 Urine and saliva as noninvasive sources of biomarkers Body fluids carry information (biomarkers) about the physical health of the human body. They are widely used for diagnostic and therapeutic purposes. It is well established that these biomarkers can provide useful information about the metabolic state of an organism. These disease-related compounds may be part of the cascade of reactions that occur in the body’s response to damage. These sources can be used for invasive or noninvasive detection for disease diagnosis. On-site analysis of biomarker levels in liquid media facilitates the assessment of disease diagnosis. In general, compounds in the body are first released into the circulatory system and then passed into biological fluids such as blood, breath, urine, blood, and saliva. However, the collection of blood or tissue is not practical, as it requires specific instruments and trained operators, and it is also uncomfortable for many patients. Therefore, the search for analytes for disease detection can be done through biofluids such as urine and saliva. These fluids also carry a great deal of vital physical and chemical information about the human body. In addition, they are more convenient because of their safe, painless collection, which is more acceptable to children, the elderly, pregnant women, mentally and physically handicapped patients, and high-risk patients. Most

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importantly, the feasibility of the sample offers the potential for rapid monitoring of human disease. The kidneys concentrate the analytes in the urine before they are excreted from the body, which is an advantage over other biofluids. Urine testing is an established method of detecting many diseases noninvasively.

15.3 Biomarkers in the bloodstream can infiltrate the acini and eventually be secreted into the saliva Salivary amylase, a form of amylase found in human saliva, catalyzes the hydrolysis of starch into maltose and sometimes glucose in the mouth. The main diagnostic advantage of saliva is simple, safe, and noninvasive sample collection with a reduced risk of infection compared to invasive methods. Saliva, as a noninvasive and safe source, could therefore replace blood in the diagnosis and prognosis of diseases. Human saliva is 99% water and the rest is made up of organic molecules such as salivary amylase, mucopolysaccharide, mucin, and lysozymes, and some inorganic materials including sodium salts, potassium, calcium, chlorate, bicarbonate, and phosphate, as well as organic compounds such as uric acid (2,6,8-trihydroxypurine, UA), lactate, hormones, polypeptides, and proteins, such as immunoglobulins, enzymes, and mucins (Goll & Mookerjee, 1978). Other constituents that may represent potential biomarkers, including neopterin, nicotine, nitrates, nitrites, and glutathione, can also be found in saliva. Recent research suggests that salivary fluid may have the potential for noninvasive, continuous, and instantaneous monitoring of various diseases. With an abundance of compounds in urine and saliva, these are promising biofluids for the continuous and real-time monitoring of various diseases.

15.3.1 Recognition of particular compounds as an indicator of diseases Several compounds can reflect pathologies in the human body. To name but a few, with great ease of collection, salivary immunoglobulins (sIgA), in particular immunoglobulin A (Salimetrics), are the most studied markers of the mucosal immune system. It is a very important biomarker as selective sIgA deficiency has been observed in people with a high incidence of infection (Levinsky, 1984) or low saliva flow (Fox et al., 1985). Biomarkers such as salivary C-reactive protein and neopterin also provide information about inflammation (Ouellet-Morin et al., 2011; Punyadeera et al., 2011; Ozmeric¸ et al., 2002). Similarly, cardiac troponin T and creatine kinase-MB have also been used in biomedical work on cardiovascular stress (Mirzaii-Dizgah & Riahi, 2013) and muscle injuries (Mirzaii-Dizgah et al., 2012), respectively. Specific interleukins can be detected in saliva (Ives et al., 2011; Cullen et al., 2015).

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Myoglobin is muscle specific and its detection in urine is diagnostically relevant and indicates trauma-induced muscle damage (Uberoi et al., 1991). 3-Methylhistidine is a commonly used marker of muscle damage that directly represents myofibrillar breakdown (Bird et al., 2006). Urine and saliva were used to assess “stress” through cortisol (Gouarne et al., 2005; Salimetrics), testosterone (Testoste´rone Salimetrics; Blazevich & Giorgi, 2001), epitestosterone (Robinson et al., 2006), tetrahydrobiopterin (BH4) (Lindsay et al., 2016), and cannabinoids (PharmaDrugs, 2022).

15.4 Current electrochemical sensor devices Electrochemical sensors have been considered the most sensitive devices for creatinine monitoring. Studies on nonenzymatic electrochemical creatinine sensors are limited compared to those using enzymatic sensors. Although not prevalent, nonenzymatic sensors have good storage stability and robust repeatability, unlike their enzymatic counterparts. They are easily implemented and exhibit metrologically relevant properties with minimal interference. With these nonenzymatic sensors, creatinine interacts with nanoparticles on an electrode (Chen & Lin, 2012) or as a composite (Viswanath et al., 2017). Creatinine detection has also been achieved using molecularly imprinted polymer (MIP) technology. In fact, over the past two decades, a variety of MIP-based creatinine sensors have been identified (Sreenivasan & Sivakumar, 1997; Subrahmanyam et al., 2001). Zhang et al. (2002) exploited the Langmuir model for creatinine detection. Other MIP-based creatinine sensors have been reported. For the creatinine detection, some authors have synthesized poly(ethylene-co-vinyl alcohol) (EVAL) as a functional monomer (Lee et al., 2008), methacrylic acid (Miura et al., 2013), acrylamido methylpropane sulfonic acid (Reddy & Gobi, 2013), poly(melamine-cochloranil) (Patel et al., 2008), b-cyclodextrin (b-CD) (Hsieh et al., 2006; Tsai & Syu, 2005), and 4-vinylpyridine (Hsieh et al., 2006). Among the sensors proposed in the literature, some use gold electrodes as support (Khadro et al., 2010; Panasyuk-Delaney et al., 2002). This type of electrodes has been exploited by Delaney et al. who coated them with a monolayer of hexadecane thiol (Panasyuk-Delaney et al., 2002). In other reported works, hanging mercury drop electrodes were used (Lakshmi et al., 2006). Glucose is not commonly measured in blood in routine practice, although it is an important molecule and plays a key role in the diagnostic work-up of diabetes. Blood glucose concentration could be monitored by saliva (Satish et al., 2014). Given its easy accessibility and a large number of biomarkers, saliva is a biofluid of choice for monitoring and screening general health and certain diseases (Viswanath et al., 2017). Some analytical devices have been identified in recent years (Mannoor et al., 2012; Kim et al., 2015). To correlate blood and salivary glucose levels, several authors have developed glucose

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detection devices (Dhanya & Hegde, 2016; Kadashetti et al., 2015). This demonstrates the possibility of using saliva as a noninvasive biofluid for diabetes diagnosis. One example is the work of Mitsubayashi et al. who developed a device for quantifying salivary glucose (Arakawa et al., 2016). Garcia-Carmona et al. have even reported a device for analyzing biofluids in newborns, including their saliva (Garcia-Carmona et al., 2019).

15.5 Applications of gas sensors in oncology or virology as tools for the detection of biomarkers Nanotechnology represents a promising means for detection, staging, and treatment of cancer (Fortina & Kricka, 2007; Bajaj & Miranda, 2009). The combination of nanotechnology with the need for medicine to find a noninvasive method to detect and prevent cancer is a rapidly evolving field. Current screening techniques, such as colonoscopy, are invasive, traumatic, and unpleasant for already debilitated patients (Hompes & Cunningham, 2011). It is known that volatile organic compound (VOC) emissions are linked to tumor growth, which is accompanied by gene and/or protein changes that may lead to these volatile emissions (Horvath & Lazar, 2009; Buzewski & Rudnicka, 2012). VOCs can be considered as biomarkers for different types of cancers, and their analysis is a new frontier in medical diagnostics because it is noninvasive and potentially inexpensive (Amann & Spanel, 2007; Mazzone, 2008). Tumor VOCs can be detected directly from the headspace of cancer cells or through an exhaled breath. A medical study has been performed by Malagu` et al. (2014) that aimed to use chemoresistive gas sensors for the detection of VOCs as indicators of colorectal cancer biomarkers. An array of 12 metal-oxide semiconductors combining SnO2 and TiO2 materials working at their best temperatures have been used to discriminate gases of oncological interest. The findings reveal that the most relevant VOC biomarkers of colorectal cancer are benzene compounds and the molecule, 1-iodo-nonane (C9H19I).

15.6 Experimental 15.6.1 Chemicals and reagents Creatinine (98%) was purchased from Riedel-de Haen (Seelze, Germany); polyvinyl carboxylic chloride (PVC-COOH), glucose, ethanol, 0.01 M phosphate-buffered saline (PBS), sodium hydroxide (NaOH) (96%), picric acid (99%), 1,4-dioxane (99.8%), and urea (99.5%) were purchased from Sigma Aldrich, France. N-(3-dimethylaminopropyl)-N0 -ethyl carbodiimide hydrochloride (EDC), ammonium persulphate (APS), N, N0 -methylene-bisacrylamide (NNMBA), acrylamide (AAM), N, N, N0 , N0 -Tetramethyl ethylenediamine (TEMED), N-hydroxysuccinimide (NHS), potassium ferricyanide

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K3[Fe(CN)6], and potassium ferrocyanide K4[Fe(CN)6] were all obtained from Fluka. The PBS used as electrolyte was prepared using a solution of monobasic potassium phosphate (KH2PO4) and dibasic sodium phosphate (Na2HPO4). To 800 mL of distilled water, 8 g NaCl, 0.2 g KCl, 1.44 g Na2HPO4, and 0.24 g KH2PO4 were added. The solution was then made up to 1 L with distilled water. The resulting pH was 7. The pH was adjusted with NaOH and HCl. Here, the focus will be on faradic processes using a redox couple in an equimolar proportion of the reduced and oxidized forms to simplify the analysis. Thus ferri/ferrocyanide was chosen because, when present in the electrolyte solution, they are not adsorbed on the electrode surface and involve only one electron in the electrochemical reactions. Indeed, for the preparation of the redox probe, 164 mg of potassium ferricyanide K3[Fe(CN)6] (with a molar mass of 329.24 g/mol) is dissolved in 100 mL of PBS buffer at pH 7.4. Then, 211 mg potassium ferrocyanide K4[Fe(CN)6] (molar mass 422.41 g/mol) is dissolved in 100 mL PBS. The two solutions are mixed to obtain 5 mM ferri/ferrocyanide used as a redox probe for electrochemical measurements. Then, 2-amino-2-hydroxymethyl-1,3-propanediol (TRIS 0.5 M) was obtained from Panreac Quimica. In this work, the buffer solutions used were TRIS and PBS. To prepare the TRIS solution and ammonium persulphate, 60 g and 13 g were dissolved in 100 mL of PBS, respectively. From a standard solution of the analyte [1 mg in 1 mL PBS (pH 7.4)], less concentrated solutions were prepared by precise dilutions of the previous solution using the same PBS buffer. To ensure complete dissolution of creatinine in PBS, a vortex and hot plate shaker were used. All solutions in the adopted range were stored at 4 C.

15.6.2 Polymer synthesis Acrylamide, as a functional monomer, was used because of its stability and availability in large quantities. Furthermore, it polymerizes under mild conditions and has uncharged groups that can establish hydrogen bonds and dipole
dipole interactions with creatinine. Indeed, the polymerizations take place in a liquid medium consisting of a mixture of acrylamide and the cross-linker bis-acrylamide (N, N0 methylene-bis-acrylamide) that react together. The acrylamide polymerizes in the presence of free radicals typically provided by ammonium persulphate. Then, in the presence of TEMED as a catalyst, the added ammonium persulphate is converted to a sulfate ion free radical (SO4-) which initiates the polymerization of acrylamide. Furthermore, in its geometric structure, acrylamide has a double bond between two carbons, which is a prime target for the sulfate radical. This results in a radical acrylamide molecule. This reacts with an identical

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FIGURE 15.1 Polymerization reaction between the functional monomer and the analyte (creatinine).

acrylamide molecule to form straight-chain acrylamide (polyacrylamide) polymers. To obtain a stable cross-linked polymer matrix, it becomes necessary to use a cross-linking agent that will link these chains. Bis-acrylamide, equivalent to 2 methyl-linked acrylamide monomers, with two free radical attack sites (two double bonds between carbon atoms), is a cross-linking agent of choice (Kanai et al., 2013). In the absence of bis-acrylamide, acrylamide would polymerize into long strands (linear polymer). Bis-acrylamide contains two double bonds, which allow the compound to act as a crosslinking agent between the acrylamide chains. A very interesting mesh formation is obtained that should capture functional species. Thus the functional groups of the polymer will covalently bind the amine groups of the creatinine molecules that are added. It could also link glucose molecules through hydrogen bonding. Fig. 15.1 illustrates the polymerization reaction with creatinine.

15.6.3 Electrochemical sensors fabrication steps 15.6.3.1 Creatinine molecularly imprinted polymer sensor For creatinine detection, a sensor based on MIP on gold screen-printed electrodes (Au-SPE) was developed. Before any modification, the working electrode (Au-SPE) was rinsed three times with ethanol. A 5-μL volume of an 8.8 mg/mL solution of polyvinyl carboxylic acid (PVC-COOH) dissolved in 1.4 dioxanes was deposited as a homogeneous layer on the electrode. To activate the -COOH groups, 5 μL of an aqueous solution containing 4 mg of N-(3-dimethylaminopropyl)-N0 -ethyl carbodiimide hydrochloride (EDC) and 1 mg of NHS prepared in 1 mL of distilled water was incubated on the

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modified electrode. A rinse with distilled water followed to remove unreacted carbodiimide species. Next, a 3.5 μL volume of the previously prepared creatinine standard solution (at 1 mg/mL in PBS buffer solution at pH 7.4) was deposited on the surface of the Au-SPE/PVC-COO- and incubated for 4 h at 4 C. The Au-SPE/PVC-COO-/Cre electrode was then rinsed twice with PBS buffer solution at pH 7.4 to remove the creatinine molecules that failed to bind. The next step consisted of a TRIS solution for 30 min to block the -COO2 groups that did not bind with creatinine molecules. This was followed by a rinsing step with distilled water. The pre-polymerization solution was then prepared by adding 13 mg of ammonium persulphate to a solution already containing 11 mg of AAM as the functional monomer and 71 mg of NNMBA as the cross-linking agent in 2 mL of PBS to create a mold around the template. To accelerate the polymerization, 20 μL of a solution of TEMED was also added to the pre-cure solution. Finally, a volume of 50 μL of the pre-polymerization solution was deposited on Au-SPE/PVC-COO2/ Cre overnight at 4 C. The analyte was then removed from the polymer layer by incubating the Au-SPE/PVC-COO2Cre/polymer in distilled water at room temperature for 15 min and with ethanol for 10 min. The new electrode obtained is called Au-SPE/MIP. It was ready for use after being rinsed with distilled water. For comparison, the nonimprinted polymer-modified electrode (Au-SPE/ NIP) was prepared in the same way but without adding creatinine. The MIP sensor was used to detect creatinine in urine samples. The Jaffe´ method was used as a validation technique.

15.6.3.2 Glucose molecularly imprinted polymer sensor On the other hand, a MIP approach was adopted to develop a nonenzymatic electrochemical sensor to quantify salivary glucose. Here, the same procedure for preparing the pre-polymerization solution as the previous sensor is adopted except for the addition of glucose. AAM was used as the functional monomer and NNMBA as the cross-linking agent. In contrast, an electropolymerization method is used to immobilize the MIP by cyclic voltammetry (CV) in a potential range of 20.35 to 0.8 V at a scan rate of 0.1 V/s for 10 cycles. During electropolymerization, a membrane (analyte 1 polymer) gradually formed on the working electrode. The glucose molecules migrated through the polymer and became trapped there. This ensures the formation of a poly-AAM/glucose membrane. After this step, the glucose molecules are extracted from the membrane to create memory sites of similar size, shape, and functionality to glucose. For comparison, a NIP sensor was prepared in the same way but without the addition of glucose. The electrodes modified with a MIP and a NIP were then dried and stored at room temperature (25 C) until analysis. In a practical application, this MIP sensor was used to determine the concentration of

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salivary glucose in relation to the blood glucose level determined by a glucose meter.

15.6.4 Physicochemical characterization The morphology of the electrodes was studied using characterization techniques, such as Fourier transform infrared (FTIR), atomic force microscopy (AFM), and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS). To highlight the immobilization process of MIP to glucose, the morphology and chemical composition of the electrode surface at different stages of immobilization (Au-SPE, Au-SPE/MIP, and that of the extraction step) were observed by SEM-EDS (EDS, FEI QUANTA 250). In addition, AFM characterization was performed using a device (NANOVEA, United States), having a maximum resolution of 110 μm in a scan area of (76 μm/76 μm) at 1 line/ 2 s. The electrodes were dried and held at a temperature of 4 C overnight before analysis. SEM images were used to study how the materials change the geometric surface of the Au-SPE at low magnification ( 3 2500) and an accelerating voltage of 15 kV. For this purpose, by the FTIR technique, the spectrum of the surface sample was obtained based on the sensitivity of chemical functions to specific wavelengths between 4000 and 400/cm. Therefore, the apparatus allowed, via the detection of the characteristic vibrations of chemical bonds, to carry out a topography of the electrode’s surfaces. The AFM study was carried out in contact mode where the tip pressed against the surface of the substrate. Thus the tip swept and rubbed the electrode surface following its relief. The deformation of the lever, which was illuminated by a laser, was measured by a photodetector and recorded on a computer, which could reconstruct a 3D image of the surface. For SEM analysis, an electron gun and an electronic column produced a fine electronic probe on the working electrode, a microscope stage to move it in three directions, and detectors for capturing and analyzing the emitted radiation. The interaction between the electron probe and the electrode generated secondary, low-energy electrons that were accelerated to a secondary electron detector that amplified the signal. At each point of impact corresponded an electrical signal. The intensity of this electrical signal depended both on the nature of the electrode at the point of impact that determined the secondary electron yield and on its topography. By scanning the beam on the electrode, a map of the scanned area was obtained. As known, each atom has a unique number of electrons that reside in normal conditions in specific positions. In an SEM, when X-rays are generated, we have a two-step process. In the first step, the electron beam hits the electrode and transfers a part of its energy to the existing atoms on the electrode.

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This energy is used by the electrons of atoms to transit from an energy level to a higher or to be removed from the atoms. If such a transition occurs, the electron leaves behind a “hole.” In the second stage of the process, the positively charged holes attract electrons from higher energy levels. When an electron from such higher energy fills the hole of the lower, the energy difference of that transition is released as an X-ray. This X-ray has characteristic energy of the energy difference between these two levels. It depends on the atomic number, which is a unique property of each chemical element. In this way, X-rays are a “fingerprint” of each element and were used to identify the chemical elements that exist on the electrode surface of concern. Absorbance measurements were performed using an ultraviolet
visible (UV
Vis) 220 spectrophotometer. The absorbances of the urine samples were measured with this in the range of 200
700 nm using quartz cuvettes (1 cm wide). The wavelength was calibrated at 520 nm.

15.6.5 Electrochemical measurements The different electrochemical processes are characterized by the electrochemical cell described in Fig. 15.2. To study the electrochemical behavior of the electrodes, electrical characterization techniques, such as CV, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) were used. CV and DPV measurements were performed using PBS as the supporting electrolyte containing 5 mM of [Fe(CN)6]3-/4- solution as the electroactive probe. For the

FIGURE 15.2 Photograph of the electrochemical measurement system via the screen-printed sensors.

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CV measurements, a potential range of 20.4 V to 0.6 V was used with a scan rate of 30 mV/s. For the DPV, a potential window of 20.1 V to 0.2 V was used with a sweep rate of 10 mV/s. The same potentiostat was used to perform analyses using EIS in a frequency range between 0.1 Hz and 50 kHz with 10 mV as the open circuit AC voltage amplitude. The impedance of the electrode is determined by applying a low amplitude (peak-to-peak) sinusoidal potential and measuring the resulting sinusoidal current. The EIS data can be represented by Bode and Nyquist diagrams (Lakshmi et al., 2006). The results of the electrochemical impedance measurements were analyzed based on the best-fit equivalent circuit analysis, using the implemented EIS spectrum analysis software.

15.7 Results and discussion 15.7.1 Morphological characterization of the fabricated sensor Fig. 15.3 shows the results of the SEM-EDS analyses of the unmodified electrode surface, after its modification with glucose MIP, and after elution of the glucose molecules. The structure of the unmodified Au-SPE surface is shown in Fig. 15.3A, that of the electrode modified with MIP is shown in Fig. 15.3B, and the structure of the electrode after the glucose extraction step is shown in Fig. 15.3C. In the latter two cases, the electrode surfaces were found to be clearly modified compared to the first. The SEM image (Fig. 15.3C) of the polymer-modified electrode (Au-SPE/MIP) shows a uniformly formed mesh structure over the entire surface compared to the image of the unmodified Au-SPE, which showed a plate-like structure. In the case of the electrode obtained after the extraction step, a plate-like structure was observed but the surface was more porous. SEM studies clearly revealed that the surface of the electrode without glucose (extraction phase) had a relatively more porous structure with smaller particles that could promote effective interaction with the target molecules. Simultaneously, the chemical composition of the electrodes was studied by the energy-dispersive X-ray spectroscopy (EDS) technique using the same apparatus. This provided information on the chemical composition of a point or area of interest on the gold screen-printed working electrode. For the results after analysis, the Kratio represents the ratio of the characteristic intensities measured on the sample and the control. In practice, there are “matrix effects” arising from the nature of the interactions of electrons and X-rays with the material, which modify the measured intensities and depend on the unknown composition of the sample. Therefore, the intensities measured from the sample must be corrected using the coefficients of (Eq. 15.1). To do this, the peaks in the region of interest are deconvoluted and a conventional ZAF-type matrix effects correction procedure is applied to the net

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FIGURE 15.3 Scanning electron microscopy (SEM) images for: (A) Au-SPE, (B) Au-SPE/ MIP after electropolymerization of molecularly imprinted polymer (MIP). (C) Au-SPE/MIP after glucose extraction, and spectra obtained using energy-dispersive X-ray spectroscopy (EDS) for: (D) Au-SPE, (E) Au-SPE/MIP after electropolymerization of MIP, (F) Au-SPE/MIP after glucose extraction.

intensities (I) to calculate the elemental concentrations. Z is an atomic number correction that accounts for differences in backscattered electron yields between the pure element and the electrode (pure elements with a higher atomic number will produce fewer X-rays because some of the electrons in

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the beam leave the electrode before losing all their energy); A is the absorption correction which compensates for X-rays generated in the electrode but which cannot escape when absorbed in the electrode (low-energy X-rays tend to be strongly absorbed); and F is the fluorescence correction which corrects for the generation of X-rays by other higher energy X-rays. The concentration of the ith element on the sample is calculated using the following equation (Goldstein et al., 2017): Weight ð%Þ 5

Ci 5 Z:A:F Ccont

Ii Icont

5 Z:A:F Kratio ;

ð15:1Þ

where CCconti is the weight, which corresponds to the relative concentraIi tion of element i in the sample compared to the control Icont , which corresponds to the ratio K, and could be defined as the ratio of the characteristic intensities measured on the sample compared to the control. ZAF is the correction factor that permits to determine the element i. Indeed, Table 15.1 shows that the area of interest of the Au-SPE was composed of 81.16% by weight gold (Au), 11.05% by weight oxygen (O), and 7.79% by weight aluminum (Al). The high presence of gold demonstrates that the Au-SPE has not been modified. Aluminum and oxygen are the constituent atoms of the electrode support which is made of ceramic. Table 15.2 shows that after being modified with MIP, the layer on the surface of interest of the electrode contained carbon (C), oxygen (O), sodium (Na), and sulfur (S) in percentage (18.32% of the weight of C, 23.76% of the weight of O, 5.94% of the weight of Na, and 4.01% of the weight of S). The appearance of Na, S, C, and O in the chemical composition proved that the pre-polymerization solution (polymer 1 glucose) dissolved in PBS was effectively immobilized on the electrode surface. Table 15.3 shows that after the extraction of the glucose molecules from the MIP layer, the layer on the electrode surface of interest consisted of 9.11% by weight C and 6.45% by weight O. The presence of C and O at a lower percentage compared to the MIP result proves a successful elution of glucose molecules from the Au-SPE/MIP surface. In all these cases, the images obtained by SEM and the spectra given by EDS showed a homogeneous distribution of the elements on the electrode surface, demonstrating its successful modification. Indeed, the topography of the electrode surface after each step of the MIP sensor development was analyzed using the AFM technique. The root mean square (RMS) value of roughness which is related to the physicochemical properties of the surfaces could be acquired from the images. Fig. 15.4 shows the AFM topographies of the electrode surface after each modification step. Fig. 15.4A shows the image of the unmodified Au-SPE with a RMS (Rq) roughness of 724 nm, while Fig. 15.4B shows the surface of the gold electrode after its modification with the MIP, exhibiting a higher Rq value (1090 nm) due to the immobilization of the polymer-glucose membrane. In

TABLE 15.1 Chemical composition of Au-SPE found using energy dispersive X-ray. Elements

Weight (%)

% Atomic

Net intensity

Error (%)

Kratio

Z

A

F

O (K)

11.05

49.65

128.15

11.41

0.0426

1.5495

0.2488

1.0000

Al (K)

7.79

20.74

211.04

7.02

0.0675

1.3816

0.6273

1.0001

Au (M)

81.16

29.61

769.82

3.11

0.7487

0.8941

1.0319

0.9996

TABLE 15.2 Chemical composition of Au-SPE/MIP after electro-polymerization of molecularly imprinted polymer (MIP) found using energy dispersive X-ray spectroscopy. Elements

Weight (%)

% Atomic

Net intensity

Error (%)

Kratio

Z

A

F

C (K)

18.32

36.61

163.53

11.06

0.0714

1.1398

0.2464

1.0000

O (K)

23.76

35.64

386.80

9.55

0.1106

1.0883

0.3085

1.0000

Na (K)

5.94

6.19

135.73

8.83

0.0428

0.9859

0.5283

1.0001

S (K)

4.01

2.99

114.68

7.42

0.0443

0.9635

0.8293

0.9979

TABLE 15.3 Chemical composition of Au-SPE/MIP after glucose extraction found using energy dispersive X-ray spectroscopy. Elements

Weight (%)

% Atomic

Net intensity

Kratio

Z

A

F

C (K)

9.11

48.98

259.72

Error (%) 9.70

0.1022

1.3521

0.3275

1.0000

O (K)

6.45

26.05

201.78

11.29

0.0519

1.2952

0.2452

1.0000

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FIGURE 15.4 Images obtained using atomic force microscopy (AFM) for (A) Au-SPE, (B) Au-SPE/MIP after electropolymerization of molecularly imprinted polymer (MIP), (C) Au-SPE/ MIP after glucose extraction.

this case, homogeneous surface topography was noticed. After elution of the glucose molecules, the surface topography changed (Fig. 15.4C) and a decrease in roughness was observed (Rq 5 0.87 μm). This indicates not only the successful immobilization of the MIP on the electrode surface but also the satisfactory extraction of the template. In summary, the morphological and chemical composition analysis results obtained by SEM-EDS and AFM are in good agreement and indicate the good modification of the electrode surface for each experimental step demonstrating the feasibility of the MIP sensor.

15.7.2 Voltammetric array and electrochemical impedance spectroscopy responses 15.7.2.1 Creatinine molecularly imprinted polymer sensor The electrochemical characterization results of the different preparation steps of the creatinine MIP sensor using the two electrochemical systems (CV and EIS) are shown in Fig. 15.5. Using CV (Fig. 15.5A), the signal corresponding to the creatinine deposit admits a larger oxidation current peak than that of PVC-COOH. This is probably caused by the movement of H1 to form NH2 (tautomerism) in an aqueous solution. This generates cations that attract the [Fe(CN)6]3-/4- anions causing easy electron transfer between the electrode and the probe. After

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FIGURE 15.5 Study of the responses according to the development stages of the sensor based on molecularly imprinted polymer (MIP) represented by the: (A) cyclic voltammograms, (B) Nyquist diagrams of the redox probe (5 mM [Fe(CN)6]3-/4-), observed after PVC-COOH deposition, after creatinine deposition, after polymer deposition, and after creatinine extraction.

polymer deposition, a decrease in the peak oxidation current was observed due to the formation of a compact layer on the electrode that prevents charge transfer. But after extraction, the amplitude of the signal becomes larger than the previous one. This is explained by the formation of memory sites that reduce the compactness of the membrane on the electrode and facilitate the movement of electrons from the redox probe [Fe(CN)6]3-/4-. The diagrams obtained by the EIS technique are presented in Fig. 15.5B in which a significant variation of the charge transfer resistances after the different steps of the development of the sensor based on a MIP is noticed. These variations follow the same trend as those observed by the CV technique. This reveals that the two techniques show consistent results.

15.7.2.2 Glucose molecularly imprinted polymer sensor For the glucose MIP sensor, the behavior of the electrode, after each step of the sensor assembly, was studied using a PBS electrolyte solution containing 5 mM [Fe(CN)6]3-/4-. The results obtained for each step of the glucose sensor preparation are presented in Fig. 15.6. Indeed, for the characterization using CV, a specific voltammogram of the couple [Fe(CN)6]3-/4- is observed for the unmodified Au-SPE. The Au-SPE modified with MIP displays a signal that is different in shape and amplitude from the previous voltammogram. This demonstrates that the electrode surface has been successfully modified. It is called AuSPE/MIP. After the characterization of the NIP sensor, a slightly higher current amplitude is observed. This can be explained by the absence of glucose molecules in the layer deposited on the electrode. Here, in comparison with MIP, the redox probe ([Fe(CN)6]3-/4-) was able to access the electrode surface more easily for charge transfer. This confirms that the glucose molecules, in the case of the MIP test, were indeed trapped in the polymeric matrix preventing the [Fe(CN)6]3-/4- from accessing the electrode.

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FIGURE 15.6 Study of the responses as a function of the development stages of the sensor based on molecularly imprinted polymer (MIP) represented by the cyclic voltammograms.

FIGURE 15.7 Study of the responses as a function of the development stages of the sensor based on molecularly imprinted polymer (MIP) represented by the Nyquist diagrams of the redox probe (5 mM [Fe(CN)6]3-/4-), observed for Au-SPE, after electropolymerization of the MIP, and after electropolymerization of the nonimprinted polymer (NIP).

Useful information on changes at the electrode interface was also provided by the EIS to characterize the sensor fabrication procedures. The Nyquist representation generally consists of a semicircle part corresponding to the electron transfer and a linear part related to the diffusion process. Thus Fig. 15.7 shows the Nyquist diagrams of the electrodes, given by EIS at each preparation stage. The faradic impedance measurements were in good agreement with the results given by CV as the diameters of the semicircles for the Nyquist diagrams correlated with the variations in the oxidation current values after the different stages of sensor development.

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FIGURE 15.8 Current variation curves of the sensor based on molecularly imprinted polymer (MIP) in the presence of creatinine as a function of (A) polymerization time, (B) extraction time, and (C) incubation time.

To improve the performance of the MIP sensor, some parameters were optimized (Fig. 15.8). The prepolymerization solution was first deposited on the Au-SPE/PVC-COO2/Cre with an incubation time of one night. In the second step, this time was optimized using 3, 4, 5 h polymerization and 6 h as incubation times. Fig. 15.8A shows the evolution of the current as a function of the incubation time which illustrates that an optimal signal is obtained for a duration of 5 h. After this incubation time, no higher current peaks are recorded. Subsequently, an elution step was performed using ethanol and distilled water. This deposition (rinsing) initially lasted 45 min. This time was then revised to 15, 20, 25, 30, and 35 min. The optimal extraction time was obtained for a duration of 25 min during which the signal remained constant (Fig. 15.8B). Finally, a study concerning the optimization of the incubation time of a concentration of creatinine on the surface of the MIP sensor was carried out revealing that the optimal incubation time remains close to 30 min (Fig. 15.8C).

15.7.3 Repeatability, reproducibility, selectivity, and stability of the sensor 15.7.3.1 Creatinine molecularly imprinted polymer sensor The signals shown in Fig. 15.9 represent the results found when detecting creatinine in synthetic media. It can be seen that the signal varies as the concentration of the analyte increases. This demonstrates the retention capacity of the proposed MIP sensor. Urea and glucose were used to test the selectivity of the printed polymer sensor to creatinine (Horvath & Lazar, 2009; Buzewski & Rudnicka, 2012). Fig. 15.10 shows the differences observed for these three analytes using the same MIP sensor. Concerning the analysis of interferents, the signals obtained by using DPV show small variations in terms of current amplitude while the Nyquist diagrams show a very negligible variation of the charge transfer resistance (Rct). This certainly means that the surface of the fabricated MIP sensor is not really sensitive in the presence of urea and glucose.

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FIGURE 15.9 Responses of the molecularly imprinted polymer (MIP)-based sensor to increasing concentrations of creatinine as: (A) Pulsed differential voltammograms and (B) Nyquist diagrams generated using PBS as electrolyte containing 5 mM [Fe(CN)6]3-/4- as electroactive species.

FIGURE 15.10 Comparison of the calibration curves of the nonimprinted polymer (NIP) sensor vs the molecularly imprinted polymer (MIP) sensor exposed to creatinine, and interfering molecules (urea and glucose).

All these observations suggest a clear conclusion that the recognition sites of the MIP sensor are specific to creatinine.

15.7.3.2 Glucose molecularly imprinted polymer sensor Once the retention capacity of the glucose sensor has been established (Fig. 15.11), its selectivity was investigated. Selective recognition of target molecules is of great importance for an MIP sensor, allowing it to be distinguished from interfering species with nearly similar structures. In this work, the selectivity test for the glucose MIP sensor was performed using two interfering analytes coexisting in physiological saliva: lactose and sucrose (Amann & Spanel, 2007). The responses of the developed MIP sensor to glucose were first compared to those obtained in the presence of the same

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FIGURE 15.11 Responses of the molecularly imprinted polymer (MIP) sensor to increasing concentrations of glucose as: (A) pulsed differential voltammograms and (B) Nyquist diagrams generated using PBS as electrolyte containing 5 mM [Fe(CN)6]3-/4- as electroactive species.

FIGURE 15.12 Comparison of the calibration curves of the molecularly imprinted polymer (MIP) sensor versus the non-imprinted polymer (NIP) sensor exposed to glucose and interfering molecules (sucrose and lactose).

concentrations of the above-mentioned interfering species and then compared to the cross-reactivity of the interferents using a mixing method. Fig. 15.12 shows a comparison of the calibration curves of the imprinted and NIP-based sensors. It can be seen from the figure that the MIP-based sensor did not show significant responses to either analog, illustrated by small changes in their current amplitudes and an almost negligible change in the charge transfer resistance of the Nyquist plots. Indeed, the slopes of the analogs were significantly lower than those of glucose, indicating that the proposed MIP sensor exhibited remarkable selectivity toward glucose. In conclusion, it can be stated that the interfering species used did not cause any obvious interference and that the memory sites of the MIP are specific to glucose.

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In addition, the reproducibility, repeatability, and stability of the MIP sensor were also studied. For this purpose, three MIP sensors were prepared in a similar way and used for the detection of the same glucose concentrations to test the reproducibility. A relative standard deviation (RSD) of 3.4% was obtained, which confirms the reproducibility of the sensor. The repeatability of the sensor was also investigated by making several measurements of the same glucose concentration and the sensor responses were studied, giving a satisfactory RSD of 4%. Generally, stability is of great importance for the development of MIP sensors. In this study, it was investigated by monitoring the current response of the sensor to a glucose concentration at regular time intervals for a period of 3 months. After this time, the MIP sensor retained 85% of its initial response. This means that the proposed sensor has acceptable stability.

15.7.4 Real samples detection 15.7.4.1 Creatinine detection in human urine The analytical applicability of the present detection device was evaluated with a creatinine assay in human urine samples from a group of healthy individuals with different creatinine levels. Given the larger clinical range of creatinine in the urine sample (500
1500 μg/mL) (Mazzone, 2008) and the linear range of determination of the present sensor (0.1 ng/mL
1 μg/mL), it could therefore be used to determine the creatinine level in urine samples. Indeed, after the development stage of the MIP creatinine sensor, its biological application was then explored. For this purpose, different biological urine samples were collected in polystyrene bottles, put in the freezer, diluted in PBS, and shaken before analysis. A classification study of the different urine samples was previously performed using the Jaffe´ UV-Vis method (Table 15.4). Three types of samples were used: for each creatinine level (low ,10 mM, medium between 10 and 22 mM, and high .22 mM), two urine samples were tested using the MIP sensor developed to measure creatinine levels. Fig. 15.13 shows an example of a result obtained using DPV and EIS; the measurement was carried out on a urine sample of a volunteer (P122) with an elevated creatinine level taking into account the value obtained by UV-Vis. For each sample, the quantification test is performed repeatedly to take the mean value of the creatinine concentration. For the values obtained by EIS, an optimal adjustment was necessary using the EIS spectrum analysis software to determine the charge transfer resistance (Rct). The Rct values obtained were used to determine the creatinine level. For the DPV and EIS techniques, creatinine concentrations in human urine samples were determined by the calibration curves in Fig. 15.10. The creatinine concentrations in the urine samples obtained by both techniques are in agreement with those

TABLE 15.4 Results of the molecularly imprinted polymer (MIP) sensor for reproducibility of urine creatinine measurements.

Code Spectrophotometer (10 μg/mL) 3

High creatinine levels

Medium creatinine levels

Low creatinine levels

P88

P128

P99

P122

P77

P118

2.703

2.975

1.074

1.662

0.780

0.599

2.748

3.054

1.142

1.685

0.791

0.554

2.760

3.099

1.346

1.787

0.803

0.576

RSD (%) (n 5 3)

0.890

1.680

9.730

3.180

1.190

3.190

DPV (10 μg/mL)

2.668

2.975

0.974

1.705

0.565

0.498

2.670

2.990

1.030

1.800

0.650

0.510

2.720

2.980

1.200

1.740

0.700

0.580

0.890

0.250

8.900

2.200

8.730

6.830

98.700

100.800

99.600

105.200

82.000

88.400

2.670

2.850

1.090

1.300

1.010

0.600

2.700

2.910

1.730

1.900

0.590

0.540

2.730

2.880

1.100

1.870

1.020

0.570

0.900

1.040

22.900

16.330

22.900

4.290

99.800

96.800

121.900

102.200

111.500

3

RSD (%) (n 5 3) Recovery (%) EIS (10 μg/mL) 3

RSD (%) (n 5 3) Recovery (%)

95.10

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FIGURE 15.13 Responses of the molecularly imprinted polymer (MIP)-based sensor exposed to a urine sample (P122) as: (A) pulsed differential voltammograms and (B) Nyquist diagrams generated using PBS as electrolyte containing 5 mM [Fe(CN)6]3-/4- as electroactive species.

FIGURE 15.14 PLS-R prediction models of urine sample creatinine content given by the molecularly imprinted polymer (MIP)-based sensor: (A) DPV and (B) EIS as a function of concentration given by the Jaffe´ method. DPV, differential pulse voltammetry; EIS, electrochemical impedance spectroscopy.

obtained by the Jaffe´ method via the spectrophotometer. This method presented for the assessment of creatinine in human urine describes meaning RSD of 5% for the DPV and 11% for the EIS techniques with recoveries of 96% and 105%, respectively. This revealed satisfactory accuracy and precision of measurement. A statistical technique named partial least squares regression (PLS-R) was used to correlate the two measurements. Fig. 15.14 describes the results of the PLS-R model for the prediction of creatinine concentrations in human urine. In this study, the PLS-R model was constructed using all the data without separating them in the training matrix and the validation matrix. A latent variable and a leave-one-out cross-validation technique were used to build this model. Fig. 15.14 shows the results of DPV and EIS, respectively, as a function of creatinine concentrations determined by the Jaffe´ method. Good linearity of the calibration plots was obtained in both cases with a correlation coefficient of 0.99. From these data, the ability of the MIP sensor to assess urine creatinine content seems to be the most remarkable result.

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15.7.4.2 Glucose detection in human saliva Knowing that the clinical glucose range in the saliva is 0.5
216.1 μg/mL, the MIP sensor could be applied to saliva samples after its development stage. For this purpose, saliva samples from six healthy students were exposed to the MIP sensor and the results were compared with their corresponding blood glucose levels given by the glucometer as a reference method. The saliva samples were collected under fasting conditions using the following procedure: Rinse the mouth with water so that the measurements are not influenced by food. Release the saliva into the tube and keep it at 4 C. Immediately after collection, the saliva samples were heated to 100 C (Malagu` et al., 2014) to remove any toxins that might influence the measurements. Centrifugation was then adopted at 1500 rpm for 15 min to remove any residual particles. Each saliva sample was split into three aliquots and used directly to avoid degradation and the consequent change in their properties; after analysis, they were stored at 4 C in a refrigerator. During the biological detection step, 5 μL of the saliva sample was deposited on the working electrode of the MIP sensor. A 30 min incubation was followed to ensure good retention of the glucose molecules. Measurements were performed three times to take the average value of the glucose concentration. As a result, fasting blood glucose values ranged from 940 to 1470 μg/mL and salivary glucose values from 2.2 to 8.6 μg/mL. Fig. 15.15 shows first the relationship for predicting the blood glucose concentration using PLS-R, knowing the salivary glucose concentration delivered by the developed sensor. Then, the relationship between the blood and salivary glucose concentration is plotted. Indeed, the results delivered by the MIP sensor were thus exploited to build a prediction model using the PLS-R technique. This model could be used as a relationship between the developed MIP sensor and the glucometer. This could be of interest for the diagnosis of diabetes mellitus. Fig. 15.15A shows the regression model obtained for the determination of the glucose

FIGURE 15.15 PLS-R prediction models of the glucose content of saliva samples given by: (A) electrochemical sensor versus blood glucose concentration from a glucometer and (B) calibration curve between blood and saliva glucose concentration.

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level. One latent variable and a leave-one-out cross-validation technique were used to construct this model. In this case, the PLS-R model was constructed using all the data without separating them in the training matrix and the validation matrix. A regression coefficient of R 5 0.99 was obtained. In addition, a direct calibration plot was established (Fig. 15.15B). According to this plot, an acceptable correlation was obtained with a regression coefficient of 0.96.

15.8 Conclusion Biosensors are generally classified according to the nature of the receptors and transducers that compose them. Due to their significant advantages, biomimetic electrochemical receptors have recently become very popular in the biomedical field. This technology has been exploited to develop biomimetic receptor-based sensors to quantify target analytes, namely creatinine and glucose, which play an important role in certain kidney diseases. As a first step, a biomimetic electrochemical sensor based on MIP was developed for the detection of creatinine (Cre), using Au-SPE. The first layer of polyvinyl carboxylic chloride (PVC-COOH) was first immobilized on the Au-SPE surface. Creatinine molecules were attached to the Au-SPE/PVCCOOH surface after activation by carbo-di-imide species. Then, a polymer composed of acrylamide cross-linked with N, N0 -methylene bis-acrylamide was formed around the Cre molecules to form the Au-SPE/MIP. Extraction of the Cre molecules from the formed layer left specific binding sites, thus forming the MIP sensor capable of selective recognition of creatinine at different concentrations. To confirm the sensitivity of the MIP sensor, the same procedure was adopted without the addition of creatinine to form the nonimprinted sensor (Au-SPE/NIP). Their analytical properties were studied using three electrochemical techniques: CV, DPV, and EIS. UV-Vis spectrophotometry was used as a validation method using the Jaffe´ method. The results obtained prove the selectivity of the MIP sensor to creatinine compared to urea and glucose. In a linear detection range of 0.1 ng/mL to 1 μg/mL, a detection limit of 0.016 and 0.081 ng/mL was found by using EIS and DPV, respectively. As a practical application, this MIP sensor was successfully tested on urine samples of volunteers with different creatinine levels. To correlate the two analytical instruments (MIP sensor and UV-Vis spectrophotometer), a quantitative pattern recognition method called PLS-R was used with satisfactory results. This study provided a promising strategy to fabricate MIP-based sensing devices with highly selective recognition capability, simplicity of operation, small-scale integration, and low cost. In the second phase, a study was conducted on the development of a MIP-based electrochemical sensor for the quantification of salivary glucose. Indeed, the MIP approach was used to develop a nonenzymatic electrochemical sensor for the determination of salivary glucose. Electropolymerization

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was used as a method of immobilizing the MIP (acrylamide/bis-acrylamide 1 glucose) on an Au-SPE. CV, DPV, and EIS were used for the electrochemical measurements using PBS as a supporting electrolyte containing ferri/ferrocyanide. Morphological characterizations of the developed MIP sensor were performed using AFM and SEM-EDS. Under optimal conditions, the MIP sensor could efficiently detect glucose while avoiding interference from structurally similar substances such as lactose and sucrose. In the working range of 0.5
50 μg/mL, detection and quantification limits of 0.59 and 1.9 μg/mL, respectively, were found. Furthermore, using PLS-R, the determination of glucose in human saliva was correlated with that of fingerprick blood with satisfactory results (R2 5 0.99). As a result, this work demonstrated a cheap, simple, and effective detection device for nonenzymatic glucose detection, which makes it a promising tool for the future evolution of noninvasive, accurate, and reliable diabetes diagnosis.

Acknowledgments Authors gratefully acknowledge Moulay Ismaı¨l University of Meknes for financial support of the project “Research support”. This work has been funded in part by CANLEISH under H2020-MSCA-RISE-2020 project, grant agreement number: 101007653: Non-invasive volatiles test for canine leishmaniasis diagnosis.

Declaration of competing interest We declare that we have no personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

References Amann, A., & Spanel, P. (2007). Breath analysis: The approach towards clinical applications. Mini Reviews in Medicinal Chemistry, 7, 115
129. Arakawa, T., Kuroki, Y., Nitta, H., Chouhan, P., Toma, K., Sawada, S. I., Takeuchi, S., Sekita, T., Akiyoshi, K., Minakuchi, S., & Mitsubayashi, K. (2016). Mouthguard biosensor with telemetry system for monitoring of saliva glucose: A novel cavitas sensor. Biosensors and Bioelectronics, 84, 106
111. Biomarkers Definitions Working GroupAtkinson, A. J., Jr, Colburn, W. A., DeGruttola, V. G., DeMets, D. L., Downing, G. J., Hoth, D. F., Oates, J. A., Peck, C. C., Schooley, R. T., Spilker, B. A., Woodcock, J., & Zeger, S. L. (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology & Therapeutics, 69, 89
95. Bajaj, A., & Miranda, O. R. (2009). Detection and differentiation of normal, cancerous, and metastatic cells using nanoparticle-polymer sensor arrays. Proceedings of the National Academy of Sciences of the United States of America, 106, 10912
10916.

Development and characterization of an electrochemical sensor Chapter | 15

345

Bird, S. P., Tarpenning, K. M., & Marino, F. E. (2006). Liquid carbohydrate/essential amino acid ingestion during a short-term bout of resistance exercise suppresses myofibrillar protein degradation. Metabolism: Clinical and Experimental, 55, 570
577. Blazevich, A. J., & Giorgi, A. (2001). Effect of testosterone administration and weight training on muscle architecture. Medicine & Science in Sports & Exercise, 33, 1688
1693. Buzewski, S., & Rudnicka, J. (2012). Analytical and unconventional methods of cancer detection using odor. Trends in Analytical Chemistry, 38, 1
12. Chen, C. H., & Lin, M. S. (2012). A novel structural specific creatinine sensing scheme for the determination of the urine creatinine. Biosensors and Bioelectronics, 31, 90
94. Cullen, T., Thomas, A. W., Webb, R., & Hughes, M. G. (2015). The relationship between interleukin-6 in saliva, venous and capillary plasma, at rest and in response to exercise. Cytokine, 71, 397
400. Dhanya, M., & Hegde, S. (2016). Salivary glucose as a diagnostic tool in Type II diabetes mellitus: A case-control study. Nigerian Journal of Clinical Practice, 19, 486
490. Fortina, P., & Kricka, L. (2007). Application of nanoparticles to diagnostics and therapeutics in colorectal cancer. Trends in Biotechnology, 25, 145
152. Fox, P. C., van der Ven, P. F., Sonies, B. C., Weiffenbach, J. M., & Baum, B. J. (1985). Xerostomia: Evaluation of a symptom with increasing significance. Journal of the American Dental Association, 110, 519
525. Garcia-Carmona, L., Martin, A., Sempionatto, J. R., Moreto, J. R., Gonzalez, M. C., Wang, J., & Escarpa, A. (2019). Pacifier biosensor: Toward noninvasive saliva biomarker monitoring. Analytical Chemistry, 91, 13883
13891. Goldstein, J. I., Newbury, D. E., Michael, J. R., Ritchie, N. W., Scott, J. H. J., & Joy, D. C. (2017). Scanning electron microscopy and X-ray microanalysis. Springer. Goll, R. D., & Mookerjee, B. K. (1978). Correlation of biochemical parameters in serum and saliva in chronic azotemic patients and patients on chronic hemodialysis. Journal of Dialysis, 2, 399
414. Gouarne, C., Groussard, C., Gratas-Delamarche, A., Delamarche, P., & Duclos, M. (2005). Overnight urinary cortisol and cortisone add new insights into adaptation to training. Medicine & Science in Sports & Exercise, 37, 1157
1167. Hompes, R., & Cunningham, C. (2011). Colorectal cancer: Management. Medicine, 29, 254
258. Horvath, L., & Lazar, Z. (2009). Exhaled biomarkers in lung cancer. The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology, 34, 261
275. Hsieh, R. Y., Tsai, H. A., & Syu, M. J. (2006). Designing a molecularly imprinted polymer as an artificial receptor for the specific recognition of creatinine in serums. Biomaterials, 27, 2083
2089. Ives, S. J., Blegen, M., Coughlin, M. A., Redmond, J., Matthews, T., & Paolone, V. (2011). Salivary estradiol, interleukin-6 production, and the relationship to substrate metabolism during exercise in females. European Journal of Applied Physiology, 111, 1649
1658. Kadashetti, V., Baad, R., Malik, N., Shivakumar, K. M., Vibhute, N., Belgaumi, U., Gugawad, S., & Pramod, R. C. (2015). Glucose level estimation in diabetes mellitus by saliva: A bloodless revolution. Romanian Journal of Internal Medicine, 53, 248
252. Kanai, T., Sanskriti, C., Vislawath, P., & Samui, A. B. (2013). Acrylamide based molecularly imprinted polymer for detection of m-nitrophenol. Journal of Nanoscience and Nanotechnology, 13, 3054
3061.

346

Oncogenic Viruses Volume 2

Khadro, B., Sanglar, C., Bonhomme, A., Errachid, A., & Jaffrezic-Renault, N. (2010). Molecularly imprinted polymers (MIP) based electrochemical sensor for detection of urea and creatinine. Procedia Engineering, 5, 371
374. Kim, J., Imani, S., de Araujo, W. R., Warchall, J., Valde´s-Ram´ırez, G., Paixa˜o, T. R. L. C., Mercier, P. P., & Wang, J. (2015). Wearable salivary uric acid mouthguard biosensor with integrated wireless electronics. Biosensors and Bioelectronics, 74, 1061
1068. Lakshmi, D., Prasad, B. B., & Sharma, P. S. (2006). Creatinine sensor based on a molecularly imprinted polymer-modified hanging mercury drop electrode. Talanta, 70, 272
280. Lee, M. H., Tsai, T. C., Thomas, J. L., & Lin, H. Y. (2008). Recognition of creatinine by poly (ethylene-co-vinylalcohol) molecular imprinting membrane. Desalination, 234, 126
133. Levinsky, R. J. (1984). Primary and secondary immunodeficiency disorders. Archives of Disease in Childhood, 59, 393. Lindsay, A., Healy, J., Mills, W., Lewis, J., Gill, N., Draper, N., & Gieseg, S. P. (2016). Impactinduced muscle damage and urinary pterins in professional rugby: 7, 8-dihydroneopterin oxidation by myoglobin. Scandinavian Journal of Medicine & Science in Sports, 26, 329
337. Malagu`, C., Fabbri, B., Gherardi, S., Giberti, A., Guidi, V., Landini, N., & Zonta, G. (2014). Chemoresistive gas sensors for the detection of colorectal cancer biomarkers. Sensors, 14, 18982
18992. Mannoor, M. S., Tao, H., Clayton, J. D., Sengupta, A., Kaplan, D. L., Naik, R. R., Verma, N., Omenetto, F. G., & McAlpine, M. C. (2012). Graphene-based wireless bacteria detection on tooth enamel. Nature Communications, 3, 1
9. Mayeux, R. (2004). Biomarkers: Potential uses and limitations. NeuroRx: The Journal of the American Society for Experimental NeuroTherapeutics, 1, 182
188. Mazzone, P. J. (2008). Analysis of volatile organic compounds in the exhaled breath for the diagnosis of lung cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer, 3, 774
780. Mirzaii-Dizgah, I., Hejazi, S. F., Riahi, E., & Salehi, M. M. (2012). Saliva-based creatine kinase MB measurement as a potential point-of-care testing for detection of myocardial infarction. Clinical Oral Investigations, 16, 775
779. Mirzaii-Dizgah, I., & Riahi, E. (2013). Salivary high-sensitivity cardiac troponin T levels in patients with acute myocardial infarction. Oral Diseases, 19, 180
184. Miura, C., Funaya, N., Matsunaga, H., & Haginaka, J. (2013). Monodisperse, molecularly imprinted polymers for creatinine by modified precipitation polymerization and their applications to creatinine assays for human serum and urine. Journal of Pharmaceutical and Biomedical Analysis, 85, 288
294. Mosby. (2009). Mosby’s medical dictionary (8th ed). Elsevier Inc. Ouellet-Morin, I., Danese, A., Williams, B., & Arseneault, L. (2011). Validation of a highsensitivity assay for C-reactive protein in human saliva. Brain, Behavior, and Immunity, 25, 640
646. Ozmeric¸, N., Baydar, T., Bodur, A., Engin, A. B., Uraz, A., Eren, K., & Sahin, G. (2002). Level of neopterin, a marker of immune cell activation in gingival crevicular fluid, saliva, and urine in patients with aggressive periodontitis. Journal of Periodontology, 73, 720
725. Panasyuk-Delaney, T., Mirsky, V. M., & Wolfbeis, O. S. (2002). Capacitive creatinine sensor based on a photografted molecularly imprinted polymer. Electroanalysis, 14, 221
224. Patel, A. K., Sharma, P. S., & Prasad, B. B. (2008). Development of a creatinine sensor based on a molecularly imprinted polymer-modified sol-gel film on graphite electrode. Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis, 20, 2102
2112.

Development and characterization of an electrochemical sensor Chapter | 15

347

PharmaDrugs, NarcoChecks saliva test. , https://www.pharmadrugtest.com/saliva-tests/38-thcsaliva-test-cannabis.html . . Punyadeera, C., Dimeski, G., Kostner, K., Beyerlein, P., & Cooper-White, J. (2011). One-step homogeneous C-reactive protein assay for saliva. Journal of Immunological Methods, 373, 19
25. Reddy, K. K., & Gobi, K. V. (2013). Artificial molecular recognition material-based biosensor for creatinine by electrochemical impedance analysis. Sensors and Actuators B: Chemical, 183, 356
363. Robinson, N., Castella, V., Saudan, C., Sottas, P. E., Schweizer, C., Dimo-Simonin, N., Mangin, P., & Saugy, M. (2006). Elevated and similar urinary testosterone/epitestosterone ratio in all samples of a competition testing: Suspicion of a manipulation. Forensic Science International, 163, 148
151. Salimetrics. Salivary cortisol. ,https://salimetrics.com/wp-content/uploads/2018/03/salivary-cortisol-elisa-kit.pdf.. Salimetrics. Salivary secretory IgA. ,https://salimetrics.com/wp-content/uploads/2018/03/secretory-iga-saliva-elisa-kit.pdf.. Satish, B. N. V. S., Srikala, P., Maharudrappa, B., Awanti, S. M., Kumar, P., & Hugar, D. (2014). Saliva: A tool in assessing glucose levels in diabetes mellitus. Journal of International Oral Health: JIOH, 6, 114. Sreenivasan, K., & Sivakumar, R. (1997). Interaction of molecularly imprinted polymers with creatinine. Journal of Applied Polymer Science, 66, 2539
2542. Subrahmanyam, S., Piletsky, S. A., Piletska, E. V., Chen, B., Karim, K., & Turner, A. P. (2001). ‘Bite-and-Switch’ approach using computationally designed molecularly imprinted polymers for sensing of creatinine. Biosensors and Bioelectronics, 16, 631
637. Testoste´rone Salimetrics. Salivary testosterone. ,https://salimetrics.com/wp-content/uploads/ 2018/03/testosterone-saliva-elisa-kit.pdf.. Tsai, H. A., & Syu, M. J. (2005). Synthesis of creatinine-imprinted poly (β-cyclodextrin) for the specific binding of creatinine. Biomaterials, 26, 2759
2766. Uberoi, H. S., Dugal, J. S., Kasthuri, A. S., Kolhe, V. S., Kumar, A. K., & Cruz, S. A. (1991). Acute renal failure in severe exertional rhabdomyolysis. Journal of the Association of Physicians of India, 39, 677
679. Viswanath, B., Choi, C. S., Lee, K., & Kim, S. (2017). Recent trends in the development of diagnostic tools for diabetes mellitus using patient saliva. TrAC Trends in Analytical Chemistry, 89, 60
67. Viswanath, K. B., Devasenathipathy, R., Wang, S. F., & Vasantha, V. S. (2017). A new route for the enzymeless trace level detection of creatinine based on reduced graphene oxide/silver nanocomposite biosensor. Electroanalysis, 29, 559
565. Zhang, L., Cheng, G., & Fu, C. (2002). Molecular selectivity of tyrosine-imprinted polymers prepared by seed swelling and suspension polymerization. Polymer International, 51, 687
692.

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Chapter 16

Detection of triclosan and sodium lauryl sulfate in environmental samples and cosmetic product by electrochemical sensor based on biomimetic recognition combined with electronic nose Nezha EL Bari1, Soukaina Motia1, Moulay Mustapha Ennaji2 and Benachir Bouchikhi3 1

Biosensors and Nanotechnology Group, Department of Biology, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco, 2Group Research Leader Team of Virology, Oncology, and Biotechnologies, Head of Laboratory of Virology, Oncology, Biosciences, Environment and New Energies (LVO-BEEN), Faculty of Sciences and Techniques Mohammedia, University Hassan II of Casablanca, Casablanca, Morocco, 3Biosensors and Nanotechnology Group, Faculty of Sciences, Moulay Ismaı¨l University of Meknes, Meknes, Morocco

16.1 Introduction A safe and clean environment free of toxic threats, safe food and water are increasingly important concerns in our society. Wastewater is frequently considered a nuisance to be disposed of rather than a resource (Guz & Guz, 2018). Nevertheless, it is an important source of valuable substances: water, energy, nutrients, organic matter, and other by-products. It is an important part of the water cycle and needs to be managed throughout the water cycle: from abstraction, treatment, distribution, collection, and post-use treatment of freshwater to its reuse and subsequent return to the environment, where it replenishes the source for further water abstraction. Because of their high toxicity, nondegradability, and adverse effects on living beings, plants, microorganisms, and ecosystems, the analysis and control of Oncogenic Viruses Volume 2. DOI: https://doi.org/10.1016/B978-0-12-824156-1.00003-0 Copyright © 2023 Elsevier Inc. All rights reserved.

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cosmetic residues are of considerable importance for the protection of the environment and even more so for human health (Jing et al., 2020). These products are among the most challenging contaminants to detect and remove from water. Surfactants and preservatives are one of the leading cosmetic products found in wastewater. However, these cosmetic products contain chemical substances that are potentially toxic to humans (such as skin irritation, hormones modification (hyperthyroid), testosterone levels change, causing a reduction of sperm production, found in breast milk, urine, and blood) and the environment (Wallen-Russell & Wallen-Russell, 2017). Cleaning agents (anionic compounds, etc.), antibacterial preservatives (sodium lauryl sulfate [SLS], triclosan [TCS], etc.), foaming agents, emulsifiers, and additives (perfumes, etc.) can thus be considered as pollutants. To meet these legitimate demands, complex scientific research is carried out, and its effectiveness is closely linked to the quality of the analysis tools made available to it. Nowadays, there are various conventional methods for the determination of cosmetic products in environmental media, such as liquid chromatography
mass spectroscopy (Chu & Metcalfe, 2007), ion-selective electrodes (Kov´acs et al., 2001), and gas chromatography
ion trap mass spectrometry (Chau et al., 2008; Wu et al., 2007). These techniques are reliable and accurate, but they require heavy, expensive, highly sophisticated equipment, skilled labor, and sometimes lengthy sample preparation. In this respect, it is desirable to develop new reliable, sensitive, selective, and inexpensive methods for detecting and determining heavy metals. Electrochemical techniques using modified electrodes as sensors have proven to be a promising alternative to conventional methods for the qualitative and quantitative analysis of heavy metals and various organic molecules. Indeed, electrochemical detection systems offer many advantages such as simple instrumentation, high sensitivity and selectivity, ease of use, possible miniaturization of the instrumentation, minimal sample pretreatment, short analysis time, portability, and the possibility to perform analyses in the field. Neglected for a long time, the electrochemical approach has seen a resurgence of interest in the last 15 years with the appearance of new electrochemical sensors (Ghosh et al., 2019; Raziq et al., 2021). These are often simple and compact devices transforming the biochemical and/or chemical signal into an easily exploitable electrical signal. They generally consist of a selective part (sensitive layer) and a transducer system that transforms the physicochemical changes induced by the recognition in the sensitive layer into an electrical signal. They also have an operating environment that allows the electrical processing of the signals. Electrochemistry offers interesting prospects for the miniaturization and industrialization of simple, reliable, and robust sensors at low cost (Qian et al., 2021). In order to achieve levels of sensitivity comparable to those obtained by spectroscopic techniques, a great deal of research is being conducted. Metallic films are

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associated with electrodes to design electrochemical sensors that can determine residues of cosmetic products at trace levels and obtain remarkable sensitivities. Accordingly, in this contribution, the objective of this chapter is to present a brief overview of the state of the art of micropollutant analysis in wastewater samples. On the other hand, the development of electrochemical sensors to detect TCS and SLS will be discussed.

16.1.1 Wastewater as sources of micropollutants The rising global consumption of chemicals has triggered an increase in chemical pollution of groundwater and surface, with largely unanticipated effects on human health and aquatic life. Many of these “micropollutants” have an urban background and are used daily in homes, workplaces, or the urban environment. Most of them end up in sewers (Dittmer et al., 2020). This is the case for personal care products (PCPs), pharmaceuticals, and their metabolites excreted in urine and feces, and several household chemicals such as food or plastic additives or flame retardants in textiles. Among the main emerging products, PCPs involve ingredients found in shampoos, washing lotions, skincare products, dental care products, sunscreen agents, cosmetics, perfumes, hairdressing products, etc. Because of their vast consumption and type of use (often dermal application), they enter municipal wastewater mainly through washing in the shower or bath. Domestic wastewater is contaminated with nondomestic pollutants such as the mentioned products. If they are not eliminated in wastewater treatment plants (WWTPs), they can impact the fauna and flora of the receiving waters (Nguyen et al., 2021). These substances are referred to as emerging contaminants (EC). This raises a global environmental concern regarding water quality, a potentially serious threat to human health, wildlife, aquaculture life, and ecosystems. Due to their mild estrogenic effect and ubiquitous occurrence in human tissue, they may be candidates’ substances for human health concerns. In addition, they are pollutants of concern because of their endocrine disruption and their association with numerous human health problems (reproductive, developmental, and neurodevelopmental impairment) (Weatherly & Gosse, 2017). They are responsible for adverse effects on human health, such as cancer, reproductive, and developmental effects. Consequently, the wastewater is a promising environment for continuous and in situ monitoring of the so-called micropollutant.

16.1.2 Current electrochemical sensors for environmental residues Electrochemical sensors have been documented to fulfill some of these characteristics due to their high sensitivity, fast response, small equipment size, ease of installation, simplicity of sample preparation, and suitability for in

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situ analysis (Bounegru & Apetrei, 2020). There are several reports on the electrochemical detection of TCS and SLS. Although the performance and sensitivity of these methods are quite good, they are not selective, and in most cases, their preparation or real-life application is very difficult. Among several strategies, the combination of molecularly imprinted polymers (MIPs) as sensing elements in electrochemical analysis has proven to be one of the most promising techniques in recent years (Sobiech et al., 2021). On the one part, the detection of TCS was achieved using MIP technology. Indeed, Wu et al. (2017) assessed the graphene/palladium nanoparticles hybrids for TCS detection. However, in this paper, they employed graphene oxide nanosheets decorated by Fe3O4@Au nanostructure stabilized on polypyrrole for TCS sensing (Saljooqi et al., 2020). Huang et al. reported the TCS detection with carbon nanospheres as electrocatalysts (Huang et al., 2021). Thus, this work reports TCS detection by using a molecularly imprinted photoelectrochemical sensor based on g-C3N4-AuNPs (Feng et al., 2018). Finally, a study by Kong et al. employed MIP functionalized magnetic Fe3O4 for the highly selective extraction of TCS (Kong et al., 2020). Other MIP-based TCS sensors have been reported. On the other part, SLS detection has also been carried out using MIP. Among others, the work of Hao et al. used a modified eosin Y/polyethyleneimine electrode (Hao, Lei, Li, et al., 2014). In this paper, they employed a piezoelectric quartz crystal sensor (Albano & Sevilla, 2007). Devi et al. employed a polyvinyl chloride (PVC) matrix membrane sensor for TCS detection (Devi & Chattopadhyaya, 2013). However, the electrochemical detection of SLS has been used little.

16.1.3 Potential sensors for applications in the fields of virology and oncology On the one hand, cancer is one of the leading causes of death globally. On the other hand, the direct detection of virus particles can provide more information on the stage of infection. Both diseases have high morbidity and mortality rates. Rapid, accurate, and sensitive monitoring of the corresponding biomarkers is essential for successful treatment. They are key indicators of disease growth (Jørgensen, 2021). Nevertheless, conventional laboratory methods (Niu et al., 2021; Yang et al., 2021) are labor-intensive, time-consuming, and relatively insensitive. The samples must be transported to diagnostic laboratories for testing. These conditions increase costs and response times while reducing the quality of patient care. Because of these many limitations, scientists are now focusing on developing biosensor systems and electrochemical sensors for efficient, rapid, and noninvasive detection of virus and cancer biomarkers. Viral and oncological electrochemical biosensors offer attractive alternatives and promise to provide inexpensive, miniaturized, sensitive, rapid, realtime detection, and portable platforms that offer patients and medical staff

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quick and easy results. The major influence of nanotechnology on the development of biosensors is that they will revolutionize the diagnosis and therapy of diseases (Zhang & Lyu, 1948). Several bio/electrochemical sensors have been developed and tested for cancer detection, in particular, the lung (Hussain et al., 2021), the breast (Novodchuk et al., 2021), and the prostate (Altunko¨k et al., 2021). A biomimetic sensor was applied to detect miRNA related to liver cancer (Zhang et al., 2020). In addition, some of these sensors were developed for the virus, including SARS-CoV-2 (Abid et al., 2021; Kumar et al., 2022), influenza (Tram et al., 2021), and hepatitis A (Wang et al., 2021). Accordingly, accurate and early diagnosis in oncological and virological applications will help to reduce the mortality rate. There is no doubt that this decade will make extensive use of biosensor and electrochemical sensorbased systems due to their distinctiveness mentioned above.

16.1.4 Electronic nose technology Recently, electronic nose (e-nose) technology has also been promoted as a very exciting approach for environmental analysis (Park et al., 2019). This approach has demonstrated simplicity, speed, and promise in minimizing analysis time and costs. MOS multigas sensor systems and e-noses for air quality monitoring represent a promising alternative for detecting gaseous pollutants in indoor air. Since the 1980s, following the evolution of microtechnologies and analytical microsystems, e-nose systems have experienced a strong development due to their numerous advantages: compactness, ease of use, reliability, and low manufacturing cost compared to the different types of devices available on the gas sensor market (Suriya et al., 2021). However, for monitoring these two molecules, we rarely find articles that report their detection using this type of device. In cancer research, e-nose tools are commonly used to search for a typical “breath print” that can discriminate cancer patients from healthy controls. It allows the study of differences in exhaled breath composition between categories of cancer subjects according to disease severity. It is used in the diagnosis of cancers, including lung (Behera et al., 2019), prostate (Waltman et al., 2020), and bladder (Bassi et al., 2021).

16.2 Experimental 16.2.1 Chemicals and reagents TCS, carboxylic PVC (PVC-COOH), N-hydroxysuccinimide (NHS), 1,4 dioxane (99.8%), potassium ferrocyanide ([Fe(CN)6]42), potassium ferricyanide ([Fe(CN)6]32), 2,4,6-trichlorophenol, catechol, phosphate-buffered

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saline (PBS), SLS, 2-aminothiophenol (2-ATP), ethanol, Tween 80, and urea were ordered from Sigma-Aldrich. N-(3-dimethylaminopropyl)-N0 ethyl-carbodiimide hydrochloride (EDC), N,N0 -methylene-bisacrylamide (NNMBA), N,N,N0 ,N0 -tetramethyl ethylene diamine (TEMED), and acrylamide (AAM) were purchased from Fluka. Ammonium persulfate (APS) was from Scharlau Chemie S.A., methanol, (hydroxymethyl) aminomethane (TRIS 0.5 M), and acetic acid were obtained from Reactifs RAL, Panreac Quimica S.A., and VWR Chemicals, respectively. Ethylenediaminetetraacetic acid (EDTA) was from Fluka. For the sensors calibration objective, the following procedure is adopted: From a stock standard solution of TCS (1 mg/mL in methanol), working solutions were prepared by dilutions with the accuracy of the previous solution with PBS (pH 5 7.4). All these solutions of the adopted range (0.1
1 ng/mL) were stocked at 4 C until analysis. A hotplate apparatus and vortex were employed to achieve the complete solutions dissolution. In addition, the stock solution of SLS was also prepared (1 mg/mL in water). The SLS working solutions were prepared by a dilution with water. Distilled water (DW) was employed for preparing the solutions used in this study and for electrode washing. To perform the electrochemical characterization, a standard stock solution of 5 mM [Fe(CN)6]32/42 was prepared with PBS (pH 5 7.4) (Motia et al., 2021a; Nasraoui et al., 2021). This later was selected because when present in the electrolyte solution, it is not adsorbed on the surface of the electrode and implies only one electron in the electrochemical reactions. To start a test, the 1 3 8 sensor array was inserted into the potentiostat device slot. For each sensor development step, 30 μL of the electrolyte [Fe (CN)6]32/42 is dropped on the electrode for characterization to investigate the electrochemical changes (Motia et al., 2021b). This droplet was allowed to remain on the surface of the electrodes for a few seconds to allow the sample to react properly with the preloaded reagents, and finally the characterization was initiated.

16.2.2 Polymer synthesis The development of the MIP sensor consisted of functionalizing the gold screen-printed electrode (Au-SPE) with a PVC-COOH coating. The PVCCOOH polymer was selected because TCS molecules are enabled to cooperate with it by intermolecular forces under mild conditions. The reaction mechanism is mostly controlled by the creation of intermolecular hydrogen bonds between the -COOH groups of the PVC-COOH shell and the polar groups of TCS. As a result, the -COOH groups were activated by the chemistry of carbodiimides and succinimides (NHS and EDC). In the second step, a volume of the TCS stock solution was dropped to the modified electrode. The resulting Au-SPE/PVC-COOH/TCS assembly

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was carefully washed with PBS buffer to remove the unreacted TCS molecules. A TRIS solution was employed to sequester the unreacted -COOH on the electrode. Next, a polymerization step was started by preparing a mixture of AAM as a functional monomer (Sullivan et al., 2021) and a cross-linker, which was used to achieve a highly cross-linked matrix. In effect, the polymerization of the AAM was commenced. The polymerization reaction is exemplified in Fig. 16.1. In the SLS-MIP sensor, 2-aminothiophenol (2-ATP) (Menon et al., 2018) was employed to prepare the electrochemical MIP sensors. It can interact with target analytes through hydrogen bonds and be electropolymerized.

16.2.3 Electrochemical sensors fabrication steps 16.2.3.1 TCS-MIP sensor For the preparation of the TCS-MIP sensor, the following steps were adopted. The working area of the bare Au-SPE was cleaned by washing it with ethanol (99.95%) and DW. The PVC-COOH was prepared in 1,4-dioxane at 40 C because intermolecular interactions increase its melting point. So, a volume of 5 μL of a PVC-COOH solution (8.8 mg/mL in 1,4-dioxane) was dropped on the working electrode (Au-SPE) and incubated for 90 min at room temperature. Then, to activate the groups (
COOH) of the resulting Au-SPE/PVC-COOH, 5 μL

FIGURE 16.1 Schematic representation of the reaction during polymerization.

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of a solution containing (4 mg/mL) of EDC and (1 mg/mL) of NHS was incubated for 90 min. The EDC and NHS molecules, which did not react, were eliminated by rinsing thoroughly with DW. Next, a volume of 5 μL of the TCS solution (1 mg/mL in methanol) was dropped on the electrode for 3 h at 4 C, enable TCS molecules binding to the activated groups (
COOH) of the PVC-COOH film. Afterward, the unbounded templates were removed by rinsing with PBS buffer (pH 5 7.4). A TRIS solution (pH 5 9) of 5 μL was then incubated for 30 min on the Au-SPE/PVC-COOH/TCS electrode to block no specific sites significantly. Several washes with DW followed this step. Thus, a polymer solution was prepared by mixing 11 mg/mL of AAM and 71 mg/mL of NNMBA in PBS. Then, the polymerization initiator (APS 5 13 mg/mL in PBS) was added to the mixture. The polymerization was accelerated by adding the catalyst (TEMED solution 5 5%). After that, a volume of 5 μL of the obtained solution was dropped and incubated on the working electrode for 5 h at 4 C. Finally, the TCS imprinted sites were obtained after extracting the TCS template from the polymer. For this aim, 5 μL of a solution containing a mixture of methanol and acetic acid (7:3, (v/v)) was incubated on the electrode surface for 15 min. As the control step, a nonimprinted polymer electrode (Au-SPE/NIP) was elaborated in the same manner but without the TCS. The elaborated MIP and NIP sensors were stored at 4 C until use. Furthermore, the sensors were incubated with the selected concentrations of TCS solutions (0.1 pg/mL to 1 ng/mL) for 30 min. The MIP sensor was employed to detect TCS in wastewater samples for the real detection step. Then, the spectrophotometer was used as a validation method.

16.2.3.2 SLS-MIP sensor For the aim of elaborating the SLS-MIP sensor, the following steps were adopted. Before commencing any functionalization, the working electrode was cleaned by rinsing with DW and ethanol. A film of 2-ATP was first formed after deposit onto the bare Au-SPE 10 μL of a solution containing (5.4 mg/ mL of ethanol) for incubation of 12 h at 4 C. The electrodes were then rinsed with ethanol and DW to eliminate the loosely and nonbounded 2-ATP molecules. Through an electrochemical polymerization technique, both MIP and NIP films were elaborated on the Au-SPE by using cyclic voltammetry (CV). In order to obtain an imprinted layer, the electropolymerization of MIP was performed by employing 30 μL of a solution comprising 2-ATP (0.1 mg/mL) and SLS (1 mg/mL) in a supporting electrolyte (10 mM [Fe(CN)6]32/42 in PBS pH 7.2). The electropolymerization phase was performed in a potential range between 20.35 V and 0.80 V with a scan rate of 100 mV/s for 10

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cycles. In addition, to facilitate the binding and recognition of the SLS analytes, an extraction step of the template from the MIP layer was done by dipping the correct prepared electrode in DW solution for 20 min at room temperature. Similarly, a nonimprinted film (Au-SPE/NIP) was prepared in the same way but without adding the SLS template to the solution of the electropolymerization process. This was performed to validate that the remarked impact during the MIP detection step was only due to the imprinting characteristics. As a practical application, the developed SLS-MIP sensor was employed to determine the SLS concentrations in cosmetic product samples. The spectrophotometer is used as a reference method.

16.2.4 Surface morphotogical analysis As a reference method, a UV
vis spectrophotometer was employed in this study by using the device ANACHEM instrument in the range 200
700 nm employing quartz cuvettes. The wavelengths were calibrated at 452 and 654 nm for TCS and SLS to measure the absorbances of the wastewater and cosmetic samples. The morphological and composition structure characterization were fulfilled by employing the techniques of Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM) to examine all the elaboration MIP sensor steps.

16.2.5 Electrochemical measurements An electrochemical transducer (potentiostat) interfaced with a computer was employed to perform the electrochemical measurements (Fig. 16.2). To examine the electrochemical attitude of the electrodes, CV, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) were employed as electrochemical techniques. For the CV experimentation, the potential was accessed over a range of
0.4 to 0.6 V with a scan rate of 30 mV/s. The DPV characterizations were fulfilled over a potential range of
0.2 to 0.4 V at a scan rate of 10 mV/s and a pulse amplitude of 50 mV. For the EIS experimentations, an alternating current was applied with a frequency range of 0.1
50 kHz. Nyquist plots were registered at a polarization potential of 0.01 V. Based on these curves, the electrode’s charge-transfer resistance (Rct) was defined after a correct fitting with the simple Randles equivalent circuit using the implemented EIS spectrum analyzer software. To obtain meaningful information on the modifications of the Au-SPE surface, the impedance data were properly fitted by utilizing the suitable Randles equivalent circuit (Choi et al., 2020).

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FIGURE 16.2 Photograph of the electrochemical measurement setup.

16.2.6 E-nose setup and measurement In this study, the experimental device used for the analysis of the same wastewater samples is the electronic nose (e-nose) which can be divided into three main parts (Amari et al., 2009) (Fig. 16.3): G G

G

The sampling system of the odorous substance to be analyzed. A box designed to house the measuring cell, which contains the matrix of sensors sensitive to odorant compounds, temperature and humidity sensors, a microcontroller, an LCD, and an air pump. A laptop computer equipped with software for data preprocessing, classification, and identifying odors.

This e-nose system was equipped with five metal oxide Taguchi gas sensors: TGS 815 (CH4), TGS 821 (H2), TGS 822 (alcohols, xylene, and toluene), TGS 824 (NH3), TGS 825 (H2S), and TGS 842 (methane) (Haddi et al., 2011) obtained from Figaro Engineering, Inc. (Osaka, Japan). It equally contains a temperature sensor (LM335Z) and a relative humidity sensor (HIH4000
01) from National Semiconductor (Santa Clara, CA, USA) for constantly monitoring the inner sensor chamber temperature and relative humidity. The TGS 8xx sensor requires two voltage inputs to operate a 5 V heater voltage (VH) to stabilize the sensing circuit inside the sensor and a 10 V circuit voltage (VC) for measuring the sensor output. This system qualitatively investigated the five wastewater samples spiked with different TCS concentrations (0.1, 1, 10, 100, and 1000 pg/mL). Before starting the measurements, a 20 mL sample of water was placed in a 50 mL Erlenmeyer flask and kept at a temperature of 32 6 0.5 C. Then the effluent transfer takes place: the collected volatile compounds are transferred to the sensor matrix. Pure nitrogen gas with 60 mL/min flow rate is used as carrier

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FIGURE 16.3 Electronic nose system designed for triclosan analysis.

gas from the headspace to the measuring cell. The operational system comprises mainly of three parts: the sensor chamber, the sampling system, and the data acquisition system.

16.3 Results and discussion 16.3.1 Morphological characterization of the fabricated sensors 16.3.1.1 TCS-MIP sensor The AFM technique was applied to monitor the morphological features of the Au-SPE, MIP surface before and after the removal of TCS. The AFM-3D morphological image of bare Au-SPE, presented in Fig. 16.4, discloses a rugged surface structure with a root mean square (Rq) roughness of 0.69 μm, achieved in scan areas of 10 3 10 μm 2. It should be mentioned here that this surface is formed by small grain structures. After the polymer was assembled on the bare Au-SPE surface, the Rq of the coating decreased to 0.21 μm (Fig. 16.4), unveiling a more homogeneous surface with a surface morphology distinctly different from that of the bare Au-SPE. This type of grain superstructure is typically encountered in polymeric layers. After extraction, the AFM-3D image of the MIP sensor revealed a different surface morphology, but it maintained a homogeneous shape (Fig. 16.4), even though the roughness had risen to 0.84 μm. These morphological variations of both surfaces confirm the formation of free imprinted sites, and hence the successful elimination of the TCS molecules. 16.3.1.2 SLS-MIP sensor To examine the morphology of the composition structure of the MIP sensor during its elaboration process, two techniques were employed, namely FTIR.

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FIGURE 16.4 AFM-3D morphology of (A) Bare Au-SPE, (B) and Au-SPE after polymerization, and (C) extraction of TCS. AFM, Atomic force microscopy; Au-SPE, gold screen-printed electrode; TCS, triclosan.

The characterization was achieved for modified Au-SPE after the electropolymerization and after the extraction of SLS molecules. As for the method of FTIR, the Au-SPE electrodes were positioned in an FTIR spectrophotometer sample holder (HATR, ABB, MB3000). Infrared spectra (64 scans) were recorded at room temperature in the wavenumber range of 500
4000/cm and a resolution of 4/cm. Simultaneously, Fig. 16.5 compiles the FTIR spectra of the MIP sensor elaboration. In this, the presence of SLS molecules is demonstrated by sulfate vibration bands S 5 O (993 and 1121/cm) and C
H bands (3032 and 2920/cm). The presence and absence on the spectra (Fig. 16.5) of the sulfate functions belonging to SLS validate the legend of the SLS analytes and their removal from the MIP matrix. By employing the FTIR method, the obtained outcomes successfully validate SLS bounding and then the complete removal from the MIP mesh. In a nutshell, the findings of the morphology and chemical composition analyses obtained by FTIR and AFM are in a good concordance and suggest the correct modification of the electrode surface for each experimental step, thereby supporting the applicability of the MIP sensor.

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FIGURE 16.5 FTIR spectra of (A) Au-SPE after polymerization and (B) extraction step. FTIR, Fourier transform infrared spectroscopy; Au-SPE, gold screen-printed electrode.

16.3.2 Electrochemical characterization of the sensors’ fabrication stages 16.3.2.1 Electrochemical characterization: TCS-MIP sensor The outcomes of the electrochemical characterization of the different elaboration steps of the TCS-MIP sensor employing the two electrochemical techniques (CV and EIS) are presented in Fig. 16.6.

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FIGURE 16.6 Electrochemical characterization of the modification steps of the Au-SPE using 5 mM of [Fe(CN)6]32/42 in PBS (pH 5 7.4) by (A) CV and (B) EIS techniques. Au-SPE, Gold screen-printed electrode; EIS, electrochemical impedance spectroscopy.

The CV technique is commonly used to highlight changes in the surface of the gold electrodes. The CV voltammograms registered during the progressive manufacture of the MIP sensor are illustrated in the graph below. The deposition of PVC-COOH provided a barrier for electron transfer to occur at the electrode interface. This widening in peak separation after the PVC-COOH layer formation is caused by the repulsive interaction of anionic probe ([Fe(CN) 6 ] 32/42) and poly-anions (
COO
) at the surface interface. This finding provides evidence of the formation of a PVC-COOH film at the Au-SPE surface. After the deposition of TCS, the redox current peak becomes larger than that obtained with PVC-COOH. It is well documented that TCS analytes are electrochemically active (Fujioka et al., 2014). This can be attributed to a decrease in repulsive interactions on the resulting electrode. It is more likely that the mechanism is regulated by an increase in the concentration of TCS at the electrode membrane, improving the global conductivity of the electrode. Furthermore, after the polymer deposition, a dense polymer film coated the surface of the gold Au-SPE electrode. This MIP formation had a tendency to impede the charge transfer from the redox probe to the electrode posting a lower signal in terms of magnitude. The EIS technique can also be useful as an additional confirmatory tool for the chemical changes occurring on the surface of the Au-SPE. The obtained findings from the CV technique corroborate those registered by the EIS technique, which also supports the corresponding variations in the semicircle diameters (Rct).

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16.3.2.2 Electrochemical characterization: SLS-MIP sensor In the case of the SLS-MIP sensor, the performance of the electrode after each step of the sensor preparation was evaluated by using a PBS electrolyte solution containing 5 mM [Fe(CN)6]32/42. The following and corresponding results are provided in Fig. 16.7. CV was applied for characterization in a [Fe(CN)6]32/4
5 mM solution containing PBS. The obtained data are displayed in Fig. 16.7. As can be seen, the CV response of the MIP electrode is higher than that of the NIP, suggesting the SLS was effectively scavenged on the 2-ATP mesh, stopping the [Fe(CN)6]32/42 from diffusing through the polymer toward the Au-SPE surface. In fact, the lower current of the NIP electrode is likely caused by the absence of SLS molecules in the polymer matrix. Impedance spectroscopy was further employed to characterize the modified MIP and NIP electrodes. This figure depicts the corresponding Nyquist plots. The impedance measurements are in good accordance with the CV results as the diameter of the semicircles for the Nyquist plots is found to be in close correlation with the variations in peak current. The Nyquist representation is composed of a semicircle part related to the electron transfer and a small linear part linked to the diffusion process. To summarize, the impedance measurements were in close accordance with the findings of CV, as the diameters of the semicircles in the Nyquist diagrams correlated with the variations in the oxidation current levels after the different steps in the development of the sensor. In order to assess the suitability of the sensor for the detection of TCS in wastewater samples, the parameters influencing its behavior, namely polymerization, elution, and incubation times, were further optimized. After each

FIGURE 16.7 Electrochemical characterization of the MIP and NIP sensors using 5 mM of [Fe (CN)6]32/42 in PBS (pH 5 7.4) by (A) CV and (B) EIS. MIP, Molecularly imprinted polymer; NIP, nonimprinted; EIS, electrochemical impedance spectroscopy.

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deposition, a redox probe, typically [Fe(CN)6]32/42, was employed to investigate the charge transfer processes onto the electrode. Optimization of the polymerization sequence was achieved using different ratios and polymer incubation times (4
12 h). In order to check the density of the polymer, different concentration ratios of the complex (AAM 1 NNMBA) were tested for the best ratio by measuring the sensor responses. The concentrations used are quoted in Table 16.1. According to the outputs, the binding efficiency of the three MIPs for different ratios was distinguished. The best response was attained with MIP2, as attested by a current (15.3 μA) almost two times higher than the other MIPs. These findings support the conclusion that the concentrations of AAM and NNMBA are critical for binding the molecules, thus enhancing the effectiveness of the binding. Therefore, the ratio used in MIP2 was selected in this work. As the polymer solution was dropped onto the Au-SPE, a chemical reaction was produced on the electrode, quantified by [Fe(CN)6]32/42. The current signals generated by CV are displayed in Fig. 16.8. The highest [Fe (CN)6]32/42 current peak, corresponding to polymer deposition, was obtained between 5 and 6 h of polymerization. Above these values, a reduction in the response is observed. Therefore, the experimental polymerization time was selected to be 5 h. The evolution of the DPV responses for various elution times is illustrated in the graph. The [Fe(CN)6]32/42 current peak corresponding to the TCS deposit enhanced further with increasing elution times, achieving a maximum at 15 min, and staying almost unchanged after this time. This indicates that the elution of TCS was finished within 15 min. Therefore, the optimal elution time assumed was 15 min. The incubation time is also of great significance for the sensitivity of the MIP sensors. Fig. 16.8 depicts the peak current of the sensor responses to different TCS incubation times (10
60 min). The findings indicate that the MIP sensor response to TCS rises with incubation times from 10 to 30 min, and is not significantly changed from 30 to 60 min. Thus, 30 min was considered to be the optimized incubation time.

TABLE 16.1 Composition and reaction conditions of prepared polymers. MIP

Functional monomer (AAM) (mg)

Cross-linker (NNMBA) (mg)

Current peak (μA)

MIP1

11

14

5.1 6 0.4

MIP2

11

71

15.3 6 1.4

MIP3

11

22

9.04 6 0.14

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FIGURE 16.8 The impact of analytical parameters on the MIP sensor response regarding the TCS detection. MIP, Molecularly imprinted polymer; TCS, triclosan.

16.3.3 Reproducibility, selectivity, and stability of the sensor 16.3.3.1 Analytical parameters: TCS-MIP sensor After preparation and throughout each stage of the detection process, the sensor was stored securely at 4 C in a refrigerator and all measurements were conducted at room temperature (25 C). During the MIP test, DPV and EIS techniques were applied to check small changes on the sensor surface. The obtained data is depicted in Fig. 16.9. Different concentrations of TCS were used, ranging from 0.1 pg/mL to 1 ng/mL; the cavities within the MIP matrix were then glued with TCS molecules. As TCS concentrations were raised, the current peaks in the voltammograms became higher by the DPV technique. With regard to the EIS technique, the Nyquist plots, reported in Fig. 16.9, proved that the Rct values, obtained from the diameter of the semicircle, diminished with elevating TCS concentrations. This implies that the electron transfer resistance at the electrode was linked to the TCS concentrations. In effect, the diameter of the semicircle decreased after that, evidencing the trapping of TCS molecules by the Au-SPE/PVC-COOH/MIP surface. The rise in electron transfer was likely caused by the increase in TCS molecules at the working electrode, involving a more conductive membrane. The findings from the DPV technique were congruent with those obtained from EIS technique. An expected increase in peak current (DPV) and a concomitant decrease in Rct (EIS) can be regarded as proportional to the increase in TCS concentrations.

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FIGURE 16.9 MIP sensor responses at different TCS concentrations using 5 mM of [Fe (CN)6]32/42 in PBS (pH 5 7.4) by (A) DPV and (B) EIS. MIP, Molecularly imprinted polymer; TCS, triclosan; EIS, electrochemical impedance spectroscopy.

It is well documented that the selectivity characteristic is an identified significant factor for the implementation of molecularly imprinted sensors in real sample analysis. To this end, MIPs need to have a specific selectivity toward the target analytes. Therefore, the specificity of the sensor developed for TCS was tested and evaluated against some of the potentially interfering molecules, namely 2,4,6-trichlorophenol and catechol (Liu et al., 2009). As depicted in Fig. 16.10, the sensor did not present significant responses to these analogs, exhibiting only limited changes in their current amplitude responses. In fact, the slopes of the interfering were found significantly smaller than those of TCS, stating that the proposed MIP sensor exhibited notable selectivity to TCS.

16.3.3.2 Analytical parameters: SLS-MIP sensor In order to verify the absorption process for varying concentrations of SLS, DPV and EIS techniques were used. Fig. 16.11 illustrates the DPV responses of the MIP sensor for the detection of SLS over a concentration range of 0.1
1 ng/mL. Throughout the procedure, [Fe(CN)6]32/42 was mediated between the printed electrode and the substrate solutions. As can be observed, the redox current peaks diminish with the increase in SLS concentrations, which can be assigned to the gradual heavy film spanning the Au-SPE surface, confirming the attachment of SLS molecules that impedes the electron transport of the redox probe [Fe(CN)6]32/42. Regarding Fig. 16.11, Nyquist plots according to the different SLS concentrations are plotted.

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FIGURE 16.10 Responses of the MIP sensor toward interfering species. MIP, Molecularly imprinted polymer.

FIGURE 16.11 (A) Differential pulse voltammograms and (B) Nyquist plots in the synthetic detection by MIP sensor. MIP, Molecularly imprinted polymer.

In order to examine the selectivity feature of the MIP electrochemical sensor for the quantification of SLS analyte, the interference of some analogs with similar molecular structures, namely Tween 80, urea, and EDTA, was

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considered (Hao, Lei, Bing, et al., 2014). As depicted in Fig. 16.12, the monitored current responses of the MIP sensor with respect to SLS were much greater than those of the other analogs, pointing to more efficient adsorption and binding capacity for the template molecules. These findings signify that the engineered MIP sensor could effectively prevent interference, which renders it more relevant for the specific detection of SLS. In summary, it can be concluded that the interfering analytes employed did not interfere and that the MIP sensor was selective to SLS. In parallel, the reproducibility and stability of the MIP sensor were also examined. The reproducibility is achieved by using six electrodes manufactured under the same conditions employed to determine the SLS contents. The DPV technique results indicate a relative standard deviation (RSD) value of 4.9%, which connotes the correct reproducibility of the sensor. In addition, the operational stability of the designed sensor was verified to check the capability of the MIP sensor to sustain its initial response. The sensor was stored at 4 C in a refrigerator in PBS solution (pH 5 7.2) throughout the stability test. As such, the sensor’s response to SLS is verified for 3 months at 4 C and it retained 92.56% of the initial response with no noticeable worsening in its analytical behavior. This signifies that the proposed sensor has reasonable stability.

16.3.4 Practical application 16.3.4.1 TCS detection by MIP in wastewater In the real-world detection step, 30 μL of the wastewater sample was applied to the working electrode of the MIP sensor. A 30 min incubation ensued to

FIGURE 16.12 MIP sensor outcomes for interfering species. MIP, Molecularly imprinted polymer.

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ensure good retention of the TCS molecules. Measurements were performed three times to take the average value of the TCS concentration. The designed MIP sensor was used to determine the TCS in five wastewater samples collected in the city of Meknes and its surroundings (Morocco): Ain Karma, Bab Bouamair, Kasba, Toulal, and the Quinta do Conde WWTP (Portugal). The TCS levels of these samples were determined by means of both techniques (DPV and EIS). The obtained outcomes are outlined in Table 16.2. Based on the DPV and EIS techniques, the TCS content measurements of these samples yielded good results. These findings indicate a satisfying accuracy and precision of the suggested MIP sensor at an acceptable RSD of less than 12%. An RSD of the equivalent order of magnitude was also obtained using spectrophotometric measurements. Consequently, the TCS contents in environmental water samples obtained by the MIP sensor developed by DPV and EIS measurements were compared and found to agree with those obtained by spectrophotometry as a reference method.

16.3.4.2 TCS detection by e-nose in wastewater Wastewater samples enriched with TCS were used for qualitative analysis using the e-nose system. Fig. 16.13 shows typical responses generated by the TGS 842 sensor in the presence of different wastewater samples (enriched and nonenriched). It can be seen that the sensor response changes characteristically depending on the sample. This may be due to the difference between the presented VOCs. Radar plots of the wastewater enriched with TCS expressed as the normalized values deduced from the six gas sensors are depicted in Fig. 16.14. We observed that the shape of the radial plots varies with the change in TCS in the sample. The PLS model was created using all the data without separating them into training or validation matrices (Saeed et al., 2021). The correlation is achieved with R 5 0.98 between the concentrations predicted by the e-nose and the actual concentrations by the MIP sensor, as depicted in Fig. 16.15. This enables us to assert that the developed e-nose is indeed an accurate predictor of TCS concentrations in wastewater samples. Based on these findings, the capability of the MIP sensor to evaluate the TCS content of the wastewater is the most outstanding achievement. 16.3.4.3 SLS detection by MIP in cosmetic products The freshly designed MIP sensor was evaluated to detect SLS in different cosmetics samples to prove its suitability for practical applications. The obtained outcomes are presented in Table 16.3. The RSD values achieved are good percentages between 0.03% and 7.38%. For a

TABLE 16.2 TCS (triclosan) determination in wastewater samples. Wastewater samples

MIP sensor

Spectrophotometer

DPV (pg/ mL)

RSD (%) (n 5 3)

EIS (pg/ mL)

RSD (%) (n 5 3)

Spectro (pg/ mL)

RSD (%) (n 5 3)

Ain Karma/Morocco

1.76 6 0.42

3

1.6 6 1.2

8

2.88 6 0.33

5

Bab Bouamair/Morocco

4.9 6 0.3

4

4.8 6 1.2

3

5.2 6 0.3

3

Kasba/Morocco

13.9 6 2.0

7

13.6 6 1.0

7

12.9 6 0.2

3

Toulal/Morocco

4.6 6 1.1

2

4.6 6 0.3

5

5.3 6 1.1

5

Quinta do Conde WWTP/ Portugal

4.8 6 0.2

7

2.2 6 0.2

12

3.4 6 0.3

2

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FIGURE 16.13 Electrical conductance changes in the presence of wastewater samples using the TGS 842 sensor.

FIGURE 16.14 Radar plots of the e-nose responses toward the six sensors (Gs as a feature).

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FIGURE 16.15 PLS prediction model of TCS content in wastewater samples using MIP sensor versus e-nose. MIP, Molecularly imprinted polymer; TCS, triclosan.

TABLE 16.3 Determination of SLS (sodium lauryl sulfate) in cosmetic products samples. Cosmetic product samples

MIP sensor

Spectrophotometer

DPV (103 ng/ mL)

RSD (%) (n 5 3)

EIS (103 ng/ mL)

RSD (%) (n 5 3)

Spectro (103 ng/ mL)

RSD (%) (n 5 3)

Shampoo 1

5.25

0.10

5.21

0.09

5.04

0.30

Shampoo 2

0.86

0.95

0.86

0.55

0.83

0.60

Toothpaste

77.55

0.03

77.53

0.09

73.26

3.85

Shower gel

2.65

5.38

2.31

7.38

2.54

8.20

confident assessment of SLS in cosmetic product samples, the obtained results of the MIP sensor are also checked against the results of the spectrophotometric method. As demonstrated in this table, the results

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FIGURE 16.16 PLS prediction model of SLS content in cosmetic samples using MIP sensor versus spectrophotometer. PLS, Partial least squares; SLS, sodium lauryl sulfate; MIP, molecularly imprinted polymer.

registered by both methods are in good accordance. Considering these findings, the global results mainly prove that the proposed approach for SLS determinations based on the electrochemical MIP sensor is precise, responsive, coherent, and trustworthy. A well-recognized statistical method, referred to as PLS, was applied to scale this study to predict a correlation between the designed MIP sensor and the spectrophotometer. The ultimate objective was to examine whether the MIP sensor could predict SLS levels in cosmetic samples. To construct suitable models, a latent variable and a leave-one-out cross-validation technique were employed. Fig. 16.16 displays the PLS-predicted SLS contents vs. actual SLS values for cosmetic samples. Calibration curves with respect to correct linearity were achieved with satisfying regression coefficients of 0.99. Because of the good correlation between the MIP sensor technique and the spectrophotometer, the designed MIP sensor could be applied as a rapid and alternative method to determine SLS content in cosmetic samples.

16.4 Conclusion The development of materials with biomimetic properties has been one of the key challenges for many researchers over the past decades. The evolution of molecular imprinting technology has given a considerable boost. Indeed,

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since their invention, imprinted materials have been used as elements capable of mimicking the ability of biological systems to recognize specific ligands. Because of their high sensitivity, selectivity, and stability, printed materials have been successfully used in various fields. This technology has been harnessed to develop receptor-based biomimetic sensors to quantify the target analytes, notably TCS and SLS. In the first part of this research study, the development of an electrochemical sensor based on a MIP, assembled on an Au-SPE, dedicated to detecting TCS in environmental water sources, is reported. To realize this objective, an acrylamide/ bisacrylamide solution was polymerized after bonding the TCS to the PVCCOOH layer on the Au-SPE. The fabrication of the sensing device and its retention capabilities were characterized by CV, DPV, EIS, AFM, and FTIR. Negligible responses were obtained during the unprinted polymer (NIP) test as a control experiment. The sensor efficiently detected TCS by avoiding interference from structurally similar substances such as 2,4,6-trichlorophenol and catechol. Under optimal conditions, the sensor responses were found to be logarithmic in the concentration range of 0.1
1000 pg/mL. Indeed, compared to the reported work, this sensor has a lower limit of detection and limit of quantification of 0.23 and 0.78 pg/mL, respectively. The developed sensor was effectively applied to wastewater samples to detect TCS and showed satisfactory performance. Simultaneously, the wastewater samples were analyzed by the e-nose device in conjunction with the developed MIP sensor and showed good response and correlation with (R 5 0.98). The second part of this study presents a new MIP sensor for the electrochemical detection of SLS. The MIP-based Au-SPE was prepared by electrochemical polymerization of 2-ATP in the presence of SLS as the target molecule. As a control, a NIP sensor was also constructed in an identical manner omitting the SLS template. In addition, the selectivity of the proposed MIP sensor was investigated using analogs such as ethylene diamine tetraacetic acid (EDTA), Tween 80, and urea, revealing a satisfactory selectivity toward SLS. The electrochemical characterization of the developed sensor was performed using CV, DPV, and EIS techniques. The morphology of the MIP sensor was studied by AFM and FTIR. Some experimental parameters, such as the number of cycles for electropolymerization, incubation time of 2-ATP, and incubation time of elution and SLS, were optimized to improve the sensor’s performance. Under optimal conditions, the electrochemical sensor has a logarithmic working range of 0.1
1000 pg/mL, and a detection limit of 0.18 pg/mL. In addition, compared to the reported work, the sensor has remarkable properties, such as higher sensitivity and selectivity, good reproducibility, wider logarithmic range, lower detection limit, and long-term stability. As a real-world application, the developed MIP sensor has been well used to determine SLS content in environmental waters and cosmetic samples. A UVVis spectrophotometer was used as a reference method. A PLS-R technique was used to investigate the correlation between the spectrophotometer and the MIP sensor technology with a satisfactory regression coefficient (R 5 0.99).

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Acknowledgments The authors acknowledge the University Moulay Ismail of Meknes, as well as all the national and international donors who made it possible to finance this work, notably UMI through the project “Appui a` la recherche” and the European Commission for Framework Program for Research and Innovation through the project “TROPSENSE.”

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data and supplementary material related to this article can be found in the online version.

References Abid, S. A., Suhail, A., Al-Kadmy, I. M., Sattar, A. A., Beshbishy, A. M., Batiha, G. E. S., & Hetta, H. F. (2021). Biosensors as a future diagnostic approach for COVID-19. Life Sciences, 273, 119117. Albano, D. R., & Sevilla, F., III (2007). Piezoelectric quartz crystal sensor for surfactant based on molecularly imprinted polypyrrole. Sensors and Actuators B: Chemical, 121, 129
134. ¨ . B., O ¨ zku¨tu¨k, E. B., & Erso¨z, A. (2021). Development of ¨ nlu¨er, O Altunko¨k, N., U potentıometric bıosensor for diagnosis of prostate cancer. Materials Science and Engineering: B, 263, 114789. Amari, A., El Bari, N., & Bouchikhi, B. (2009). Conception and development of a portable electronic nose system for classification of raw milk using principal component analysis approach. Sensors & Transducers, 102, 33. Bassi, P., Di Gianfrancesco, L., Salmaso, L., Ragonese, M., Palermo, G., Sacco, E., & Racioppi, M. (2021). Improved non-invasive diagnosis of bladder cancer with an electronic nose: A Large pilot study. Journal of Clinical Medicine, 10, 4984. Behera, B., Joshi, R., Vishnu, G. A., Bhalerao, S., & Pandya, H. J. (2019). Electronic nose: A non-invasive technology for breath analysis of diabetes and lung cancer patients. Journal of Breath Research, 13, 024001. Bounegru, A. V., & Apetrei, C. (2020). Development of a novel electrochemical biosensor based on carbon nanofibers
gold nanoparticles
tyrosinase for the detection of ferulic acid in cosmetics. Sensors, 20, 1
17. Chau, W. C., Wu, J. L., & Cai, Z. (2008). Investigation of levels and fate of triclosan in environmental waters from the analysis of gas chromatography coupled with ion trap mass spectrometry. Chemosphere, 73, S13
S17. Choi, W., Shin, H. C., Kim, J. M., Choi, J. Y., & Yoon, W. S. (2020). Modeling and applications of electrochemical impedance spectroscopy (EIS) for lithium-ion batteries. Journal of Electrochemical Science and Technology, 11, 1
13.

376

Oncogenic Viruses Volume 2

Chu, S., & Metcalfe, C. D. (2007). Simultaneous determination of triclocarban and triclosan in municipal biosolids by liquid chromatography tandem mass spectrometry. Journal of Chromatography A, 1164, 212
218. Devi, S., & Chattopadhyaya, M. C. (2013). Determination of sodium dodecyl sulfate in toothpastes by a PVC matrix membrane sensor. Journal of Surfactants and Detergents, 16, 391
396. Dittmer, U., Bachmann-Machnik, A., & Launay, M. A. (2020). Impact of combined sewer systems on the quality of urban streams: Frequency and duration of elevated micropollutant concentrations. Water, 12, 1
18. Feng, S., Wei, X., Zhong, L., Li, J., & Novel, A. (2018). Molecularly imprinted photoelectrochemical sensor based on g-C3N4-AuNPs for the highly sensitive and selective detection of triclosan. Electroanalysis, 30, 320
327. Fujioka, M., Okano, H., & Ogawa, K. (2014). Inflammatory and immune responses in the cochlea: Potential therapeutic targets for sensorineural hearing loss. Frontiers in Pharmacology, 5, 287. Ghosh, M., Shinde, V. S., & Rueping, M. (2019). A review of asymmetric synthetic organic electrochemistry and electrocatalysis: Concepts, applications, recent developments and future directions. Beilstein Journal of Organic Chemistry, 15, 2710
2746. Guz, A. P., & Guz, E. (2018). Assessment of odour nuisance of wastewater treatment plant. Environmental Engineering V, 261
268. Haddi, Z., Amari, A., Alami, H., El Bari, N., Llobet, E., & Bouchikhi, B. (2011). A portable electronic nose system for the identification of cannabis-based drugs. Sensors and Actuators B: Chemical, 155, 456
463. Hao, X., Lei, J. L., Bing, N., & Luo, Li, H. Q. (2014). An electrochemical sensor for sodium dodecyl sulfate detection based on anion exchange using eosin Y/polyethyleneimine modified electrode. Analytica Chimica Acta, 852, 63
68. Hao, X., Lei, J. L., Li, N. B., & Luo, H. Q. (2014). An electrochemical sensor for sodium dodecyl sulfate detection based on anion exchange using eosin Y/polyethyleneimine modified electrode. Analytica Chimica Acta, 852, 63
68. Huang, X., Jiang, A., Jin, X., Yu, H., Zhang, Y., Xia, S., & Dionysiou, D. D. (2021). Nitrogendoped hollow carbon nanospheres as highly efficient electrocatalysts for detection of triclosan. Journal of Environmental Chemical Engineering, 9, 106022. Hussain, S. H., Huertas, C. S., Mitchell, A., Deman, A. L., & Laurenceau, E. (2021). Biosensors for circulating tumor cells (CTCs)-biomarker detection in lung and prostate cancer: Trends and prospects. Biosensors and Bioelectronics, 197, 113770. Jing, Z., Jingcheng, J., Junjie, H., Yanfei, W., Xiaodong, J., & Haihong, G. (2020). Pollution status and control countermeasures for pharmaceutical and personal care products in China. Asian Journal of Ecotoxicology, 3, 21
27. Jørgensen, J. T. (2021). Predictive biomarkers and clinical evidence. Basic & Clinical Pharmacology & Toxicology, 128, 642
648. Kong, X., Li, F., Li, Y., He, X., Chen, L., & Zhang, Y. (2020). Molecularly imprinted polymer functionalized magnetic Fe3O4 for the highly selective extraction of triclosan. Journal of Separation Science, 43, 808
817. Kov´acs, B., Cso´ka, B., Nagy, G., & Ivaska, A. (2001). All-solid-state surfactant sensing electrode using conductive polymer as internal electric contact. Analitica Chimica Acta, 437, 67
76. Kumar, N., Shetti, N. P., Jagannath, S., & Aminabhavi, T. M. (2022). Electrochemical sensors for the detection of SARS-CoV-2 virus. Chemical Engineering Journal, 430, 132966.

Detection of triclosan and sodium lauryl sulfate Chapter | 16

377

Liu, Y., Song, Q. J., & Wang, L. (2009). Development and characterization of an Amperometric sensor for triclosan detection based on electropolymerized molecularly imprinted polymer. Microchemical Journal, 91, 222
226. Menon, S., Jesny, S., & Kumar, K. G. (2018). A voltammetric sensor for acetaminophen based on electropolymerized-molecularly imprinted poly (o-aminophenol) modified gold electrode. Talanta, 179, 668
675. Motia, S., Bouchikhi, B., & El Bari, N. (2021a). An electrochemical sensor based on molecularly imprinted polymer conjointly with a voltammetric electronic tongue for quantitative diphenyl phosphate detection in urine samples from cosmetic product users. Sensors and Actuators B: Chemical, 332, 129449. Motia, S., Bouchikhi, B., & El Bari, N. (2021b). An electrochemical molecularly imprinted sensor based on chitosan capped with gold nanoparticles and its application for highly sensitive butylated hydroxyanisole analysis in foodstuff products. Talanta, 223, 121689. Nasraoui, S., Al-Hamry, A., Teixeira, P. R., Ameur, S., Paterno, L. G., Ali, M. B., & Kanoun, O. (2021). Electrochemical sensor for nitrite detection in water samples using flexible laserinduced graphene electrodes functionalized by CNT decorated by Au nanoparticles. Journal of Electroanalytical Chemistry, 880, 114893. Nguyen, H. T., Yoon, Y., Ngo, H. H., & Jang, A. (2021). The application of microalgae in removing organic micropollutants in wastewater. Critical Reviews in Environmental Science and Technology, 51, 1187
1220. Niu, C., Dong, L., Gao, Y., Zhang, Y., Wang, X., & Wang, J. (2021). Quantitative analysis of RNA by HPLC and evaluation of RT-dPCR for coronavirus RNA quantification. Talanta, 228, 122227. Novodchuk, I., Bajcsy, M., & Yavuz, M. (2021). Graphene-based field effect transistor biosensors for breast cancer detection: A review on biosensing strategies. Carbon, 172, 431
453. Park, S. Y., Kim, Y., Kim, T., Eom, T. H., Kim, S. Y., & Jang, H. W. (2019). Chemoresistive materials for electronic nose: Progress, perspectives, and challenges. InfoMat, 1, 289
316. Qian, L., Durairaj, S., Prins, S., & Chen, A. (2021). Nanomaterial-based electrochemical sensors and biosensors for the detection of pharmaceutical compounds. Biosensors and Bioelectronics, 175, 112836. ¨ pik, A., & Syritski, V. (2021). Development Raziq, A., Kidakova, A., Boroznjak, R., Reut, J., O of a portable MIP-based electrochemical sensor for detection of SARS-CoV-2 antigen. Biosensors and Bioelectronics, 178, 113029. Saeed, F., Khan, M. A., Sharif, M., Mittal, M., Goyal, L. M., & Roy, S. (2021). Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification. Applied Soft Computing, 103, 107164. Saljooqi, A., Shamspur, T., & Mostafavi, A. (2020). A sensitive electrochemical sensor based on graphene oxide nanosheets decorated by Fe3O4@ Au nanostructure stabilized on polypyrrole for efficient triclosan sensing. Electroanalysis, 32, 1297
1303. Sobiech, M., Luli´nski, P., Wieczorek, P. P., & Mar´c, M. (2021). Quantum and carbon dots conjugated molecularly imprinted polymers as advanced nanomaterials for selective recognition of analytes in environmental, food and biomedical applications. TrAC Trends in Analytical Chemistry, 142, 116306. Sullivan, M. V., Dennison, S. R., Hayes, J. M., & Reddy, S. M. (2021). Evaluation of acrylamide-based molecularly imprinted polymer thin-sheets for specific protein capture-a myoglobin model. Biomedical Physics & Engineering Express, 7, 045025. Suriya, P., Naveenkumar, K., Sangeetha, S. P., Divahar, R., & Vijaykumar, S. (2021). A Study on detection Of E-coli bacteria in drinking water using e-nose system. IOP Conference Series: Materials Science and Engineering, 1119, 012009.

378

Oncogenic Viruses Volume 2

Tram, N. D. T., Zhu, X., Ee, P. L. R., & Pastorin, G. (2021). Carbon nanomaterials for the development of biosensors for microbe detection and diagnosis. Carbon Nanostructures for Biomedical Applications, 48, 293
330. Wallen-Russell, C., & Wallen-Russell, S. (2017). Meta analysis of skin microbiome: New link between skin microbiota diversity and skin health with proposal to use this as a future mechanism to determine whether cosmetic products damage the skin. Cosmetics, 4, 1
19. Waltman, C. G., Marcelissen, T. A., & van Roermund, J. G. (2020). Exhaled-breath testing for prostate cancer based on volatile organic compound profiling using an electronic nose device (Aeonoset): A preliminary report. European Urology Focus, 6, 1220
1225. Wang, L., Liang, K., Feng, W., Chen, C., Gong, H., & Cai, C. (2021). Molecularly imprinted polymers based on magnetically fluorescent metal
organic frameworks for the selective detection of hepatitis A virus. Microchemical Journal, 164, 106047. Weatherly, L. M., & Gosse, J. A. (2017). Triclosan exposure, transformation, and human health effects. Journal of Toxicology and Environmental Health, Part B, 20, 447
469. Wu, J. L., Lam, N. P., Martens, D., Kettrup, A., & Cai, Z. (2007). Triclosan determination in water related to wastewater treatment. Talanta, 72, 1650
1654. Wu, T., Li, T., Liu, Z., Guo, Y., & Dong, C. (2017). Electrochemical sensor for sensitive detection of triclosan based on graphene/palladium nanoparticles hybrids. Talanta, 164, 556
562. Yang, D., Fan, P., Lianhong, Z., Yu, J., Xiaowen, G., Jie, Y., & Chaojie, Z. (2021). Serum metabonomics study of papillary thyroid carcinoma based on HPLC/Q-TOF-MS. Frontiers in Cell and Developmental Biology, 9, 78. Zhang, S., Cheng, J., Shi, W., Li, K. B., Han, D. M., & Xu, J. J. (2020). Fabrication of a biomimetic nanochannel logic platform and its applications in the intelligent detection of miRNA related to liver cancer. Analytical Chemistry, 92, 5952
5959. Zhang, Y., & Lyu, H. (1948). Application of biosensors based on nanomaterials in cancer cell detection. Journal of Physics: Conference Series, 2021, 012149.

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A Acridines, 209
210 Acrylamide (AAM), 321
323, 353
354 Acrylamido methylpropane sulfonic acid, 320 Acute avian leukemia retrovirus, 191
192 Acute myeloid leukemia, 201
203 Adenoviruses, 82 Adult stem cells (ASCs), 101 Adult T-cell leukemia/lymphoma (ATLL), 233 Aerobic systems, 40 Age, 7 “Aggregate” detection, 27 AIDS-associated Kaposi’s sarcoma, 236 Alcohol, 8 Aldehydes, 55 Alkaline phosphatase (ALP), 27 Alpha-fetoprotein (AFP), 229 Aluminum, 329 2-amino-2-hydroxymethyl-1,3-propanediol, 322 2-aminothiophenol (2-ATP), 353
355 Ammonium persulfate (APS), 321
322, 353
354 Amplicor polymerase chain reaction, 27 Anal cancer, 239 Antagonizing transcription factor (AATF), 152
154 Anti-HPV vaccines, 298 Antigen-explicit T cells (AST), 69
70 Applicability domain (AD), 276
277 Arsenic (Ar), 41 Artificial Intelligence (AI), 72
73 approaches, 263 Artificial neural network (ANN), 273
274 Assisted Model Building with Energy Refinement (AMBER), 257
259 AMBER-GS, 257
259 AMBER03, 257
259 AMBER94, 257
259

AMBER-96, 257
259 AMBER99SB, 257
259 AMBER99φ, 257
259 Atomic force microscopy (AFM), 325, 357 Au-SPE/MIP, 323
324 AURKA, 206 Aviadenovirus, 82, 88

B Bacterial vector vaccines, 308 Basic helix
loop
helix (bHLH), 198 Basic helix
loop
helix zipper (bHLHZip), 197
198 Bio-indicator, 51 Bio/electrochemical sensors, 353 Bioaccumulation, 52
53 Bioconcentration factor (BCF), 52 Biofluids, 318
319 Bioinformatics, 109
110 approach, 232 resources, 135
138 Biological behavior, 271 Biological database, 255
256 Biomarkers, 221
224 in bloodstream, 319
320 gas sensors in oncology or virology as tools for detection of, 321 urine and saliva as noninvasive sources of, 318
319 Biomimetic sensor, 353 Biosensors, 352
353 Biotechnology, 151
152 Bis-acrylamide, 322
323 Bivalve molluscs, 40, 49
54 lamellibranchs, 50
51 Mytilidae as bioindicators, 51
52 BLAST tool, 256 Bloodstream, biomarkers in, 319
320 Blue mussel (Mytilus edulis), 51 Breast cancer, 1
8

379

380

Index

Breast cancer (Continued) anatomy, 2
3 endogenous hormonal factors, 5 early age of first menstruation, 5 late menopause, 5 Epstein
Barr virus, 9
10 exogenous hormonal factors, 6 food needs and sources, 12
13 generality, 2 genetic, family, demographic, and health factors, 6
8 human papilloma viruses, 9 lifestyle and nutrition factors, 8 mechanism of action, 14
15 mouse mammary tumor virus like, 8
9 reproductive factors, 6 risk factors, 5
8 storage sites, 13 symptoms, 3
4 types, 4, 4t viral etiology, 8 vitamin D, 10
12 and breast cancer prevention, 15
16 new therapy for breast cancers prevention, 14 receptors, 13
14 Breastfeeding, 6 Breath print, 353 Burkitt’s lymphoma, 224
225

C c-MYC, 191
192, 195
196 G-quadruplexes and expression of, 207
210 Cadmium (Cd), 41, 44
46 behavior in aquatic environments, 45 carcinogenic effect, 45
46 properties, 44 sources, 44
45 toxicity, 45 Cancer, 54
55, 81
82, 352 biomarkers Epstein Bar virus-associated, 225
226 hepatitis B virus-associated, 229
230 hepatitis C virus-associated, 230
232, 232t HHV-8-associated, 236
238 HTLV-1-associated, 234
235 human papillomaviruses-associated, 239
243 cancer-causing viruses, 81
82, 151 Epstein-Barr virus-associated, 224
225

hepatitis B virus-and hepatitis C virusassociated, 228
229 HHV-8-associated, 236 HTLV-1-associated, 233 human papillomaviruses-associated, 239 immunoediting hypothesis, 63
64 MYC in normal tissues and, 193
194 mechanisms of MYC activation in cancer, 194t Cannabinoids, 320 Carbon (C), 329 Carboxylic PVC (PVC-COOH), 353
354 Carcinogenesis, 25, 290 Catechol, 353
354 CD40, 259 Cell cell-based vaccines, 181
182 homologs of v-MYC, 191
192 transformation mechanisms, 291
292 Cellular vaccines, 311 Cervarix, 155
156 Cervical cancers (CCs), 23, 239, 257
259 Cervix, 299 Checkpoint kinase 1 (CHK1), 207 ChEMBL, 269 Chemical conjugation, 174 Chemical database, 271 Chemistry at HARvard Macromolecular Mechanics (CHARMM), 257
259 Chemotherapy, 62
63, 98 integration of oncolytic viral therapy in, 71 Chromatography, 124 Chromium (Cr), 41 Chronic hepatitis, 230
231 Chronic hepatitis B (CHB), 173 Chronic sarcoma, 159 Circuit voltage (VC), 358 Classic Kaposi’s sarcoma, 236 Classification-based QSAR approaches, 274
275 Cobalt (Co), 42 “Cocktail” detection, 27 Comparative genomics, 111
112 Comparative molecular field analysis (CoMFA), 260 Comparative molecular similarity indices analysis (CoMSIA), 260 Computational process, 126
127 Computer-aided drug discovery, 259
260 Computer-based drug design (CADD), 259
260 Copper (Cu), 41

Index Core protein, 228 Cosmetic products, MIP in, 369
373 Cosmetic residues, 349
350 Curation of chemical, 269
270 Cutaneous squamous cell carcinoma (CSCs), 282 Cyclic voltammetry (CV), 324
325, 356
357 CV technique, 362 Cyclin-dependent kinase inhibitor 2A (CDKN2A), 26 Cyclin-dependent kinases, 204
205 Cyclophilin A (cyp A), 88
89 Cysteine (Cys), 53
54 Cytokine-instigated executioner cells (CIK), 69
70

D Database building, 270 Dendritic cells (DCs), 173
174, 309 Deoxyribonucleic acid (DNA), 23 hybridization method, 238
239 methylation, 154
155 microarray genotyping, 33 oncovirus, 147
148 tumor viruses, 83
85, 150
151 vaccines, 179, 309 Deubiquitinating enzymes, 205
206 Diagnostic biomarkers, 221
224 2,6-diamido anthraquinones, 209
210 Differential pulse voltammetry (DPV), 326
327, 357, 365 1,4dioxane, 353
354 Distilled water (DW), 354 Domestic wastewater, 351 Drugs, 64
65

E E1 gene, 285 E2 gene, 286 E2-binding sites (E2BS), 154
155 E3 gene, 287 E4 gene, 286 E5 gene, 286 E6 gene, 286
287 E7 gene, 287 E8 gene, 287 Early control region (E control region), 25 Early region, 285
287 EBV-associated gastric cancers (EBVaGCs), 225

381

EBV-associated Hodgkin’s lymphoma (EBVaHL), 226 EBV-associated nasopharyngeal carcinoma biomarkers (EBVaNC biomarkers), 226 Electrochemical detection systems, 350 Electrochemical impedance spectroscopy (EIS), 326
327, 357 creatinine molecularly imprinted polymer sensor, 333
334 responses, 333
336 Electrochemical sensor devices, 320
321 for environmental residues, 351
352 experimental, 353
359 chemicals and reagents, 353
354 E-nose setup and measurement, 358
359 electrochemical measurements, 357 polymer synthesis, 354
355 surface morphological analysis, 357 fabrication steps, 323
325, 355
357 creatinine molecularly imprinted polymer sensor, 323
324 SLS-MIP sensor, 356
357 TCS-MIP sensor, 355
356 practical application, 368
373 SLS detection by MIP in cosmetic products, 369
373 TCS detection by e-nose in wastewater, 369 TCS detection by MIP in wastewater, 368
369 results, 359
373 characterization of sensors fabrication stages, 361
364 morphological characterization of fabricated sensors, 359
360 reproducibility, selectivity, and stability of sensor, 365
368 Electrodes, 320 Electronic nose technology (e-nose technology), 353 setup and measurement, 358
359 in wastewater, 369 Electrons, 325
326 Elimination, 54 Embryonic stem cells (ESCs), 101 Emerging contaminants (EC), 351 Endemic Kaposi’s sarcoma, 236 Energy-dispersive X-ray spectroscopy (EDS), 327
329 Envelope glycoproteins (Env), 83
85

382

Index

Environmental analysis, 353 Environmental residues, electrochemical sensors for, 351
352 Enzyme-linked immunosorbent assay (ELISA), 124 Epidermal growth factor receptor (EGF receptor), 25 Epidermodysplasia verruciformis (EV), 282 Epigallocatechin-3-gallate (EGCG), 259
260 Epigenomics, 113 Epitestosterone, 320 Epstein
Barr virus (EBV), 8
10, 63, 82, 86
87, 112
113, 147
148, 221, 224
226, 253
254, 259 EBV-associated cancer biomarkers, 227t EBV-positive Burkitt’s lymphoma, 226 Epstein Bar virus-associated cancer biomarkers, 225
226 Epstein-Barr virus-associated cancers, 224
225 infection, 224 life cycle, 224 statistical analysis of oncovirus, 157
158 Essential elements, 42 Estrogen receptor (ER), 100 Ethanol, 353
354 Ethylenediaminetetraacetic acid (EDTA), 353
354 Eulamellibranchs, 50 External validation, 276

F Fabricated sensors morphological characterization of, 327
333, 359
360 SLS-MIP sensor, 359
360 TCS-MIP sensor, 359 FastA tool, 256 Fibroblast cells, 236
237 Filbranches, 50 Finite volume technique (FV technique), 157 Fluorescence polarization (FP), 201
203 Fold recognition, 256
257 Food and Drug Administration (FDA), 72
73 Food needs and sources, 12
13 Fourier transform infrared spectroscopy (FTIR), 357 Free radicals, 40 Functional genomics, 111
112 Functional proteomics, 123
124

G G-quadruplexes and expression of c-MYC, 207
210 G-tetrad, 208 G4 ligands database (G4LDB), 209 Gardasil, 155
156, 299 Gardasil 9, 155
156, 299 Gas chromatography, 350 Gas sensors in oncology or virology as tools for detection of biomarkers, 321 Gastric cancer, 224
225 Gene expression programming (GEP), 274 Generative topographic mapping (GTM), 274 Genes, 63 prediction, 256 Genetic fusion, 174 Genetic mutations, 6 Genetic programming (GP), 274 Genetically modified oncolytic virus, 70 Genetics of virus, 94
96 Genome-scale biomolecular networks, 241 Genomics, 110
113 of oncoviruses and bioinformatics, 254
255 sequencing of oncovirus organisms, 112t Genotype, 148
149 Genotyping by sequencing, 30
33 using Luminex technology, 33
34 Glucose, 320 glucose-regulated proteins, 229 molecularly imprinted polymer sensor, 324
325, 334
336 sensor, 337
338 Glutamine metabolism, 207 Glypican 3 (GPC-3), 229 Gold screen-printed electrodes (Au-SPE), 323
324, 354 GROningen Machine for Chemical Simulations (GROMACS), 257
259 Group 1 carcinogens, 221

H Hash-coding algorithm, 271 HBV-associated hepatocellular carcinoma (HBVaHCC), 229 HCV-associated hepatocellular carcinoma (HCVaHCC), 230
231 Heat shock proteins (HSPs), 229 Helix
loop
helix leucine zipper (bHLHZ), 197
198 Hepatitis B surface S antigen (HBsAg), 174

Index Hepatitis B virus (HBV), 82, 87, 112
113, 147
148, 173, 221, 226
232, 253
254 on cancer progression, 162 cell-based vaccines, 181
182 DNA-based vaccines, 179 efficacy of therapeutic vaccines, 183 harmlessness, 183 HBV-induced cancer, 226
228 hepatitis B virus-associated cancer biomarkers, 229
230, 230t and hepatitis C virus-associated cancers, 228
229 immunization coverage, 183
184 mRNA-based vaccines, 180 nanovaccines, 182 proteins/peptides vaccines, 181 statistical analysis of oncovirus, 158 therapeutic vaccines, 178 virus-like particle-based hepatitis B vaccines, 173
178 Hepatitis C virus (HCV), 82, 112
113, 147
149, 183
184, 221, 226
232, 253
255 on cancer progression, 162 and hepatitis B virus-associated cancers, 228
229 hepatitis C virus-associated cancer biomarkers, 230
232, 232t statistical analysis of oncovirus, 158
159 Hepatocellular carcinoma (HCC), 173, 228
229 HER2/new gene, 94
96 Herpes simplex virus, 90
91 Hi-value, 277 Hidden Markov models (HMMs), 157 HMMER3 HMM technique, 157 High-end sequencing techniques, 256 High-grade squamous intraepithelial lesion (HSIL), 289
290 High-risk oncogenic HPV (HR-HPV), 23 High-throughput methods, 112
113 Hodgkin’s lymphoma, 224
225 Homology modeling, 256
257 Hormonal therapy, 100 Hormone replacement therapy (HRT), 6 HSV1716, 62
63 Human aortic smooth muscle cells (AoSM), 236
237 Human genome, 209 Human herpesvirus 4. See Epstein
Barr virus (EBV)

383

Human herpesvirus-8 (HHV-8), 82, 112
113, 147
148, 221, 235
238, 253
254 HHV-8-associated cancer biomarkers, 236
238, 238t HHV-8-associated cancers, 236 Human immunodeficiency virus (HIV), 82, 88
89, 151 statistical analysis of oncovirus, 159
160 Human MYC gene, 197
198 Human Na 1 /I-symporter (hNIS), 67 Human oncoviruses, 149 Human papillomavirus (HPV), 9, 23, 82, 89
90, 112
113, 147
148, 238
243, 253
254, 257
259, 281, 297 bacterial vector vaccines, 308 on cancer progression, 161
162 cell transformation mechanisms, 291
292 cellular vaccines, 311 classification, 282
283 diagnosis of human papillomavirus viral genome, 26
34 identification of human papillomavirus without genotyping, 27
29 DNA vaccines, 309 E6/E7 mRNA and protein detection, 34 efficacy, 300
301 epidemiology, 281
282 etiopathogenesis, 24
26 factors influencing vaccination coverage, 305 genome structure, 24
25 genotyping, 30
34 HPV 16, 257
259, 281
282 HPV 18, 257
259, 281
282 human papillomaviruses-associated cancers, 239 biomarkers, 239
243, 242t immunization procedures and doses, 299
300 infection evolution, 289
290 L2-based human papillomavirus prophylactic vaccines, 303
304 mechanism of human papillomavirus infection in cervix and carcinogenesis, 25
26 molecular mechanisms of HPV-induced carcinogenesis, 290
291 peptide-based vaccines, 310 protein vaccines, 310
311 replication cycle, 288
289 RNA-based vaccines, 309 safety and security, 302
303

384

Index

Human papillomavirus (HPV) (Continued) statistical analysis of oncovirus, 158 structure, genomic organization, and viral proteins, 284
288 therapeutic vaccines, 305
311 transmission, 283
284 horizontal transmission, 284 vertical transmission, 284 vaccine coverage, 304 vaccines prophylactic against, 298
299 vaccinia virus, 308 viral vector vaccines, 308 Human T-cell lymphotropic virus-1 (HTLV1), 112
113, 221, 233
235, 253
254 HTLV-1-associated cancer biomarkers, 234
235, 235t HTLV-1-associated cancers, 233 statistical analysis of oncovirus, 160 Human T-cell lymphtropic virus, 147
148 Human umbilical vein endothelial cells (HUVECs), 236
237 Human urine, creatinine detection in, 339
341 Hybrid Capture II (HC2), 27 Hybrid Capture III (HC3), 27 25-hydroxyvitamin D3 (25(OH) D3), 11
12

I I-Tasser, 256
257 Iatrogenic Kaposi’s sarcoma, 236 Immune inhibitor checkpoints, integration of oncolytic viral therapy with, 71 Immunization procedures and doses, 299
300 Immunoediting, 63 Immunofluorescence assay (IFA), 152 Immunotherapy, 96
98 Infectious disease, 181 Interferons (IFNs), 64
65 Interleukin-6 (IL-6), 229 Interleukins, 240 Internal validation, 275
276 International Agency for Research on Cancer (IARC), 221 Ion trap mass spectrometry, 350 Ion-selective electrodes, 350 Ionizing radiation, 7 Iron (Fe), 42

K K-means, 274
275 Kaposi’s sarcoma (KS), 147
148, 236 lesions, 236

Kaposi’s sarcoma herpesvirus (KSHV). See Human Herpesvirus-8 (HHV-8) Kaposi’s sarcoma-associated herpesvirus (KSHV), 147
148 statistical analysis of oncovirus, 159 Kernel partial least squares (KPLS), 273 KSHV. See Human Herpesvirus-8 (HHV-8)

L L-MYC, 191
192 L1 protein, 287 L2 protein, 287 L2-based human papillomavirus prophylactic vaccines, 303
304 Lamellibranchs, 50
51 food, 51 habitat, 50 metallic pollution bioindicators, 51 Laminine 5 (LN5), 288 Langmuir model, 320 Late region (L region), 25, 287
288 Latency-associated nuclear antigen (LANA), 235
236 Latent membrane proteins (LMPs), 224 LMP1, 225, 259 Lead (Pb), 41
44 behavior in aquatic environments, 43 carcinogenic effect, 43
44 sources, 43 toxicity, 43 Learning process, 271
272 Least-squares fitting, 275
276 Leporipoxvirus, 82, 92
93 Leucine zipper (LZ), 198 Ligand-based drug design (LBDD), 259
260 Liquid chromatography, 350 Liquid phase in situ hybridization, 27
28 Liver cancer, 228
229 Liver cirrhosis, 230
231 Liver transplantation, 229 Local Lazy Regression (LLR), 274 Long control region (LCR), 24, 284
285 Long terminal repeats (LTR), 191
192 Low-and middle-income countries (LMIC), 304 Low-grade squamous intraepithelial lesion (LSIL), 289
290 Luminex technology, genotyping using, 33
34

M Machine Learning (ML), 72
73

Index MALDI-TOF-MS, 124 Mammography density, 8 Manganese (Mn), 42 Manganese superoxide dismutase (MnSOD), 231 Mass spectroscopy (MS), 124, 350 Mastadenovirus, 82 Mercury (Mg), 41, 46
48 behavior in aquatic environments, 48 carcinogenic effect, 48 property, 46
47 sources, 47 toxicity, 47
48 Merkel cell carcinoma (MCC), 254
255 Merkel cell polyomavirus (MCPyV), 147
148, 253
255 statistical analysis of oncovirus, 160
161 Merkel cells, 254
255 Mesenchymal immature microorganisms (MSCs), 69
70 Metabolic regulatory networks, 241 Metabolomics, 127
135 metallomics, 130
135 Metallic films, 350
351 Metallomics, 130
135 Metallothioneins (MTs), 53
54 Methacrylic acid, 320 Methylmercury (MeHg), 46
47 Micro RNAs (mi RNAs), 152
154 Micropollutants, wastewater as sources of, 351 MicroRNAs (miRNAs), 195
196 Mitogen-activated protein (MAP), 25 Mixed lineage leukemia, 204
205 Modified vaccinia virus Ankara (MVA), 308 Molecular biology, 151
152 Molecular descriptors, 270
271 Molecular dynamics simulations (MD simulations), 257
259 Molecular mechanisms of HPV-induced carcinogenesis, 290
291 Molecularly imprinted polymer electrochemical measurements, 326
327 experimental, 321
327 chemicals and reagents, 319
320 physicochemical characterization, 325
326 real samples detection, 339
343 creatinine detection in human urine, 339
341 glucose detection in human saliva, 342
343 results, 327
343

385

morphological characterization of fabricated sensor, 327
333 Molecularly imprinted polymer technology (MIP technology), 320 Molecularly imprinted polymers (MIPs), 351
352 in cosmetic products, 369
373 in wastewater, 368
369 Molluscs, 49 Mouse mammary tumor virus like (MMTVlike), 8
9 mRNA-based vaccines, 180 mTOR serine/threonine kinase, 205 Multigas sensor systems (MOS), 353 Multiple linear regression analysis (MLR analysis), 267
268, 272 Multivariate analysis, 271
272 Mussels, 50 MYC biological role of MYC genes, 192
193 boxes, 197
198 expression, 205 gene inhibitors, 200
201 MYC-driven cancers, 203
204 MYC
Max interaction, 198
200 amino acid composition of c-MYC
Max complex, 198t with small molecule inhibitors, 201
203 oncogenes biological role of MYC genes, 192
193 G-quadruplexes and expression of cMYC, 207
210 indirect targeting of MYC, 203
204 MYC as potential target for antitumor therapy, 200
201 MYC in normal tissues and cancer, 193
194 MYC signal transduction pathway, 195
196 MYC
Max interaction, 198
200 structure of MYC, 197
198 synthetic lethality with MYC, 206
207 targeting MYC stability, 205
206 targeting MYC transcription, 204
205 targeting MYC
Max interaction with small molecule inhibitors, 201
203 targeting of MYC expression, 205 proteins, 192
193 signal transduction pathway, 195
196 transcription, 204
205 MYC transactivation domain (MYC TAD), 197
198

386

Index

Myelocytomatosis virus MC29, 191
192 viruses, 191
192 Myoglobin, 320 Mytilidae as bioindicators, 51
52

N

N,N,N0 ,N0 -tetramethyl ethylenediamine (TEMED), 321
322, 353
354 N,N0 -methylene-bis-acrylamide (NNMBA), 321
322 N,N0 -methylene-bisacrylamide (NNMBA), 353
354 N-(3-dimethylaminopropyl)-EDC, 321
324 N-(3-dimethylaminopropyl)-N0 ethylcarbodiimidehydrochloride (EDC), 321
324, 353
354 N-hydroxysuccinimide (NHS), 321
322, 353
354 N-MYC, 191
192 N-terminal transactivation domain (NTD), 197
198 NANnoscale Molecular Dynamics (NAMD), 257
259 Nanotechnology, 352
353 Nanovaccines, 182 Neoplasm, 233 Neopterin, 319 Neuroblast, 204
205 Nickel (Ni), 41 Noncoding region (NCR), 284 Nonenzymatic electrochemical creatinine sensors, 320 Nonessential elements, 42
48 Nonstructural protein, 228 Nuclear magnetic resonance spectroscopy (NMR), 122, 126, 199
200

O Omics, 110
141 bioinformatics resources, 135
138 databases and tools, 139
141 domains, 110f genomics, 111
113 metabolomics, 127
135 proteomics, 122
127 transcriptomics, 113
121 Oncogenic HPV, 24 Oncogenic viruses, 147
148 Epstein-Barr virus, 224
226 hepatitis B virus and hepatitis C virus, 226
232 human herpesvirus-8, 235
238

human oncogenic viruses, 222t human papillomavirus, 238
243 human T-cell lymphotropic virus-1, 233
235 Oncology, 103 applications in fields of, 352
353 as tools for detection of biomarkers, 321 Oncolytic virotherapy, 62
66, 163
164 applications, 66
71 diagnosis, 67
68 genetically modified oncolytic virus, 70 integration of oncolytic viral therapy in chemotherapy, 71 integration of oncolytic viral therapy in radiotherapy, 70
71 integration of oncolytic viral therapy with immune inhibitor checkpoints, 71 tumor targeted cell delivery by oncolytic virotherapy, 69
70 cancer immunoediting hypothesis, 63
64 future concerns, 73 integration of artificial intelligence or machine learning into cancer research, 72
73 limitations, 71
72 pharmacokinetics, 64
66 Oncolytic virus (OV), 61 history, 62
63 Oncoproteins, 285 Oncorine H101, 62
63 Oncotherapy, 101
103 future, 103
104 Oncovirology, 253
254 Oncovirus, 81
82, 147
148 cancer, 253
254 and cancer progression, 161
162 classification, 149
151 general properties of, 63 genetics of virus, 94
96 mechanism, 83
85 molecular tools used for oncovirus detection, 151
155 prevalence, 148
149 statistical analysis of oncovirus, 157
161 stem cell transplant therapy, 101
103 types and mechanism, 86
93 types of treatment, 96
101 vaccines available for, 155
157 Open reading frames (ORFs), 24, 284 Oral contraceptives, 6 Oropharyngeal cancer, 239 Oropharyngeal squamous cell carcinoma, 242
243

Index Orthopoxvirus, 82, 93 Ovarian carcinoma, 201
203 Overweight, 8 Oxidative stress, 54
55 Oxygen (O), 329

P p53, 63 p53 and retinoblastoma protein (pRb), 25 Pancreatic ductal adenocarcinoma (PDAC), 201
203 Papillomaviruses, 281 Parity and early age at first motherhood, 6 Partial least square (PLS), 272
273, 369 Partial least squares regression (PLS-R), 341 Parvovirus, 82, 91
92 B19, 91
92 Peptide-based vaccines, 310 Personal care products (PCPs), 351 Pharmacokinetics of oncolytic viral therapy, 64
66 Phosphate-buffered saline (PBS), 321
322, 353
354 Phyre2, 256
257 PI3K/AKT/mTOR pathway, 205 Poly(ethylene-co-vinyl alcohol) (EVAL), 320 Polymer solution, 364 synthesis, 322
323, 354
355 Polymerase chain reaction (PCR), 27 amplification technique, 29 consensus, 29 Polyomavirus, 82, 90, 148 Polyvinyl carboxylic chloride (PVC-COOH), 321
324 Polyvinyl chloride (PVC), 352 Post transcriptional regulatory networks, 241 Potassium ferricyanide ([Fe(CN)6]32), 321
322, 353
354 Potassium ferrocyanide ([Fe(CN)6]42), 321
322, 353
354 Predictive biomarkers, 221
224 Principal component regression (PCR), 272 Principal components (PCs), 272 Prognostic biomarkers, 221
224 Prophylactic HPV vaccine, 298 Prophylactic vaccine, 298
299 Protein
protein interactions (PPIs), 199 Proteins, 124 expression proteomics, 122
123 protein
protein interaction, 241 proteins/peptides vaccines, 181 vaccines, 310
311

387

Proteomics, 122
127 of oncoviruses and bioinformatics, 254
255 techniques, 124
126 Protobranchs, 50 PubChem, 269 Purvalanol B, 207 Putative G4-forming sequences (PQS), 209 Pyridostatin, 209
210

Q Quantitative structure
activity relationship methodology (QSAR methodology), 267 applicability domain, 276
277 artificial neural network, 273
274 classification-based QSAR approaches, 274
275 external validation, 276 generation, 275 handling and curation of chemical and biological data, 269
270 internal validation, 275
276 kernel partial least squares, 273 model application for prediction of compounds activity, 277 model examination and validation, 275 molecular descriptors, 270
271 multiple linear regression analysis, 272 multivariate analysis, 271
272 partial least square, 272
273 principal component regression, 272 structures drawing and database building, 270 Quarfloxin CX-3543, 209
210

R Radiation therapy, 99 Radiotherapy, integration of oncolytic viral therapy in, 70
71 Rapid diagnostic tests (RDTs), 151
152 Reactive oxygen species (ROS), 54 Relative standard deviation (RSD), 339, 368 Retinoblastoma (Rb), 63 Retinoblastoma tumor suppressor protein (pRb), 236
237 Retinoid receptor X (RXR), 14
15 Retroviruses, 83
85, 233 Reverse hybridization, 30 Rhadinovirus, 82 RNA oncovirus, 147
148 RNA-based vaccines, 309

388

Index

RNA (Continued) tumor viruses, 83
85, 151 Robetta, 256
257 Root mean square (RMS), 329
333 Root mean square error (RMSE), 275
276 Rous sarcoma virus (RSV), 83
85, 151, 221

S Safety, 302
303 Saliva glucose detection in, 342
343 infiltrate acini and eventually be secreted into, 319
320 as noninvasive sources of biomarkers, 318
319 Salivary amylase, 319 Salivary C-reactive protein, 319 Salivary immunoglobulins (sIgA), 319 Scanning electron microscopy coupled with energy-dispersive spectroscopy (SEMEDS), 325 Screening techniques, 321 Sensor electrochemical characterization of sensors fabrication stages, 361
364 reproducibility, selectivity, and stability of, 365
368 SLS-MIP sensor, 366
368 TCS-MIP sensor, 365
366 SLS-MIP sensor, 363
364 composition and reaction conditions of polymers, 364t TCS-MIP sensor, 361
362 Septibranchs, 50 Sequence analysis, 256
257 Sequencing, genotyping by, 30
33 Sequestration, 53
54 Serological screening method, 226
228 Sexually transmitted disease (STD), 88
89 Shope papilloma tumor virus (SPV), 81
82 Signal amplification method, 27
28 Simian virus 40 (SV40), 152
154 Simplex virus, 82 Single-dose vaccination, 300 Single-nucleotide polymorphisms (SNP), 193 Slow Off-rate Modified Aptamer (SOMAmer), 234
235 Small cell lung carcinoma, 204
205 Small molecule inhibitors, targeting MYC
Max interaction with, 201
203 Smoking, 8 Sodium (Na), 329

Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), 124 Sodium hydroxide, 321
322 Sodium lauryl sulfate (SLS), 350 detection by MIP in cosmetic products, 369
373, 372t SLS-MIP sensor, 356
357 Soluble OX40, 234 Statistical analysis of oncovirus, 157
161 Statistical method, 373 Stem cell transplant therapy, 101
103 Storage sites, 13 Structural genes, 191
192 Structural genomics, 111
112 Structural proteomics, 123 Structure-based drug design (SBDD), 259
260 Structure
activity relationship studies (SAR studies), 267
268 Sulfur (S), 329 Surface morphological analysis, 357 Surgery, 100
101 SwissModel, 256
257 Syndecan-1 (SDC1), 239 Syndecans, 239 Synthetic lethality with MYC, 206
207 Systems biology approach, 261
262

T T-cell acute lymphoblastic leukemia, 204
205 Talimogenelaherparepvec (T-Vec), 62
63 Targeted antitumor therapy, MYC as potential, 200
201 Targeted therapy, 98
99 Telagenastat, 207 Telomestatin, 209
210 Terminal differentiation mechanisms, 192
193 Testosterone, 320 Tetrahydrobiopterin, 320 The Cancer Genome Atlas (TCGA), 225 Therapeutic vaccines, 178, 305
311 efficacy of, 183 Thioredoxin (TRX), 231 Threading, 256
257 Three-dimensional space (3D space), 270 3D descriptors, 271 Tolerable upper intake (TMA), 12 Topological indices, 271 Trace metal(lic) elements (TMEs), 39, 41
49 effects of metal toxicity on human health, 49

Index

389

origin and cycle, 41 oxidative stress and cancer, 54
55 properties, 41
48 response of marine organisms to, 52
54 transfer of trace metal elements in trophic chain, 48
49 Transcription activation domain (TAD), 197
198 Transcription factor IIH complex (TFIIH), 204
205 Transcriptomics, 113
121 Transition metals, 40 2,4,6-trichlorophenol, 353
354 Triclosan (TCS), 350 detection by MIP in wastewater, 368
369, 370t TCS detection by e-nose in wastewater, 369 TCS-MIP sensor, 355
356 Tumor necrosis factor receptor (TNFR), 259 Tumor necrosis factor receptor-associated factor 3 (TRAF3), 259 Tumor targeted cell delivery by oncolytic virotherapy, 69
70 Two-dimensional differential gel electrophoresis (2D-DIGE), 124 Two-dimensional gel electrophoresis (2-DE), 124 Two-dimensional space (2D space), 270

Viral MYC oncogene (v-MYC oncogene), 191
192 Viral proteins, 284
288 Viral vector vaccines, 308 Virology, 243 applications in fields of, 352
353 as tools for detection of biomarkers, 321 Virus-like particles (VLPs), 173, 298 vaccination strategy, 298 virus-like particle-based hepatitis B vaccines, 173
178 Vitamin D, 1, 10
12 absorption, 1 biosynthesis, 11
12 and breast cancer prevention, 15
16 D2 and D3, 10 deficiency, 1 generality, 10
11 new therapy for breast cancers prevention, 14 relationship between vitamin D and breast cancer, 14 Vitamin D receptor (VDR), 11, 13
14 Vitamin D response element (VDRE), 15 Volatile organic compound (VOC), 321 Voltammetric array, 333
336 creatinine molecularly imprinted polymer sensor, 333
334 Vulvar cancer, 239

U

Wastewater, 349 e-nose in, 369 MIP in, 368
369 as sources of micropollutants, 351 Wastewater treatment plants (WWTPs), 351 Water cycle, 349 Western blotting, 124 Williams map, 277 World Health Organization (WHO), 173, 282, 297

Ubiquitin ligases, 197
198 Ubiquitin
proteasome system, 193, 203
204 Ultraviolet
visible (UV
Vis), 326 Upstream regulatory region (URR). See Long control region (LCR) Uric acid (UA), 319 Urine as noninvasive sources of biomarkers, 318
319 testing, 319

V Vaccination coverage, 305 Vaccines, 297 available for oncovirus, 155
157 Vaccinia virus, 308 Vaginal cancer, 239 Varicellovirus, 82 4-vinylpyridine, 320 Viral cancers, 221 Viral etiology, 8

W

X X-ray powder diffraction (XRD), 125
126 X-rays, 327
329 crystallography, 125
126, 199
200 Xenobiotics, 50

Z 0-D descriptors, 271 Zinc (Zn), 41 ZINC49069570, 259
260 ZINC49115270, 259
260