Genetic Polymorphism and cancer susceptibility 9813366982, 9789813366985

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Aga Syed Sameer Mujeeb Zafar Banday Saniya Nissar   Editors

Genetic Polymorphism and Cancer Susceptibility

Genetic Polymorphism and Cancer Susceptibility

Aga Syed Sameer • Mujeeb Zafar Banday • Saniya Nissar Editors

Genetic Polymorphism and Cancer Susceptibility

Editors Aga Syed Sameer Basic Medical Sciences, College of Medicine King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) Jeddah, Saudi Arabia

Mujeeb Zafar Banday Department of Biochemistry Government Medical College Karan Nagar, Srinagar, Jammu and Kashmir, India

King Abdullah International Medical Research Centre (KAIMRC), National Guard Health Affairs (NGHA) Jeddah, Saudi Arabia Saniya Nissar Department of Biochemistry Government Medical College Karan Nagar, Srinagar, Jammu and Kashmir, India

ISBN 978-981-33-6698-5 ISBN 978-981-33-6699-2 https://doi.org/10.1007/978-981-33-6699-2

(eBook)

# The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

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Foreword 1

It gives me extreme pleasure and honor to write a foreword for the book entitled “Genetic Polymorphisms and Cancer Susceptibility” which has been edited by one of my colleagues, Dr Syed Sameer Aga. This book focuses on genetic polymorphisms and their role in modulating the risk of developing various cancers. The book contains various chapters focusing on the role played by various important genes and their specific polymorphisms in different cancers. The first chapter of this book “Mutations and Polymorphisms” lays the foundation of the book in discussing the basic differences between polymorphisms and mutations of the genome. There are dedicated chapters discussing specific cancers and the genes and the polymorphisms involved in their carcinogenesis. Chapter 3, microRNAs and breast cancer focuses on the role played by various miR SNPs in cancer susceptibility as well as cancer prognosis. Chapter 4 of the book discusses various SNPs in the inflammatory pathway genes and their role in gastric cancer risk. Chapter 7 written by editors themselves provides us an in-depth insight on the role played by viruses via inflammatory pathway genes and the associated genes in cervical carcinogenesis.

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I wish the editors of this book a huge success and expect the trio of editors will continue to publish similar books in future benefitting the scientific community with their expertise in this field. Basic Medical Sciences, College of Medicine-Jeddah King Saud bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, National Guard Health Affairs Jeddah, Kingdom of Saudi Arabia

Sheikh A. Saeed

Foreword 2

Dr. Aga Syed Sameer has been a prolific writer and a passionate researcher since his starting days of career at Sher-i-Kashmir Institute of Medical Sciences, Srinagar, India. As a research fellow, Sameer impressed one and all in the system by his commitment, humility, scientific aptitude, and passion towards research. His quality research publications in high impact scientific international journals impressed one and all. All clinical departments loved to get associated with the department of immunology in general and with Sameer in particular to get into some form of high impact credible research. His love for writing never faded and, wherever, he worked, it gave him an identity at all places of work. Today, I really feel honored, happy, and humbled to write a foreword on his extraordinary beautifully written Springer publication on “Genetic Polymorphisms and Cancer Susceptibility.” I found this book a masterpiece, deliberating in a detailed and an analytical way on polymorphisms playing their role in human cancers. I am sure that all students, teachers, and clinicians dealing with cancer research on human beings in any form will find this beautiful manuscript interesting and of great help in planning and executing their research on human cancers. I sincerely recommend this book to all oncologists and basic scientists. The book is evidence based and supported with credible and high impact references throughout its content. I congratulate Dr. Sameer and his team for putting in so much of hard work to bring out this great manuscript to light for our better

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understanding of role of polymorphisms in human carcinogenesis. I am sure Sameer will continue to grow, prosper, and contribute more and more quality work to the research world. Colorectal Division, Department of General and Minimal Surgery Sher-i-Kashmir Institute of Medical Sciences Srinagar, J&K, India

Fazl Q. Parray

Foreword 3

It gives me an immense pleasure to write a foreword for this book entitled “Genetic Polymorphisms and Cancer Susceptibility” edited by Dr Syed Sameer Aga and his team whom I know very well and have been associated with them for various research projects. I am delighted to know that they have undertaken this scientific project, which I am sure is going to be a great contribution to the scientific literature. They have the potential, knowledge, and dedication to accomplish even very difficult goals. The title of the book itself conveys the importance of the subject. In fact, innumerable work has been published on gene polymorphisms across the globe. The book has encompassed all the important aspects of gene polymorphisms and their relevance in various human cancers. The chapters have been well written by a galaxy of authors recognized in the field. Dr. Sameer has been associated with cancer research right from the time he joined our institute. He did not look back and worked quite hard to publish his work in reputed national and international journals and in the form of books with well-known publishing houses. The book is going to describe the role of gene polymorphisms in the genesis of various cancers and thereby guide and facilitate the management of this dreadful group of diseases. Many researchers, cancer biologists, doctors, related academicians, and finally the patients are going to be benefited by this book.

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Foreword 3

I really feel honored to present this foreword and introduce this book edited by a dedicated team and wish this book and the editors all the success. Department of General and Minimal Invasive Surgery and Colorectal Surgery Sher-i-Kashmir Institute of Medical Sciences Srinagar, J&K, India

Nisar A. Chowdri

Preface

The human genome, as the genome of any other species, encodes the necessary information, which determines the phenotypic characteristics of an individual and the differences in these characteristics, the individual exhibits, in comparison to other individuals. The inherent property of the genome, which determines these differences, is termed as genetic variation. The human genome, indeed, exhibits a dynamic variation between different individuals and across different geographical and/or racial/ethnical populations. However, human genome has remained well conserved throughout the period of human evolution and the DNA of two individuals differs only by around 0.5%, which means that the DNA of two individuals is almost 99.5% identical and the genetic variation accounts for only about 0.1–0.4 % of the total genomic DNA or genome. Gene/genetic polymorphism is the most common and dynamic form of genetic variation present throughout the human genome and represents the most common type of sequence variation. The most prevalent type of gene polymorphism is the single nucleotide polymorphism (SNP), which accounts for more than 90% of all the human genetic variations. Single nucleotide polymorphisms, therefore, represent a major source of genetic variations present in the human genome and play a crucial role in determining the interindividual phenotypic differences. Among the varied roles played by single nucleotide polymorphisms (SNPs) in regulating various interindividual phenotypic differences, the role of SNPs in predicting or indicating or indeed determining the risk of development and progression of various diseases in humans including various cancers is of greatest importance. Not only the susceptibility of individuals towards various diseases but SNPs may also modulate the response of different individuals to environmental stresses and treatment of diseases by various drugs. Therefore, the deep understanding of the polymorphisms as important genetic variation and in particular SNPs becomes imperative for the evolutionary and population geneticists not only to understand the disease mechanisms but also to affect the development of precision (personalized) medicine. However, the actual role played by various SNPs in regulating the interindividual phenotypic differences including the differences in disease susceptibility, responses to the drugs, and the mechanisms involved thereof is still work in progress and a lot needs to be done in this regard. Our book entitled, “Genetic Polymorphisms and Cancer Susceptibility” aims to bring together and provide an overview of the important roles played by genetic xiii

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polymorphisms especially single nucleotide polymorphisms (SNPs) in determining the risk of development and progression of various human cancers. The introductory chapter of this book (Chapter 1: Polymorphisms and Mutation: What Is difference?) provides a brief insight into various types of genetic variations with emphasis on gene polymorphism especially SNPs and how gene polymorphisms differ from gene mutations. The second chapter (Chapter 2: Single Nucleotide Polymorphisms and Pharmacogenomics) describes the role and possible applications of SNPs in pharmacogenomics including the role played by SNPs in modulating the drug response. The third chapter of this book (Chapter 3: Breast Cancer Susceptibility and MicroRNA Polymorphisms) discusses the possible association between SNPs in microRNAs, known as “polymirs” and the risk of development and progression of breast cancer. The fourth chapter of this book (Chapter 4: Gastric Cancer: Role of Genetic Polymorphisms of Inflammatory Pathway Molecules) describes the role played by various SNPs in the various genes especially those involved in the inflammatory response to Helicobacter pylori infection and their possible role in modulating the risk of development and progression of gastric cancer. The fifth chapter (Chapter 5: Colorectal Cancer and Genetic Polymorphism in Key Regulatory Low Penetrance Genes) provides a detailed description of colorectal cancer and the possible role of various SNPs especially in key regulatory but low penetrance genes in modifying the susceptibility towards development and progression of colorectal cancer. The sixth chapter of this book (Chapter 6: Role of Genetic Polymorphisms in Breast Cancer) discusses various SNPs which have been associated with an increased risk of breast cancer and some of which could possibly serve as potential biomarkers for early diagnosis and personalized targeted therapy of the disease. The seventh chapter (Chapter 7: Cervical Cancer and Genetic Polymorphisms of Essential Immune Pathogenic Response Genes) describes the role of various SNPs in several immune pathogenic response genes in modulating the risk of development and progression of cervical cancer. The eighth chapter (Chapter 8: Thyroid Cancer and Genetic Polymorphism) provides a brief insight into genetic characterization of thyroid cancer and discusses the role of various SNPs in thyroid cancer risk modulation, diagnosis, and therapeutics. The ninth chapter (Chapter 9: Bladder Cancer and the Role of Toll-Like Receptor (TLR) Polymorphisms) discusses the possible role of various polymorphisms in toll-like receptor (TLR) genes in modulating the risk of bladder cancer. The final and tenth chapter of this book (Chapter 10: Genetic Polymorphisms and their Role in Lung Cancer) describes the role of various SNPs in genes, which play important roles in various major lung cancer pathways. We hope our effort in the form of this book will emerge as a valuable resource of research-based knowledge for professionals, researchers, students, and everyone else in the field. Jeddah, Saudi Arabia Srinagar, Jammu and Kashmir, India Srinagar, Jammu and Kashmir, India November 2020

Aga Syed Sameer Mujeeb Zafar Banday Saniya Nissar

Acknowledgements

Foremost, we would like to express our sincere gratitude and reverence to all the authors whose contribution made the pursuit of this book possible and the overall experience very knowledgeable. We are grateful to all of them for being patient enough with our constant demands and for toiling hard enough to make the inclusion of their contribution in this book possible. We also thank all our supervisors, co-supervisors, and mentors whose advice, encouragement, tolerance, and freedom of work made us who we are today. Their judicious guidance, innovative ideas, spotless suggestion, and constant encouragement are duly acknowledged. We, Dr. Sameer and Dr. Saniya, would like to express our deep sense of gratitude and appreciation to Prof. Mushtaq A. Siddiqi, Vice-Chancellor, Islamic University of Science and Technology, Awantipora, Kashmir, for being an extremely inspirational, motivating, and educative person in our lives. Combined with his friendly nature and discipline, he has been approachable at any time, providing instant support and guidance throughout our initial stages of research work. I, Dr. Mujeeb, would like to express my gratitude to my supervisor, Dr. Ehtishamul Haq, Associate Professor, Department of Biotechnology, University of Kashmir Hazratbal, Kashmir, for his guidance and support, which have been invaluable not only in work but also in various other aspects of life. I, Dr. Saniya would extend my sincere thanks to Dr. Fouzia Rashid, Assistant Professor, Department of Clinical Biochemistry, University of Kashmir, Hazratbal, Kashmir for her excellent support, supervision, and guidance throughout my research period. I am highly thankful to her for inspiring, motivating, and helping me during my research days through thick and thin of the lab work and in life in general. We all also feel delighted in expressing our sincere thanks to Dr. Nissar A. Chowdri, Professor and Head, Department of General and Minimal Invasive Surgery and Department of Colorectal Surgery, SKIMS, Soura, Srinagar, for his continuous support and encouragement. He has been keenly interested in our work right from the initial stages of our research in SKIMS and University of Kashmir. We also owe our sincere gratitude to Dr. Fazl Q. Parray, Professor, Department of General and Minimal Invasive Surgery, Sher-i-Kashmir Institute of Medical Sciences (SKIMS),

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Soura, Srinagar, for his lifelong mentorship and his keen interest in our work right from the initial stages. We also extend our deep respect and appreciation to all the authors who contributed their valuable work for this book and did exhaustively respond to each editorial demand which we put forth during the writing and editing phase. These eight months’ journey was a long toil for which we thank all of you. We emphatically owe our profound gratitude to Dr. Bhavik Sawhney and Ms. Priya Shankar at Springer Nature, who were very kind and cooperative throughout the period of this endeavor. We would like to express our heartfelt thanks to our friends for being the moral support ever and always and for being the factor who inculcated in us the resilient spirit to pursue our professional and personal goals. Last but not least, we are also immensely indebted to our parents and siblings for their support and giving us the strength of will to pursue what we believed and because of which we have reached this stage of life where we are able to contribute something substantial.

Contents

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Mutations and Polymorphisms: What Is The Difference? . . . . . . . . Aga Syed Sameer, Mujeeb Zafar Banday, and Saniya Nissar

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Single Nucleotide Polymorphisms and Pharmacogenomics . . . . . . . Azher Arafah, Shafat Ali, Sabhiya Majid, Samia Rashid, Shabhat Rasool, Hilal Ahmad Wani, Iyman Rasool, and Muneeb U. Rehman

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Impact of MicroRNA Polymorphisms on Breast Cancer Susceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nusrath Yasmeen, Vikram Kumar, and Krutika Darbar Shaikh

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From Inflammation to Cancer: Role of Genetic Polymorphisms of Inflammatory Pathway Molecules in Gastric Cancer . . . . . . . . . . Israa Abdullah Malli

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Colorectal Cancer and Genetic Polymorphism in Key Regulatory Low Penetrance Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Mujeeb Zafar Banday, Aga Syed Sameer, and Saniya Nissar

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Role of Genetic Polymorphisms in Breast Cancer . . . . . . . . . . . . . . 165 Mohammad Rafiq Wani

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Genetic Polymorphisms of Essential Immune Pathogenic Response Genes and Risk of Cervical Cancer . . . . . . . . . . . . . . . . . . . . . . . . . 191 Saniya Nissar, Aga Syed Sameer, and Mujeeb Zafar Banday

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Thyroid Cancer and SNPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Mosin S. Khan and Syed Mudassar

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The Role of Toll-Like Receptor (TLR) Polymorphisms in Urinary Bladder Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Payam Behzadi

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Genetic Polymorphism and Their Role in Lung Cancer . . . . . . . . . 319 Sheikh M. Shaffi

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Editors and Contributors

About the Editors Aga Syed Sameer is currently working as an Assistant Professor of Biochemistry in the Department of Basic Medical Sciences, College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia. He is also serving as the Chairman of the Quality Unit in College of Medicine, KSAU-HS. He is a lifetime member of many societies related to genetics, molecular biology, and medical education like the Association of Clinical Biochemists of India, Society of Indian Academy of Medical Genetics, Middle East Molecular Biology Sources, Asian Council of Science Editors, Saudi Society of Medical Education, etc. He is also serving as the Associate Editor in four Journals, Frontiers in Oncology— Molecular Biosciences section, BioMed Research International, World Journal of Biological Chemistry, and Current Drug Metabolism. He also serves as a full-time reviewer for various journals including European Journal of Cancer Prevention (Lippincott Williams & Wilkins), PLOS One and PLOS Genetics (Public Library of Science), World Journal of Gastroenterology (Baishideng Publishing), Gene and Meta Gene (Elsevier Publishing), Bioscience Reports (Portland Press), Journal of Clinical Laboratory Analysis (John Wiley & Sons), Tumor Biology (Sage Journals), etc. His research interests are focused on cancer biology and he has published more than seventy-five research and review articles in high repute journals with an h-index of 20 and authored ten books. Mujeeb Zafar Banday is presently working as a Senior Researcher and Lecturer in the Department of Biochemistry, Govt. Medical College, Srinagar. He has completed his Ph.D., M.Phil., and M.Sc. in Biochemistry from the University of Kashmir, J&K, India. He is serving as the review editor and associate reviewer for the journal, Frontiers in Oncology—Cancer Epidemiology and Prevention (Frontiers Group) and full-time reviewer for various journals including Gene (Elsevier Publishing), Bioscience Reports (Portland Press), BioMed Research International (Hindawi Publishing), and Toxicology and Environmental Health Sciences Review (Springer Nature). He has authored more than twenty research and review articles and three books.

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Saniya Nissar is presently working as a Senior Researcher and Lecturer in the Department of Biochemistry, Govt. Medical College, Srinagar. She has completed her PhD, MPhil, and MSc in clinical biochemistry from the University of Kashmir, J&K, India. She works as freelance scientific content writer with many scientific magazines. She has published more than thirty research and review articles and authored four books.

Contributors Shafat Ali Cytogenetics and Molecular Biology Laboratory, Centre of Research for Development, University of Kashmir, Hazratbal, Srinagar, Jammu and Kashmir, India Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India Azher Arafah Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia Mujeeb Zafar Banday Department of Biochemistry, Government Medical College, Srinagar, Kashmir, India Payam Behzadi Department of Microbiology, College of Basic Sciences, Shahr-eQods Branch, Islamic Azad University, Tehran, Iran Mosin S. Khan Department of Biochemistry, Government Medical College Srinagar & Associated Hospitals, Srinagar, Jammu and Kashmir, India Department of Clinical Biochemistry, Sher-I-Kashmir Institute of Medical Sciences (SKIMS), Srinagar, Kashmir, India Vikram Kumar Faculty of Biotechnology, Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur, India Sabhiya Majid Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India Israa Abdullah Malli Department of Basic Medical Sciences, College of Medicine-Jeddah, King Saud Bin Abdulaziz University for Health, King Abdulaziz Medical City, Ministry of National Guard—Health Affairs, Jeddah, Saudi Arabia Syed Mudassar Department of Clinical Biochemistry, Sher-I-Kashmir Institute of Medical Sciences (SKIMS), Srinagar, Kashmir, India Saniya Nissar Department of Biochemistry, Government Medical College, Srinagar, Kashmir, India

Editors and Contributors

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Samia Rashid Department of Medicine, Government Medical College (GMCSrinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India Iyman Rasool Department of ENT, Government Medical College (GMC-Baramulla), Kanth Bagh, Srinagar, Jammu and Kashmir, India Shabhat Rasool Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India Muneeb U. Rehman Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India Aga Syed Sameer Basic Medical Sciences, College of Medicine, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Jeddah, Saudi Arabia King Abdullah International Medical Research Centre (KAIMRC), National Guard Health Affairs (NGHA), Jeddah, Saudi Arabia Sheikh M. Shaffi Department of Biochemistry, Government Medical College, Anantnag, Jammu and Kashmir, India Krutika Darbar Shaikh King Abdullah International Medical Research Centre (KAIMRC), National Guards Health Affairs, Jeddah, Saudi Arabia Faculty of Anatomy, University College of Pre-Professional Studies, King Saud Bin Abdul Aziz University for Health Sciences, Jeddah, Saudi Arabia Hilal Ahmad Wani Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India Mohammad Rafiq Wani Section of Genetics, Department of Zoology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Nusrath Yasmeen Amity Institute of Biotechnology, Amity University, Jaipur, Rajasthan, India Faculty of Pharmacology, College of Nursing, King Saud Bin Abdul Aziz University for Health Sciences, King Abdullah International Medical Research Centre (KAIMRC), National Guard Health Affairs, Jeddah, Saudi Arabia King Abdullah International Medical Research Centre (KAIMRC), National Guards Health Affairs, Jeddah, Saudi Arabia

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Mutations and Polymorphisms: What Is The Difference? Aga Syed Sameer, Mujeeb Zafar Banday, and Saniya Nissar

Abstract

The human genome exhibits a dynamic but limited variation between different individuals and across different geographical and/or racial/ethnical populations which accounts for only 0.1–0.4% of the total genomic DNA or genome. These genetic variations are generally described in terms of mutation and genetic polymorphism. Mutation is defined as the irreversible sequence variation in the DNA which essentiality encompasses all types of variations occurring in the human genome spontaneously or non-spontaneously. Genetic polymorphism which is the most common and dynamic form of genetic variation present throughout the human genome is defined as the presence of two or more alternative forms of an allele in the genome of any individual, which results in distinct phenotypes in the same population. Genetic polymorphism represents most of the variations present in the human genome and includes four different annotated types, viz. single nucleotide polymorphisms (SNPs), copy number variants (CNVs), insertions or deletions (indels), and structural variants. Of these, single nucleotide polymorphisms (SNPs), which involve the substitution of a single nucleotide by another nucleotide at a specific location within the genome, account

A. S. Sameer (*) Basic Medical Sciences, College of Medicine, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Jeddah, Saudi Arabia King Abdullah International Medical Research Centre (KAIMRC), National Guard Health Affairs (NGHA), Jeddah, Saudi Arabia e-mail: [email protected] M. Z. Banday · S. Nissar Department of Biochemistry, Government Medical College, Srinagar, Kashmir, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_1

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for more than 90% of all the human genetic variations and thus constitute an important aspect of genetic variation exhibited by the human genome. Keywords

Polymorphism · Mutations · Genome wide association studies · Single nucleotide polymorphism · Genetic variations

Abbreviations ACE CNPs CNVs DNA GP GPC GVs GWAS HGP Indels ISVs lncRNAs LCVs mCNVs MEIs miRNA NUMTs SNP UTR

1.1

Angiotensin-converting enzyme Copy number polymorphisms Copy number variants Deoxyribo nucleic acid Genetic polymorphisms Genome project consortium Genetic variations Genome wide association studies Human genome project Insertions or deletions Intermediate-sized variants Long noncoding RNAs Large-scale copy number variants Multi-allelic copy number variants Mobile element insertions MicroRNA Nuclear mitochondrial sequences Single nucleotide polymorphism Untranslated regions

Introduction

Deoxyribonucleic acid (DNA) along with carbohydrates, lipids, and proteins constitute four main classes of macromolecules, which are prerequisite for any form in which life exists. DNA is the fundamental determinant of all cellular processes or responses whether those associated with normal cellular physiology or those associated with diverse pathophysiological conditions. DNA is fundamentally responsible for all the known and expressed phenotype including the color of hair, skin, and eyes, vulnerability to various diseases, response to various drugs, and immune response to various pathogens and vaccines. DNA acts as the main control center for determining how, when, what to do, or what will happen (Sukhumsirichart 2018).

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Mutations and Polymorphisms: What Is The Difference?

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The Human Genome Project (HGP), a global research project, was undertaken from 1990 to 2003, with the primary aim to determine the sequence of nucleotides, which constitute the human DNA, and to physically identify and map all the estimated 100,000 genes, which make up the human genome and to decipher their function (Salzberg 2018; Lander et al. 2001). The Human Genome Project (HGP) has enabled us to have a peek into the intricacies of DNA and established that human DNA contains about 3 billion base pairs, which are present in every human cell with a nucleus (Venter et al. 2001; Lander et al. 2001). HGP established two important facts: One, that the human genome has remained well conserved throughout the period of human evolution with DNA of two individuals differing only by around 0.5%, which means that DNA of two individuals is almost 99.5% identical. Second, that against an initially estimated number of 100,000, DNA actually contains much less number of genes, approximately ~24,000 (International Human Genome Sequencing Consortium I 2004; Salzberg 2018). However, since 2004 numerous scientific teams have unearthed that human genome is more complex, dynamic, and diverse in its structure (Karki et al. 2015). The total number of genes in human genome database (HGDB) is around 43,162. Of these, 21,306 genes code for proteins (coding genes), whereas 21,856 genes do not code for proteins (noncoding genes). The total number of transcripts is 323,824 and when compared with total number of genes, the number of transcripts per gene comes out to be 7.5. However, recently it has been proposed that the human genome database should be updated and diversified to include almost 5000 new coding and noncoding genes in it. The proposed list of new genes includes 4997 genes in total, of which 1178 are coding genes and 3819 are noncoding genes. The proposed list also includes 97,511 new transcripts or splice variants of coding genes (Pertea et al. 2018; Willyard 2018). Box 1.1 Introduction to Human DNA Human beings are diploid organisms, which mean humans have two sets of chromosomes, each inherited from one of the parents. The nucleus of human somatic cells contains 46 chromosomes organized into 23 chromosome pairs. Out of these, 22 pairs are autosomes or non-sex chromosomes with each pair containing two identical copies of a specific chromosome. The remaining pair consists of two identical or non-identical allosomes or sex chromosomes. In females, the allosome pair consists of two identical copies of X-chromosome, whereas in males, the allosome pair consists of one copy each of X-chromosome and Y-chromosome. In addition, mitochondria, an important cellular organelle also contains its own DNA, present as 16-kilobase pair (16 kb) mitochondrial genome. In humans, a copy of genome, organized into 23 pairs of chromosomes, has an estimated length of about 3.2 gigabase pairs and contains around 43,162 genes. Of these, 21,306 genes code for proteins (coding genes), whereas 21,856 genes do not code for proteins (noncoding genes). Protein coding (continued)

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Box 1.1 (continued) genes comprise only about 1.0–1.5% of the total human DNA. The major portion (98.5–99%) of the total human DNA is noncoding and does not code for proteins. The non-coding DNA consists of telomeres, introns, and various gene regulatory sequences including promoters, enhancers, silencers, and insulators. It also contains sequences, which provide instructions for the production of various types of noncoding ribonucleic acid (RNA) molecules including transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNA), small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), small interfering RNAs (siRNAs), small temporal RNAs (stRNAs), and heterogeneous nuclear RNAs (hnRNAs). The human genome exhibits a dynamic variation between different individuals and across different geographical and/or racial/ethnical populations. However, this genetic variation accounts for only 0.1–0.4% of the total genomic DNA or genome (Jorde and Wooding 2004; Karki et al. 2015). 1000 Genomes Project Consortium (1KGPC/1000GPC) identified more than 88 million genetic variations based on the study of high-quality individual haplotypes from 26 different populations across different regions of the world including Europe (EUR), Africa (AFR), Americas (AMR), East Asia (EAS), and South Asia (SAS). These genetic variations include 84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/ deletions (indels), and 60,000 structural variations (SVs) (Auton et al. 2015). An integrated map of six types of structural variations, linking population genetics with the functional impact of SVs, has been constructed through the analysis of 2504 genomes from 26 different populations. The analysis identified 2929 multiallelic copy number variants (mCNVs), 6025 biallelic duplications, 42,279 biallelic deletions, 16,631 mobile element insertions (MEIs), 786 inversions, and 168 insertions in nuclear mitochondrial sequences (NUMTs) (Fig. 1.1) (Sudmant et al. 2015). The 1000 Genomes Project, an international research project, which was completed in 7 years (2008–2015), concluded that human genome has an abundance of structural variations and a single diploid genome contains a median of 18.4 Mbp of structural variations (>50 kb). These structural variations arise due to a series of complex evolutionary processes (Birney and Soranzo 2015). Of all the known genetic variations present in the human genome, polymorphism especially the single nucleotide polymorphism (SNP) represents the most common type of sequence variation and accounts for more than 90% of all the human genetic variations (Ding 2007; Orr and Chanock 2008; Risch 2000).

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Mutations and Polymorphisms: What Is The Difference?

Fig. 1.1 Different types of genetic/structural variations reported by 1000 genomes project. mCNVs multi-allelic copy number variants, NUMTs nuclear mitochondrial sequences, MEIs mobile element insertions

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Deletions

Inversions

Duplications

Genetic/ Structural Variations Insertions in NUMTs

mCNVs

MEIs

Box 1.2 Definitions of Common Terms Used in Genetics Genome: The sum of all the DNA or complete set of chromosomes present in a viable cell (of an individual). Genetic variation: The variation of the sequence or structure in the gene and alleles thereof. It is because of them that an individual acquires uniqueness from the others within the population. The term variant defines a specific region of the human genome, which differs between two genome samples. Genetic marker: A specific region of DNA or a DNA sequence with a known chromosomal location, which shows variation or is polymorphic in nature and can be used in identification of different individuals or species or mapping of genes involved in specific diseases. Locus: The location of a DNA marker or a specific gene within a specific chromosome. Allele: Different version of the same gene or gene variant. Genotype: The genetic constitution (whole DNA) of the living organism (Cell). Phenotype: External visible characters of the living organism. It is governed by the genotype and the environment in which organism lives. Homozygous: An individual, which possess identical alleles for a particular trait. Heterozygous: An individual, which contains both dominant and recessive alleles of the particular trait. Penetrance: Ability of a gene (or genes) to be expressed phenotypically to any degree. (continued)

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Box 1.2 (continued) Polymorphism: The phenomenon by which a specific DNA sequence or a gene exists in one of the two or more variant forms with each form capable of expressing itself as an alternative phenotype. Mutation: A heritable and often deleterious change in a DNA sequence or a gene, which usually has higher penetrance (expressivity) and hence higher ability to affect phenotype. Single Nucleotide Polymorphism: The most common type of genetic variation, which results from a single base pair substitution within the DNA sequence. It is abbreviated as SNP and pronounced as snip. SNPs, as the name indicates, result from a single base pair substitution within the DNA sequence. The other common type of genetic variation is the insertion or deletion polymorphism, often known as indel polymorphism (Robert and Pelletier 2018). Various studies have established that sequence variations, especially SNPs in coding or noncoding regions, of the human genome may predict or indicate predisposition to certain diseases and modulate the response to drugs (Karki et al. 2015; Kruglyak 1999). Therefore, the deep understanding of the polymorphism as important genetic variation and in particular SNPs becomes imperative for the evolutionary and population geneticists not only to understand the disease mechanisms but also to effect the development of the precision (personalized) medicine.

1.2

Mutations

Mutations (Lit. change) are the basic events, which are responsible for the generation of almost all genetic variations (GVs). In its contextual definition, as endorsed by many scientists, mutation is defined as the irreversible sequence variation in the DNA which essentially encompasses all types of variations occurring in the human genome spontaneously or non-spontaneously (Beaudet and Tsui 1993; Beutler 1993; den Dunnen and Antonarakis 2000). However, the persistence of the GVs in the genome is heavily dependent upon the interactions of numerous factors including environment. The single simple definition of the mutation is a contentious one and mutation is always defined based on their contextual effects and penetrance within the individual (den Dunnen and Antonarakis 2000). Therefore, a mutation in context to its penetrance usually represents a rare change in sequence of the gene, which usually manifests in the diseases causing attribute (Condit et al. 2002; Karki et al. 2015). The polymorphism has therefore been essentially meant to represent a neutral or harmless mutation present in a population with a frequency of 1% or more (Condit et al. 2002). Mutations can be further classified either as germline if they are inherited from parents or as somatic if they are acquired over the life mainly because of environmental effects. The germline mutations are present or arise in the gametes, sperm,

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Table 1.1 The generalized classification of the mutations Type of mutation Point/ substitution Silent

Missense

Nonsense Frameshift Gross mutations Duplications Inversions Deletions Insertions

Translocations

Change and effect Usually involves a change of single nucleotide New codon formed codes for the same amino acid because of codon degeneracy. The change in DNA sequence therefore has no altering effect on the amino acid sequence of the translated polypeptide New codon formed codes for a different amino acid. The change in DNA sequence therefore results in substitution of one amino acid by another within the polypeptide chain Codon changes to a stop codon. The change in DNA sequence therefore results in premature truncation of polypeptide chain Insertion or deletion in codon changes everything beyond it and hence changes the entire reading frame of the gene beyond the affected site Usually explained in context to whole chromosome, hence involves many genes Involves duplication or copying of a part of the chromosome and generates duplicate segments, which may result in increased gene expression Involves removal of a segment of chromosome and its insertion back into the chromosome but in reverse order A portion or segment of a chromosome is completely removed or deleted. It results in loss of genes contained within that segment A portion or segment of one chromosome is removed and this removed chromosomal segment is added onto another chromosome, which may alter the DNA sequence and disrupt genes on both the affected chromosomes Two chromosomes exchange chromosomal portions or segments, which may alter the DNA sequence and disrupt genes on both the affected chromosomes

ovum, or both. When the affected gametes (sperm and ovum) fuse, the mutations are passed onto the zygote and can be found in each cell of the progeny derived from this zygote. The somatic mutations, however, are important in the disease processes including carcinogenesis (Karki et al. 2015). Depending upon the severity or extent of change in the DNA, a mutation can also be categorized as point, if it involves the change of single nucleotide, or gross, if it involves change of a stretch of DNA or entire chromosome change (GHR 2020). The classification of mutations is summarized in Table 1.1.

1.3

Genetic Polymorphisms

Genetic polymorphism is the most common and dynamic form of genetic variation present throughout the human genome. It is defined as the presence of two or more alternative forms of an allele in the genome of any individual, which results in distinct phenotypes in the same population (Singh and Kulathinal 2013). Therefore, the gene locus (of the gene) should possess two or more alternative forms of an allele, wherein the most common allele is present at a frequency of 99% or less and

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the less common allele present at a frequency of 1% or more (Brookes 1999; Singh and Kulathinal 2013). The lower limit of the frequency of the most common allele should not be beyond 95% (Singh and Kulathinal 2013). With the advent of technology and availability of the cheap next generation sequencing (NGS) an accelerated annotation of the genetic variations has been made possible since the completion of HGP almost two decades ago (Srinivasan et al. 2016; Sudmant et al. 2015). Four different types of genetic variations have been annotated until now. These include single nucleotide polymorphisms (SNPs), copy number variants (CNVs), insertions or deletions (indels), and structural variants (Srinivasan et al. 2016). Human genome is composed of almost 3.3 billion nucleotides and the current knowledge about it demonstrates that almost 98.5% of it is noncoding i.e., does not code for any protein and only about 1.5% of the genome comprises protein coding sequences (Lander 2011). It has been estimated that human genome harbors one genetic variation for every 100–300 base pairs, which is inherent to the DNA copying mechanism (Gonzaga-Jauregui et al. 2012; Lander 2011; Srinivasan et al. 2016). Further, a typical individual possesses around 150 rare variants of coding genes, which affect around 1% of the genes (Lander 2011; Sudmant et al. 2015).

1.3.1

Types of Polymorphisms

As already mentioned, four different types of genetic variations have been annotated until now and these include single nucleotide polymorphisms (SNPs), copy number variants (CNVs), insertions or deletions (indels), and structural variants (Srinivasan et al. 2016). Copy number variations (CNVs) are important GVs, which occur, frequently in the human genome, as well as in other mammalian species (Auton et al. 2015; Freeman et al. 2006; Jorde and Wooding 2004) (Fig. 1.2). A CNV is a DNA segment or a sequence, one kilobase (kb) or larger in size, which is present in different copy numbers in a particular genome in comparison to the normal reference genome (Redon et al. 2006). A CNV in its sequence and structure may consist of simple tandem duplication (Fig. 1.3a) or may be segmental, which involves a complex, multi-site structural loss or gain of homologous sequences (Fig. 1.3b) (Freeman et al. 2006; Redon et al. 2006). CNVs have been reported to cover around 12% of the human genome, which constitutes around 360 megabases distributed across 1500 identified sites (Clancy 2008). In an individual, CNVs are distributed across almost whole of the genome and around 6–19% of the chromosomes are known to contain CNVs (Redon et al. 2006). CNVs are present in the genome in any of the three important structural variant forms, which include large-scale copy number variants (LCVs), intermediate-sized variants (ISVs), and copy number polymorphisms (CNPs). However, the DNA segments containing retroposonal insertions are not included in CNVs (Freeman et al. 2006).

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Mutations and Polymorphisms: What Is The Difference?

Presence or Absence of Variations

Gene A

9

Gene C

Gene D

Gene B

Gene D

Gene B

Gene C

Gene C

Gene D

Gene B

Gene C

Gene C

Gene D

Gene B

Gene C

Gene D

Gene E

Chromosome 1

Gene A Chromosome 1 Copy Number Variations

Gene A

Gene D

Chromosome 2

Gene A Chromosome 2 Normal Reference Chromosome

Gene A Chromosome 1

Fig. 1.2 The simple generalized representation of the copy number variations (CNV) in chromosomes

CNVs lead to drastic changes in the gene sequence and overall gene structure and therefore exert a strong influence on the expression, phenotypic variation, and adaptation in an individual. CNVs have been reported to cause various microdeletion and microduplication diseases and increase predisposition to lethal genetic variations and complex traits (Buckland 2003; Freeman et al. 2006; Inoue and Lupski 2002; Lupski and Stankiewicz 2005; McCarroll et al. 2006). CNVs also affect expression of genes through position effects, which often result from translocation and through varied chromosomal changes, CNVs contribute to the process of evolution (Freeman et al. 2006; Redon et al. 2006). Insertion/deletion (indel—I/D) polymorphism constitutes the type of genetic variation whereby a specific nucleotide sequence of varied length (1–100 bps) is inserted into or deleted from the affected DNA sequence or gene. Indels are distributed extensively across the human genome and affect many important genes. 1000 Genomes Project Consortium (1KGPC/1000GPC) identified around 3.6 million short insertions/deletions (indels) (Auton et al. 2015). A simplest and classical example of indel polymorphism is the 287 bp Alu repetitive sequence or element present in the intron 16 of the gene coding for angiotensin-converting enzyme (ACE). This indel produces three genotypes: insertion/insertion (II) homozygote, insertion/deletion (ID) heterozygote, and deletion/deletion (DD) homozygote (Pinto and van Gilst 1999; Sameer et al. 2010) (Fig. 1.4). The 287 bp Alu element indel polymorphism has been reported to cause differential ACE serum concentration and activity with D allele associated with increased ACE serum activity. The DD homozygous genotype or absence of 287 bp Alu element has been associated with two times increased mean plasma concentration and activity of ACE in comparison to II homozygous genotype or presence of 287 bp Alu element (Rigat et al. 1990). Angiotensin-converting enzyme (ACE) gene, a 21 kb gene (chromosomal location 17q23.3) consists of 26 exons and 25 introns. The indel polymorphism in the

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a Genome 2

Gene A

Gene B

Gene C

Gene C

Gene C

Gene D

Chromosome 2

3x Tandem Repetition

Gene A

Gene B

Gene C

Gene D

Gene B

Gene C

Gene C

Chromosome 1

Genome 1

Gene A

Gene D

Chromosome 2

2x Tandem Repetition

Gene A

Gene B

Gene C

Gene D

Gene B

Gene C

Gene D

Chromosome 1

b Genome 2

Gene A

Gene A

Gene B

Gene C

Chromosome 2 Segmental Duplication

Gene A

Gene B

Gene C

Gene D

Gene B

Gene C

Gene D

Chromosome 1

Genome 1

Gene A Chromosome 2

No Segmental Duplication

Gene A

Gene B

Gene C

Gene D

Chromosome 1

Fig. 1.3 (a) The copy number variation (CNV) in tandemly duplicated Gene C in two genomes, (b) the copy number variation (CNV) in segmental duplicated gene A, B, and C in chromosome 2 of the genome 2 in comparison to genome 1. The depiction is for the same gene battery present in different loci in different chromosomes

intron 16 of ACE gene results from the insertion or deletion (presence or absence) of 287 bp Alu element and produces three genotypes: insertion/insertion (II) homozygote, insertion/deletion (ID) heterozygote, and deletion/deletion (DD) homozygote. D allele and DD genotype of ACE gene 287 bp Alu element have been associated with increased ACE serum concentration and activity. Single nucleotide polymorphisms (SNPs) represent the most common type of genetic variation or sequence variation and accounts for more than 90% of all the human genetic variations (Ding 2007; Orr and Chanock 2008; Risch 2000). SNPs involve the substitution of a single nucleotide by another nucleotide at a specific location within the genome. These substitutions are of two types, transition and

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Mutations and Polymorphisms: What Is The Difference?

5′ UTR

5′ Upstream Enhancer

Promoter

Start Codon

Exon 16

Chromosome 17q23.3

11

Gene Structure - Exons 1–26 & Introns 1–25

Intron 16

Exon 17

287 bp A/u

Intron 25

Stop Codon 3′ UTR

Exon 26

Splice Site

3′ Intergenic Region

Fig. 1.4 The insertion/deletion (Indel) polymorphisms in angiotensin-converting enzyme (ACE) gene

Fig. 1.5 SNP of an allele and the effect on phenotype

transversion, are usually stable, and not believed to have any drastic effect on the organisms (Brookes 1999; Robert and Pelletier 2018; Shastry 2009). Transition involves substitution of one purine (adenine or guanine) by another purine (guanine or adenine) or substitution of one pyrimidine (cytosine or thymine) by another pyrimidine (thymine or cytosine), whereas transversion involves substitution of purines by pyrimidines and vice versa (Fig. 1.5). Of the two SNP types, transitions are more common. SNPs being the most common type of genetic variation are present at a frequency of 1% or more within a given population (Brookes 1999; Robert and Pelletier 2018; Shastry 2009). Within the human genome, about ten million SNPs have been identified, each of which occurs at an interval of approximately 100–300 base pairs (bp) with an average occurrence every 300 bp (Lander 2011; Orr and Chanock 2008; Risch 2000; Sudmant et al. 2015). SNPs because of their abundance and wide distribution throughout the human genome have emerged as important genetic markers for mapping of various human diseases and have proven to be a vital tool for understanding population genetics and uncovering mechanisms of evolution (Ismail and Essawi 2012).

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5′ UTR

5′ Upstream Enhancer

Promoter

Start Codon

TA TA

Exon 1

Coding Region of Gene

Intron 1

Exon 2

Intron 2

Stop Codon

Exon 3

3′ UTR

Splice Site

3′ Intergenic Region

Fig. 1.6 The general gene structure and the different sites where SNPs can occur within it (shown by fire arrows)

The usual frequency of difference in single base between two identical chromosomes of a homologous chromosome pair within the human genome is 1 per 1000 bp. This roughly means that the heterozygous form of a SNP is present at a frequency of almost 0.1% within an individual (Brookes 1999; Orr and Chanock 2008; Sudmant et al. 2015). Since the human genome is diploid in nature and SNPs involve the substitution of one nucleotide by another, SNPs usually exist in either of the two forms based on the presence of either of the two alternative alleles. This means that a SNP present at specific chromosomal loci can have different alternative forms. If the SNP contains an allelic form which is present more commonly within a population, it is termed as “major allele” or “more common form.” In contrast, if the SNP contains an allelic formic, which is present rarely or less commonly within a population, it is termed as “minor allele” or “less common form.” Therefore, a single human diploid genome can have various genotypic forms for a particular SNP including homozygous for major allele, homozygous for minor allele, or heterozygous for major and minor alleles (Crawford and Nickerson 2005). Many SNPs are functionally and phenotypically associated with other SNPs or with different gene regions through linkage disequilibrium and other possible mechanisms, which makes it extremely difficult to clearly describe the effect of a particular SNP on a specific phenotype considering other associated SNPs may also affect that phenotype (Abecasis et al. 2010; Sudmant et al. 2015). The effect of a particular SNP on a specific phenotype varies depending on the location of that SNP within the genome. Based on the location where single base substitution occurs within the genome, SNPs can be categorized into two types: coding region SNPs and noncoding region SNPs. The coding region SNPs are located in exons, the regions of gene that code for the proteins. In contrast, the noncoding region SNPs are located in the noncoding regions of the gene, that is, the regions that do not code for proteins and include 50 -untranslated region (50 -UTR) enhancer regions, 50 -UTR promoter regions, introns, and 30 -untranslated regions (30 UTRs) (Fig. 1.6) (Aerts et al. 2002; Gonzaga-Jauregui et al. 2012). It has been reported that around 50% of the SNPs are present in noncoding regions and 50% of the SNPs are located in coding regions of the genome. Of all

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the SNPs located in coding regions, 50% SNPs cause missense mutations and the other 50% SNPs result in silent mutations (Brookes 1999; Sudmant et al. 2015). Additionally, SNPs can also be categorized as bi-allelic, tri-allelic, or tetra-allelic depending upon the number of alleles affected by the substitution (Ismail and Essawi 2012). The coding region SNPs present in exons affect gene transcription and translation and hence modulate the structure and function of the protein (Aerts et al. 2002; Deng et al. 2017; Gonzaga-Jauregui et al. 2012; Lupski 2016; Snyder et al. 2010). Furthermore, SNPs in coding regions can be either synonymous or non-synonymous. The synonymous SNPs are silent and despite single nucleotide substitution does not cause any change in the amino acid sequence because of the degeneracy of the codon. The synonymous SNPs, however, can affect the messenger RNA (mRNA) splicing, structure and stability, secondary structure conformation, and translation dynamics (protein translation and cotranslational protein folding). The non-synonymous SNPs affect protein structure and function and result in altered protein activity. The non-synonymous SNPs cause change in the sense codon at the substitution site (missense change) and result in the substitution of one amino acid by another which results in change in the structure, conformation, and functionality of the protein but the protein remains viable. Alternatively, these SNPs can change sense codon into a stop codon, which causes premature termination of polypeptide chain, and result in the production of truncated, non-functional protein (Ismail and Essawi 2012). The SNPs present in the noncoding regions of the genome can affect the gene expression, especially if these SNPs are located in the regulatory regions of the gene, which may affect the binding of transcriptional machinery or regulatory proteins. The SNPs located in the promoter region of a particular gene can affect its expression by altering a range of activities including binding of transcription factor(s), promoter activity, promoter clearance, DNA methylation, and histone modifications. The SNPs present in introns can alter protein expression through generation of mRNA splice variants and promote and disrupt the binding and function of cis-regulatory elements and long noncoding RNAs (lncRNAs). The SNPs in 50 -UTRs affect transcriptional activity and protein translation, whereas SNPs in 30 -UTRs affect miRNA binding and regulation of its degradation and protein translation. The SNPs located in regions away from the genes may inhibit or promote gene transcription through long-range cis effects (Table 1.2) (Fig. 1.7) (Brookes 1999; Deng et al. 2017; Karki et al. 2015; Robert and Pelletier 2018; Shastry 2009). Therefore, the effect of a particular SNP on the gene expression and the subsequent effect on various physiological and pathophysiological processes including susceptibility to various diseases depend to a large extent on the location of the SNPs within the gene or the genome (Deng et al. 2017; Haraksingh and Snyder 2013; Lupski 2016; Snyder et al. 2010).

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Table 1.2 Molecular mechanisms of region-based SNP Location of SNPs Promoter

Possible molecular mechanism Gene regulation: It occurs through modulation of binding of transcription factor(s) to promoter region, which affects promoter activity and promoter clearance Epigenetic regulation: It occurs through modulation of DNA methylation or histone modification by affecting, for example, GATC sequence Non-synonymous coding SNPs: These SNPs affect protein structure and function and result in altered protein activity Synonymous coding SNPs: These SNPs affect messenger RNA (mRNA) splicing, structure and stability, secondary structure conformation, and translation dynamics (protein translation and cotranslational protein folding) The intronic SNPs can affect mRNA splicing, genomic imprinting, and chromatin looping and binding of cis-regulatory elements and long noncoding RNAs (lncRNAs) 50 -UTR SNPs: These SNPs affect transcriptional activity and protein translation 30 -UTR SNPs: These SNPs affect miRNA binding and regulation of its degradation and protein translation These SNPs affect long-range cis-regulation and protein translation through modulation of tRNA and rRNA function

Exons

Introns

UTRs

Non-definite regions

Types of SNPs

Synonymous SNP Non-Coding Region

Coding Region

Missense SNP

Results in change of a codon and hence 1 amino acid in proteins

Nonsense SNP

Creates a stop codon & hence causes truncation of protein at SNP

Non-Synonymous SNP In Promoter or Intronic Regions

Located in the exons

Affects the expression of gene

Affects the functionality of the proteins

Fig. 1.7 The types of SNPs and their effects

1.3.2

Applications of SNPs

Almost half of the SNPs are located in the noncoding regions of the human genome, which gives them a very low penetrance, and apparently no or very little role in

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Mutations and Polymorphisms: What Is The Difference?

15

health or development of an individual. However, with the advancement in the mapping of SNPs coupled with the cataloguing of data from a large number of meta-analysis studies carried out in different regions of the world, it has emerged that SNPs play multiple roles. It has been revealed that thousands of SNPs are involved directly or indirectly in modulating the susceptibility to several diseases including cancer (Goldstein and Cavalleri 2005; Lander 2011; Sudmant et al. 2015). HapMap with its databases of around ten million SNPs in human genome has enabled to link various SNPs as the predisposing risk to various multifactorial diseases like rheumatoid arthritis, atherosclerosis, asthma, multiple sclerosis, obesity, diabetes, and cancers (Table 1.3 lists various public SNP databases available currently) (Ramírez-Bello 2019; Shastry 2009). SNPs have emerged as important tool in understanding the population genetics. SNPs have played a vital role in establishing the evolutionary divergence of humans from the other primates (like chimpanzees). It has been reported that most of the present-day SNPs in human genome have originated before the divergence of human race into various populations but after their divergence from primates (Mountain et al. 1992). This has also been established through the discovery that most of the SNPs in the human genome are not found in the primates though about 85% of those do exist commonly in all human races (Barbujani et al. 1997). Furthermore, SNPs have helped in establishing the genealogy and ethnicity within the human population, as there exists a substantial heterogeneity among human population of different races and ethnicities (Goldstein and Cavalleri 2005; Goldstein and Weale 2001; Weir et al. 2005). The SNPs also find wide application in the field of pharmacogenomics, which involves the study of how the genes and hence the SNPs affect the metabolism, response, and efficacy of drugs (Alwi 2005). It has emerged that a number of uncontrollable factors like the response to drugs, adverse drug reactions, required drug dosage, etc. affect the metabolism and efficacy of drugs. The treatment protocols based on earlier notion that “one size fits all” are not fully effective. Therefore, a tailor-made approach involving precision or personalized medicine is now being adopted taking into consideration the effect of the human genome on the metabolism, response, and efficacy of drugs. SNPs of particular pathways especially the xenobiotic genes [cytochrome p450 (CYP) genes] (PharmVar SNP database) have emerged as valuable tools for pharmaceutical industry and they have helped greatly in predicting the drug response and prevent adverse reactions (Chaudhary et al. 2015; Katara 2014). Personalized medicine has taken a huge leap with the advent of HapMap of SNPs and has helped in the assessment of predisposition to diseases, screening and diagnosis of diseases, disease prognosis, stratification of patients, estimation of drug dosage, monitoring of drug response, and assessment and/or prediction of drug resistance (Laing et al. 2011). SNP maps have also revolutionized the oncology in designing the targeted therapies to treat basic and aggressive cancers based on the SNPs of the involved genes (Rodríguez-Antona and Taron 2015).

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Table 1.3 Various publicly available SNP databases and their importance Site URL for SNP searches https://www. ncbi.nlm.nih. gov/snp/ https://www. genome.gov/ 10,001,688/ internationalhapmap-project http://asia. ensembl.org/ info/ Genome/ variation/index. Html https://www. hgvs.org/centralmutation-snpdatabases http://gvs.gs. washington.edu/ GVS http://www. pharmgkb.org

Database dbSNP

Host NCBI

HapMap

The HapMap consortium

Ensembl

EMBL-EBI

HGVbase

HGVS

GVS

NHLBI

PharmGKB

Stanford university

RTPDB

CNHGC

http://www. rtpdb.com/

NIEHS SNPs

NIEHS-EGPUW

http://egp.gs. washington.edu

Seattle SNPs

US NHLBI (PGA)

https://pga.gs. washington.edu/

Santa Cruz

University of California

https://genome. ucsc.edu/

PharmVar

Pharmacogene variation (PharmVar) consortium

http://www. cypalleles.ki.se https://www. pharmvar.org/

Importance and Usage SNPs from the complete human genome Contains SNPs from four populations

Content Almost 6.67 million validated SNPs 10 million SNPs

Contains SNPs from various species

Almost six million validated SNPs

SNPs from the complete Genome. Collates all databases Provides access to dbSNP and HapMap SNPs SNPs in genes involved in drug metabolism SNPs in genes involved in cancers

SNPs from different populations

SNPs in genes involved in environmental response SNPs in candidate genes and pathways that underlie inflammatory response SNPs from the complete human genome SNPs in human cytochrome P450 genes

11 million SNPs

23,910 SNPs from 167 genes 708 SNPs from 261 genes of 16 tumor types and 16 treatment types 83,000 SNPs

38,200 SNPs from 327 genes

Almost six million validated SNPs SNPs from 30 CYP 450 Genes (continued)

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Mutations and Polymorphisms: What Is The Difference?

17

Table 1.3 (continued) Database COSMIC

Host Welcome sanger institute

Site URL for SNP searches https://cancer. sanger.ac.uk/ cosmic

Importance and Usage SNPs in gene involved in cancers

Content Information on 32,000 genomes

NCBI National Center for Biotechnology Information, EMBL-EBI European Molecular Biology Laboratory-European Bioinformatics Institute, NIEHS National Institute of Environmental Health Sciences, HGVS Human Genome Variation Society, EGP Environmental Genome Project, RTPDB Radiotherapy Prognosis Database, CNHGC Chinese National Human Genome Center, UW University of Washington, NHLBI National Heart Lung and Blood Institute’s, PGA programs for genomic applications, COSMIC catalogue of somatic mutations in cancer

1.4

Conclusions

Numerous GWAS carried out across the spectrum of diseases in various populations around the world, have enabled the identification of thousands of disease related variants. It can easily be said that genetic polymorphisms in general and SNPs in particular possess a great potential in unraveling the mechanism of how genotype affects phenotype through gene–gene and gene–environment interactions.Additionally, carrying out large collaborative studies in the field of genetic polymorphisms will also help in elucidation of complex disease mechanisms, better risk profiling, diagnosis and prognosis of diseases, and in creating specific and effective personalized therapeutics.

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Single Nucleotide Polymorphisms and Pharmacogenomics Azher Arafah, Shafat Ali, Sabhiya Majid, Samia Rashid, Shabhat Rasool, Hilal Ahmad Wani, Iyman Rasool, and Muneeb U. Rehman

Abstract

Pharmaco-genomics determines the individual genetic mechanism for drug response and has the ability to transform tailored medication into clinical practice. A huge number of individuals die every year from adverse drug response since each person reacts differently to similar drug. The science of pharmaco-genomics

A. Arafah Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia S. Ali Cytogenetics and Molecular Biology Laboratory, Centre of Research for Development, University of Kashmir, Hazratbal, Srinagar, Jammu and Kashmir, India Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India S. Majid · S. Rasool · H. A. Wani Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India S. Rashid Department of Medicine, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India I. Rasool Department of ENT, Government Medical College (GMC-Baramulla), Kanth Bagh, Baramulla, Jammu and Kashmir, India M. U. Rehman (*) Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia Department of Biochemistry, Government Medical College (GMC-Srinagar) & Associated SMHS Hospital, Karan Nagar, Srinagar, Jammu and Kashmir, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_2

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has emerged as a potential discipline in the development of drugs and clinical medicine during the last several decades. It has offered a hope of protection for the patients against lethal health complications that arise from the adverse drug responses. The new medication approaches utilizing the science of pharmacogenomics reduce the patient exposure to less or non-effective drugs or drugs with adverse effects. Same drugs have been reported to induce specific reactions in each individual owing to different nucleotide sequences in genes that encode the essential biological molecules such as drug-metabolizing enzymes, drug transporters, and drug targets. Single nucleotide polymorphisms (SNPs) are very helpful in determining the susceptibility of individuals to different diseases and drug reaction. These polymorphisms are the most prevalent in the genome of humans and account for 90% of genetic variance amongst individuals. Pharmacogenomics may help in understanding the strong effects of inherited single gene variations on drug mobilization and action. The detection and characterization of SNPs related with disease risk and adverse drug response (ADR) is essential before using them as genetic tools. The credibility of SNP application in the diagnosis of diseases and possible ADR has been increased by the completion of HapMap project but still there are some challenges associated with it. The present chapter attempted to present the general role and effect of SNPs on pharmacogenomics as well as their utility in clinical practice. Keywords

Pharmaco-genomics · Single nucleotide polymorphism · Tailored medication · Diseases · Sequencing · Drug response

Abbreviations 5-FU ADR APOE CYP DNA DPYD LD NAT 2 SNP TPMT TPMT UGT1A1 VDR

5-fluorouracil Adverse drug response Apolipoprotein E Cytochrome P450 Deoxyribonucleic acid Dihydropyrimidine dehydrogenase linkage disequilibrium (LD) N-acetyl transferase 2 Single nucleotide polymorphism Thiopurine S-methyltransferase Thiopurine methyltransferase Uridine diphosphate glucuronosyltransferase 1A1 Variable drug response

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2.1

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Introduction

Single nucleotide polymorphism (SNP) occurs when a base of specific nucleotide is substituted by another base at certain location of genome. Abundant number of SNPs exist in human genome and about 5,000 pharmaco-genomics associated SNPs have been reported on Pharmaco-genomics Knowledgebase (PharmGKB). SNPs exist all through the genome and produce diverse alleles (Zaid et al. 2017). During the whole genome sequencing in humans mismatched base pairing was observed. DNA is made of four bases that include adenine, cytosine, thymine, and guanine. Among these bases adenine commonly forms bonds with thymine and that of guanine with cytosine. This mispairing of bases was observed around every 1000 base pairs and considered as SNPs when present in a minimum of 1% of individuals of entire population (Kirk et al. 2002). SNPs exist in coding as well as non-coding regions of DNA. SNPs of coding region have been categorized into synonymous and non-synonymous SNPs. Synonymous SNPs do not alter sequence of amino acids in proteins while as non-synonymous SNPs modify the sequence of amino acids in proteins. Non-synonymous SNPs are further classified as missense and non-sense SNPs (Hunt et al. 2009) (Fig. 2.1). Such genetic polymorphisms even in an individual gene including drug receptor and transporter coding genes as well as the gene coding for cell signaling pathways may be a crucial predictor of clinical reaction (Nebert et al. 2013). The series of SNPs existing together in a specific part of genome give rise to haplotype. Initially about 300,000 SNPs were estimated. However, the figure was raised to 1.4 million in 2001 (Thorisson and Stein 2003), 3.1 million in 2007 (Frazer et al. 2007), and 6.4 million currently available in the database of HapMap project. In humans SNPs represent over 90% of genetic polymorphisms

Fig. 2.1 Classification of single nucleotide polymorphisms (SNPs)

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Table 2.1 Bio-informatics databases for SNPs S No. 1.

Bio-informatics Databases dbSNP Kaviar

2.

SNPedia

3. 4.

OMIM dbSAP

5.

Human gene mutation database HapMap project GWAS central

6. 7. 8. 9.

1000 genomes project PharmGKB

Description NCBI (National Centre for biotechnology information) database for SNPs Multisource SNP-compendium Wiki-type database for annotating, interpreting, and analyzing individual genome Database that related polymorphisms with diseases Single amino-acid polymorphism-based database detecting protein variants Provides functional SNP-related or inherited human disease associated gene mutations Allows tag SNPs identification for haplotype collection Allows visual interrogation of exact summary level details in a single or multiple genome-wide association studies A catalog with detailed data about human genome variations A data resource of SNPs related with drug reaction and disease outcome

(Collins et al. 1998; McPherson et al. 2001). Various bio-informatics databases for SNPs are described in Table 2.1. A general medical problem nowadays is variable drug response (VDR) against any drug. These variabilities range from drug failure to adverse drug response (ADR). It has now become evident that such inter-individual variations are hereditary and caused by genomic variations among individuals (Wang et al. 2012). Genomic composition is a significant element in the ultimate effect of the individual’s response to the medication in question. Pharmaco-genomics is indeed a discipline that originates as a result of junction of the pharmacology with genomics. Simply speaking, pharmaco-genomics study the effect of genomic variations on the individual drug response (Weng et al. 2013). These changes may be attributed to differences in drug targets or variations in drug carrying or metabolizing enzymes, namely CYP450 (Klein and Zanger 2013). Variations in drug targets may result in differences in potency of drugs, but variations in enzymes alter either drug efficiency or toxicity. Research in the field of pharmaco-genomics has acquired tremendous significance with modern developments in genomic sequencing and molecular genetics. The novel technology facilitates fast screening of specialized polymorphisms, and also the identification of target genetic sequences that code for enzymes, ionic channels, and certain other kinds of receptors associated with drug response (Lennard et al. 2013; Nelson 2013). The Human Genome project (HGP) helped in determining the molecular structure of the enzymes that in turn make it possible to correlate variability between genotypes and phenotypes. Such developments can enable the identification of persons that may suffer harmful medication reactions. This all became possible by SNPs identification (Chang et al. 2012).

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Several SNPs are linked with different drug metabolisms (Goldstein 2001; Lee 2004; Yanase et al. 2006). SNPs can occur as deletions that either impede or facilitate enzymatic activity and result in lower levels of drug metabolism (Butler 2018). The correlation of a broad variety of human disorders such as infectious diseases (leprosy, hepatits, AIDS, etc.), autoimmune disorders, neuropsychiatry defects, and several other ailments (Table 2.2) with various SNPs may be render as important pharmaco-genomic targets for treatment (Fareed and Afzal 2013).

2.2

Techniques for the Identification of SNPs

It is necessary to identify and characterize large numbers of SNPs before extensively utilizing them as genetic tools. In order to carry out association studies optimized marker sets need to be constructed from several lakh SNPs. Five approaches are widely used for the identification of SNPs (or mutation) (Wang et al. 1998; Nees and Woodworth 2001; Khan 2004) (Fig. 2.2).

2.2.1

Single Strand Conformation Polymorphisms (SSCPs)

The DNA segment containing the presumed SNP is amplified with PCR, followed by denaturation, and run through non-denaturing polyacrylamide gel for the identification of SSCP. The single-stranded DNA fragments formed acquire secondary structures determined by their sequence of nucleotides while moving through the gel. The DNA fragments that contain SNPs are recognized due to their unusual movement behavior and verified via Sequencing. This technique though is commonly used and fairly easy but its performance rate for the identification of SNPs varies, usually varying from 70% to 95% (Wang et al. 1998). This low capacity technique needs intensive labor, however, highly competent approaches based on capillary instead of gels are evolving (Wenz et al. 1999).

2.2.1.1 Hetero-Duplex Analysis This technique detects the hetero-duplex produced when denatured strands of PCR product obtained from a person heterozygous for the SNP undergo re-annealing. The hetero-duplex is detected either by high-performance liquid chromatography column where it shows deferential retention or by band shifting on gel. HPLC developed as a well-known approach for the detection of SNPs by using hetero-duplex because of its cost effectiveness, simplicity, and high detection rate (95–100%) (O’Donovan et al. 1998). With the help of Trans-genomic Wave system, a rational output can be obtained at the rate of 10 min/ sample (Buyse et al. 2000).

UGT1A1 (UDP-glucoronosyltransferase 1A1) ADRB2 (Adrenoceptor beta 2) OPCML (Opiod binding protein/ cell adhesion molecule like) APOA1/C3/A4/A5 (Apolipoprotein gene cluster) APOB (Apolipoprotein B) gene

APOE (Apolipoprotein E) gene

CYP2D6*4 (cytochrome P450 2D6*4) ApoER2 (Apolipoprotein E receptor 2) gene PPARG (peroxisome proliferator activated receptor gamma) (pro12Ala) MEF2D (Myocyte specific enhancer factor 2D)

4.

8.

9.

10.

13.

12.

11.

7.

5. 6.

3.

2.

SNPs Matriptase
2 gene/TMPRSS6 (Transmembrane serine protease 6) ALOX5(Archidonate 5-lipooxygenase) DRD2 (dopamine receptor D2)

S. No 1.

T2DM (type 2 diabetes mellitus) Migraine

Alzheimer’s disease Dyslipidemia

Dyslipidemia

Dyslipidemia

Dyslipidemia

Asthma Schizophrenia

Colorectal cancer Breast cancer

Asthma

Associated disease Breast cancer

Table 2.2 SNPs in association with human diseases

FRET (fluorescence resonance energy) and FP (fluorescence polarization) methods GWAS (genome-wide association studies)

PCR and HRM analysis

Dewar et al. (1997), Matheson et al. (1995) Panichareon et al. (2012)

– PCR (polymerase chain reaction), HRM (high resolution melting) analysis GWAS (genome-wide association studies) GWAS (genome-wide association studies) GWAS (genome-wide association studies) –

Freilinger et al. (2012)

Altshuler et al. (2000), Sarhangi et al. (2020)

Martinelli-Boneschi et al. (2013), Lu et al. (2014) Thongket et al. (2015)

Kathiresan et al. (2009b)

Kathiresan et al. (2009b)

Kathiresan et al. (2009b)

Shatalova et al. (2006)

Pyrosequencing™

Gemignani et al. (2005)

Kalayci et al. (2006)

– APEX system

References Hartikainen et al. (2006)

Study technique TaqMan® assay

28 A. Arafah et al.

23.

22.

21.

20.

C7orf10 (chromosome 7 poen reading frame 10) gene MMP16 (matrix metalloproteinase 16) gene ALOX5(Archidonate 5-lipooxygenase) ALOX12(Archidonate12lipooxygenase)

PHACTR1(phosphate and actin regulator 1) APOE (Apolipoprotein E) gene

18.

19.

TGFBR2 (transforming growth factor beta receptor 2)

TGFBR2 (transforming growth factor beta receptor 2) PHACTR1(phosphate and actin regulator 1) ASTN2 (Astroactin 2)

17.

16.

15.

14.

Genotyping Genotyping Genotyping

Atherosclerosis Schizophrenia Obesity

Colorectal cancer

WGA (whole genome amplification) and genotyping Genotyping

GWAS (genome-wide association studies) GWAS (genome-wide association studies) Genotyping

GWAS (genome-wide association studies) –

GWAS (genome-wide association studies) GWAS (genome-wide association studies) GWAS (genome-wide association studies) GWAS (genome-wide association studies)

Breast cancer

Lung cancer

Migraine

Monogenic Marfan’s syndrome Myocardial infarction Alzheimer’s disease Migraine

Migraine

Migraine

Migraine

Kleinstein et al. (2013), Habermann et al. (2013), Kraus et al. (2013), Resler et al. (2014) Burdon et al. (2010) Kim et al. (2010) Xiao et al. (2011)

Connor et al. (2015)

Wei et al. (2020)

Rozanov et al. (2004)

Marlaire et al. (2004)

Richard et al. (1997)

Kathiresan et al. (2009a)

Mizuguchi et al. (2004)

Freilinger et al., 2012

Freilinger et al. (2012)

Freilinger et al. (2012)

2 Single Nucleotide Polymorphisms and Pharmacogenomics 29

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A. Arafah et al.

Fig. 2.2 Techniques used for the identification of single nucleotide polymorphisms

2.2.2

Direct DNA Sequencing

Presently, the direct DNA sequencing is the recommended high-performance approach for the detection of SNPs. With the completion of sequencing process under limited human interference, only one capillary device (e.g. Applied Biosystems 3700) may produce sequences from over 1500 DNA segments each of 500 bps just within 48 hours. With the help of dye-terminator sequencing 95% hetero-zygotes can be identified, however, for the detection of all sequences highly expensive, complex, and labor-intensive dye-primer chemistry is required (Gross et al. 1999).

2.2.3

Variant Detector Arrays

This approach is comparatively new high-output method used for the identification of SNPs. It enables the detection of SNPs by hybridizing PCR products to oligonucleotides placed on glass chip and the calculation of difference in the strength of hybridization among compatible and incompatible oligo-nucleotides. The rate of detection of Variant detector arrays (VDA) is equivalent to that of the dye-terminator sequencing. This technique enables a fast scanning of vast numbers of DNA sequences. With the application of this approach 2500 SNPs were identified in 2 Mb of DNA in humans by Wang et al. (1998) and 874 SNPs were identified by Halushka et al. (1999) in 75 hypertension candidate genes.

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2.2.4

31

DNA Microarray Technology

This technology is the most recent and advanced tool for SNP study (Nees and Woodworth 2001; Khan 2004). It provides an important biotechnological breakthrough for tracking genetic expression of thousands of genes just in one experiment with the application of optics, chemistry of DNA, and silicon chip technology. In short, a robot adds 20 thousand to 1 lakh unique molecules of DNA to the silicon wafer surface and the level of expression of all these genes can be observed in only one experiment via utilizing a standard microarray assay. Now microarray techniques are commonly utilized to monitor genetic expression of huge figure of genes, and often used for genotyping, analyzing DNA sequences, and molecular diagnostics. With these techniques the DNA segments that even differ only by a SNP can be differentiated which makes the microarray technology as a potent approach for the identification of new molecular targets for drugs as well as elucidation of drug action mechanisms. Moreover, the microarray techniques are capable of monitoring large-scale gene expression profile in respond to pharmacological agents, and thus furnish data related to drug efficiency and toxicity (Nees and Woodworth 2001).

2.2.4.1 SNPs and Tailored Medication The genetic variation among human individuals occurs and understood due to SNPs. The SNPs are the key role players in the concept development of tailored medication (Wang et al. 2012). In the genome of human beings about 3 billion nucleotide pairs are present, and a modification in a single nucleotide base pair existing in a minimum of 1% population is described as a SNP. These minor genetic variations are most widespread among individuals and produce huge functional differences. Different biotechnology companies during this current era of Next Generation Sequencing (NGS) provide relatively cost-effective genotyping of SNPs, and for predicting SNPs from data already sequenced, numerous computational software systems and algorithms are available and by now put into practice (Useche et al. 2001; van Oeveren and Janssen 2009). The relationship of SNPs in humans with different pharmacologic processes such as drug reaction and toxicity has been observed which provided some information available on PharmGKB (Whirl-Carrillo et al. 2012), and Human HapMap about the effect of unique SNPs complement of patient on the response to drugs. 2.2.4.2 Inter-Individual Variability in Drug Response The therapeutic response to prescribed drugs remarkably differs among individuals and the resulting effects are often patient specific (Marchant 1981). This interindividual heterogeneity is often challenging for developing a protocol for prescribed dose since majority of medications are quite effective just in about 25% to 60% of cases (Wilkinson 2005). Some patients do not adequately react and gain from initial medication. For instance, the average percentage of patients with depression (38%), asthma (40%), diabetes (43%), arthritis (50%), and cancer (75%) do not respond to preliminary treatment (Spear et al. 2001).

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Fig. 2.3 Inter-individual variations in drug response

Similar Patients

Similar Disease Severity

A

B

C

SNPs

Similar Drug Treatment

Variable Drug Response

Individual Patient Response

A

B

C

Adverse Response

Positive Response

No Response

Different individual patients may react to the same medication and dosage differently (Fig. 2.3). Often an effective dose of a drug may be fatal for a specific patient or lead to therapeutic failure in several others (very low concentration of drugs at regular doses) which leads to significant negative effects or almost no effects. In case of treatment with drugs that have known adverse side effects and narrow clinical indices, the constant drug screening is advised to prevent unpredicted and harmful consequences (Evan and McLeod 2003). The problem may worsen further due to potential drug-disease and drug–drug interaction when a patient suffers from many diseases and receives multiple drug treatments (Rettie and Tai 2006). For instance, the regular dose of Warfarin used for the treatment of thrombosis and embolism may vary 20–30 times among patients in several cases of disease (Rettie and Tai 2006). In case of Simvastatin similar dose-dependent variations among individual patients in response to this drug has been observed (Chasman et al. 2004). The optimal daily dosage of simvastatin for blood cholesterol control is prescribed at 40 mg. A study of 156 cohorts reported that the treatment of patients with

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Single Nucleotide Polymorphisms and Pharmacogenomics

33

simvastatin at the dose of 160 mg/day decreased low-density lipoprotein (LDL) level in 95% cases only, however, no LDL decline was observed in remaining 5% cases (Davidson et al. 1997). The inter-individual dosage-dependent variation has been associated with ABCG2 (ATP-binding cassette subfamily G membrane 2), and HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) (Chasman et al. 2004; Tomlinson et al. 2010).

2.3

Factors Leading to Variation in Inter-Individual Drug Response

The specific response of an individual to a drug depends on a variety of multifold and intricate factors (Fig. 2.4) that include unique genetic composition (mutations like gene deletions, gene duplications, and SNPs). Such genetic factors either alone or together with physiological factors (gender, ethnicity, age, and body size), environmental factors (smoking, toxin exposure, and diet), pathological conditions (obesity, renal and hepatic function, and diabetes) affect drug response (Akhondzadeh, 2014). Kalow et al. (1998) suggest that different genetic factors help in determining about 20–95% heterogeneity in inter-individual drug response. Moreover, genetic factorsrelated individual drug response variability. is usually irreversible, whereas the effects due to other factors are often transitory (Ma and LU 2011). Vesell (1989) reported greater population drug response variability in all individuals of the population compared to intra-patient variability

Fig. 2.4 Factors responsible for inter-individual drug response variation

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at occasions. This finding supports inheritance as the key determinant of drug response.

2.4

Inter-Individual Drug Response Determinants

Specific medical disorders were diagnosed based on symptoms that may represent multiple illnesses or linked to family background. In past years, physicians may just strive to prevent or manage illness at its commencement (Akhondzadeh et al. 2009). More specific techniques are now developed for the diagnosis of genes and genetic variants believed to relate to altered inter-individual drug reaction or disease conditions and in this area HGP completion has made significant contribution. Pharmaco-genomics helps researchers to determine unique gene variants which could be responsible for the specific drug reaction of a person via the detecting the involved specific genetic loci (Ferrara 2007). In recent years’ microarrays or biochips have been utilized in the technique of genetic studies such as whole genome SNP profiling, gene expression, multi-gene analysis, and haplotyping (Borges et al. 2015; Siest 2015), for determining the drug response of individuals at different levels that may well help in the discovery and development of drugs (Nair 2010). Gene polymorphisms can affect the drug impact by either modifying its pharmacokinetics or pharmacodynamics or both, and these are the two key factors that confer inter-individual variations in drug response. Pharmacokinetics determines the required concentration of a drug to arrive at a specific target, whereas pharmacodynamics analyzes the level of respond of different targets including ion channels, enzymes, and receptors to a variety of drugs (Pirmohamed and Park 2001; Pirmohamed 2014). The pharmacokinetic and pharmacodynamic properties of either drugs utilized or their metabolic products or both are altered at the target sites due to polymorphisms in the drug transporter and phase-I drug-metabolizing enzyme genes that result in the variability of drug response. Variations even in a single-base (SNPs) or closely linked cluster of SNPs (haplotypes) of genes participating at any level in the pathways of drug pharmacokinetics and pharmacodynamics may, in principle, influence an individual’s overall drug responses (Eichelbaum et al. 2006; Lin 2007).

2.5

Application of SNPs in Clinical Trails

The development of tailored medication is guided by SNPs. The key information obtained from gene polymorphisms may be utilized for deciding right drug doses. The inter-individual genetic variation makes drugs well effective in some individuals and ineffective in others (Roden et al. 2011). This gene polymorphism-based variability in drug reaction may be utilized in the clinical of novel drugs. In the beginning of clinical trial the evaluation of the functional association of SNPs may help in selecting the individuals physio-genetically suitable for responding to drug under trail (Laing et al. 2011). The identification of suitable volunteer individuals for

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Single Nucleotide Polymorphisms and Pharmacogenomics

35

drug trail can be performed by genetic tests which offer details about their genetic variations, metabolic conditions, drug targets, polymorphisms in metabolic enzymes, and existence of particular disorders. In this way limited number of participants chosen will provide statistically significant outcomes, lowering the total expenses and enhancing the efficacy of clinical trials. SNPs may help in the development of tailored medication for a broad range of disorders (Laing et al. 2011). Pharmaco-genomics, therefore, primarily depends on SNPs for optimizing the safety of drugs and effective treatment well appropriate for the specific genetic profile of a particular patient, i.e., tailored medication or right medication for right patient.

2.6

Utilizing SNP Maps in Pharmaco-Genomics

SNPs maps can be utilized in the field of pharmaco-genomics via two important approaches (Jeanette and McCarthy 2000) the candidate gene approach and linkage disequilibrium mapping.

2.6.1

Candidate Gene Approach

This approach utilizes paradigms of biology or previous understanding of pathogenesis of diseases to detect disease-related genes. SNPs present in such genes are evaluated in patients registered for cohort, familial, or case-control studies for clinical correlation with diseases. It is believed that such susceptibility genes specifically affect the probability of disease development in patients. This technique by now has been utilized for the identification of candidate genes that affect the drug reaction. The variants of drug-metabolizing enzyme gene such as thiopurine methyltransferase (TPMT) have been associated with adverse drug responses (Krynetski and Evans 1999). Similarly, variants of drug target gene such as 5-lipoxygenase (ALOX5) have been related with drug reaction variability (Drazen et al. 1999) and gene variants of apolipoprotein E (APOE)-a disease susceptibility gene have also been linked with cholinesterase inhibitor response in patients suffering from Alzheimer’s disease (Poirier et al. 1995).

2.6.2

Linkage Disequilibrium Mapping

This approach is a substitute for candidate gene technique and depends on disequilibrium linkage or non-random interaction between closely positioned SNPs. There are thousands of unknown SNPs in genome that need identification and mapping for their position. Even though these unidentified SNPs may indeed be present as susceptibility SNPs in genes but most of them are found between genes in the huge non-coding regions of DNA and do not play an apparent part in drug reaction. After finding the relationships via linkage disequilibrium (LD) these unknown

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biomarkers can help in identification of a genomic region containing a susceptibility gene having unknown identity and location. Subsequently, in order to understand the fundamental relationship extra substantial efforts are needed using positional cloning to identify the particular gene along with SNPs present in it. Monogenic disease causing genes have been effectively diagnosed with the application of linkage disequilibrium mapping in families having many affected individuals (Friedman et al. 1995). Although currently different association studies use LD mapping technique but it has failed to identify genetic predictors each of drug reaction or disease in unrelated persons.

2.7

Pharmaco-Genomic Effects of SNPs

The increasing number of instances are available that correlate SNPs and other gene polymorphisms with imperfect protein activity, possibility of over-toxicity, changes in therapeutic advantages. Genetic variants of cytochrome P450 enzymes and thiopurine methyl transferase present important instances for this kind of polymorphism. Currently several programs are available that predict effects of SNPs (Table 2.3).

2.7.1

Pharmaco-Genomic Effects of Cytochrome P450 SNPs

Cytochrome P450 iso-enzymes (CYP450s) constitute a super-family of enzymes consisting of haemoproteins. These enzymes are present on endoplasmic reticular membrane and catalyze the metabolic process of a broad spectrum of internal and external substances. CYP450s act as the key cause of variation in drug reaction and pharmacokinetics (Danielson 2002). So far in human beings 57 functional genes and 58 pseudo-genes have been documented as CYP super-family members (Table 2.4). However, merely some enzymes of CYP1, CYP2, and CYP3 family cause biotransformation of majority of exogenous substances including 70–80% of clinically used drugs (Nelson 2013). All CYPs exhibit variations in their expression that is influenced by numerous bio-factors and mechanisms such as gene polymorphisms, state of disease, hormones, etc. (Zanger and Schwab 2013). SNPs are the main CYP polymorphisms. SNPs of members of CYP genes (Table 2.5) introduce heterogeneity in their expression level and eventually in activity that results in severe impact on drug reaction and efficacy (Nebert et al. 2013). Gene polymorphisms are extremely ethnically based and play a key part in the functioning of certain CYP representatives including 2A6, 2B6, 2C9, 2D6, 2C19, and 3A5 as well as produce phenotypically distinct metabolizers, viz. poor, intermediate, extensive, and ultra-rapid. CYP450s play a significant role in the biotransformation of many drugs administered as pro-drugs (inactive drug form) into their active form (Huttunen et al. 2008; Ortiz de Montellano 2013). SNPs of CYP representative genes affect the biotransformation of pro-drugs. Therefore, CYP450 polymorphisms besides affecting the

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Single Nucleotide Polymorphisms and Pharmacogenomics

37

Table 2.3 Various programs for the prediction of SNP effects S No 1.

Program/tool SIFT

2.

Mutation taster

3. 4. 5.

SuSPect Predict SNP EnsemblVariant effect predictor

6. 7.

SNAP2 LIST

8.

PolyPhen2

9.

SNPViz

10.

PROVEAN

11.

PhyreRisk

12.

Missense3D

Description A software for evaluating the effect of laboratory-induced mutation (missense or non-synonymous) on the function of a protein on the basis of amino-acid sequence homology and physical properties This tool helps in evaluating DNA gene variants to find their propensity for disease. It helps in estimating the effect of variants on gene product or protein via performing a series of silico tests Predicts human gene variants Predicts human disease-related gene mutations This tool measures the impact of variants such as deletions, SNPs, insertions, structural variants or copy number variations on genes, transcripts, protein sequences, and regulatory regions Annotates functional sequence variant effects This software determines the possible deleterious effects of gene resulting from modifying their protein functions. This tool predicts the potential effect of amino-acid substitution on human protein structure and activity simply via relative and physical aspects This software gives a 3-dimensional image of affected protein, displays the modifications in amino acids that helps in determining mutant protein’s pathogenecity This program predicts the effect of amino-acid substitution and indel on protein activity and filters sequence variations for the identification of functionally vital indel or non-synonymous variants This database that maps gene variants into experimental and predicted protein structures Provide stereo-chemical information of the impact of missense variations on the structure of protein

metabolism of drugs influence the pro-drug biotransformation as well as cause variability in drug reaction against the similar drug. This potent attribute of CYP450 is employed for the delivery of targeted drugs (Huttunen et al., 2008). The information of families of CYP450 and their interactions with drugs can be obtained from the available biological databases (Preissner et al. 2010).

2.7.2

Pharmaco-genomic effects of Thiopurine Methyltransferase (TPMT) SNPs

The enzyme thiopurine S-methyltransferase metabolizes thiopurine drugs such as azathioprine, 6-thioguanine, and 6-mercaptopurine, and other heterocyclic sulfhydryl and aromatic compounds (Rutherford and Daggett 2008). These cytotoxic compounds are commonly utilized for the treatment of inflammatory bowel diseases

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Table 2.4 CYP families of humans and their role CYP family CYP1 CYP2

Alleles CYP1A1, CYP1A2, CYP1B1

CYP5 CYP7

CYP2A6, CYP2A7,CYP2A13, CYP2B6,CYP2C8, CYP2C9, CYP2C18, CYP2C19,CYP2D6, CYP2E1, CYP2F1, CYP2J2, CYP2R1, CYP2S1, CYP2U1, CYP2W1 CYP3A4, CYP3A5, CYP3A7, CYP3A43 CYP4A11, CYP4A22, CYP4B1, CYP4F2, CYP4F3, CYP4F8, CYP4F11, CYP4F12, CYP4F22, CYP4V2, CYP4X1, CYP4Z1 CYP5A1 CYP7A1, CYP7B1

CYP8

CYP8A1, CYP8B1

CYP11 CYP17

CYP11A1, CYP11B1, CYP11B2 CYP17A1

CYP19 CYP20 CYP21 CYP24 CYP26 CYP27

CYP19A1 CYP20A1 CYP21A2 CYP24A1 CYP26A1, CYP26B1, CYP26C1 CYP27A1, CYP27B1, CYP27C1

CYP39

CYP39A1

CYP46 CYP51

CYP46A1 CYP51A1

CYP3 CYP4

Role Metabolism of drugs and steroids particularly estrogen Metabolism of drugs and steroids

Metabolism of drugs and steroids including hormone testosterone Metabolism of fatty acids or archidonic acid

Synthesis of thromboxane A2 Biosynthesis of bile acid via 7α-hydroxylation of cholesterol Prostacyclin synthesis (CYP8A1), biosynthesis of bile acid (CYP8B1) Biosynthesis of steroids 17α- hydroxylation, biosynthesis of steroids Biosynthesis of steroids Function not known Biosynthesis of steroids Degradation of vitamin D Hydroxylation of retinoic acid Biosynthesis of bile acid (CYP27A1), activation of vitamin D3 by 1α-hydroxylation of vitamin D3 (CYP27B1) 7α-hudroxylation of 24-hydroxycholestrol Hydroxylation of cholesterol Biosynthesis of cholesterol

and leukemia. With the discovery of TPMT gene polymorphisms (Table 2.6) and the effects they exert on drug reaction, it has become an important topic of research globally among medical researchers and biologists. Among the presently known 36 TPMT gene (http://www.imh.liu.se/) resulting in intermediate to no activity of TPMT, the majority of which possess non-synonymous type of SNPs (Salavaggione et al. 2005). Individuals carrying two homozygous non-functional variants of TPMT have a higher risk of adverse side effects with thiopurine medication during their care. Non-functional alleles are fairly uncommon and differ among ethnic groups in their existence and frequency such as TPMT*3A allele is the most widespread in

CYP3A4

Exon 4 Exon 5 Exon7 Intron6 50 -promoter

Exon 7

Exon 12 Exon 9

Exon 11

Exon 6 Exon 7, intron 10 –

Exon 10

CYP1A2*3 CYP1A2*4 CYP1A2*6 CYP1A2*7 CYP3A4*1B

CYP3A4*2

CYP3A4*3 CYP3A4*6

CYP3A4*11

CYP3A4*15A CYP3A4*16B

CYP3A4*21

CYP3A4*20

Intron1

CYP1A2*1 K

A956G (Y319C)

25889_25890insA (488Framshift)

G14269A (R162Q) C15603G (T185S)

T23171C 17661_176622insA (227Framshift) C21867T (T363M)

T15713C


164A,
740G,
730 T T5347T A2499T G5090T G3533A A392G

Han-Chinese (0.5%)

German-Caucasian ( G 460G > A

Position Exon 5 Exon 7, exon 10 Exon 7

TMPT*4

IVS91G > A

TMPT*5 TMPT*6 TMPT*7 TMPT*8

146 T > C 539A > T 681 T > G 644G > A

Intron 9/exon 10 Exon 4 Exon 8 Exon 10 Exon 10

Activity Decreased Decreased

References Do and Dobrovic (2015) Thatcher (2015)

Decreased

Marrugo-Ramirez et al. (2018), Hofman (2019) Dagogo-Jack and Shaw (2018)

Decreased Decreased Decreased Decreased Intermediate

Dundas et al. (2008) Dundas et al. (2008) Riemann et al. (2007) Chomczynski and Sacchi (2006)

Caucasian population with nearly 5% frequency. Similarly, TPMT*3C allele most commonly exists in the populations of East-Asian and African-American populations. Likewise, TPMT*8 allele is more commonly present in the population Africa (http://www.pharmgkb.org). TPMT served as a model for numerous genetic polymorphism-based clinical trials since the advent of perception of pharmacogenomics and today it presents an excellent instance of successful translation of pharmaco-genomics into regular medical practice (Appell et al. 2013).

2.7.3

Pharmaco-Genomic Effects of N-Acetyl Transferase 2 (NAT2) SNPs

The gene NAT2 is a key enzyme that catalyzes N-acetylation of sulfasalazine. It is positioned on short arm of chromosome 8 (8p22). NAT2 gene polymorphisms influence the acetylation rate as a result of which the efficiency and toxicity of the drug sulfasalazine is modified (Ladero 2008). This drug is normally employed for treating rheumatic arthritis and it works by inhibiting the function of neutrophils, decreasing the levels of immunoglobulin as well as interferes with the functions of T lymphocytes via suppressing necrosis factor -jB (Gadangi et al. 1996). The patients that suffer from rheumatic arthritis and possess the variants of NAT2 gene including NAT2*5A, NAT2*5B, NAT2*5C, NAT2*6, and NAT2*7 are more susceptible to adverse drug reaction compared to wild type haplotype (NAT2*4) (Taniguchi et al. 2007; Ladero 2008). With the help of genomic data available related to NAT2 gene the severity of rheumatic arthritis patients will be classified as highly severe (homozygous NAT2*4), moderately severe (heterozygous NAT2*4 along with its allele variants), and rarely severe (homozygous allele variant) (Kumagai et al. 2004).

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2.7.4

43

Pharmaco-Genomic Effects of Uridine Diphosphate Glucuronosyltransferase1A1 (UGT1A1) SNPs

Polymorphisms of the UGT1A1 gene promoter region profoundly influence the pharmaco-kinetic and pharmaco-dynamic properties of drug irinotecan benefits and risks (Carlini et al. 2005; Toffoli et al. 2006; Hoskins et al. 2008; Liu et al. 2014). The drug irinotecan together with fluorouracil or leucovorin is the most common first-line chemotherapeutic agent used for colorectal cancer treatment (Meyer and Cohen 2011; Hill and Sharma 2011). Inside the body irinotecan reacts with carboxyl-esterase which metabolizes the drug to produce an active, toxic, and more potent metabolite 7-ethyl-10-hydroxycamptothecin (SN38) that needs to be removed to avoid the increased frequency of adverse effects such as diarrhea and neutropenia induced by irinotecan. The UGT1A1 gene-encoded uridine diphosphate glucuronosyltransferase1A1 enzyme mainly mediates SN38 elimination via inactivating and converting this toxic metabolite (SN 38) of irinotecan into SN38 glucuronide (SN38G) (Kawato et al. 1991). Individuals that possess the allele UGT1A1*28 with seven TA repeats in TATA box of UGT1A1 promoter exhibit significant reduction in UGT1A1 expression leading to reduced glucuronidation of SN38 and increased susceptibility to neutropenia as compared to wild-type allele UGT1A1*1 with six TA repeats (Liu et al. 2014). Patients with homozygous alleles of UGT1A1*28 gene react less effectively compared to those with one or two wild-type alleles (Iyer et al. 2002). UGT1A1*28 genotype moderately predicts the acute hematological toxicity induced by irinotecan at intermediary dosages and potently prognostic at higher dosages but such patients exhibit similar toxicity incidence when compared with other patients (Hoskins et al. 2007). The UGT1A1*28 polymorphism is not alone leading to neutropenia induced by irinotecan but other polymorphisms including UGT1A1, UGT1A7, and UGT1A9 as well as haplotype assessment is essential for predicting toxicity induced by irinotecan rather than depending on single UGT1A1 polymorphism. With SNPs the patients at higher risk of acute hematological toxicity can be better predicted.

2.7.5

Pharmaco-Genomic Effects of Dihydropyrimidine Dehydrogenase (DPYD) SNPs

Patients exhibiting diminished activity of DPYD accumulate 5-fluorouracil (5-FU) and as a result suffer from lethal toxicities such as mucositis, neural complications, neutropenia, and even death (Diasio 2001; Lyss et al. 1993). The drug 5-FU is usually used together with many chemotherapeutic agents for the management of solid tumors like colorectal cancer (Schwab et al. 2008). This pro-drug undergoes compound metabolic reactions. Nearly 5 percent of inoculated 5-FU after anabolism is transformed into cytotoxic nucleotides that inhibit TYMS gene-encoded thymidylate synthase or gets incorporated in genetic material (DNA and RNA) responsible for its antitumor action, whileas most of the given 5-FU gets catabolized

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into biologically inactive metabolite, 5,6-dihydro-5-fluorouracil via the action of DPYD, a rate limiting enzyme (Heggie et al. 1987). Diminished activity of DPYD results from DPYD gene polymorphism (Schwab et al. 2008). The most prevalent polymorphism in DPYD is DPYD*2A which is related to reduced activity of DPYD. Most of the patients with DPYD*2A polymorphism experienced acute toxicity due to 5-FU (Van Kuilenburg et al. 2002).

2.8

Cancer Pharmaco-Genetics and Treatment

Cancer pharmaco-genetics nowadays receives more public interest owing to potential of individualized treatment for cancer, mitigating toxicity whereas optimizing effectiveness. Cancer pharmaco-genetics enables researchers to identify patients with severe risk of toxicity or others expected to gain from a personalized medication, which thereby assists researchers’ progress towards the eventual objective of individualized therapy for cancer management. Cancer involves patient’s germline genome as well as tumor somatic genome. On the other hand, cancer pharmacogenomics faces difficulty of performing clinical trials, easy accessibility to healthy participants for drug trials, and multi-genic drug reaction monitoring. For certain types of chemotherapy drugs, it has become a fact that the outcome of cancer management on the basis of genetic polymorphisms is predicted before treatment, for instance, thiopurine S-methyltransferase (TPMT) gene variants in response to thiopurines, UGT1A1 variants to irinotecan, DPD (dihydropyrimidine dehydrogenase), and GSTs (Glutathione-S-transferases) to 5-Fluorouracil. TPMT gene variants can alter the outcome of treatment in patients that receive chemotherapy drugs. Of certain gene variants, TPMT*2 (238G > C), TPMT*3A (460 > GA and 719A > G), TPMT*3B (460G > A), and TPMT*3C (719A > G) explain around 95% of cases of with low or intermediate enzymatic activity (Otterness et al. 1997; McLeod et al. 2000). Some reports have demonstrated that patients with TPMT deficiency have extremely higher risk of acute hematopoietic toxicity when administered with usual thiopurine doses (Lennard and Van Loon 1989; Evans et al. 1991). Studies have also shown that TPMT heterozygous patients are intermediately susceptible to toxicity associated with dosage-limit (Black et al. 1998; Relling et al. 1999). Inadequate drug response has been reported in patients with cancer in head and neck that were treated with cisplatin and 5-FU, which could be attributed to elevated intracellular cisplatin concentration, resulted from intermediate or low enzymatic activity of TPMT (Dhawan et al. 2017). Similarly, UGT1A1*28 variant is correlated with decreased expression of UGT1A1 and results in decreased glucuronidation of SN-38 (Iyer et al. 2002). Studies also reported that UGT1A1*28 variants result in dramatically elevated active metabolite SN-38, thereby raising the risk of side effects like leukopenia and diarrhea during treatment with irinotecan (Ando et al. 2000; Iyer et al. 2002). In a study by Font et al. (2003) about 34% patients with non-small cell lung cancer having normal genotype show disease regulation compared to 54% of patients with variant genotypes.

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In DPD gene several polymorphisms linked with decreased activity of DPD has been reported (Wei et al. 1996; McLeod et al. 1998). The DPD*2A gene variant originates due to G > A transition occurring at GT splice donor site flanking DPD gene exon 14 (IVS14 + 1G > A). A correlation has been observed between the reduced activity of DPD and acute or lethal toxicity from normal 5-FU doses (Van Kuilenburg et al. 2002). A study on carcinoma patients (neck and head carcinoma) with IVS14 + 1G > A gene variant demonstrated low response to 5-FU treatment (Dhawan et al. 2017). DPYD*9A variant has been related with 5-FU therapeutic effect in patients with acute lymphoblastic leukemia (Zhao et al. 2016). GSTs have many gene variants that may contribute to poor (e.g. GSTP1 variant) or total absence (e.g. GSTM1 and GSTT1 variants) of enzymatic function. A study has found association between CYP450 and GST polymorphisms and head and neck carcinomas (Ruwali and Parmar 2010). GSTP1 SNP was found linked with the overall survival of 107 metastatic colorectal cancer patients that were given a combined chemotherapy of 5-FU and oxaliplatin (Stoehlmacher et al. 2002). A non-synonymous SNP of XPD gene (Xeroderma pigmentosum group D) at the codon 751 with lysine replaced by glutamine has been substantially correlated with clinical outcome in metastatic colorectal cancer patients given the treatment of platinum agents (Park et al. 2001).

2.9

Limitations of Using SNPs as a Pharmaco-Genomic Analytical Tool

Pharmaco-genomics needs a broad array of medical and genetic data for correlating them. Pharmacogenomic studies until now are largely relying on limited data obtained from clinical trials on small group of volunteer patients making the linkage disequilibrium estimation usually impossible and inaccurate. Moreover, the clinical trials of novel therapeutic drugs are often performed on only some ethnic populations such as Americans, Europeans or Caucasians but in fact, the drugs are marketed globally and used by almost all racial groups around the globe. Thus, majority of pharmaco-genomic studies are typically restricted to such genetically heterogeneous populations wherein clinical trials are performed (McGraw and Waller 2012), and which cannot represent the heterogeneity globally. Even though the cost for sequencing has been reduced with the advent of Next Generation Sequencing technology but for a common individual it is still quite expensive to get his genome sequenced for SNPs as well as correlating clinical data and SNPs and subsequent analysis also acts as a limiting factor (Gattepaille et al. 2013).

2.10

Conclusion

Genetic polymorphisms even in an individual gene including drug receptor and transporter coding genes as well as the gene coding for cell signaling pathways may be a crucial predictor of drug reaction. SNPs have been seen linked with different

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drug metabolism and their identification will help in identifying persons that may suffer harmful medication reactions. Pharmaco-genomics is well capable of utilizing these individual genetic variants for developing tailored medicines and their transformation into clinical practices as a result reducing the patient exposure to less or non-effective drugs or drugs with adverse effects. This field of science has evolved as a hope of treatment for patients suffering from untreatable diseases as well as protection for patients producing lethal health complication as a result of adverse drug responses. However, further extensive large sample sized clinical trial studies are necessitated for validating the transformation of pharmaco-genomics into clinical practice.

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Impact of MicroRNA Polymorphisms on Breast Cancer Susceptibility Nusrath Yasmeen, Vikram Kumar, and Krutika Darbar Shaikh

Abstract

MicroRNAs (miRNAs) are considered as the micromanagers of gene expression. They are said to be involved in several physiological and pathological processes. They are capable of altering the hallmarks of cancer and hence are considered as prominent cancer biomarkers. More recently, the microRNAs related polymorphism, i.e. single nucleotide polymorphisms (SNPs) or (poly-miRs’) are reported and are exhaustively being studied. SNPs or (poly-miRs’) might occur in the miRNA biogenesis pathway, in miRNAs themselves or in their target binding sites, the consequences of such polymorphism, and its impact on life still remain dubious. miRNAs have known to be implicated in several diseases including cancer, revealed from a pool of studies performed in past years. Moreover, several

N. Yasmeen (*) Amity Institute of Biotechnology, Amity University, Jaipur, Rajasthan, India Faculty of Pharmacology, College of Nursing, King Saud Bin Abdul Aziz University for Health Sciences, King Abdullah International Medical Research Centre (KAIMRC), National Guard Health Affairs, Jeddah, Saudi Arabia King Abdullah International Medical Research Centre (KAIMRC), National Guards Health Affairs, Jeddah, Saudi Arabia e-mail: [email protected] V. Kumar Faculty of Biotechnology, Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur, India K. D. Shaikh King Abdullah International Medical Research Centre (KAIMRC), National Guards Health Affairs, Jeddah, Saudi Arabia Faculty of Anatomy, University College of Pre-Professional Studies, King Saud Bin Abdul Aziz University for Health Sciences, Jeddah, Saudi Arabia # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_3

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studies have reported a strong association between SNPs to susceptibility as well as the prognosis towards cancer. Polymorphisms in miRNAs are even found to be associated with breast cancer and evidential support suggests that they substantially increase breast cancer susceptibility. In this chapter, we will summarize and discuss the most recent shreds of evidence explaining the role of miRNAs and the poly-miRs’ in cancer pathogenesis, with particular emphasis on breast cancer susceptibility and prognosis. Keywords

Breast cancer susceptibility · miRNA · miRNA polymorphism · miRSNPs

Abbreviations 30 -UTR ADRB2 Ago BRCA1 BRCA2 C. elegans CI CLDN12 CLL CSCs DFS DGCR8 DHFR EIF2C1/2 EMT Expo/Xpo GEMIN 3 GEMIN 4 GWASs HCV HIF1AN KRAS-V LNA miRNA miRNPs MMP OR OS Pol II Pol III

30 -untranslated regions Gene coding for beta-2 adrenergic receptor (β2 adrenoreceptor) Argonaute Breast cancer type 1 susceptibility protein Breast cancer type 2 susceptibility protein Caenorhabditis elegans Confidence interval Gene coding for Claudin-12 Chronic lymphocytic leukemia Cancer stem cells Disease-free survival DiGeorge syndromecriticalregiongene8 Dihydrofolate reductase Eukaryotic translation initiation factors 2C 1 and 2 Epithelial-mesenchymal transition Exportin Gem-associated protein 3 Gem-associated protein 4 Genome-wide association studies Hepatitis C virus Gene coding for Hypoxia-inducible factor 1-alpha inhibitor V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog Locked nucleic acid microRNA miRNA-containing ribonucleoprotein particles Metalloproteinase Odds ratio Overall survival Polymerase II Polymerase III

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Impact of MicroRNA Polymorphisms on Breast Cancer Susceptibility

Pre-miRNA Pri-miRNA RA RAN RISC RPS6KB1 SLE SNPs TARBP 2 TNRC6A ZNF839

3.1

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Precursor microRNA Primary miRNA transcript Rheumatoid arthritis RAS-related nuclear protein RNA-induced silencing complex Ribosomal protein S6 kinase B1 Systemic lupus erythematosus Single nucleotide polymorphism Transactivation-responsive RNA-binding protein2 Trinucleotide repeat-containing gene 6A Zinc finger protein 839

Introduction

Cancer is an umbrella term used for an array of malignant neoplastic diseases. It is characterized by several hallmarks, e.g. invasion, angiogenesis, and metastasis. Cancer develops due to modulation in genetic and epigenetic patterns that are responsible for altering the phenotype of the cells in which they occur (Ahmad and Shah 2020). Among different cancer types, breast cancer is the second most prevalent female cancer type and is the predominant carcinoma leading to increased mortality rates among women worldwide. According to the recent statistics, 2 million new breast cancer cases were diagnosed in 2018 (23% of all cancers) and nearly 6.6% of cancer deaths were reported globally ranks it to be second among women fatalities (Zaidi and Dib 2018). Several epidemiologic studies revealed that breast cancer etiology is the most complicated among various cancer types. It varies according to time of exposure to hormone replacement therapies (HRTs), the menopausal status of women, age and ethnicity of the patient, genetic predisposition, and environmental factors. Moreover, differences in the risk and prognosis of the disease are associated with the given multiple endogenous and exogenous factors (Shah et al. 2014). MicroRNAs (miRNAs) are endogenously synthesized, single-stranded, small non-coding RNA molecules that are 19–25 nt long (Ragusa et al. 2015). MiRNAs are evolutionarily conserved non-coding proteins that bind to complementary sequences in the 30 untranslated region (30 UTR) of target mRNAs and prevent their translational inhibition or degradation (Liu and Xu 2011).

3.1.1

Brief Overview of History and Biogenesis of MicroRNA(miRNA)

In the year 1993, Ambros and Rukvun et al. discovered a small endogenous RNA lin-4 that is used to regulate developmental timing in nematode Caenorhabditis

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Fig. 3.1 Schematic illustration of microRNA biogenesis

elegans. The regulatory effect of lin-4 was due to the downregulation of the lin-14 transcription factor, this downregulation is essential during the transformation of the larval stage from L1 to L3 (Bhaskaran and Mohan 2014). Later in 2000, two separate groups discovered that let-7, a small RNA, was crucial for the transformation of a larval stage to adult by targeting lin-41 in C. elegans (O’Carroll and Schaefer 2013). The biogenesis of miRNA is a sequential multi-step process as shown in Fig. 3.1. miRNA biogenesis begins with the generation of long primary miRNAs (500–3000 nucleotides long) initially transcribed by RNA polymerase II. Primary miRNA has a 50 guanosine cap and a 30 polyadenylated tail. These can be either coding or non-coding regions (present in the core of the intron of a coding gene). The primary miRNAs are then cleaved by Drosha/DGCR8, i.e., a multiprocessor complex to form precursor miRNAs (pre-miRNAs), that are 60–70 nucleotides long. The Drosha is a nuclear RNase III enzyme and DGCR8 is DiGeorge syndrome critical region gene 8 (DGCR8) or Pasha constituting components of microprocessor complex. The hairpin-like structured precursor miRNAs(pre-miRNAs) that are generated are exported from the nucleus to the cytoplasm, following Drosha processing by Exportin-5 (EXP 5/XOP5)/Ran-GTP complex. In the cytoplasm, the pre-miRNAs are finally processed into mature ~18–23-nucleotide-short double-stranded miRNA duplexes. Dicer, TRBP, or PACT, and Argonaute (Ago1–4) proteins modulate the

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processing of pre-miRNA along with the assembly of the RNA-induced silencing complex (RISC) (He et al. 2016). The mature miRNA is subsequently processed by Dicer, an RNase III endonuclease, into short double-stranded miRNA duplexes. Two strands of mature RNA are separated. One strand of the duplex is termed the guide strand (unstable base pairing at the 50 end) along with RNA-binding proteins such as trinucleotide repeatcontaining gene 6A (TNRC6A), links with catalytic Argonaute (AGO) proteins, forming a micro-ribonuclear protein complex (miRNP) called RISC. RISC includes GEMIN3 and GEMIN4 that have a significant role in miRNA processing and target gene silencing. The function of the guide strand is as a mature miRNA, and the other strand, known as the passenger strand. Ago2 is a very crucial protein as it has slicer activity. It transits between nucleus and cytoplasm via GW182 family of proteins. The Ago2 proteins mediate complexation between miRNA and its target mRNA at 30 untranslated region in a sequence specific fashion. This complexation further induces target repression through poly(A)tail shortening of mRNA, also translational repression and mRNA destabilization. However, scientific data reveals that miRNA biogenesis can be microprocessor independent through the mirtron pathway. Short introns with hairpin potential, named “Mirtrons,” can be spliced and debranched into pre-miRNAs by bypassing Drosha cleavage (Lin and Gregory 2015; Wu et al. 2018). The biogenesis of miRNA is illustrated herein Fig. 3.1.

3.1.2

Functional Abilities of MicroRNA(miRNA)

miRNAs are non-coding RNA molecules, and their discovery has led to an improvised understanding of gene expression and regulation. The biological functions of miRNAs in different cellular processes and their abnormal expression levels in various diseases can be unraveled by highly sophisticated experimental models such as transgenic overexpression studies and animal knockout models (Hammond, 2015). The mechanism of action of miRNAs remains elusive and several theories suggesting their modus operandi have been proposed. The most widely used theory reports that miRNAs cause mRNA degradation, translational repression by base pairing with mRNA. The translational repression might be due to decreased translational efficiency or due to aberrant mRNA levels. Subsequently, insufficient complementarity leads to mRNA degradation and translational inhibition (Bhaskaran and Mohan 2014). In normal physiological processes, miRNAs are crucially involved in feedback circuits constituting buffering effects that confer robustness to miRNA functions. Several recent experimental studies proved that miRNAs are involved in crucial biological processes such as apoptosis, cellular proliferation, and differentiation, along with developmental events like early embryonic stem cell development, survival, and differentiation, organ development, cellular signaling, and communication, cell fate determination (Rajman and Schratt 2017). Not only limited to these actions, but miRNAs are also found to play a role in hematopoietic lineage differentiation, host-viral interactions, and tumorigenesis. Furthermore, miRNAs are

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involved in mature neuronal survival, function, and maintenance. For example, evidence from several studies confirmed that miR-133b/Pitx3 is responsible for the maturation and regulation of the midbrain function. Also, it is proved that miR-134 regulates the growth and synaptic plasticity of the nervous system (Huang et al. 2011). Cellular proliferation in glioma cells was inhibited by overexpression of miR-128 (Zhang et al. 2009). miR-223 was discovered to be responsible for finetuning inflammatory response and of granulocyte production. Suggesting the loss or deregulation of miRNAs and their genetic ablation could derail the immune system in mammals leading to pathogenic immune disorders and also cancer (Yuan et al. 2018).

3.1.3

miRNA in Human Cancer

Cancer is a multitudinous disease arising due to derailed genome function. The sequential carcinogenic events leading to mortality are Initiation, promotion, malignant conversion, progression, and metastasis. Understanding the complexity and diversity of the functional roles of miRNAs towards development of disease have paved a way for further investigational studies underpinning the role of individual miRNAs in cancer. miRNAs are now being considered as prominent diagnostic and therapeutic tools. Insights into the aberrant miRNAs expression levels in cancer cells indicate that regulation of miRNAs could be associated with genetic mutations, like deletion or insertion. Recent years have witnessed an upsurge in the investigational research finding the association between miRNA and cancer, despite the underlying complications due to the genetic diversity of cancer cell lines and tumor types and also the fact that many miRNAs might be deregulated in the same tumor. Moreover, the functional role of individual miRNA in oncogenesis might be context-dependent as several transcripts regulate gene expression by miRNA. Different oncogenic stages mentioned above exhibit different gene expression profiles. Accordingly, miRNAs are regarded as the regulatory molecules with dual functions in cancer, they can act either as oncogenes termed “oncomirs” or tumor suppressors (Jansson and Lund 2012). Oncomirs negatively regulate genes, responsible for cell differentiation and apoptosis, leading to tumor development. For instance, in breast carcinoma, miR-29a is oncogenic whereas it is a tumor suppressor in lung cancer. MiRNAs involved in regulating apoptosis are termed as “apoptomiRs” which can be either pro- or antiapoptotic. miRNAs also might modulate cellular proliferation, metastasis. It is also studied that miRNAs are involved in stem cell differentiation; influence cancer stem cells (CSCs) formation and prominent epithelial-mesenchymal transition (EMT) invasion which is crucial for the development of drug resistance. Cancer onset and its progression are linked to aberrant expression of miRNA levels that are associated with the modulation of cell signaling pathways. MicroRNAs are regarded as powerful gene regulators, as single miRNA can target up to several hundred mRNAs (Acunzo et al. 2015). The miRNAs involved in breast oncogenesis are listed in (Table 3.1).

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Table 3.1 Examples of aberrantly expressed microRNA in breast cancer Type of miRNAs Tumor suppressor

OncomiRs

3.1.4

Family of miRNA Let-7d

Expression of miRNA in breast cancer Downregulated

miR-31 miR-200c miR-10b miR-21 miR-221/222

Downregulated Downregulated Upregulated Upregulated Upregulated

References Thammaiah and Jayaram (2016) Tang et al. (2012) Tang et al. (2012) Piasecka et al. (2018) Han et al. (2012) Piasecka et al. (2018)

miRNA and Other Human Diseases

Substantial evidence from the previous studies performed in the past few years revealed that miRNAs are aberrantly expressed in several pathological conditions such as obesity, insulin resistance, type 2 diabetes, fatty liver disease, and coronary artery disease. Recent investigations have found an association between heart function and microRNA expression levels. Evidence suggests that miR-1, miR-133, and miR-208 are prominent regulators of myocyte differentiation and heart development. Data suggest that miR-1 is implicated in the modulation of Ca2+ handling proteins and is linked to ventricular arrhythmias. Subsequently, it is reported that certain microRNAs like miR-24, miR-195, miR-199a are upregulated in cardiac hypertrophy and end-stage heart failure (Romaine et al. 2015). miR-142 s, miR-181, and miR-223, and predominantly expressed in hematopoietic cells regulating hematopoietic lineage differentiation. MiRNAs also contribute to the pathogenesis of autoimmune disorders such as Systemic lupus erythematosus (SLE), Rheumatoid arthritis (RA), and Sjogren’s syndrome. It is observed that miRNA-126 are downregulated which causes decreased production of IFN-α hence implicated in the pathogenesis of SLE. Upregulated expression of mir-146a and downregulated expression of miR363, miR-498 was observed in CD4+ T cells of RA patients (Chen et al. 2016a). miR-192 and miR-377 downregulation leads to extracellular matrix accumulation, podocyte dysfunction, albuminuria, and EMT in diabetic nephropathy (Chandrasekaran et al. 2012). miR-29a/b, miR-107, and miR-15a are downregulated suggesting a possible association between miRNAs and Alzheimer’s disease. Evidence from a recent study suggests the possible role of miR-30d towards insulin gene transcription and insulin secretion in diabetic patients (Maqbool and Hussain 2014). Several such associations between miRNA levels and various diseases are unfolding regularly from experimental studies.

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Polymorphism in miRNA and Cancer

MicroRNAs (miRNAs) act as microregulators capable of translation of several genes and gene expression, unlike any other gene their coding sequences are prone to genetic variations. Several kinds of genetic variations exist of which single nucleotide polymorphisms (SNPs) have evolved as the most prominent resource in exploring and understanding the mechanism of carcinogenesis. Single nucleotide polymorphism (SNP) is a common variation in the DNA sequence that occurs when nucleotides (A, T, C or G) change in nearly 1% of a certain population. As evidenced from a recent study in humans it is reported that >95% of single nucleotide polymorphisms (SNPs) were linked to non-coding sequences as identified by genome wide association studies (GWASs) (Maurano et al. 2012). miRNAs are acknowledged for their contribution to the control of numerous metabolic pathways, such as cellular growth and differentiation. They are also considered crucial for their roles in cell proliferation, cell differentiation, apoptosis, and metabolism (Chen et al. 2016b) as depicted in Fig. 3.2. Moreover, after the evidence of the existence of miRNA-related polymorphisms, they were regarded as a gold mine for numerous studies especially in the field of molecular epidemiology. Consequently, scientists began to assess the association of genetic polymorphisms with cancer risk. However, the imperative role on point mutations in miRNA genes or miRNAs-SNPs in cancer development and progression was confirmed by Croce’s group who reported for the first time the downregulation due to frequent deletions of miR-15 and miR-16-1 in human B cell chronic lymphocytic leukemia (CLL) (Ryan et al. 2010). Based on their potential role MiRNAs are regarded as double-edged swords, as they can function both as oncogenes (oncomirs) or as tumor suppressors by virtue of different molecular mechanisms (Joseph and Nair 2013). Oncogenic miRNAs, commonly termed oncomirs, are upregulated in cancerous cells and their target being tumor suppressor genes. Increased expression of oncomirs may lead to inhibition of the expression of tumor suppressor genes. Some examples of miRNAs upregulated in breast cancer are miR-10b, miR-18a, miR-21, miR-27a, and miR-206. In contrast, tumor suppressor miRNAs (such as miR-205, miR-27b, miR-17-5p) are downregulated in cancer cells than in normal cells and act on oncogenes increasing the oncogenic effects that promote tumorigenesis (Veeck and Esteller 2010). Currently, in the field of genetics, phenotypic variations for traits are considered to be of medical importance. The degree of phenotypic variation and miRNA expression differs between different cell types, cell task complexity, cell engagements, and the cell’s exposure to a variety of environmental signals hence can exert an array of functional effects based on the cellular context (Du et al. 2014). The last decade had witnessed tremendous progress in the field of miRNAs and cancer, particularly related to miRNA expression patterns, which are emerging as favorable diagnostic tools and predictive markers of cancer progression and prognosis of patients (Bensen et al. 2018). The discovery of germline variants or polymorphism in miRNA that are associated with cancer susceptibility is currently a matter of intense research. The term miRSNP/miRNA polymorphism was coined and

Impact of MicroRNA Polymorphisms on Breast Cancer Susceptibility

Fig. 3.2 Illustration depicting the various causes affecting the miR-polymorphism and the effects exhibited due to these alterations which might impact disease susceptibility and drug response

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defined as a novel class of SNPs/polymorphisms seen in miRNA primary sequences, miRNA target sites, or miRNA biogenesis genes that interfere with the function of a microRNA. Recent evidence suggests that expression of at least 20–30% of human protein-coding genes is modulated by miRNAs. miR-polymorphisms in the human genome might responsible for various genetic alterations such as chromosomal translocations, insertions, and deletions, etc., leading to depletion or gain of a microRNA site/function. The heterozygous and homozygous form of miRSNPs/ polymorphisms could be present in a population (Mishra et al. 2008). This alteration is made possible by modifying the complementarity between the miRNA and its target gene having a profound effect on cancer risk, treatment efficacy, and patient prognosis (Li et al. 2016). Single nucleotide polymorphisms in miRNA (miRSNPs) in the miRNA sequences are a class of functional polymorphisms in the human genome which are considered to the most prominent. Evidence from literature elucidates that the predominant role of miRNAs is to regulate protein translation by base pairing with complementary sequences within the 30 untranslated regions (UTR) of target mRNAs. The role of miRNAs may be modified through variations in their individual sequence (miRSNPs) or in their target sequences (called “miR-TS-SNPs”). Consequently, altering a single base in a miRNA sequence may affect multiple genes and their binding specificities, profoundly influencing one or more of the sequential steps in miRNA biogenesis. All these changes/polymorphisms are attributed to the mode of action of miRNA that is highly sequence dependent. Moreover, they could potentially alter the efficiency of miRNA binding to the target sites and also the expression of the miRNA expression of the corresponding gene and its target along with gene transcription (Palmero et al. 2011). Not only that, miRSNPs in regulatory regions of coding genes or at quantitative trait loci or in the miRNA-binding sites of mRNAs can affect the biogenesis and function of miRNAs. Subsequently, they can also alter the processing of pri- or pre-miRNA, and influence miRNA–mRNA interactions.

3.2.1

SNPs in miRNA Biogenesis Genes and Breast Cancer

Aberrant expression levels of mature miRNAs’ and their association with the development of cancer is now being investigated. The critical functions of DROSHA, DGCR8, Exportin-5, RAS-related nuclear protein (RAN), and DICER in miRNA biogenesis, makes it is very imperative to assume that genetic polymorphisms in these genes might influence the processing of miRNAs and, hence, cancer susceptibility. Undoubtedly, several studies have demonstrated that polymorphism in miRNA target sites, and genes involved in various steps of the miRNA processing machinery can affect cell phenotype and disease susceptibility. DROSHA DROSHA with its essential cofactor DGCR8 forms the microprocessor complex. This microprocessor protein complex is very crucial to execute the first step in microRNA processing. Dysregulation of DROSHA and DGCR8 also plays a

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role in cancer susceptibility. In a case-control study done by Jiang et al. it was reported that rs417309 in DGCR8 was consistently associated with breast cancer risk in Chinese women with an odds ratio (OR) of 1.50 (95% confidence interval (CI) ¼ 1.16–1.93) (Jiang et al. 2013a). Another report also shows Non-coding RNA associated rs2291109 (A > T) in DROSHA with increased breast cancer susceptibility [Odds ratio (OR) ¼ 0.81; 95% Confidence Interval (CI) 0.66–0.99] in Chinese patients. Exportin 5 Exportin 5(XPO5) is a nucleo-cytoplasmic transporter protein classified as a member of the importin-β family of proteins. XPO5 is responsible for nuclear pre-miRNA transport and stabilization in a RAN-GTP dependent manner recognizing it to be a rate-limiting step in miRNA biogenesis. Leaderer et al. in their study demonstrated that two missense SNPs in XPO5, rs34324334 (S241N) and rs11544382 (M1115T), have significant associations with breast cancer risk (OR ¼ 1.59, 95% CI: 1.06–2.39). They also reported that the variant alleles of both rs11544382 (OR ¼ 1.82, 95% CI: 1.09–3.03) and rs34324334 (OR ¼ 1.76, 95% CI: 1.10–2.83) were ominously associated with breast cancer risk in post-menopausal women (Leaderer et al. 2011). RAS-Related Nuclear Protein (RAN) RAS-Related Nuclear Protein (RAN) is a small G protein essential for the translocation of pre-miRNA and proteins through the nuclear pore complex in a RAN-GTP dependent manner. Polymorphism in RAN can alter miRNA transport and expression and may contribute to pathogenic changes. RAN protein is also involved in downstream regulation of the PI3K signaling pathway, which is further involved in cancer cell invasion and metastasis. Cho et al. reported that RAN rs14035 along with CT + TT genotype was associated with decreased colorectal cancer (CRC) risk in male patients without diabetes mellitus (DM) as well as in patients with rectal cancer. Additionally, they reported that RAN rs14035 CC genotype was related to a decreased risk of CRC with respect to tumor size and metabolism of homocysteine and folate (Cho et al. 2015). However, such studies revealing the association between RAN and breast cancer risk are yet to be reported. DICER DICER1 is an enzyme which is responsible for the cleavage of miRNA precursors. It is known to be involved in the oncogenic process of different cancers. Pre-miRNA processing is mediated by DICER1along with a TARBP2 (transactivation-responsive RNA-binding protein2). TARBP2 is a protein factor of the miRNA loading complex (includes DICER1, AGO2, and TRBP2) that are essential for the formation of RNA-induced silencing complex (RISC) (OsuchWojcikiewicz et al. 2015). In a population-based case control study done in Korean Women, Sung et al. reported that no association between rs1057035 (C > T), located in the 30 -UTR of DICER and risk of breast cancer (OR ¼ 0.64, 95% CI: 0.23–1.77, P ¼ 0.39), However, they reported that DICER1 rs1057035 was significantly associated with both disease-free survival (DFS) and overall survival (OS). Consequently, they reported that the genetic variants TC/CC of rs1057035 were found to

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be associated with disease progression by 1.72-fold increase (95% CI, 1.00–2.99) and risk of death by2.08-fold increase (95% CI, 1.01–4.28) (Sung et al. 2011; Bermisheva et al. 2018). Argonaute 1 (AGO1) and 2 (AGO2) Argonaute RISC Component 1 (AGO1) and 2 (AGO2), also identified as “Eukaryotic Translation Initiation Factors 2C 1 and 2 (EIF2C1/2),” belong to Argonaute protein family. These Argonaute proteins bind to mature miRNA to form RNA-induced silencing complex (RISC), which carries miRNAs to their target 30 UTR binding sites. Argonaute proteins have several key roles, as in transcriptome silencing, immune system regulation, cell differentiation, and angiogenesis. Fawzy et al. reported significant association between genetic variants rs636832 and rs2977490 of AGO1 and AGO2 proteins, respectively, on breast cancer (BC) risk in Mediterranean population. They proved that AGO1*G variant showed a significant BC risk under recessive model [adjusted odds ratio (95% confidence interval); 4.90 (1.03–23.39), P ¼ 0.024], and was also associated with lymph node infiltration (P ¼ 0.037), distant metastasis (P ¼ 0.019), advanced clinical stage (P < 0.001), recurrence (P ¼ 0.032), and shorter overall survival (P ¼ 0.001). Furthermore, AGO2*G/G genotype exhibited association with poor pathological grade (P ¼ 0.029) (Fawzy et al. 2020).

3.2.2

SNPs in miRNA 30 UTR Region and Breast Cancer

SNPs located in the 30 UTR of the target mRNAs are referred to as poly-miRs responsible for alteration in polyadenylation, protein-mRNA interactions, which can either facilitate or hinder miRNA–mRNA interactions (Slaby et al. 2012). Consistent with the predominant role of miRNAs in gene regulation, some SNPs in miRNA 30 UTR region of miRNA-binding site are found to interfere with miRNA function and lead to differential gene expression. Increased expression levels of SET8 expression were observed in various cancer types. SNP locus rs16917496 (T > C) in the 30 -untranslated region (30 -UTR) of SET8 a polymorphism within miR-502 binding site was reported to be associated with susceptibility to breast cancer. SET8 can alter cancer prognosis by modifying its expression, which could be suppressed by miR-502 (Liu et al. 2016). The SNPs rs1051424 and rs11704 were observed in the putative target sites of miRNA within the 3’-UTR of RPS6KB1 and ZNF839, respectively. RPS6KB1, is a ribosomal protein S6 kinase B1, that belongs to S6 kinase family of serine/threonine kinases which are found to be regulated by phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR)pathway. It was reported that RPS6KB1 located on chromosome 17q23 is often amplified and overexpressed in breast cancer, and this phenotype correlates with poor prognosis (Yang et al. 2017). Currently, it is reported that 27 single nucleotide polymorphisms noticed in the 30 UTR zone of the miRNA genes are detected to be associated with the increased risk of breast cancer as shown in (Table 3.2).

Positive regulation of cell proliferation

DNA repair

Promoting uncontrolled cellular proliferation Promote proliferation possibly through downregulation of the expression of the tumor suppressor p21

TGFBR1

BRCA1

BRCA2

RNF 115

Gene function Nuclear hormone receptor involved in the regulation of eukaryotic gene expression increase cellular proliferation in breast tissue

Gene ESR 1

miR-1182 miR-544 miR-639 miR-149 and miR-345 miR-320

G>T

rs8176318

rs17354678

rs11169571 T/C

miR-486-5p

miR-638

T˃C

rs799917

C˃T

miR-628-5p

G-- > A

rs334348

rs686

miR-3636, miR-3662 And miR-186 miR-let-7b

T/ A

rs1062577

Modified microRNA miR-453

Genetic variants C/ T

SNP rs2747648

Table 3.2 Association of miRSNPs and their respective genes with breast cancer susceptibility

Increased breast cancer risk by two folds Increased breast cancer risk

TGFBR1 tumor-specific mutation in breast cancer Increased breast cancer risk Increased breast cancer risk Increased BC risk

Increased BC risk

Association with breast cancer Increased breast cancer risk

(continued)

Kontorovich et al. (2010) JacintaFernandes et al. (2020)

Ahmad et al. (2019)

Nicoloso et al. (2010) Shi et al. (2017)

Dehghan et al. (2017)

References Tchatchou et al. (2009)

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DNA repair and meiotic and mitotic recombination Negative regulation of

Intracellular signaling pathways that control growth, differentiation, apoptosis, cell motility, migration, and survival Tumor invasion and metastasis cellular differentiation Transcriptional activation of the p53 protein; cell cycle progression; apoptosis Abnormal changes of normal cells, and to accelerate the formation of the tumor solid

RAD 52

Itgb4

KRAS

SET 8

MMP9

IQgap 1

Gene function Cell cycle arrest

Gene ErbB4

Table 3.2 (continued)

T˃G

G/T

rs712

Let-7

Let-7

miR-502

miR-491-5p

A>C T/C

miR-34a

miR-124

miR-511-5p, miR-4659a-5p, miR-4659b-5p, and miR-6830-3p Let-7

Modified microRNA miR-548

G/A

A/T

A>C

(c. *3622A > G)

Genetic variants C˃A

rs61764370

rs16917496

rs1056628

rs743554

rs1042538

rs7963551

rs12471583

SNP rs13423759

No association on BC risk

Increase the BC risk

Increased breast cancer menace Increased breast cancer risk at an early age

Reduced breast cancer risk Increased breast cancer risk Increased breast cancer associated risk

Associated with ER/PR negativity and advanced breast cancer

Association with breast cancer

Pirooz et al. (2018) Song et al. (2009);Wei et al. (2018) Sanaei et al. (2017); Mohthash et al. (2020) Huang et al. (2011), Du et al. (2017)

Jiang et al. (2013b) Zheng et al. (2011) Brendle et al. (2008)

References Bidkani et al. (2018) Tabatabian et al. (2020)

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SNPs in Pri-, Pre-, Mature miRNA and Breast Cancer Susceptibility

Breast cancer has a genetic predisposition attached to its occurrence. SNPs are commonly observed in the mature miRNA, pre- and pri-forms of the miRNA sequence with a potential towards altering the processing and their binding of miRNAs to their target genes. Polymorphisms occurring in the seed region of a microRNA gene may contribute to its oncogenic or tumor suppressive functions and subsequently affect cancer risk or susceptibility. Hu et al. investigated the associations of SNPs (rs2910164, rs2292832, rs11614913, and rs3746444) linked to the pre-miRNAs transcript types (hsa-mir146a, hsa-mir-149, hsa-mir-196a2, and hsa-mir-499) with breast cancer susceptibility in Chinese female population and reported that hsa-mir-196a2 rs11614913:T > C and hsa-mir-499 rs3746444:A > G variant genotypes were significantly associated with increased risks of breast cancer (odds ratio, 1.23; 95% confidence interval, 1.02–1.48 for rs11614913:T > C; also OR, 1.25; 95% CI, 1.02–1.51 for rs3746444: A > G in a dominant genetic model (Hu et al. 2009). Zhao et al. in their study reported that SNP rs6505162 in pre-miR-423 affects the mature miR expression, and miR-423 has definitely an oncogenic role in breast tumorigenesis (Zhao et al. 2015). Aberrant expression of miR-146a was reported in breast cancer tissues. A G > C polymorphism (rs2910164) was identified in the miR-146a precursor, that can lead to mismatch in base pair sequence from a G: U pair to a C: U particularly in its stem region (Lian et al. 2012). Shen et al. identified that rs2910164 can lead to the pathogenesis of breast cancer, this is because BRCA1 and BRCA2 key breast cancer genes are the target genes of miR-146a.miRNAs due to their multitudinous functions might also be used as biomarkers for the early diagnosis of cancer. Consequently, patients susceptible to breast cancer possessing at least one miR-146a-variant allele could be diagnosed earlier than the patients with no corresponding variant allele (Shen et al. 2008). Bahreini et al. found a link between SNP in miR-559 and breast cancer susceptibility. They reported that the miR-559 SNP rs58450758 that is located in the coding region of the pre-miR-559 hairpin stem-loop structure was associated with increase in risk of breast cancer incidence. According to their reports, the non-dominant genotypes (CT + TT) were found to be associated with breast cancer in patients (OR 3.62; 95% CI, 1.95–6.69; p < 0.0001) (Bahreini et al. 2020). Through a meta-analysis Tan et al. reported statistically significant association between mir-499a rs3746444 polymorphism and an increased breast cancer susceptibility under all genetic models (homozygous, OR ¼ 1.33, 95% CI ¼ 1.03–1.71, P ¼ 0.03; heterozygous, OR ¼ 1.08, 95%CI ¼ 1.00–1.16, P ¼ 0.04; dominant, OR ¼ 1.15, 95% CI ¼ 1.02–1.30; P ¼ 0.03; recessive, OR ¼ 1.35, 95% CI ¼ 1.06–1.72, P ¼ 0.01; allele, OR ¼ 1.12, 95% CI ¼ 1.00–1.26, P ¼ 0.04) (Tan et al. 2020).

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SNPs in miRNA-Coding Genes

miR-let 7 Du et al. explored the effects of polymorphism on miR-let-7-related genes (mirSNPs) on breast cancer risk and clinical outcomes. They reported that rs1042713 in the ADRB2 gene was associated with the susceptibility to both HR-positive and HR-negative breast cancer. This association was attributed to the conformational change in the protein, induced by rs1042713 in the ADRB2 gene which might have led to changes in the interaction within the signaling pathways, advancing to carcinogenesis in both HR-positive and HR-negative breast cancer. Additionally, they demonstrated that HIF1AN rs11292, a let-7 miRNA-related SNP (mirSNP), was associated with breast cancer risk. Moreover, they proved that rs1017105 CLDN12 expression can be regarded as an independent predictor of poor disease-free survival rate in ER-negative breast cancer patients (Du et al. 2019). miR-27a The miRNA-27 family consists of miR-27a and miR-27b, and the former plays a vital role in tumor development. The G-allele of rs895819 (A > G), located in the terminal loop of the pre-miR27a oncogene, was reported to be significantly associated with reduced familial breast cancer risk (OR ¼ 0.88;95% CI: 0.78–0.99) study done in German population (Li et al. 2019). miR-34 SNP rs4938723 CC of miR-34b/34c genotype was reported to be associated with decreased breast cancer mortality. Bensen et al. reported no significant association between the pri-miR-34b/c rs4938723 variant and susceptibility to BC, and found that this polymorphism was in fact associated with BC survival (CC vs. TT + TC: hazard ratio, 0.57; 95% CI, 0.37–0.89; P ¼ 0.01) (Sanaei et al. 2016). miR-146a The functional microRNA polymorphisms miR-146a rs2910164 G > C is reported to be associated with breast cancer (BC) risk. A meta-analysis done by Zhang et al. 2017 testified that rs2910164 polymorphism has no association with breast cancer risk (Zhang et al. 2017). miR-196a2 miR 196a2 is a miRNA gene that is aberrantly expressed in BC in comparison with normal tissues. Its over expression was reported to be a biomarker for the prognosis of BC patients. The variant rs11614913 in pre-miR-196a2 represents a miRNA variant that can extend in the mature miR-196a2 sequence. This SNP rs11614913 not only affects the maturation of mir-196a but also the regulation of the target mRNA. Moreover, it is reported that the SNP rs11614913 is associated with augmented susceptibility to BC. The predicted targets for miR-196a2 are DHFR and Thymidylate synthase. A recent meta-analysis substantiated that the rs11614913 CC genotype of miR-196a2 was known to be linked with an increased breast cancer risk compared with the TT + CT genotypes (Zhang et al. 2014). miR-423 microRNA-423 is an oncogenic factor which is frequently upregulated in cancer. This gene has coding sequences for miR-423-3p and miR-423-5p. The

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polymorphism rs6505162 at the 30 non-coding sequence located within hsa-mir-423 miRNA gene was reported to be associated with breast cancer (Kontorovich et al. 2010). miR-499 Afsharzadeh et al. in their study reported that rs11614913 (T to C) together with miRSNP A to G (rs3746444) in mir-499 is significantly associated with increased breast cancer susceptibility. In addition to this a miRSNP C to T (rs93410170) in the 30 UTR of the estrogen receptor-α (ER-α) was implicated in breast cancer susceptibility by modulating miR-206. Moreover, an integrin, β-4 SNP, was predicted to influence breast cancer survival and tumor aggressiveness. Here a chromosomal translocation was responsible for alteration in the let-7 miRNA mediated regulation of group A2 with high mobility leading to oncogenic transformation) (Afsharzadeh et al. 2017). miR-520f Meshkat et al. in their study demonstrated that the GA genotype of the rs75598818 SNP, on stem-loop region of the mir-520f reduced risk of BC development (GA versus GG, OR ¼ 0.50, 95% CI: 0.25–0.98, P ¼ 0.041). Moreover, they even reported that A allele mir-520f rs75598818 might lead to reduced production of miR520f-3p, a tumor suppressor miRNA with a role in pathogenesis of BC. They showed that the GA genotype might be associated with HER-2 positivity. Their findings suggest a possibility of varied functional aspects attributed to miR-520f-3p in normal and breast cancer cells. Similarly, SNPs in miR-559 (rs58450758) and miR-618 (rs2682818) were reported to be associated with increase breast cancer susceptibility. SNPs in the microRNA genes which were studied until recently are discussed in (Table 3.3).

3.3

Therapeutic Implications of miRNA

There is enormous research done in the discovery of miRNA and its association with diseases. Rendering the therapeutic usefulness of miRNAs into clinical setting has been improvised by the utilization of novel high-throughput technologies on a variety of patient samples, like blood, tissue, serum, cerebrospinal fluid and (fresh and formalin-fixed paraffin-embedded [FFPE]). These technologies are not only known to lower the laboratory costs, enhance operational productivity, and the yield but also to decipher smaller molecules. Albeit, there are enormous therapeutic implications associated with miRNAs, yet the emergence of miRNA drug candidates has not taken a lead to be translated into FDA approved drug molecules, they are dormant either in Phase I or Phase II clinical trials. miRNA therapeutics is the most widely evolving treatment modality with its clinical applications not limited to cancer malignancies alone but involve a wide array of diseases like cardiac disorders, sepsis, neurological ailments, hepatic disorders, etc. (Sethi et al. 2018). However, miRNAs targeting is most prominently used in onco-therapeutics opening a new genre of cancer treatment. For example, miR-17 family is currently being

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Table 3.3 SNPs in miRNA-coding region MicroRNA type miR-10b

miR-27a

Gene target HOXD10, TBX5, KLF4, PTEN, and syndecan-1 SPRY 2; TMEM170B; BAK; SET8

Associated polymiR rs4078756

C˃T; A>G

rs895819

Decreased risk of breast cancer

Increased risk of TNBC Debatable

miR-34a

p53

GA/AA

rs72631823

miR-34 b/c

p53

T/C

rs4938723

miR-101

SOX2 and NRF2

C>G

rs1053872

C˃T

rs462480

C>G

rs7536540

miR-146a

ERBB2

G/C

rs2910164

miR-185

RhoA and Cdc42

C/T

rs2008591

A/G

rs887205

miR-196a2

BRCA1

C˃T

rs11614913

miR-218-2

SLIT2

G/A

rs11134527

miR-301b

CDKN2A, LMNB1 NSRP1

miR-423

Association with breast cancer Increased BC risk

Genetic variants AG/GG

Increased risk of BC Increased risk of BC No association Dubious

Reduced risk of BC Reduced risk of BC Controversial

Increased risk of BC

rs384262 C/A

rs6505162

Debatable

References Chen et al. (2017)

Mashayekhi et al. (2018), Barjui et al. (2017), Morales et al. (2018), Li et al. (2019), Dai et al. (2020) Kalapanida et al. (2018) Sanaei et al. (2016) Yi et al. (2019)

Bodal et al. (2017), Zhang et al. (2017), Meshkat et al. (2018) Bensen et al. (2013) Bensen et al. (2013) Bodal et al. (2017), Dai et al. (2016), Morales et al. (2018) Danesh et al. (2018) Danesh et al. (2018) Smith et al. (2012); MoazeniRoodi et al. (2019), Pourmoshir et al. (2020) (continued)

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Table 3.3 (continued) MicroRNA type miR-499

miR-520f

miR-559

miR-605

miR-618

Gene target FOXO4, PDCD4,Sox6 and rod 1 ERBB2

ERBB2, MTA1, MTA2, CCND1 and ULK1 ABL2, KRAS, RAF1, CBL, ELK1, ETS1 and MDM2 LPR12

Genetic variants AG/GG

Associated polymiR rs3746444

G/A

rs75598818

C>T

rs58450758

A>G

C>A

Association with breast cancer Increased risk of BC

References Dai et al. (2016), Tan et al. (2020)

Decreased susceptibility to BC Increase breast cancer susceptibility

Meshkat et al. (2018)

rs2043556

Increase breast cancer susceptibility

Kazemi and Vallian (2020)

rs2682818

Increased BC risk in non-familial early-onset BC

Morales et al. (2016)

Bahreini et al. (2020)

scrutinized as a therapeutic option for the treatment of vulvar carcinoma. Treatment of cancer is made possible by two methods; one is to inhibit the overtly expressed miRNA using antisense miRNA derivatives called antisense oligonucleotides, antagomiRs, miR sponges or LNA constructs. Second method is called “miRNA replacement therapy” which helps in restoration of tumor suppressive miRNAs’ using miRNA mimetics like siRNA-like duplex or chemically modified oligoribonucleotide. Anti-miRNAs are used to target diseases causing miRs. Miravirsen is the only miRNA-based drug until now that has reached clinical trials. Miravirsen is locked nucleic acid (LNA) antisense oligonucleotide inhibitor which targets miR-122 (Kwok et al. 2017). Furthermore, Hepatitis C virus (HCV) infected patients were tested with Anti-miR-122. Anti-miR-10b was shown to reduce metastasis of breast cancer in animal studies. In spite of the wide therapeutic applications and inevitable role of miRs in treatment of variety of diseases, further studies related to their precise mechanism of action and identifying their targets are warranted (Seven et al. 2014).

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Conclusion and Future Prospects

Over the past decade, the number of experimental studies on miRNAs has increased exponentially, but the data gathered, and the novice miRNA being discovered advocates that we are still at the beginning. Consequently, microRNAs (miRNAs) gene regulators are being considered as of paramount clinical importance due to their involvement in wide array of processes such as cancer diagnosis and predicting their usefulness as prognostic biomarkers and novel anticancer therapeutic agents that can be used alone or in combination with current targeted therapies. The discovery of single nucleotide polymorphisms (SNPs) in the miRNA regions or transcripts along with binding sites or target genes could provide insights about the link between associations of various miRSNPs and susceptibility of cancer. Single nucleotide polymorphisms (SNPs) are benign genetic variants that are extensively studied in several diseases to know their exact mechanism. However, changes or SNPs in miRs can alter various activities of protein formation and function like expression, binding affinity folding, etc. Moreover, elaborate knowledge of miRSNP their potential mechanisms can be used to identify several cancer subtypes and classify cancer patients accordingly. Not only this, it can be used to provide better treatment options with accuracy and efficacy. Therefore, further characterization of miRSNPs along with more association studies with larger sample sizes is warranted to make accurate predictions of cancer risk and can provided better diagnostic tests.

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From Inflammation to Cancer: Role of Genetic Polymorphisms of Inflammatory Pathway Molecules in Gastric Cancer Israa Abdullah Malli

Abstract

Gastric cancer is a pathological alteration that affects the cellular lining of the stomach, causing some of the gastric cells transformed to become malignant cells. Gastric cancer is still on the top five leading causes of cancer death in both genders worldwide. During the past decade, new important data indicated that the prevalence of gastric cancer is changing due to multiple etiology. However, Helicobacter pylori infection, geographical locations, inflammation, and cancer subtype, age, and environmental changes are reported risk factors that are commonly associated with gastric cancer. Helicobacter pylori infection is recognized as a principal gastric cancer-associated risk due to its direct effects on gastric tissues. It was reported that it affects cellular cancer formation by increasing proliferation rate, upregulating inflammatory response and apoptosis. There are also several human polymorphisms identified and associated with gastric cancer; most of them occur within immune response genes. This book chapter will discuss recent pathological and epidemiological aspects from the perspective of polymorphisms and gastric carcinoma. It will also summarize our understanding of the genetic basis of gastric cancer, which will be remodeled to the new recognition of gastric cancer as a multifactorial disease with distinguished pathogenicity and polymorphism. This understanding will have important implications in the future of the management and prevention of gastric cancer. This chapter will discuss molecular alterations that have been characterized for GC.

I. A. Malli (*) Department of Basic Medical Sciences, College of Medicine-Jeddah, King Saud Bin Abdulaziz University for Health, King Abdulaziz Medical City, Ministry of National Guard—Health Affairs, Jeddah, Saudi Arabia e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_4

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Keywords

Gastric cancer · Stomach cancer · Gene polymorphism · Polymorphisms · Helicobacter pylori

Abbreviations APCs BabA CagA CGC CIMP CIN DLBCL EMT GAPPS GC GERD GISTs GLP HDGC HNPCC ICCs MALT MAPK MSI MUC1 NCGC NK OipA PGL PMNs SabA SNPs Tis TNFα VacA WHO

Antigen-presenting cells Blood group antigen-binding adhesin Cytotoxin-associated gene Cardia gastric cancer CpG island methylator phenotype Chromosomal instability Diffuse large B-cell lymphoma Epithelial-mesenchymal transition Gastric adenocarcinoma and proximal polyposis of the stomach Gastric cancer Gastroesophageal reflux disease Gastrointestinal stromal tumors Gastric linitis plastica Hereditary diffuse gastric cancer Hereditary nonpolyposis colon cancer Interstitial cells of cajal Mucosa-associated lymphoid tissue Microtubule-associated protein kinase Microsatellite instability Mucin 1 Non-cardia gastric cancer Natural killer Outer inflammatory proteins Primary gastric lymphoma Polymorphonuclear cells Sialic acid-binding adhesin Single nucleotide polymorphisms Carcinoma in situ Tumor necrosis factor-alpha Vacuolating cytotoxin World Health Organization

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4.1

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Introduction

Gastric cancer is classified among the top most common malignancy that increases the mortality rate due to cancer around the world (Bray et al. 2018). It is a pathological alteration that affects the cellular lining of the stomach, causing the transformation of some of the gastric cells to become malignant cells (Venerito et al. 2018). Gastric cancer is considered a civilized disease as it has been associated with the early development of modernization (Ang and Fock 2014). The first traced cases of gastric cancer were believed to be reported by Ebers Papyrus. However, the gastric cancer diagnosis was unknown as the characterization of benign and malignant tumors was not determined yet (Santoro 2005). Gastric cancer tends to develop slower than other forms of cancers as pre-cancerous cellular modifications occur in the mucosa lining of the stomach over many years before true gastric cancer cells develop and progress. However, gastric cancer is classified among the most aggressive types of cancers (Mairimclean et al. 2014). Thus, it is often diagnosed at a later stage, which contributes to its poor prognosis (Carcas 2014). The global shift toward organic and preservative-free nutrients is expected to contribute to enhancing gastric cancer prevention and believed in reducing gastric cancer cases (Correa and Piazuelo 2012). In 2012, the International Journal of Cancer had estimated 951,000 new cases and 723,000 deaths of gastric cancer (Ferlay et al. 2015). However, the disease progress rate continued in 2018 and the World Health Organization (WHO) reports that gastric cancer was expected to be the third among the most common with 27,600 new cases and 11,010 gastric cancer deaths in the USA (Jemal et al. 2010). The global estimate of gastric cancer is reported to be more than 1.22 million new cases, and 865,000 deaths occurred worldwide of gastric cancer (Etemadi et al. 2020). Gastric cancer is a heterogeneous multifactorial disease of molecular complexity that demands continued updates and research toward advancement. Genetic, Helicobacter pylori infection, nutritional factors, and environmental factors are common etiology that create some challenges for the diagnosis, prevention, and alternative therapeutic solutions of molecular markers (Baniak et al. 2016; Zamani et al. 2018).

4.1.1

Classifications

4.1.1.1 Histological Classifications Gastric cancer is considered one of the slow progressing diseases that tend to develop slowly with no specific signs and symptoms over many years, while pre-cancerous alteration at the gastric mucosa originates. Based on the type of affected cells it originates from, gastric cancer can be classified into four types: adenocarcinoma, primary gastric lymphoma (PGL), carcinoid tumor, and leiomyosarcoma. Adenocarcinoma arises from columnar glandular epithelium cells lining of the stomach; it accounts for nearly more than 90% of stomach cancer (Taghavi et al. 2012).

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Adenocarcinoma The adenocarcinoma was first classified based on the morphology, growth, and differentiation of cancer cells until Lauren et al. had reported the histological based classification of gastric cancer in 1965. According to Lauren’s, gastric cancer is classified into two main subtypes (Lauren 1965; Van Cutsem et al. 2016). Both types do not transform into the other because they have defined characteristics and numerous variations in their origin, etiology, pathology, epidemiology, and behavior (Carcas 2014). The intestinal gastric cancer is the most common type which is prevalent in high-risk areas; it is usually associated with high-risk populations, elderly adult males, it has a better prognosis than diffuse gastric cancer (Yasui et al. 2011). Intestinal gastric cancer is caused by the bacteria Helicobacter pylori, which releases some virulence factors that invade the epithelial cells and cause extensive cellular damage (Suzuki et al. 2012). It is characterized by metaplasia that originates from the glandular involvement of adhesive tubular cells that might induce high vascularity and possible invasion from the lymphatic circulation (Waldum and Fossmark 2018). On the other hand, the diffuse gastric cancer is characterized by single non-adhesive cells that infiltrate as scattered small groups of cells (Du 2006). Despite that, the diffuse type of gastric cancer is more common in low-risk areas and mostly affecting a much younger female population; it is reported to have a worse prognosis and short duration compared to the intestinal type (Assumpção et al. 2020). Primary Gastric Lymphoma The primary gastric lymphoma (PGL) arises from lymphocytes that are found in the wall of the stomach. It has two predominant histological subtypes: mucosaassociated lymphoid tissue (MALT) and diffuse large B-cell lymphoma (DLBCL). However, gastric lymphoma is considered an uncommon malignancy compared to adenocarcinoma, and it accounts only for 4% of gastric cancer cases (Hosseini and Dehghan 2014). B lymphocytes are responsible for recognizing and responding to any pathogen that crosses the epithelial layer. An excessive B cell proliferation during a chronic Helicobacter pylori infection makes B cells more prone to having mutations and developing a diffuse well-differentiated lymphocyte (Juárez-Salcedo et al. 2018). Carcinoid Tumor The carcinoid tumor originates from neuroendocrine cells like the G cells, the hormone-producing cells of the stomach. This form of gastric tumor accounts for only 3% of gastric cancer cases and usually localizes without spreading to nearby organs. Carcinoid tumor causes a well-differentiated mass that protrudes from the mucosa and appears as a polyp (Nikou and Angelopoulos 2012). It has three types: Type I and II ECL-cell carcinoids present with little to no symptoms. Thus, it is usually accidentally discovered during endoscopy. The third type, Type III ECL-cell carcinoids, is considered the more aggressive type, which presents with more

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pronounced symptoms such as bronchial tube constriction, severe abdominal pain, and diarrhea (Dias et al. 2017). This form of cancer is not unique to G cells of the stomach; it can form malignancy at other locations also where G cells are found, such as in the pancreas and intestine (Oronsky et al. 2017). Leiomyosarcoma The leiomyosarcoma of gastric cancer arises from stromal smooth muscle cells that cover the gastric wall. It is considered an extremely rare type of gastric cancer characterized by undifferentiated cells (Agaimy and Hartmann 2010). Although gastrointestinal stromal tumors (GISTs) are reported to originate from the interstitial cells of Cajal (ICCs), a specialized cell is located in the gastrointestinal tract, but it is commonly associated with stomach malignancy (Kang et al. 2019).

4.1.1.2 Anatomical Classifications The stomach has five regions: the cardia, the upper part next to the esophagus and has the esophagogastric junction; the fundus, the second part after the cardia; the body or the corpus, the main part that connects upper to lower; the pyloric antrum, the lowest part of the stomach; the pylorus, the last part that has a pyloric sphincter to be connected to the small intestine (Ellis 2011). The first three parts of the stomach (cardia, fundus, and body) are called the proximal stomach, which is responsible for digestive enzymes production and intrinsic factor secretion that is needed for vitamin B12 absorption. The lower two parts are called the distal stomach (Mahadevan 2014). According to the adenocarcinoma location, GC can be classified based on major topographical subsites into cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC) (Hu et al. 2012). According to 2012 estimates, the worldwide cardia and non-cardia gastric cancer incidence rates have been reported 260,000 cases of CGC and 691,000 cases of NCGC. Each type possesses distinct characteristics in their descriptive epidemiology and risk factors; however, NCGC is more frequent than CGC, with an average ratio of 2:1 (Colquhoun et al. 2015). Cardia gastric cancer affects the cardia region, the upper part of the stomach, at the gastroesophageal junction. CGC is thought to be mainly caused by damage to the cells at the gastroesophageal junction as a result of chronic irritation of the gastroesophageal junction by stomach acidity which leads to gastroesophageal reflux disease (GERD) (Carr et al. 2013). This CGC is found to be associated with an increased intake of saturated fat and increased BMI that lead to severe obesity. Therefore, obesity increases the risk of cardia stomach cancer, but it shows no association with NCGC (Mukaisho et al. 2015). CGC can be established with or without Helicobacter pylori infection. If the disease starts independent of Helicobacter pylori, it manifests after a longstanding mucosal inflammation without atrophic gastritis, while atrophic gastritis can be observed when it is associated with Helicobacter pylori infection (Pennathur et al. 2013). Non-cardia gastric cancer affects the non-cardia region, the main area of the stomach wall. The non-cardia region of the stomach holds unprocessed food 4–5 h during digestion. Thus, the non-cardia type is affected by the exposure to nutritional factors such as alcohol consumption, cigarette smoking, food-preservative, and high

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dietary intake of salt (Piazuelo and Correa 2013). Some food might contain carcinogens such as nitrates that are generally found in preservative-used food and meat. These materials interact with the mucosal lining, irritate, and damage the gastric tissue, forming lesions that can develop to cancer. Some types of NCGC are commonly associated with chronic Helicobacter pylori infection, which usually progresses to cause chronic gastric inflammation and GC. However, the diffuse type of NCC has been associated genetically with E-cadherin gene silence mutation and less likely to be associated with nutritional and other factors such as diet or smoking (Anderson et al. 2010).

4.1.2

Grading of Gastric Cancer

Gastric cancer forms when malignant or cancerous cells arise in the stomach. From outside the gastric wall has four defined layers: mucosa, submucosa, serosa, and muscularis propria. The innermost and the most affected layer by gastric cancer is the mucosa. It comes in direct contact with the food and gastric contents (Hosseini and Dehghan 2014). Mucosa has three layers: epithelial layer, the innermost layer of mucosa that absorbs food, secretes mucus, gastric acid, and digestive enzymes; lamina propria, the middle layer of mucosa where the blood and lymph vessels and mucosa-associated lymphoid tissue located; muscular mucosa, the outermost layer of mucosa that contracts and mixes the food with gastric enzymes (Goldberg and Raufman 2015). Submucosa, the next supporting layer of the mucosa. Then muscularis propria, a thick muscular layer. Finally, serosa, the outer layer of the stomach. Inside the epithelial layer, gastric pits have foveolar cells that secrete mucus to protect the stomach from autodigestion. Parietal cells located in the body and fundus areas maintain the acidic pH by secreting hydrochloric acid (HCL). Chief cells and G-cells, which secrete digestive enzymes such as pepsinogen and gastrin, respectively (Curcic et al. 2014). Grading gastric cancer is a crucial step to evaluate stomach cancer and develop an individualized treatment plan. It is based on a scale of the depth of invasion, size, and growth (T), lymph nodes involvement (N), and distant metastasis (M). This scale is described in detail in Table 4.1 (Sano et al. 2016; Washington 2010).

4.1.3

Risk Factors

In the USA, gastric cancer was on the top in the leading causes of cancer deaths until the late 1930s. Today, there is a global decline in the incidence of gastric cancer on the list of the leading causes of death (Bray et al. 2018). Like most forms of cancer, gastric cancer is considered a multifactorial disease that might originate due to different causes. Risk factors for gastric cancer are genetic makeup and family history of gastric cancer, age, smoking, alcohol consumption, and obesity (Eusebi et al. 2014). Furthermore, Helicobacter pylori infection, nutritional factors, and gender are specific risk factors that are commonly associated with intestinal subtype

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Table 4.1 Gastric cancer grading system Grade Grade 0 T0-N0-M0 Grade T1-N0-M0 I (A) Grade T1-N1-M0 I T2-N0-M0 (B)

Grade II (A)

T1-N2-M0 T2-N1-M0 T3-N0-M0

Grade II (B)

T1-N3aM0 T2-N2-M0 T3-N1-M0 T4a-N0M0

Grade III (A)

T2-N3aM0 T3-N2-M0 T4a-N1M0 T4b-N0M0

Grade III (B)

T1/2-N3bM0 T3-N3a-

Description Carcinoma in situ (tis), where cancer has not spread into the inner layers, lymph nodes (N0), nor metastasized (M0) Cancer tissue reaches the upper layer of the mucosa (T1) but does not reach the muscle layer with no lymph nodes involvement (N0) or metastasis (M0) Cancer tissues reach the upper layer of the mucosa (T1) and to a maximum of two lymph nodes (N1) but no metastasis (M0). or Cancer tissues reach beneath the upper layer of cells into the muscularis propria layer (T2), with no involvement in lymph nodes (N0) or distant tissue (M0) Cancer tissues reach the upper layer of the mucosa (T1) but not the main muscle layer, with three to six nearby lymph nodes involvement (N2) but no distant tissue involvement (M0). or Cancer tissues reach beneath the top layer of cells (T2) with not more than two nearby lymph nodes (N1) but no metastasis (M0). or Cancer tissues reach beneath the top layer into the subserosa (T3). However, it has not spread to lymph nodes (N0) nor metastasized (M0) Cancer reaches the second layer, such as mucosa into the lamina propria (T1), and has reached 7 to 15 nearby lymph nodes (N3a) with no metastasis (M0). or Cancer reaches the muscularis propria (T2), and it has reached three to six nearby lymph nodes (N2) with no metastasis (M0). or Cancer reaches the subserosa layer (T3) with one to two lymph nodes (N1) involvement but no metastasis (M0) or Cancer reaches the stomach wall into the serosa (T4a) but no spread to lymph nodes (N0) nor metastasis (M0) Cancer reaches the outer muscular layers of the stomach wall (T2) with spread to 7 to 15 lymph nodes (N3a) but no metastasis (M0). or Cancer penetrates all muscular layers into the connective tissue or Cancer penetrates all layers of the muscle into the connective tissue but not into the peritoneal lining (T4a) with spread to one to two lymph nodes (N1) but no metastasis (M0). or Cancer penetrates all layers of the muscle into the connective tissue and has grown into nearby organs or structures (T4b) with no spread to lymph nodes (N0) but no metastasis (M0) Cancer reaches the inner layer of the wall of the stomach or the outer muscular layers of the stomach wall (T1/T2) and spread to 16 or more lymph nodes (N3b), but no metastasis (M0). (continued)

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Table 4.1 (continued) Grade

Description M0 T4b-N2M0 T4a-N3aM0

Grade III (C)

Grade IV

T3/T4aN3b-M0 T4b-N3aM0

or Cancer penetrates all stomach layers into the connective tissue but not into the peritoneal lining or serosa (T3) with spread to 7 to 15 lymph nodes (N3a) but no metastasis (M0). or Cancer reaches the main muscle layer into the connective tissue and organs or structures (T4b) and spread to not more than seven lymph nodes (N2), but no metastasis (M0). or Cancer penetrates all muscle layers into the connective tissue and grows into the peritoneal lining or serosa (T4a) with spread to 7 to 15 lymph nodes (N3a) but no metastasis (M0) Cancer penetrates all layers into the connective tissue and may have grown into the peritoneal lining or serosa T3/T4a with spread to more than 16 lymph nodes (N3b) but no metastasis (M0). or Cancer penetrates all layers into the connective tissue and nearby organs (T4b) with spread to 7 or more lymph nodes (N3a) but no metastasis (M0) The most advanced stage where cancer of any size has spread and metastasized beyond the stomach into distant parts of the body

gastric cancer (Eusebi et al. 2014). The overall improvement and the global shift toward healthier practices, such as the rapid growth of the organic food industry, consumption of preservative-free diet, and tobacco cessation believed to change the trend and decrease the incidence of the disease (Zabaleta 2012). Also, the availability of fresh vegetables and fruits, which believed to decrease the consumption of preservatives, salted, and smoked foods (González and Agudo 2012). Other researchers believed that Helicobacter pylori discovery and antibiotics availability are major factors that reduce the rate of gastric cancer. Despite the global decline in the incidence of intestinal and diffuse gastric cancer, gastric cancer is still listed among one of the most aggressive types of cancer (Nagini 2012). Each of the previously mentioned risk factors mediates an individual impact on the disease pathology depending on the disease subtype. Other protective factors to prevent gastric cancer include a high intake of fruits, vegetables, fiber, and folate. Therefore, regulation or dysregulation of gastric cancer etiology leads to a better or poor prognosis (Resende et al. 2010). Figure 4.1 illustrates the risk factors for gastric cancer.

4.1.3.1 Helicobacter Pylori Infection and Pathogenesis Helicobacter pylori is a flagellated, spiral, and Gram-negative organism (Wroblewski et al. 2010). It is classified as a fastidious organism that requires special lab conditions to be grown; it is a microaerophilic bacterium, requires little oxygen (O2) and more carbon dioxide (CO2). Also, it is an acidophilic bacterium as it

From Inflammation to Cancer: Role of Genetic Polymorphisms of Inflammatory. . .

Fig. 4.1 Risk factors for the progression of gastric carcinoma. Created with BioRender.com

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colonizes nearly 50% of the human’s stomachs’ mucosal layer (Yamaoka 2010). Warren and Marshall had first isolated Helicobacter pylori in 1981. However, the global attention of understanding gastric cancer pathogenesis and subtypes has suddenly shifted toward Helicobacter pylori in 1983, as it had been associated with peptic ulcers (Marshall 2005). Marshall et al. isolated this spiral curve, which was later identified as Helicobacter pylori, from the stomach contents of gastric ulcer patients. As a recognition of their great work and discoveries by Robin Warren, who was a clinical pathologist, Western Australia, and was the first to observe the curved bacteria under the microscope, and Barry Marshall, the person who consumed the organism to develop gastritis, were awarded the Nobel Prize in 2005 (Moss 2013). Since then, there were great efforts toward Helicobacter pylori eradication to became the first-line focus for researchers, who shifted their efforts toward Helicobacter pylori early diagnostic tools, treatment options, and prevention (Thung et al. 2016). In 1994, the International Agency for Research on Cancer reported that Helicobacter pylori is classified among the group I carcinogen and a major risk factor associated with gastric cancer development (Nishizawa and Suzuki 2015). It was found that Helicobacter pylori infection predisposed the infected subject to both forms of gastric cancer subtypes (Polk and Peek 2010). Helicobacter pylori infection leads to gastritis, gastric atrophy, and a decrease in the stomach acidity as a defense mechanism in response to an increase in the concentration of serum gastrin. Thus, hypergastrinemia, which leads to gastritis, is believed to promote stomach carcinogenesis and has been linked to poor prognosis of gastric cancer and enhancement of pathological behavior (Fossmark et al. 2015). Gastritis caused by the infection is considered the strongest remarkable risk factor for gastric cancer. However, not all cases of peptic ulcers as a result of Helicobacter pylori develop cancer after the infection (Polk and Peek 2010). Developing the risk of gastric cancer can be triggered by multiple factors; the presence of a specific strain of Helicobacter pylori and its virulence factors, the infected host genotypes, and the specific host–microbe interactions in terms of host inflammatory responses and subtypes of gastric cancer, which have different epidemiological and pathophysiological features (Peek 2016). Helicobacter pylori expresses several virulence factors that promote infection, Debowski et al. reported that the ability of Helicobacter pylori to acquire resistance and survive in the acidic environment that reaches pH as less as 1.0 is caused by urease enzyme that converts urea (CH4N2O) into ammonia (NH3) and carbon dioxide (CO2) (Debowski et al. 2017). Wroblewski et al. documented that urease is required not only in disturbing the gastric pH but also in altering the tight junctions between gastric epithelial cells; thus, urease activity considers a rate-limiting step in the establishment of chronic infection (Wroblewski et al. 2009). Moreover, cytotoxic-associated gene A (CagA), a common virulence protein that was well studied in the literature which arose from cag island, is the most studied virulence factor that is associated with Helicobacter pylori pathogenesis. It has been documented that it has at least 20 known cellular binding partners (Backert et al. 2010). CagA gene encodes a protein with the type IV secretion system, which enables it to be exported and translocated from bacterial cells to form dimers in

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the epithelial cells (Mentis et al. 2019). Ohnishi et al. have reported that CagApositive Helicobacter pylori infection is associated with gastric cancer development compared to the wildtype (Ohnishi et al. 2008). Furthermore, the vacuolating cytotoxin (VacA) gene is another virulence factor of Helicobacter pylori that promotes cellular impairment by vacuole formation and cytochrome C release (Qadri et al. 2014). Decreasing cellular activation, inhibiting cellular proliferation, and reducing signaling pathways result in mitochondrial damage and induction of apoptosis (Ishaq and Nunn 2015). Outer-inflammatory proteins (OipA) and other virulence factors have also been associated with gastric cancer pathogenesis and progression of gastric cancer; the interaction of these virulence factors is believed to induce a synergistic effect toward cancer development (Sokolova and Naumann 2017). In 2014, a study investigated genetic deviations presented in gastric epithelium with Helicobacter pylori infection from patients with and without gastric tumors. Yamaoka had presented some evidence that Helicobacter pylori disease patterns presented differently based on geographic distribution. Helicobacter pylori and its virulence factors vary according to geographical distribution. Thus, East Asian populations were found to report a high incidence of gastric carcinoma and a high prevalence of Helicobacter pylori infection compared to South Asian and other African nation populations (Yamaoka 2010). Peek and Blaser have proposed the mechanisms by which Helicobacter pylori induces gastritis inside the gastric mucosa; thus, increasing the risk for an intestinal form of gastric cancer (Peek and Blaser 2002). The colonization of gastric epithelial cells with Helicobacter pylori initiates this process by inducing a potent inflammatory response and a signal transduction cascade of events that involves host–cell protein interaction (Nešić et al. 2010). Phosphorylated CagA is involved significantly by increasing the expression level of proinflammatory cytokines, which recruit neutrophils and macrophages to the gastric lining to initiate an inflammatory response leading to an increase in the risk of developing severe gastritis, gastric atrophy, and peptic ulcer (Fazeli et al. 2016). Continuous inflammation generated by the prolonged immune activation leads to mucosal damage and chronic gastritis that cause DNA damage as a result of reactive oxygen species (ROS), that is released by activated immune cells (Na and Woo 2014). Over time, as a result of repetitive injury and repair, gastric epithelium undergoes transdifferentiation (morphological and molecular) changes to become epithelial-mesenchymal transition (EMT) cells (Marck and Bracke 2013). These cells resemble the intestinal epithelium in a process called metaplasia, foci of dysplasia develop in the area to invade the lamina propria forming an invasive carcinoma that leads to loss of parietal, chief, and glandular cells atrophy (Carcas 2014). The hallmark of transdifferentiation is defined by the presence of intracellular Cag-A that downregulates E-cadherin and upregulates N-cadherin, which is a mesenchymal marker (Corso et al. 2011). Thus, transdifferentiation changes such as altering the cellular adhesion, increasing cellular detachment and motility while losing cellular polarity will be observed (Huang et al. 2015). Acquired motility of poorly attached epithelial cells enhances the spread and

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invasion of the carcinogenic cells to nearby tissues and distant locations (Diepenbruck and Christofori 2016). Furthermore, Helicobacter pylori causes genetic instability in the progression of gastric cancer via the aberrant promoter methylation (Maleki and Röcken 2017). Metaplastic cells are prone to accumulate mutations in some genes that are involved in cell cycle and division, which results in DNA damage (Li et al. 2015). Somatic mutations were accumulated in multiple genes inside infected gastric tissues with Helicobacter pylori. Therefore, increased cytidine activity inside gastric inflamed tissues upregulates the formation of somatic mutations and promotes gastric cancer formation in subjects with Helicobacter pylori infection (Shimizu et al. 2014). Mutations can occur in both sides of cell cycle regulatory pathways: tumor suppressor genes which downregulate the cell cycle by coding for proteins that stop the cell division and promote apoptosis or proto-oncogenes which upregulate cell division and normally code for accelerator proteins that promote the cell cycle (Machlowska et al. 2018). Tumor suppressor genes such as p16 are downregulated by the Helicobacter pylori, which induce promoter hypermethylation in gastric cancer (Liu et al. 2019). Because of such mutations, metaplastic cells start dividing uncontrollably, and more mutations are passed with each division to become malignant cells with special ability to invade nearby cells or metastasis and spread to distant sites (Cai et al. 2019). Even though the presence of the same etiological agent, Helicobacter pylori, predisposes some people to gastric cancer, it is likely to protect them from other forms of cancer (Carcas 2014). Polyzos et al. showed some evidence that Helicobacter pylori decreases the risk of developing esophageal cancer by reducing the incidence of GERD, metaplasia of the distal part of the esophagus (Polyzos et al. 2018). Therefore, regardless of the great decline in the rate of Helicobacter pylori infection due to the availability of antibiotics and increase in the awareness toward clean food consumption, Helicobacter pylori infection is considered a promoter in chronic gastric cancer cases (Akhavan-Niaki and Samadani 2014).

4.1.3.2 Other Risk Factors Gastric cancer global distribution varies fundamentally around the world. These variations across geographical regions are justified by a plentiful factors that are associated with gastric cancer incidence, patients’ survival, and mortality rate (Resende et al. 2011). In the 2018 status report, the global incidence and mortality, for both sexes combined, of gastric cancer were reported to cause more than 1 million new cases and nearly 800,000 deaths. Among males, gastric cancer was the fourth on the list of the most common cancer and the leading cause of cancer death (Bray et al. 2018). According to the same report, the eastern Asian population was shown on the top of the list. Almost 32% of the world’s cases are diagnosed in Asia; nearly half of that percentage is diagnosed in China, followed by Eastern Europe in the second and South America in the third place (Ferlay et al. 2010a). Despite the global migration, the first generation of Asian diaspora, who migrate from countries with higher incidence rates, maintain the high risk of developing the disease from their country of origin. However, the incidence rate declines in the following generations, the

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second and third, due to the environmental factors and lifestyle influences (Anderson et al. 2010). Despite the degree of variability among mostly affected populations within a similar geographic region, other demographic factors such as ethnicity, socioeconomic status, and lifestyles might influence the gastric cancer distribution rate. J. Ferlay et al. reported in the USA socioeconomic differences between classes play a role in gastric cancer distribution; it was found that gastric cancer cases reported more at lower-income citizens among the African American population. Also, ethnic predisposition can be supported by an increasing percentage of gastric cancer reported cases among Hispanics and Native Americans compared to Caucasian Americans (Ferlay et al. 2013). For a long time, cigarette smoking, salty food, and food preservatives have been connected with an increased risk of developing CGC (Piazuelo and Correa 2013). However, less developed countries carry a greater gastric cancer burden than developed countries, as NCGC is more likely to affect persons of lower socioeconomic classes (Blot and Tarone 2015). Moreover, Western and European populations experience less burden of Helicobacter pylori compared to the Asian population who reported having the majority of the world’s gastric cancer cases (Ferlay et al. 2010b). Also, the risk of Helicobacter pylori infection has been associated with lower socioeconomic status, poverty, overcrowding, and unsanitary conditions (De Sablet et al. 2011) (Fig. 4.2). Moreover, it is also reported that environmental and nutritional factors can justify some differences in the development of gastric cancer. For example, Tan et al. presented that the prevalence of Helicobacter pylori infection reported being high in many African countries. Today, the rate of gastric cancer in the same countries is very low. He justified discrepancies in his results to be caused by the coinfection with intestinal helminths rather than the differences in ethnicity and genetic makeup of the individuals living in these countries (Tan et al. 2012). He hypothesized that coinfection with intestinal helminths changes the T-helper cell response from an inflammatory (Th1-type) to a non-inflammatory (Th2-type) immune response. However, in 2000, Fox et al. responded to that hypothesis and suggested with some evidence that coca chewing, which was associated with a reduction in gastritis, might be responsible for providing some protection (Fox and Wang 2000).

4.2

Pathogenesis of Gastric Cancer

As previously mentioned, intestinal and diffuse are the two main subtypes of gastric cancer (Ferlay et al. 2010b). The intestinal subtype of adenocarcinoma manifests at a later age, presents as a well-differentiated large irregular ulcer located on the antrum part of the stomach, and progresses fast in a relatively well-defined series of steps. This subtype of gastric cancer (Shah et al. 2011). This subtype of gastric cancer is generally developed due to a premalignant change in terms of atrophic gastritis, which develops on a chronic inflammation background induced by Helicobacter pylori infection (Piazuelo and Correa 2013). Also, Peek and Blaser have proposed the mechanisms by which gastritis is induced by Helicobacter pylori inside the

Fig. 4.2 Role of Helicobacter pylori in the progression of gastric cancer. Created with BioRender.com

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gastric mucosa and increasing the risk for intestinal-type of GC (Peek and Blaser 2002). It has been proposed that gastritis during Helicobacter pylori infection generates a special altered environment where inflammatory cytokines, chemokines, and growth factors activate signaling transduction cascade inflammatory events, which may lead to chronic gastritis (Machado et al. 2010). Infection induced inflammatory response enhances lymphocytes, neutrophils, and macrophages recruitment. Also, persistent inflammation is commonly associated with genetic variations and DNA damage that upregulate the status of chronic gastritis, which is believed to predispose the host to develop gastric tumorigenesis (Malfertheiner 2011). Gastric cancer presents with genetic instability in terms of permanent DNA damage and impairment of DNA repair mechanisms. Helicobacter pylori infection is believed to generate mitochondrial-DNA mutations, increase the rate of DNA damage, and decrease DNA repair (Ding et al. 2010). Spontaneous mutations at the epithelial cells are induced by DNA methylation, oxidative damage, chromosomal or microsatellite instability, which downregulate major DNA repair pathways (Sayed et al. 2020). Alternatively, the diffuse subtype of adenocarcinoma affects both genders equally but is considered more common in younger generations. The mechanism of cancer cell formation of the diffuse subtype of gastric cancer has been clearly explained and well-documented (Assumpção et al. 2020). It arises from normal gastric mucosa and characterizes by non-adherent cell formation; adhesion proteins were downregulated to enhance the formation of cancer lesions and spread to adjacent cells. It can manifest at any part of the stomach, and it is mostly related to genetic mutations in the CDH1 genes that code for a membrane adhesion molecule called E-cadherin (Fitzgerald et al. 2010). This protein is responsible for the tight junction between epithelial cells and signals transduction to regulate the cell cycle. This disturbance in the function of E-cadherin to maintain cellular junction results in uncontrolled cell division and loss of cellular communication (Jafferbhoy et al. 2013). Metastases of cancer cells from one part to another in the stomach structure, which progresses the disease rapidly and worsens the condition (Tomita et al. 2011). Also, its desmoplasia will be associated with inflammation and generated as a result of the chronic inflammation, but with the absence of atrophic gastritis that usually characterizes the intestinal subtype; therefore, this subtype of gastric cancer has a poor prognosis (Bass et al. 2014). However, the diffuse type has more pronounced desmoplasia, adhesion, and dense fibrosis with thick and rigid stomach wall caused by the detachment of cancer cells to invade connective tissue at the submucosa causing gastric linitis plastica (GLP) (Hanahan and Weinberg 2011). Unlike the intestinal subtype, this type is rarely associated with Helicobacter pylori (Yakirevich and Resnick 2013). Thus, diffuse gastric cancer has been associated with Helicobacter pylori infection by some researchers, but many others rejected its responsibility in developing gastric cancer. Bornaschein and his colleagues have reported that Helicobacter pylori infection increases the individual risk of developing gastric cancer compared to noninfective subjects that might result from the inflammation (Bornschein et al. 2010). The presence of an acid-resistant bacteria that invades the gastric lining with a reduction of stomach acidity as a result of anti-acid medication contributes to the development of diffuse gastric cancer (Khatri et al. 2020).

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Gastric malignancies’ symptoms are vague and not specific in general, however, malaise, loss of appetite, epigastric pain, nausea, and dyspepsia. Later, pronounced symptoms are associated with an advanced stage that may include weight loss, significant blood loss, anemia, hematochezia, and hematemesis (Hu et al. 2012). Cancer reaches body parts such as lymph nodes, liver, lungs, peritoneum, and metastasis’ symptoms include Sister Mary Joseph sign, a mass around the belly button, and Troisier’s sign, enlargement of the left supraclavicular node (Zdilla et al. 2019). The most common complications of gastric adenocarcinoma include metastasis to nearby or distant organs and tissues such as lymph nodes, peritoneum, and liver. Also, the formation of paraneoplastic neurological syndromes, the appearance of brownish spots all over the skin because of overstimulation of growth factors to keratinocytes to release tissue factor which then activates the coagulations cascade (Taketa et al. 2012). Diagnosis of gastric cancer is essentially made obtaining a biopsy during endoscopy to visualize the tumor directly. Medical imaging using X-rays with barium contrast of the upper GI tract to determine if cancer is spread to other body parts to identify complications like ulcers. Also, abdominal pelvic CT can be used to evaluate if the cancer is spread to close by organs or lymph nodes to evaluate the stage of the tumor. Treatment of gastric cancer depends on the cancer stage; for initial stages, surgery can be performed to treat cancer; for advanced stages, treatments include a combination of chemotherapy, surgery, radiation, and targeted therapy to increase the chance of survival even though palliative surgery can only relieve the pain. However, the overall survival rate is very low because gastric cancer is mostly diagnosed at advanced stages.

4.3

Molecular Basis of Gastric Carcinogenesis

Regardless of the nature of the etiological or carcinogenetic factors for gastric cancer, signal transduction cascades of molecular events take place to enable the communication between malignant and stromal cells (Machlowska et al. 2018). Therefore, following distinct pathways and utilizing specific mechanisms of cellular growth, differentiation, or a program of apoptosis, cell death. The fate of cells will determine the future of the tumor, whether it will become localized or get metastasized (Riquelme et al. 2015). Gastric adenocarcinoma development is a complex process involving multiple genetic alterations. As previously mentioned, based on tissue pathology, intestinal and diffuse are the two main subtypes of gastric adenocarcinoma (Ferlay et al. 2010b). Many gastric adenocarcinomas are occasionally occurring in irregular or random instances, with many of those related to genetic, epigenetic, and environmental risk factors. Many studies have associated environmental and microbial factors to gastric cancer, while others associate molecular variations with gastric cancer (Pan et al. 2018). For example, chronic microbial infections such as Helicobacter pylori infection, the causative agent of some types of peptic ulcers, and Epstein-Barr virus (EBV), the causative agent of mononucleosis, are reported to predispose the subject to develop gastric cancer (Iizasa et al. 2012).

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As a result of both infections, acute gastritis contributes to an increase in the level of DNA methylation in the gastric lining. It upregulates cancer cell formation, which supports diffuse gastric cancer molecular mechanisms, and highlights its development (Peleteiro et al. 2012). The exact mechanism that is responsible for making this type of cancer a more aggressive phenotype is yet to be uncovered; it may occur sporadically, as a result of familial factor, or as a hereditary factor; thus, maintaining genomic instability appears to be a key regulator for gastric cancer (Hudler 2012, 2015). Single-gene molecular studies suggested that intestinal and diffuse-type gastric cancer evolves via different genetic pathways (Palli et al. 2010). Some inherited factors are associated with gastric cancer; however, most of the genetic changes that have been linked with gastric cancer are acquired (Sereno et al. 2011). Acquired factors can result from somatic and spontaneous mutations that are generated from gastric adenocarcinoma and have shown genetic instability in terms of chromosomal instability (CIN) and microsatellite instability (MSI), which are considered the initial triggers for gastric carcinogenesis (Maleki and Röcken 2017). Besides the two main types of genomic instability implicated in the gastric cancer pathophysiology, the CpG island methylator phenotype (CIMP), which are epigenetic alterations, is linked with gastric cancer (Puneet et al. 2018).

4.3.1

Hereditary Genetic Factors

The majority of gastric cancer are originating from non-inherited factors; thus, less than 1–3% of gastric cancer cases are connected to hereditary factors (Sereno et al. 2011). Among these, hereditary diffuse gastric cancer (HDGC) syndrome, an autosomal dominant caused by a mutation in the CDH1 gene. This gene encodes E-cadherin or cadherin-1, an adhesion protein responsible for the tight junction between epithelial cells and signals transduction to regulate the cell cycle (Paredes et al. 2012). Mutation in this gene recognizes as a predisposition syndrome that increased the risk for the development of gastric cancer, although it is classified as a rare syndrome (Van Roy 2014). Hereditary diffuse gastric cancer can appear in any part of the stomach, and it affects both genders equally but is considered more common in younger generations (Fitzgerald et al. 2010). The mechanism of cancer cell formation of the hereditary diffuse gastric cancer has been clearly explained and well-documented. It arises from normal gastric mucosa with no premalignant stage and characterizes by non-adherent cell formation; adhesion proteins were downregulated to enhance the formation of cancer lesions and spread to adjacent cells (Assumpção et al. 2020). Thus the loss of function of cadherin-1 will result in uncontrolled cell division, loss of cellular communication, cellular detachments, de-differentiation, and metastasis (Piazuelo and Correa 2013). Metastases of cancer cells from one part to another in the stomach structure, which progresses the disease rapidly and worsens the condition (Tomita et al. 2011). Also, desmoplasia will be associated with inflammation and generated as a result of the chronic inflammation, but with the absence of atrophic gastritis that usually characterizes the intestinal

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subtype; therefore, this subtype of gastric cancer has a poor prognosis (Bass et al. 2014; Jafferbhoy et al. 2013). However, the diffuse type has more pronounced desmoplasia, adhesion, and dense fibrosis with thick and rigid stomach wall caused by the detachment of cancer cells to invade connective tissue at the submucosa causing GLP (Hanahan and Weinberg 2011). Mimata et al. have shown evidence that hereditary diffuse gastric cancer mutation alone is not enough to cause gastric cancer; they proposed multiple additional factors that are needed to enhance the molecular mechanisms of gastric cancer pathogenesis (Mimata et al. 2011). Additional mechanisms might also be involved in the pathogenesis of HDGC, such as CDH1 promoter methylation, mutations in p53, and missense mutation at c-Met genes (Corso et al. 2011; Ma et al. 2016). Hereditary gastric cancer is associated with either the polyp type such as gastric adenocarcinoma and proximal polyposis of the stomach (GAPPS), a malignancy of polyps; or the non-polyp type such as hereditary nonpolyposis colon cancer (HNPCC), which associates with a mutation in DNA repair genes (Carneiro 2012). Also, rare forms of hereditary gastric cancer syndrome, such as Li–Fraumeni syndrome which is caused by a mutation in STK11 (Masciari et al. 2011; Van Lier et al. 2010).

4.3.2

Acquired Genetic Factors

Chromosomal Instability Based on the molecular classification of gastric cancer, chromosomal instability is defined as the accumulation of a high rate of mutations as a result of structural rearrangements at microsatellite regions of the DNA (Shah et al. 2011). Moreover, gastric cancer risk factors such as alcohol consumption, tobacco smoking, high salt diet, Helicobacter pylori infection have a great impact on the chromosomal stability in susceptible individuals (Na and Woo 2014). Infection induced inflammatory response enhances immune cells’ recruitment such as lymphocytes, neutrophils, and macrophages. Furthermore, persistent inflammation is commonly associated with genetic variations and DNA damage that upregulate the status of chronic gastritis, which is believed to predispose the host to develop gastric tumorigenesis (Malfertheiner 2011). Gastric cancer is characterized by genetic instability in terms of permanent DNA damage and DNA repair mechanisms. Helicobacter pylori infection is believed to generate mitochondrial-DNA mutations, increase the rate of DNA damage, and decrease DNA repair (Ding et al. 2010). Spontaneous mutations at the epithelial cells are induced by DNA methylation, and genomic oxidative instability also differs according to the geographical location, histological type highlighting the importance of gene–environment interactions in disease pathogenesis (Sayed et al. 2020). Thus, the two possible mechanisms suggested the development of chromosomal instability. The first one is the belief that these mutations are to be because of dysfunction of the DNA mismatch repair proteins that can occur in both sides of cell cycle regulatory pathways. Tumor suppressor genes downregulate the cell cycle by coding for proteins that stop the cell division and promote apoptosis or proto-oncogenes, which upregulate cell

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division and normal code for accelerator proteins that promote the cell cycle (Machlowska et al. 2018). Because of such mutations, metaplastic cells start dividing uncontrollably, and more mutations are passed with each division to become malignant cells with special ability to invade nearby cells or metastasize and spread to distant sites (Cai et al. 2019). The second proposed theory for chromosomal instability was present of the defected chromosome is within cells that can be carried to the offspring during mitosis (Duijf and Benezra 2013). Microsatellite Instability It is reported to be responsible for more than 50% of sporadic cases of gastric cancers. These causes can be in terms of hypermethylation of the promoter region known as microsatellite regions due to related epigenetic changes or mutational inactivation (Velho et al. 2014). These changes cause defects in DNA mismatch repair (MMR) genes and result in the accumulation of mutations within simple nucleotide repeats by bases insertion or deletion (Li et al. 2020). Inactivation and loss of gene function, which leads to additional genetic changes is responsible for the development of a higher frequency of gastric cancer (Carcas 2014). Abnormal CpG islands’ promoter hypermethylation is another epigenetic alteration associated with the transcriptional silencing of human genes reported in cancers that affect multiple gene promoter regions but not a single gene promoter (Puneet et al. 2018). These epigenetic discrepancies have been recognized in gastric cancer and known to serve as a mechanism for the silencing of the tumor suppressor genes and other adhesion protein genes such as E-cadherin (Gigek et al. 2017). It has been shown that Helicobacter pylori increases the expression of MUC1 by the CpG hypomethylation pathway, which functions as a signal transducer and activator of transcription (Puneet et al. 2018).

4.4

Single Nucleotide Polymorphisms and Gastric Cancer

It has been proposed that gastritis during Helicobacter pylori infection generates a special altered environment because of inflammatory cytokines, chemokines, and growth factors that activate signaling transduction cascade inflammatory events, which may lead to chronic gastritis. Inflammatory response enhances lymphocytes, neutrophils, and macrophages recruitment (Machado et al. 2010). Furthermore, persistent inflammation is commonly associated with genetic variations and DNA damage that upregulate the status of chronic gastritis, which is believed to predispose the host to develop gastric tumorigenesis (Malfertheiner 2011). Besides, spontaneous mutations at the epithelial cells are induced by DNA methylation, oxidative damage, chromosomal or microsatellite instability, which downregulate major DNA repair pathways (Sayed et al. 2020). Single nucleotide polymorphisms (SNPs) are naturally occurring genetic variations of a single base within the DNA sequence that occur with variable frequency. Less than 1% of the population carries the same variation to be considered and classified as an SNP (Huebner et al. 2010). SNPs are considered one of the most important that stimulate and upregulate the expression and activity of certain pro-carcinogen genes (Skierucha et al. 2016). The frequency

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of genetic polymorphisms varies based on ethnicity and affect by environmental exposure, which promotes the development of gastric cancer (O’Brien et al. 2013). These variations appear in the genome of different ethnicity as a result of point mutations, which they regulate and correlate biological pathways, disease susceptibility, drug response, and carcinogenesis (Oh et al. 2020). Alteration of the gene expression will increase, decrease, or defect the function of its associated protein. However, not all SNPs are associated with increased risk of diseases; some have no known pathology while either is protective (Chang et al. 2014). Gastric cancer shows host genetic variants in terms of SNPs in some genes that induce DNA damage, impair DNA repair, and decrease the expression of immune response genes (Ding et al. 2010). Special SNPs within key candidate genes are major factors that contribute to sporadic cases of gastric cancer (Grabsch and Tan 2013). All of the sporadic mechanisms end up by alteration and dysregulation of signaling pathways between host cells and the surrounded environment, which consequently disrupts mechanisms of the cell cycle, proliferation, and apoptosis (Hanahan and Weinberg 2011). In terms of the infected host genotypes, presence of SNPs, several genes have been linked to the host genetic components with the risk of gastric cancer as a result of infection (Zabaleta 2012). This section aims to investigate SNPs within host genes involved in the inflammatory response to Helicobacter pylori infection and its pathological consequences such as gastric atrophy, peptic ulcer, and intestinal type of gastric cancer (McColl 2010). The risk of developing gastric cancer can be affected by multiple factors: the presence of a specific strain of Helicobacter pylori and its virulence factors, the infected host genotypes, and the specific host–microbe interactions in terms of host inflammatory responses and subtypes of gastric cancer, which have different epidemiological and pathophysiological features (Peek 2016). Gastritis is the hallmark of the immune response because of H. pylori infection; it leads to the induced gastric acid secretion and development of mucosal ulceration, which consequently leads to gastric atrophy and developing gastric cancer (Malfertheiner et al. 2014). In an attempt to address the host genetic variations and susceptibility associated with SNPs and the risk of developing gastric cancer, multiple pieces of research have been conducting searching for new polymorphisms to improve the knowledge about pathogenesis, diagnosis, treatment, and prevention of gastric cancer at a cellular level (McLean and El-Omar 2014). The following sections will address possible variations and heterogeneity caused by SNPs, which represent a spectrum of several key genetic increases, the susceptibility to gastric cancer, and influences on the pathogenesis of gastric cancer.

4.4.1

Polymorphisms in Cytokine Genes

Cytokines are small molecular weight secretory proteins that are synthesized by nearly all nucleated cells, such as lymphocytes and monocytes. Their biological activities inside the host indicated by their roles in response to infection and

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inflammatory utilizing microtubule-associated protein kinase (MAPK) pathway for initiating their gene expression (Bockerstett and DiPaolo 2017). Upon activation, cytokines positively and negatively regulate cellular genes expression by binding to cytokine receptors on the target cells and initiate signal transduction pathways through the second messenger (Morrison 2012). Many studies have documented clear associations between cytokines’ genes SNPs that regulate inflammation during gastric cancer (Hong et al. 2012). Genetic variants in cytokines and cytokines’ receptor genes associated with inflammation are considered to participate in gastric carcinogenesis initiation and promotion (Persson et al. 2010). Cytokine genes that are responsible for initiating regulate the production of inflammatory cytokines and the risk of gastric cancer. Genes of inflammatory cytokines: tumor necrosis factoralpha (TNFα), interleukin 1 beta (IL-1 β), interleukin 8 (IL-8), and interleukin 10 genes (IL-10), interleukin 17 gene (IL-17) as well as their coded proteins are the most studied for their association with gastric cancer (Bornschein et al. 2010; Peek 2016). These cytokines function as mediators of gastric cancer pathophysiology and play important roles in the upregulating other risk factors for gastric cancer (Yuzhalin 2011).

4.4.1.1 Interleukin 1 Gene For its involvement in upregulating the release of other proinflammatory cytokines during the early stage of inflammation, interleukin one beta gene (IL-1 β) considers an ultimate candidate for studying the effects of SNPs and its role during Helicobacter pylori infection and its association with the development of gastric cancer (Santarlasci et al. 2013). Chromosome 2q13-14 is the location of IL-1 gene clusters known to encode three proteins: IL-1RN, IL-1α, and IL-1β, their naturally occurring receptor antagonist that inhibits their functions (Liu and Cai 2017). Three polymorphisms have been found in IL-1β and been associated with gastric cancer; the base transition between C > T at 511 (IL-1β C511T (rs16944)), and base transition between C > T base transition at +3954 (IL-1β C511T (rs1143634)), and base transition between T > C at 31 (IL-1β C31T (rs1143627)) (Motamedi Rad et al. 2018; Zeng et al. 2003). Also, the base transition between A > T at +9589 (IL-1RN A9589T (rs454078)) was reported at receptor antagonist genotype IL-1RN and has been associated with gastric cancer risk (Bangshun et al. 2011). Since IL-1 plays an important role in inflammation and infection pathogenesis, SNPs in the IL-1 cluster encoding IL-1β and IL-1RN were associated with accelerated risk of developing gastritis and in response to Helicobacter pylori infection relatives of gastric cancer cases (Hong et al. 2016). IL1β encodes IL-1β, a potent proinflammatory protein that is released as a proprotein from activated antigen-presenting cells (APCs) such as macrophages and monocytes during an early stage of acute and chronic inflammation or infection (Gigek et al. 2017). Once activated, this potent cytokine accelerates and reinforces the importance of host–environment interactions by enhancing inflammatory response and upregulating cellular proliferation, differentiation, and apoptosis (Chen et al. 2015). As an endogenous pyrogen, IL-1β induces prostaglandin synthesis, recruits polymorphonuclear cells (PMNs) such as neutrophils, activates immune cells, and promotes B-cell proliferation and antibody

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production (Qian et al. 2010). IL-1β C511T (rs1143634) and IL-1β C31T (rs1143627) in the promoter regions cause non-random association of alleles at that loci. IL-1β C31T (rs1143627) regulates transcription factors binding capacity and therefore affecting the transcriptional activity of IL-1β (Motamedi Rad et al. 2018; Zeng et al. 2003). IL-1RN A9589T (rs454078) polymorphism leads to the presence of five different alleles of both IL-1RA and IL-1β (Kamenarska et al. 2014). Proinflammatory genetic polymorphisms tend to contribute to the risk of the development of gastric cancer, as previously shown by a study, the association of IL-1β 511C > T (rs16944) with gastric cancer risk (Kamangar et al. 2006). In this regard, a study conducted by Persson et al. documented that among non-Asian populations, IL-1β 511C > T (rs16944) and IL-1RN A9589T (rs454078) were found to be associated and linked to an increased risk of intestinal and diffuse gastric cancer. While in terms of anatomical location, Persson et al. have investigated the association between IL1B polymorphisms and gastric cancer at different geographic locations among Asian and non-Asian participants. Their findings suggested that there is an increase in gastric cancer risk among non-Asians who carry IL-1β 511C > T (rs16944). Interestingly this risk was accelerated in Helicobacter pyloripositive cases compared to the control and found to be related more to distal gastric cancer compared to cardia cancers (Persson et al. 2010). However, in Asian populations with a high incidence of Helicobacter pylori infection, IL1B C31T (rs1143627) was mainly associated with a reduced overall risk of distal and intestinal gastric cancer, while IL-1β C511T (AA;rs16944) carriers were found to have an increased risk of cardia gastric cancer specifically (Persson et al. 2010). Moreover, even though IL-1β C511T (rs16944) and IL-1RN A9589T (rs454078) are considered a positive risk factor for gastric carcinogenesis in certain populations, they were never associated with the presence of gastric cancer in others (Baum and Georgiou 2011). In 2012, Kimang’s et al. had conducted a crosssectional study among African participants to investigate the effects of proinflammatory genetic polymorphisms, IL-1β 511C > T (rs16944) and IL-1RN A9589T (rs454078), and their role in Helicobacter pylori-related gastric cancer pathology. He reported that both polymorphisms were significantly associated with gastric pathologies in the presence of Helicobacter pylori infection (Kimang’a 2012). This genetic susceptibility has been validated by Zhao et al. in three different ethnicities; they reported IL-1β C511T (rs16944) and IL-1RN A9589T (rs454078) are positively involved with gastric changes and in the development of non-cardia gastric adenocarcinoma (Zhao et al. 2012). Regardless of the carrier status of Helicobacter pylori bacteria, gastric cancer was reported with a severe and prolonged inflammatory response due to increased circulatory levels of IL-1β C511T (rs16944) and IL-1RN A9589T (rs454078). Most of the literature search has concluded that IL-1β C511T (rs16944) IL-1RN A9589T (rs454078) were related to increased risk of developing subtypes of gastric cancer, non-cardia, and intestinal gastric cancer. However, IL1B C31T (rs1143627) was associated with a decreased risk of distal and intestinal gastric cancer.

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4.4.1.2 Interleukin 8 Gene For its role as proinflammatory cytokines in upregulating the release of other initial cytokines during the early stage of inflammation and infection, interleukin 8 (IL-8) considers a possible candidate for studying the effects of SNPs and its role during Helicobacter pylori infection and its association with the development of gastric cancer (Brew et al. 2000). IL-8 belongs to the chemokine subfamily, which is encoded by IL8 and produced by a variety of tissue and blood cells. Thus, as a predominantly neutrophil chemoattractant, it attracts and activates neutrophils. High concentrations of IL-8 have the ability to attract T cell chemokines to induce a respiratory burst, neutrophils degranulation, and angiogenic activity (Persson et al. 2010). The two receptors of IL-8, IL-8RA (CXCR1) and RB (CXCR2), continuously express with high affinity on the surface of neutrophils, mainly T cells and monocytes (Brew et al. 2000). Upon Helicobacter pylori infection and colonization, as a response to inflammation, gastric epithelial cells produce a high concentration of IL-8 constitutively, and this production is enhanced following cytokine stimulation. This activation leads to extensive cellular infiltration and enzyme degranulation (Persson et al. 2010). Upon infection with Helicobacter pylori, CagA induces and increases IL-8 expression, which is related to the more severe gastritis in the host. An association between IL-8 promoter polymorphisms and gastric cancer has also been suggested for some populations (Persson et al. 2010). There are two functional polymorphisms reported in the promoter region of IL-8: base transition between at 251 (IL-8251 T/A (rs4073)) and a base transition between at 845 (IL-8845 T/C (rs2227532)) (de Oliveira et al. 2015). Therefore, both polymorphisms regulate transcription factors binding to the promoter sites, which are involved in increased or decreased gene expression. Since they are located at the promoter regions, they involve in regulating transcription factors affinity to the promoter site; therefore, they alter expression levels of mRNA and inflammatory cytokines associated with cancer development (Qadri et al. 2014). Several studies have been conducted to evaluate the influence of IL-8 polymorphisms, IL-8-251 T/A (rs4073) and IL-8-845 T/C (rs2227532), and the risks of developing chronic gastric carcinoma. De Oliveira et al. have reported since IL-8 is responsible for tumorassociated angiogenesis in several cancers, AT heterozygote of IL-8-251 T/A (rs4073) significantly upregulated and associated with changing in the levels of IL-8 production (de Oliveira et al. 2015). However, there was no reported association between the development of the clinicopathological of gastric cancer and Helicobacter pylori infection or the presence of IL-8 gene polymorphisms (Taguchi et al. 2005). In 2011, to understand the relationship between IL-8 polymorphism and the risk of atrophic gastritis and gastric cancer, Yuzhalin had investigated the host genetic susceptibility of IL-8 genes and their influence on gastric carcinogenesis caused by Helicobacter pylori infection. Examining IL-8 -251 T > A in subjects with atrophic gastritis and with gastric cancer, he reported that level of neutrophil infiltration and IL-8 production were upregulated by IL-8 -251 A/A genotype, which was associated with a higher risk of gastritis and gastric cancer compared with the T/T genotype (Yuzhalin 2011). Finally, previous studies provide some evidence that IL-8-845 T/C has an association with a higher risk of gastric cancer.

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4.4.1.3 Interleukin 10 Gene Interleukin 10 (IL-10) is a member of cytokines encoded by IL-10 located at positions 31 and 32 on the long arm of chromosome 1, which consists of five exons and four introns (Yuzhalin 2011). It is produced by B lymphocytes, macrophages, and T-helper 2 cells, and its activity is directed toward producing cells and cytokines. IL-10 functions as an anti-inflammatory to limit the production and minimize the release of proinflammatory cytokines, downregulate inflammation, inhibit T-helper 1 cell, and upregulate other lymphocytes (Cárdenas et al. 2018). Multiple polymorphisms were reported to IL-10 in the promoter region, IL10A1082G (rs1800896), IL10-592 C > A (rs1800872), IL10-819 C > T (rs1800871) have been associated with inflammation and gastric cancer (Colt et al. 2009). Even though many studies have evaluated the association between IL10 -819 C > T (rs1800871) and IL10 -592 C > A (rs1800872) and gastric cancer risk, findings were inconsistent, and gastric cancer risk was undetermined. J. Liu et al. explored the role of IL10 -819 C > T (rs1800871) in the susceptibility to gastric cancer. They concluded that IL10 -819 C > T (rs1800871) is associated with a reduced risk of gastric cancer among Asians (Liu et al. 2011). Additionally, in the presence of Helicobacter pylori infection, IL10 -819 C > T (rs1800871) was found not to be associated with reduced risk of diffuse, non-cardia, or cardia subtype gastric cancer. Thus, they concluded that IL10 -819 C > T (rs1800871) provides Asians with some protection against gastric cancer (Liu et al. 2011). Also, Xue et al. have conducted a meta-analysis trying to figure out these controversies and validate some previous results. In regard to the association between IL10 -592 C > A (rs1800872) and gastric cancer risk, they observed that there was a significant association between IL10 -592 C > A (rs1800872) and gastric cancer risk was observed in the Asian but not in the Caucasian population. Therefore, he concluded that the association is ethnicity-specific and cannot be generalized (Xue et al. 2012). Furthermore, F. Pan et al. performed another meta-analysis to determine the association between IL10- A1082G (rs1800896) and the risk of developing gastrointestinal cancers. They reported a strong link between IL10- A1082G (rs1800896) and increased risk of gastrointestinal cancers and gastric cancer in the Asian population (Pan et al. 2012). Kim et al. investigated the associations of developing gastric cancer in the Korean population with multiple factors such as Helicobacter pylori infection, tobacco smoking, and IL10 polymorphisms. They reported that in the presence of Helicobacter pylori and positive smokers, all three polymorphisms: IL10-592 C > A (rs1800872), IL10- A1082G (rs1800896), and IL10-819 C > T (rs1800871) were associated with an increased risk of intestinal-type gastric cancer. Also, Helicobacter pylori infection and smoking have a combined risk to develop intestinal-type of gastric cancer in the presence of IL10- A1082G (rs1800896) (Kim et al. 2012). 4.4.1.4 Interleukin 17 Gene Interleukin-17 (IL-17), a proinflammatory cytokine1, 2, 3 that participates, contributes to the local and systemic aspects of multiple inflammatory diseases. It is produced by T-helper 17 cells to participate in the defense against pathogens, such

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as extracellular bacterial, helminthic parasites, and fungal infections (Miossec and Kolls 2012). However, overexpression of IL-17 might contribute to autoimmunity and chronic inflammatory diseases such as rheumatoid arthritis (Jin and Dong 2013). Since chronic inflammation is the hallmark of the pathogenesis of Helicobacter pylori infection, proinflammatory cytokines IL-17 and IL-17F are the ultimate candidate to be studied for their role in Helicobacter pylori-induced gastric cancer (Yu et al. 2012). Numerous studies performed demonstrated that IL-17 stimulates tumor growth; thus, increase in its expression level might indicate a good prognosis and survival for cancer cases (Furuya et al. 2018). Several polymorphisms such as IL-17A G197A (rs2275913) and IL-17F A7488G (rs763780) have been mapped to IL-17 that encodes proinflammatory cytokines IL-17 and IL-17F (Bockerstett and DiPaolo 2017). Studies have reported multiple findings that implicate polymorphisms at IL-17 play a role in regulating the development of gastric cancer. Wu et al. had conducted a study utilizing reactionrestriction fragment length polymorphism to assess the association between polymorphisms at IL-17 and gastric cancer risk. In 2010, they reported that IL-17F A7488G (rs763780) and gastric cancer genotypes were associated with an increased risk of intestinal gastric cancer while IL-17A G197A (rs2275913) was only associated with an increased risk of poorly differentiated gastric cancer (Wu et al. 2010). Moreover, in 2015, another group had investigated the role of IL-17A G197A (rs2275913), IL-17F A7488G (rs763780), and IL-17A C > T (rs3748067) in the development of gastric carcinoma. They reported that during Helicobacter pylori infection, IL-17A G197A (rs2275913) significantly increased the risk of gastric cancer (Hou and Yang 2015).

4.4.1.5 Tumor Necrosis Factor-Alpha Tumor necrosis factor (TNF-α) is a prominent member of the proinflammatory cytokines family that is recognized as one of the most extensively studied due to its distinct functions in homeostatic and antimicrobial immunity (Olmos and Lladó 2014). TNF-α is mainly produced by activated APCs such as macrophages, T lymphocytes, and natural killer (NK) cells (Atzeni and Sarzi-Puttini 2013). TNF-α plays an essential role in controlling infection as a major neuroinflammatory cytokine that is induced by transcription factors that mediate neuroinflammation via TNF-α modulation (Muhammad 2019). During Helicobacter pylori infection, lipopolysaccharide activates proinflammatory cyclooxygenase, which induces by overexpression of TNF-α. TNF-α gene cluster has multiple polymorphisms that are linked to the development and increased risk of gastric cancer (Q. Wang et al. 2011). Analyzing TNF-α-308 G/A (rs1800629) and TNF-α-857 C/T (rs1799724) suggested TNF-α-857 C/T (rs1799724) both associated with a high-risk for developing gastric cancer in Caucasian populations during Helicobacter pylori infection (Olmos and Lladó 2014).

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Polymorphisms in Mucin Genes

As previously mentioned, Peek and Blaser have proposed the mechanisms by which gastritis is induced by Helicobacter pylori inside the gastric mucosa and increasing the risk for intestinal-type of GC (Peek and Blaser 2002). The chronic gastric infection with Helicobacter pylori relates to several mutations in genes of epithelial cells, which may prevent apoptosis but stimulate cell proliferation. Epithelial cells of gastric epithelium indicate the presence of mucosal pathogens and activate inflammatory pathways (Candido and Hagemann 2013). Mucins are recognized as a group of glycosylated glycoprotein that covers the epithelial surface of the gastrointestinal tract working in the first-line of defense for the host against physical and environmental threats and participating in epithelial protection and renewal (McGuckin et al. 2011). In 2010, an analysis of common genetic variation had indicated that the MUC1 gene has a direct association and risk of gastric cancer (Jia et al. 2010). Mucin 1 (MUC1) is encoded by the MUC1 gene and produced by the stomach to secrete mucus that lubricates the epithelial surface and captures undesired agents (Boltin and Niv 2013). During Helicobacter pylori infection, the surface of gastric epithelial cells expresses MUC1 to interact with Helicobacter pylori and prevents its binding to the epithelial cells. Several studies have examined the role of MUC1 as an important gene associated with the risk of gastric carcinogenesis, which reported that it plays an important role in the development of gastric cancer (Jing et al. 2019). In cancer cells, MUC1 believes to have an anti-apoptotic role, but in normal mucosa, MUC1 believed to have had a role in protecting gastric epithelial cells from attacks that cause inflammation and carcinogenesis (Liu et al. 2014). Also, it functions by eliminating Helicobacter pylori colonization by preventing the adhesion of its surface outer membrane protein blood group antigen-binding adhesin (BabA) to sialic acid-binding adhesin (SabA) at the gastric epithelial (Kable et al. 2017; Skoog et al. 2012). Also, it was reported that overexpression of MUC1 stimulates cell signaling cascades of events leading to escalating the activity of transcription factors and increasing cellular growth, proliferation, and decreasing cellular apoptosis (Liu and Zeng 2020). Also, in 2012, Guang et al. have reported that overexpression of MUC1 reduces CagA/β-catenin co-IP and decreases bacteria-driven β-catenin nuclear localization. Therefore, their findings suggest that MUC1 might serve as an excellent target in which regulating of MUC1 level in gastric epithelia may be a therapeutic option to reduce Helicobacter pylori virulence and inhibit IL-8 production and gastric inflammation (Guang et al. 2012). Examining insights into the pathogenesis of gastric cancer searching for novel susceptibility loci for gastric cancer drives us to MUC1 G > A (rs4072037) and MUC1 G > A (rs2070803) that are associated with MUC1 (Saeki et al. 2014). MUC1 G > A (rs4072037), an SNP within the MUC1 gene, which is located at chromosome 1q22, found to reduce the intracellular levels of reactive oxygen species (ROS); thus, it was reported that altered MUC1 expression is associated with multiple types of cancer among Chinese populations (Abnet et al. 2010). Jia et al. have reported there are two major transcripts in gastric epithelium. MUC1

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G > A (rs4072037), which is located in the second exon, determines the splicing site, which determines the type of alleles that increase the expression of some variants. Thus, functional deletion of rs4072037 loci at MUC1 was associated with the reduced transcriptional activity; therefore, a reduction in its protein expression in gastric cancer tissue (Jia et al. 2010). Thus this results in reducing MUC1 expression, enabling Helicobacter pylori to interact and penetrate through the gastric epithelium (Sheng et al. 2013). In Asia, the incidence rate of Helicobacter pylori infection and gastric adenocarcinoma is similar; however, there are subjects with positive pylori infection reported to have low incidence rates of gastric adenocarcinoma (Fock and Ang 2010). Using meta-analysis, Saeki et al. demonstrated that MUC1 G > A (rs4072037) affects promoter activity and determines the major splicing sites of MUC1 in the gastric epithelium. Therefore, individuals who carry it have a high risk of developing diffuse-type gastric cancer (Saeki et al. 2011). Also, in 2013, Saeki et al. study the susceptibility to diffuse-type gastric cancer; he reported increased susceptibility of gastric cancer-associated Japanese population who carry MUC1 G > A (rs4072037) (Saeki et al. 2013). Even though Saeki et al. have documented a strong association and increased risk to gastric cancer, Zhang et al. reported a lack of association between common polymorphisms of MUC1 G > A (rs4072037) and Helicobacter pylori infection and non-cardia gastric cancer risk in a Chinese population (Ma et al. 2013; Zhang et al. 2013). Also, in 2014, Liu et al. conducted a comprehensive database search to reveal inconsistent results with different pathology. Evaluating the relationship between MUC1 G > A (rs4072037) and gastric cancer susceptibility, they concluded this MUC1 G > A (rs4072037) might protect the subject against gastric, and this protection is associated with a significant decreased gastric cancer risk in Asians but not Caucasians (Liu et al. 2014). Similar results were replicated concerning the association of MUC1 G > A (rs4072037) with gastric cancer in the Caucasian population (Palmer et al. 2012). Furthermore, MUC1 G > A (rs2070803) is another less studied functional SNP that showed the association with diffuse gastric cancer, which was replicated in additional Japanese and Korean. Besides mentioned SNPs in the Chinese population, the association of SNPs in MUC1 with non-cardia intestinal GC was successfully replicated and demonstrated in other ethnicities, such as the Korean population (Li et al. 2012; Song et al. 2014).

4.4.3

Polymorphisms in E-Cadherin Gene

Helicobacter pylori expresses CagA that modifies intercellular adherence, disrupts the cytoskeleton, alters cell polarity, and increases the expression level of proinflammatory and inflammatory cytokines (Fazeli et al. 2016). CagA furthermore alters the β-catenin pathway; MUC1 interacts with β-catenin and CagA to form another co-IP complex, which consequently transactivates the promoter of the IL-8 gene (Guang et al. 2010). E-cadherin is encoded by the CDH1 gene located in a cluster along with other adhesion molecules such as cadherins (Xu et al. 2020). It is considered as a Ca2 +-dependent adhesion protein essential for initiating and

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maintaining the attachment of molecules to the epithelial surfaces (Baum and Georgiou 2011). E-cadherin is mainly involved in epithelial cell adhesion and the formation of zipper-like structures. Therefore, genetic alterations in E-cadherin lead to epithelial cellular adhesion impairment and dysfunction, which disturb cell migration (Paredes et al. 2012). Dysfunction or reduced expression of E-cadherin due to decreased expression at messenger RNA (mRNA) and protein levels has been reported in most of the diffuse gastric cancer. Thus, downregulation or deletions of the E-cadherin gene are associated with gastric cancer poor survival (Geng et al. 2012). Also, CDH1 may be involved in the invasion and metastasis of cancer genes by altering the transcriptional activity of epithelial cells (Menbari et al. 2013). Three SNPs have been associated with the risk of diffuse gastric cancer risk: CDH1 C-160A (rs16260), CDH1 C2076T, and CDH1 C 2253 T. CDH1 C160A located in the promoter region of CDH1 plays significant roles in cancer risk for sporadic diffuse gastric cancer (Van Roy 2014). Even though a significant association was observed between the CDH1 C-160A (rs16260) and the risk of gastric cancer, other studies showed contradictory findings (Zhan et al. 2012). In 2015, Jiang et al. had reported a lack of association between the CDH1 C-160A (rs16260) and the risk of gastric cancer (Jiang et al. 2015). A small sample size can explain the discrepancies that were reported by Zhan et al.’s study. In conclusion, the association between CDH1 C-160A (rs16260) and the risk of gastric cancer should be validated and confirmed by a large-scale study.

4.5

Conclusion

In conclusion, gastric carcinoma is one of the most widespread malignancies around the world. It has been recognized as one of the highly aggressive types of cancer. As it is usually manifested at a very late diagnosis stage, gastric cancer cases have very low survival rates. Prevention is the most effective way to decrease the risk of developing gastric cancer and reduce susceptibility rates. SNPs are considered genetic markers that predispose individuals and increase their genetic risk for gastric cancer development. Genetic alterations of genes that are responsible for inflammation are considered ultimate targets for their role in regulating gastric cancer. This chapter investigated the genetic association of SNPs and gastric cancer risk progression. In order to understand the impact of each genetic polymorphism on gastric cancer progression, this chapter reviewed polymorphisms at IL-1B, IL-8, IL-10, IL17A, IL-17F, TNF-α, MUC1, and E-cadherin genes and risk of gastric carcinoma. Therefore, molecular mechanisms of gastric cancer and its management are considered a significant factor for progressing gastric cancer development. Thus, a better understanding of the role of these SNPs creates more promises to enhance the development of personalized medicine toward treating patients infected with Helicobacter pylori and develop a higher risk of developing gastric cancer.

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Colorectal Cancer and Genetic Polymorphism in Key Regulatory Low Penetrance Genes Mujeeb Zafar Banday, Aga Syed Sameer, and Saniya Nissar

Abstract

Colorectal cancer (CRC) is one of the major solid tissue malignancies affecting the worldwide population. CRC is the third most common cancer and fourth among the major contributors of cancer-related mortality worldwide. CRC incidence rate exhibits a considerable variation with geographical location and with race and ethnicity. The variation in CRC incidence with geographical location is so conspicuous that CRC is regarded as the marker of the socioeconomic development of nations. The role played by the genetic variants especially SNPs in modulating the risk of CRC is of very diverse nature. In this chapter, we would be discussing the effects of SNPs in the low penetrance genes depending upon the type of pathways these genes are involved in. Numerous pathways, which are known to get affected by genetic variations, can be categorized as: cell cycle regulatory, transcription regulatory, DNA repair, folate metabolism, fat metabolism, xenobiotic and inflammatory pathways. Keywords

Colorectal cancer · Mutations · Polymorphism · Genetics · Risk factors · DNA repair · Susceptibility

M. Z. Banday · S. Nissar Department of Biochemistry, Government Medical College, Srinagar, Kashmir, India A. S. Sameer (*) Basic Medical Sciences, College of Medicine, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Jeddah, Saudi Arabia King Abdullah International Medical Research Centre (KAIMRC), National Guard Health Affairs (NGHA), Jeddah, Saudi Arabia e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_5

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Abbreviations 5,10-MTHFR 5-MTHF ACE ADT AJCC APC APS ASR ASRi ASRm AURKA B12 B2 B6 BER C1-THF CBS CCND1 CD CIMP CIN CNVs CRC CS CT CTH DCBE DCC DHF DHFR DHT DSBR dTMP dUMP FAP FOBT Formyl THF FS GIT GnRH GWAS HDI

5,10-Methylenetetrahydrofolate reductase 5-methyltetrahydrofolate Angiotensin-converting enzyme Androgen deprivation therapy American Joint Committee on Cancer Adenomatous polyposis coli Adenomatous polyposis syndromes Age standardized rate ASR incidence rates ASR mortality rate Aurora A kinase Vitamin B12 Vitamin B2 Vitamin B6 Base excision repair C-1-tetrahydrofolate Cystathionine β-synthase Cyclin D1 Crohn’s disease CpG island methylator phenotype Chromosomal instability Copy number variations Colorectal cancer Colonoscopy Computed tomographic Cystathionine γ-lyase Double contrast barium enema Deleted in Colorectal Cancer Dihydrofolate Dihydrofolate reductase dDihydrotestosterone Double-strand break repair Deoxythymidine monophosphate Deoxyuridine monophosphate Familial adenomatous polyposis Fecal occult blood testing Formyl tetrahydrofolate Flexible sigmoidoscopy Gastrointestinal tract Gonadotropin-releasing hormone Genome-wide association study Human development index

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HIC-1 HNPCC HOGG1 HPCC HPS HR IBD IGF-2 Indels MGMT MMR MSI MTHFR MTR MTRR NER NGS NSAIDs Plk PPARG RXR SAM SHMT1 SNP SNPs SSRs THF TP53 TS UC VC VDR XPD XRCC

5.1

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hypermethylated-in-cancer 1 Hereditary non-polyposis colorectal cancer Human 8-oxoguanine DNA glycosylase 1 Hereditary polyposis colorectal cancer Hamartomatous polyposis syndromes Homologous recombination Inflammatory bowel disease Insulin-like growth factor 2 Insertions or deletions Methylguanine methyltransferase Mismatch repair Microsatellite instability Methylenetetrahydrofolate reductase Methionine synthase Methionine synthase reductase Nucleotide excision repair Next-generation sequencing Non-steroidal anti-inflammatory drugs Polo-like kinases Peroxisome proliferator-activated receptor gamma Retinoid X receptor S-adenosyl methionine Serine hydroxymethyl transferase 1 Single nucleotide polymorphism Single nucleotide polymorphisms simple sequence repeats Tetrahydrofolate Tumor protein p53 Thymidylate synthase Ulcerative colitis Virtual colonoscopy Vitamin D receptor Xeroderma pigmentosum complementary D X-ray repair cross complementing

Introduction

Colorectal cancer (CRC) encompasses the cancers of colon, rectum, and appendix. CRC affects both the genders considerably, is one of the most common cancers worldwide, and represents nearly 10% of all the cancer cases diagnosed and cancerrelated deaths reported, annually (Dekker et al. 2019; Kuipers et al. 2015).

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Epidemiology

5.1.1.1 Etiology The emergence of colorectal tumors is multifarious and varies from benign growths to belligerently invasive tumors. As with origin, presentation is also varied and ranges from non-neoplastic polyps to neoplastic polyps (adenomatous polyps, adenomas) or to full-blown cancers. Most of the colorectal tumors (almost 95%) are adenomas (benign form) or adenocarcinomas (malignant form), which originate from epithelium derived intestinal glands. Colorectal tumors may also arise as carcinoid tumors, which are derived from gastrointestinal neuroendocrine cells, mostly in the small intestine and appendix and only occasionally in the rectum. The other colorectal tumor types include gastrointestinal stromal tumors, which originate from gastrointestinal tract (GIT) resident interstitial cell of Cajal (ICC) and lymphomas, which are predominantly derived from lymphatic system but may rarely arise or develop in the colon and rectum as colorectal lymphomas (O’Brien et al. 2004; Zauber et al. 2002). Colorectal tumors are multifarious in their etiology also and may be sporadic, inherited, or familial in their origin and occurrence (Fig. 5.1). Sporadic CRC, as the name indicates, occurs randomly without any manifestation of being or having an association with an inherited disorder. Sporadic CRC results from somatic (point) gene mutations that arise due to or are associated with normal aging, exposure to environmental factors and lifestyle including dietary habits, acting alone or in a cumulative manner (Dekker et al. 2019; Sameer 2013a). Multiple and diverse genes may be targeted by these mutations, which forms the basis of complex heterogeneous nature of this disease especially with regard to molecular pathogenesis. However, in most cases of sporadic CRC, about 70%, a specific set of genes undergo mutations in a specific progression and manifest initially as benign to precancerous morphological changes, which gradually develop into cancer. The mutations

Fig. 5.1 Etiological types of CRC and penetrance pattern of genes involved

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responsible for sporadic CRC may arise in low penetrance genes or may be low-penetrance mutated variants of high-penetrance genes. Sporadic CRC represents 70–75% of all the cases of colorectal cancer and usually occurs in individuals aged more than 50 years (Mármol et al. 2017). Inherited (hereditary) CRC represents such cases of colorectal cancer, which arise from or are associated with specific inherited disorders, which have well-defined predisposition to CRC. These inherited disorders are usually associated with inherited (germline) high-penetrance gene mutations in one of the two alleles of the targeted gene and contribute to CRC predisposition through creation of a scenario where somatic (point) mutations in the other allele of the targeted (mutated) gene may trigger morphological changes, which may gradually manifest as colorectal tumor. Further, these inherited disorders present clinically with either preponderant polyposis (colonic polyps present) or preponderant non-polyposis (colonic polyps absent) and colorectal cancers resulting from these disorders are known as hereditary polyposis colorectal cancer (HPCC) and hereditary non-polyposis colorectal cancer (HNPCC), respectively. About 5–10% of all the cases of CRC are inherited colorectal tumors (Calvert and Frucht 2002; Power et al. 2010; Mármol et al. 2017). The inherited disorders associated with CRC are of very rare occurrence but exhibit a diverse clinico-pathological presentation, which allows an extensive mechanistic understanding of almost all colorectal cancer types. Hereditary polyposis disorders, which may result in hereditary polyposis colorectal cancer (HPCC) can be grouped into two main types—adenomatous polyposis syndromes (APS) and hamartomatous polyposis syndromes (HPS). Adenomatous polyposis syndromes (APS) include familial adenomatous polyposis (FAP), Gardner syndrome, Turcot syndrome type II, and attenuated forms of familial adenomatous polyposis (Buecher 2016). Hamartomatous polyposis syndromes (HPS) include familial juvenile polyposis syndrome or juvenile polyposis syndrome (FJPS/JPS), PTEN hamartoma tumor syndromes [Bannayan-Riley-Ruvalcaba syndrome (BRRS), Cowden syndrome (CS), and Proteus Syndrome (PS)], Peutz-Jeghers Syndrome (PJS), and hereditary mixed polyposis syndrome (HMPS, a variant form of JPS characterized by the presence of both adenomatous and hamartomatous polyps) (Daniel and Howe 2008; Jelsig et al. 2014). Hereditary polyposis colorectal cancer (HPCC) represents 3–5% of all cases of colorectal cancer. Hereditary non-polyposis disorders, which may result in hereditary non-polyposis colorectal cancer (HNPCC) include Lynch syndrome, Muir-Torre syndrome (MTS), and Turcot syndrome type I. Lynch syndrome is the most common cause of HNPCC with 2–4% of all CRC cases attributed to it and hence is often synonymously used with this type of colorectal cancer. HNPCC represents 1.7–4.2% of all cases of colorectal cancer (Carethers and Stoffel 2015; Lee et al. 2017). Familial CRC represents a heterogeneous group of colorectal cancers with largely unknown molecular etiology and affects individuals with a family history of CRC but without any apparent association with specific inherited disorders. Familial incidence of CRC in such cases is too high to be classified as sporadic CRC and as it does not follow Mendelian or any other specific genetic pattern of inheritance, it cannot be classified as inherited CRC either. Familial CRC possibly results from

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mutations in predisposition genes or may arise from exposures to common environmental factors among the members of a family or both genetic and environmental factors may act in combination. The predisposition genes, which undergo mutations in familial CRC, may be lowly penetrant, moderately penetrant, or highly penetrant. Familial CRC represents 20–25% of all the cases of colorectal cancer (Calvert and Frucht 2002; Power et al. 2010; Armelao and de Pretis 2014; Stoffel and Kastrinos 2014).

5.1.1.2 Incidence, Mortality and Changing Trends CRC ranks third amongst the most common cancers, after cancers of lung and breast and fourth among the major contributors of cancer-related mortality, with about 900,000 deaths per year (Dekker et al. 2019; Bray et al. 2018; Ferlay et al. 2018a). CRC ranks second amongst the most common cancers in women (~9.5% of all cases of cancer) with breast cancer being the foremost common. It ranks third amongst the most common cancers in men (`10.9% of all cases of cancer) with lung cancer being the foremost common and prostate cancer being the second most common (Ferlay et al. 2018b; Torre et al. 2017). With regard to CRC incidence and CRC related mortality, 1.8 million new cases of colorectal cancer (1,096,000 colon cancer cases and 704,000 rectal cancer cases) and 881,000 deaths were reported worldwide in the year 2018. These numbers are expected to increase to 2.5 million new CRC cases and 1.1 million deaths by the year 2035 (Dekker et al. 2019; Ferlay et al. 2018a). CRC incidence and CRC related mortality in females is almost 25% lower than in males (Dekker et al. 2019). CRC incidence expressed in terms of age-standardized (world) [ASR (world)] incidence rates per 100,000 of population has been reported to be 23.6 for males and 16.3 for females. Overall ASR (world) incidence and mortality rates per 100,000 of population have been reported to be 19.7 and 8.9, respectively (Ferlay et al. 2018b). CRC incidence rate exhibits a considerable variation with geographical location and with race and ethnicity. The variation in CRC incidence with geographical location is so conspicuous that CRC is regarded as the marker of the socioeconomic development of nations. CRC incidence in developed nations has been reported to be 3–4 times more in comparison with developing nations and variations in incidence up to 8 times for colon cancer and 6 times for rectal cancer have been reported across different regions globally. Many countries where significant developmental progress is taking place, as evident from increasing human development index (HDI), a uniform increase in CRC incidence rates have been observed. The increase in CRC incidence rates with increasing HDI has been attributed to increased life span and change in environmental factors including such lifestyle and dietary habits, which have been associated with increased risk of CRC (Arnold et al. 2017). ASR (world) incidence rates per 100,000 of population for males and females in countries with high-HDI are 30.1 and 20.9, respectively, and for males and females in countries with low-HDI, these values are 8.4 and 5.9, respectively (Ferlay et al. 2018b, Bray et al. 2018). CRC related mortality in developed nations has been reported to be 2–3 times more in comparison with developing nations. ASR mortality rate (ASRm) per 100,000 of population for males and females in countries

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with high-HDI are 12.8 and 8.5, respectively, and for males and females in countries with low-HDI, these values are 5.7 and 3.8, respectively (Bray et al. 2018). The world regions with the highest reported incidence of CRC include Australia and New Zealand with ASR incidence rates (ASRi) of 41.7 per 100,000 for males, 32.1 per 100,000 for females and overall ASRi of 36.7 and overall ASR mortality rate (ASRm) of 11.1 per 100,000 of population. The lowest incidence of CRC has been reported from South-Central Asia with an ASRi of 6.1 per 100,000 for males, 3.8 per 100,000 for females and overall ASRi of 4.9 and overall ASRm of 3.6 per 100,000 of population (Ferlay et al. 2018b, Bray et al. 2018). The world regions with highest reported incidence of colon cancer include Southern Europe, Australia-New Zealand, and Northern Europe and the world regions with highest reported incidence of rectal cancer include Eastern Europe, Australia-New Zealand, and Eastern Asia. North America is also among the world regions with highest reported incidence of colon and rectal cancers. Based on country and gender, the highest incidence of CRC among males has been reported from Hungary and among females from Norway, with an ASRi of 70.6 and 39.3 per 100,000, respectively. Hungary also has world’s highest reported CRC related mortality rates with ASRm of 31.2 per 100,000 for males and 14.8 per 100,000 for females. Further, CRC represents the most commonly diagnosed cancer among males in Slovakia, South Korea, Japan, Saudi Arabia, UAE, Qatar, Kuwait, Oman, Bahrain, and Yemen whereas in South-Central Asia and all African regions especially Northern, Eastern, Middle and Western Africa, lowest CRC incidence has been reported among both males and females (Ferlay et al. 2018b). In Saudi Arabia, UAE, and Oman, CRC is the most common cancer and foremost cause of cancer-related mortality among males whereas in Japan, Algeria, Portugal, Belarus, and Spain, CRC is the foremost cause of cancerrelated mortality among females (Bray et al. 2018). The trends in incidence and mortality associated with CRC and the trends in relationship between these two inter-related epidemiological parameters exhibit a highly dynamic variation across different world regions. Both the colorectal cancer incidence and mortality have shown a significant increase in medium-HDI countries like China, Russia, Philippines, Brazil, and Baltic republics (Latvia, Estonia and Lithuania) where significant developmental progress is taking place. There has been a significant increase in CRC incidence but considerable decrease in CRC mortality in high-HDI countries like Denmark, United Kingdom (UK), Singapore, and Canada. The decrease in CRC mortality in these nations is largely due to improved diagnosis and treatment. The countries like France, Japan, Iceland, and most importantly the United States (US), which represent the nations with highest-HDI, have shown a significant decrease in both the incidence and mortality rates of colorectal cancer. The decrease in incidence and mortality rates is largely due to improved and timely diagnosis and prevention (through increased screening among individuals aged 50 years or above), improved treatment, in particular, appropriate surgical interventions, and in some countries including the US, due to extensive usage of non-steroidal anti-inflammatory drugs (NSAIDs) (Arnold et al. 2017; Edwards et al. 2010). In US, with a significant decrease in incidence and mortality of CRC in individuals aged 50 years or above, a considerable increase in CRC incidence among

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individuals with age less than 50 years (aged 20–49 years) has been reported over the past few decades. This is exemplified by an almost constant annual increase of 1.8% in incidence since the year 1992 and an increase in ASRi from 9.3 per 100,000 in the year 1975 to 13.7 per 100,000 in the year 2015. The mechanistic interpretation of such an increase is still awaited. Australia is another country where a similar CRC incidence trend is observed (Edwards et al. 2010; Young et al. 2015).

5.1.1.3 Survival When compared with incidence rates, which have shown an overall worldwide increase, a considerable relative decrease in mortality rates has been observed. The decreased mortality rates have been attributed to improved diagnosis and treatment. The development of secondary prophylactic strategies or methods has enabled the early detection of the disease and contributed significantly to overall increased survival. These methods, which may be non-invasive or invasive include fecal occult blood testing (FOBT) [guaiac-based FOBT (gFOBT) and fecal immunochemical test (iFOBT/FIT)] and endoscopic procedures [colonoscopy (CS), flexible sigmoidoscopy (FS), double contrast barium enema (DCBE)], and computed tomographic (CT) colonography [CTC, virtual colonoscopy (VC)] (Edwards et al. 2010). Although these strategies have resulted in overall increase in incidence rates due to increased disease detection or diagnosis, the early disease detection and preventive measures, in particular, the surgical excision of precancerous polyps, a procedure known as polypectomy, have helped in preventing the advancement of the disease to cancerous stage. All this has led to a significant overall decrease in CRC related mortality (Arnold et al. 2017; Benson et al. 2008; Ouyang et al. 2005). As with CRC incidence and mortality, survival rate also exhibits a considerable variation with geographical location, economic status and with race and ethnicity. Economic status, race, and ethnicity have been associated with variation in survival rate within a same geographical location. The important factors that determine the survival rates include availability of and access to trained physicians and surgeons, modern diagnostic procedures, and latest drug-based therapies. The overall 5-year and 10-year CRC relative survival rates have increased significantly over the last few decades and a considerable variation in these rates has been observed across different world regions or countries. The US, which represents the nations with the highest HDI, has shown a significant increase in overall 5-year and 10-year CRC survival rates. In the US, overall 5-year survival rates for stage I colon and rectal cancer are 92% and 88%, respectively. The 5-year survival rates are 87% and 81% for stage IIA colon and rectal cancers, respectively, and 65% and 50% for stage IIB colon and rectal cancers, respectively. These rates are 90% and 83% for stage IIIA colon and rectal cancers, respectively, and 72% for stage IIIB of both these cancers. The 5-year survival rates for stage IIIC colon and rectal cancers are 53% and 58%, respectively. The 5-year survival rates for stage IV, or metastatic colon and rectal cancers are 12% and 13%, respectively (SEER 2016). Within the US, a relatively decreased overall survival rate across all stages of colorectal cancer has been observed amongst Native Americans, African Americans, and deprived social class. This has been largely attributed to

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their economic status, which often manifests as non-availability or less access to healthy diet and requisite healthcare facilities including prophylactic strategies and post diagnosis care (Rawla et al. 2018). Based on the data collected between 2009 and 2015, the overall 5-year relative survival rates for colon and rectal cancers have been estimated to be 63% and 67%, respectively, and 64% for CRC in the US, a highest-HDI nation (Howlader et al. 2019). The 5-year relative survival rate is almost 40% for high-HDI nations, even less for medium-HDI countries and overall 5-year relative survival rate worldwide has been estimated to be about 20% (Torre et al. 2016; American Cancer Society 2020).

5.1.1.4 Risk Modulation Factors Globally, overall lifetime risk of colorectal cancer development is 4.4% for males and 4.1% for females. The risk depends on the absence or presence of risk modulation factors, which may be genetic or environmental, non-modifiable or modifiable and may increase or decrease the probability of CRC development. The non-modifiable or unchangeable risk modulation factors include age, gender, race, and ethnicity, personal or family history of colonic or rectal polyps, personal or family history of colorectal cancer, inherited disorders, inflammatory bowel disease (IBD), cystic fibrosis, cholecystectomy, androgen deprivation therapy, abdominal radiation, diabetes, and insulin resistance. The modifiable or changeable risk modulation factors include obesity and sedentary lifestyle, diet, smoking, alcohol, and medications (Rawla et al. 2019; Sameer 2013a, b). Non-Modifiable or Unchangeable Risk Modulation Factors The risk of CRC development increases with age, with sharp increase observed past the age of 50 years and individuals with age greater than 50 years account for 90% of the reported cases of CRC. Further, CRC incidence rate in individuals with age greater than 65 years has been reported to be as high as 12 times the CRC incidence rate in individuals with age below 45 years (Arnold et al. 2015; American Cancer Society 2020). The risk of CRC development is generally higher in males in comparison with females (Purim et al. 2013). An overall 1.5 times more CRC incidence has been reported in males in comparison with females, irrespective of age and geographical location (Bray et al. 2018). The exact reason behind this gender based difference in CRC incidence is still unknown though hormonal differences and possible differences in exposure levels to cancer promoting factors between the two sexes may partly explain this relationship. However, in comparison with males, females are at a higher risk of developing cancer on the right side of the colon which is highly aggressive, fatal, and associated with higher mortality rate in comparison to cancer on the left side of the colon (Kim et al. 2015). With regard to race and ethnicity, higher CRC incidence and lower 5-year and 10-year relative survival rates have been reported among Native Americans and African Americans in comparison with white Americans and Hispanic Americans, which among them have a comparable CRC incidence and CRC related mortality rates (LansdorpVogelaar et al. 2012).

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The risk of CRC development increases significantly with family history of colorectal cancer. Colorectal cancer diagnosed at the age of 50 years or more in a first-degree family relation increases the risk for CRC by two times. The risk of CRC development is three times if CRC is diagnosed in a first-degree family relation at the age of 50 years or less. CRC in two or more family members increases the risk of this cancer further. Overall, the family history of colorectal cancer increases the CRC risk by two to four times. There is an increased risk also if the disease is present in a second-degree family relation or beyond. It has been reported that almost 15–20% of all CRC cases arise due to the presence of this disease in the family (Kuipers et al. 2015; De Rosa et al. 2015). There are a number of inherited disorders, which increase the risk of colorectal cancer development. Colorectal cancer, which arises due to inherited disorders, is known as hereditary colorectal cancer. About 5–10% of all the cases of CRC result from inherited disorders. Lynch syndrome is the most common inherited disorder associated with CRC with 2–4% of all CRC cases attributed to it. The probability of CRC development in individuals with Lynch syndrome and age 50 years or less is 20% and it is 50% in individuals with Lynch syndrome and age 51–70 years (Bonadona et al. 2011). Another inherited disorder, familial adenomatous polyposis (FAP) has been associated with about 1% of all CRC cases and there is almost 100% probability of CRC development in individuals with this disorder even before they attain the age of 40 years (American Cancer Society 2020). Besides these two, other inherited disorders have been associated with increased risk of CRC development. The inflammatory bowel disease (IBD) is characterized by persistent and long-term colonic inflammation and mainly results from two autoimmune disorders or pathogenic conditions, ulcerative colitis (UC), and Crohn’s disease (CD). Both disorders have been associated with an increased CRC risk and 1–2% of all CRC cases have been attributed to these two conditions. The relative CRC risk associated with ulcerative colitis is more in comparison with Crohn’s disease. It has been estimated that about 2%, 8%, and 18% of individuals with ulcerative colitis develop CRC at 10 years, 20 years, and 30 years of disease duration, respectively (Lakatos and Lakatos 2008). For Crohn’s disease, 3% and 8% of individuals develop CRC at 10 years and 30 years of disease duration, respectively (Canavan et al. 2006). Cystic fibrosis has been associated with ten times increased risk of colorectal cancer development (Yamada et al. 2018). Cholecystectomy has been associated with an increased risk of colon cancer, in particular, the cancers of right-sided and proximal colon (Lagergren et al. 2001). A meta-analysis study has shown that cholecystectomy increases the risk of CRC by 22% (Zhang et al. 2017). Androgen deprivation therapy (ADT) or androgen suppression therapy is the most important treatment procedure for androgen-responsive prostate cancer. Androgens or male sex hormones, mainly testosterone and dihydrotestosterone (DHT) promote the growth and progression of various prostate diseases including prostate cancer (Eder et al. 2001). Androgen deprivation therapy is used to bring down the levels of androgens in the body to prevent them from promoting prostate carcinogenesis. ADT based on surgical castration (orchiectomy) and gonadotropin-releasing hormone (GnRH) agonists has been associated with increased risk of colorectal cancer. Orchiectomy

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Fig. 5.2 Non-modifiable or unchangeable risk modulation factors for CRC development

and gonadotropin-releasing hormone (GnRH) agonists have been reported to increase the risk of CRC by 37% and 31%, respectively (Gillessen et al. 2010). Radiation therapy used for the treatment of childhood abdominal cancers and adult cancers, in particular, prostate cancer has also been associated with an increased risk of colorectal cancer development. A radiation dose at 40 Gy has been associated with 5–10 times increased risk of developing secondary solid cancers including secondary CRC. Cancer therapy based on alkylating agents has been associated with eight to nine times increased risk of secondary CRC development (Berrington de Gonzalez et al. 2013; Nottage et al. 2012; Desautels et al. 2016). Diabetes has been associated with an increased risk of CRC independent of various shared risk factors including obesity and low physical activity (sedentary lifestyle). Various studies have reported 18–27% increased CRC risk due to diabetes (Guraya 2015; Jiang et al. 2011; Pang et al. 2018). Figure 5.2 provides a graphical representation of non-modifiable or unchangeable risk modulation factors for CRC development. Modifiable or Changeable Risk Modulation Factors Obesity and sedentary lifestyle have been associated with an increased risk of colorectal cancer development. Obesity in general and abdominal obesity in particular have been reported to increase the risk of colon cancer in males by 50% and females by 20% and obesity related increase in risk of rectal cancer development has been reported to be 20% in males and 10% in females. An increase in weight by 5 kg

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has been reported to increase the risk of CRC by 3% (Karahalios et al. 2016). Increase in BMI by one unit has been associated with 2–3% increased risk of CRC (World Cancer Research Fund 2011). Sedentary lifestyle has been reported to increase the risk of colorectal cancer development by 50% and in contrast, regular physical activity has been reported to decrease the risk of colorectal cancer development by 25%. Sedentary lifestyle is an important causative factor for obesity and obesity has also been associated with an increased CRC risk but the obesity related risk is independent of CRC risk due to sedentary lifestyle but both these risk factors may act in a cumulative manner to increase the CRC risk. Obesity and sedentary lifestyle have also been associated with decreased overall survival in CRC (Robsahm et al. 2013). Diet is an important CRC risk modulation factor, which can play a protective role against CRC or promote colorectal carcinogenesis. Diet, which consists of fresh fruits and vegetables, whole grains, milk and supply enough or recommended quantities of fiber, calcium, vitamins including vitamin D has been associated with a decreased risk of colorectal cancer development (Song et al. 2015). In contrast, the diet, which consists of red and processed meat, has been associated with an increased risk of colorectal cancer development (Song et al. 2015). Consumption of red meat and processed meat has been reported to increase the risk of CRC development by 12% and 15%, respectively (Zhao et al. 2017). Digestion of high temperature cooked red meat and processed meat promotes the formation of N-nitroso compounds, heterocyclic amines, polycyclic hydrocarbons, genotoxic and cytotoxic aldehydes, all of which are categorized as potentially carcinogenic substances (Bastide et al. 2011; Santarelli et al. 2008). Alcohol consumption and smoking have also been associated with an increased risk of colorectal cancer development. Alcohol metabolism produces acetaldehyde, a potential carcinogen that may increase the risk of CRC. Moderate alcohol consumption and excessive alcohol consumption have been reported to increase the risk of CRC development by 20% and 50%, respectively (Fedirko et al. 2011). Tobacco smoking is a well-characterized colorectal cancer risk factor. Long-term smoking has been reported to increase the CRC incidence by up to 10.8% and CRC related mortality by up to 12% (Liang et al. 2009; Zisman et al. 2006). Long-term exposure to potent carcinogenic agents such as nicotine, NNN (N0 -nitrosonornicotine), NNK (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone) and other nitrosamines in tobacco and tobacco smoke and their metabolites promotes formation and growth of adenomatous polyps and their tumorigenic progression into metastatic CRC (Liang et al. 2009; Zisman et al. 2006; Botteri et al. 2008). Several medications have been associated with a decreased risk of colorectal cancer. Longterm and continued use of low-dose non-steroidal anti-inflammatory drugs (NSAIDs) such as aspirin has been reported to decrease the risk of CRC. NSAIDs decrease gastrointestinal tract (GIT) inflammation and directly or indirectly inhibit various pro-tumorigenic mediators including cyclooxygenase-2 (COX-2) and prostaglandins [prostaglandin E2 (PGE2) and others] and modulate various tumor promoting processes including cell proliferation, angiogenesis, and apoptosis. Use of NSAIDs such as aspirin in CRC has also been associated with low aggressiveness

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Fig. 5.3 Modifiable or changeable risk modulation factors for CRC development

of cancer and increased overall survival (Rothwell et al. 2010; Algra and Rothwell 2012; Wang and DuBois 2013). However, long-term and continued use of NSAIDs has been associated with increased risk of GIT bleeding and cardiovascular disease and general use of NSAIDs for CRC prevention has not been recommended (Rothwell et al. 2010). Statins such as atorvastatin have also been reported to decrease CRC risk (Bardou et al. 2010; Liu et al. 2014). Long-term and continued use of oral bisphosphonates (BPs), characterized by anti-resorptive properties and used in the treatment and management of osteoporosis and other high bone turnover diseases including metastatic diseases of bones have been reported to decrease the risk of CRC by 13% (Thosani et al. 2013). Continued use of angiotensin-converting enzyme (ACE) inhibitors (ACE-I) for treatment of hypertension, for a period of 1 year has been reported to decrease the CRC risk by 16% and continued use for a period of 5 years has been reported to decrease the CRC risk by 25%. No further decrease in CRC risk has been reported beyond 5-year period (Makar et al. 2014). The modifiable risk modulation factors largely explain the differences in CRC incidence with socioeconomic status and geographical location (Doubeni et al. 2012). Figure 5.3 provides a graphical representation of modifiable or changeable risk modulation factors for CRC development.

5.1.2

Classification and Grading

Histologically, most of the colorectal tumors (almost 95%) are adenomas (benign form) or adenocarcinomas (malignant form), which originate from the intestinal mucosa, more specifically, epithelium derived intestinal glands (Hamilton et al. 2010). Carcinoid tumors derived from gastrointestinal neuroendocrine cells, gastrointestinal stromal tumors derived from interstitial cell of Cajal (ICC) and lymphomas are other types of colorectal cancer, which occur but rarely (O’Brien et al. 2004; Zauber et al. 2002; Fleming et al. 2012). The cancers of cecum and ascending colon

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(right-sided colon cancer) mostly grow outwards from the epithelial surface into the bowel wall (exophytic growth) whereas the cancers of descending colon and sigmoid colon (left-sided colon cancer) show circumferential growth and may lead to bowel obstruction. More than 95% of the tumor in a well-differentiated adenocarcinoma involves glandular epithelium, in moderately differentiated adenocarcinoma, glandular epithelium accounts for 50–95% of the tumor and in poorly differentiated adenocarcinoma A) SNP, ApaI (rs7975232, G > T) SNP, TaqI (rs731236, T > C) SNP, and FokI (Met1Lys; rs2228570, T > G) SNP. BsmI and ApaI SNPs are intronic SNPs located between exons 8 and 9, TaqI SNP results in a synonymous sequence change at codon 352 of VDR gene and FokI SNP leads to replacement of initiator amino acid, methionine by lysine (Nassiri et al. 2013; Bai et al. 2012). Besides, Cdx (rs11568820, G > A) SNP located in VDR gene promoter has also been evaluated for any association with cancer risk (Serrano et al. 2016). A number of GWAS and meta-analyses studies have shown that only BsmI SNP can modulate the risk of colorectal cancer and this SNP has been associated with a decreased risk of CRC (Nassiri et al. 2013; Bai et al. 2012). Various studies have shown that BsmI SNP can predict risk of colorectal cancer among Caucasian populations but not among Asians (Yu et al. 2014). FokI SNP has also been associated with an increased risk of colorectal cancer (Pan et al. 2018).

5.3.2.4 PPARG Gene Polymorphisms Peroxisome proliferator-activated receptor gamma (PPARG/PPARγ) gene (chromosomal location, 3p25.2) consists of 146,989 bases and 8 exons and encodes PPARG protein containing 505 amino acids (Wang et al. 2006). PPARG is another ligandactivated nuclear transcription factor, which is involved in activation of gene expression in response to various exogenous ligands (Liang et al. 2018; GrygielGórniak 2014; Wang et al. 2006). PPARG is involved in regulation of macronutrient metabolism. It shows a high expression in adipose tissue and regulates differentiation of adipocytes, lipid metabolism, and insulin sensitization (Janani and Ranjitha Kumari 2015). PPRG is found to play role in several pro-tumorigenic pathways like cell cycle regulation, differentiation, apoptosis, and inflammation (Grygiel-Górniak 2014; Wang et al. 2006). About 60% of sporadic human CRCs have been reported to have higher expression of PPARG and 8% of primary CRCs have shown specific LOH mutations in the PPARG gene (Wang et al. 2006; Sarraf et al. 1999). Several SNPs have been reported in PPARG gene. Out of these SNPs, the two most studied SNPs include P12A (Pro12Ala, rs1801282) and C161T (His447His, rs3856806) (Liang et al. 2018; Lu et al. 2010; Xu et al. 2010). PPARG P12A SNP is important as it causes the reduction of the promoter affinity of PPARG protein by

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approximately 50% and hence can affect the downstream pathways drastically (Wang et al. 2006; Janani and Ranjitha Kumari 2015). PPARG P12A SNP has been associated with a decreased risk of colorectal cancer [(OR, 0.84; 95% CI, 0.72–0.98), P ¼ 0.014] (Xu et al. 2010) and [(OR, 0.88; 95% CI, 0.83–0.94), P < 0.0001)] (Liang et al. 2018). PPARG C161T SNP has also been associated with a decreased risk of colorectal cancer [(OR, 0.66; 95% CI 0.44–1.00), P ¼ 0.05)]. The significant association with a decreased colorectal cancer risk has been observed in Caucasians [(OR, 0.43; 95% CI, 0.26–0.69), P ¼ 0.0006)] but not in Asians [(OR, 0.95, 95% CI 0.73–1.25), P ¼ 0.72)] (Liang et al. 2018). Therefore, it would be safe to assume that PPARG SNPs could serve as predictive CRC biomarkers in Caucasians.

5.3.3

Polymorphisms in DNA Repair Pathway Genes

Cell viability is determined directly by the intactness of the DNA molecule present within it. As DNA is the central molecule which controls all the processes occurring within the cell, any damage to it will immediately get reflected on the cell structure and function. DNA damage results from exposure to various carcinogens including various chemicals, smoke, UV rays, etc. It usually leads to the activation or increased expression of proto-oncogenes through gain of function mutations and inactivation or decreased expression of tumor suppressor genes through loss of function mutations, which are the two main events responsible for initiation and progression of carcinogenesis (Fearon 2011; Fearon and Vogelstein 1990). Humans have highly robust, tightly regulated DNA repair mechanisms, each of which can repair specific type of DNA damage and include mismatch repair (MMR), base excision repair (BER), nucleotide excision repair (NER), double-strand break repair (DSBR), and homologous recombination (HR), and non-homologous end joining (NHEJ) (Chatterjee and Walker 2017; Sameer et al. 2014). A large number of polymorphisms including single nucleotide polymorphisms (SNPs) in different genes involved in regulation and function of DNA repair mechanisms have been shown to modulate the risk of colorectal cancer. The gene polymorphisms through their affect on the structure and function of gene and its transcription can modulate regulation and function of DNA repair genes, which in turn influence the susceptibility to various diseases including cancers such as CRC (Jiraskova et al. 2018; Sameer and Nissar 2018; Nissar et al. 2015; Nissar et al. 2014).

5.3.3.1 OGG1 Gene Polymorphisms Human 8-Oxoguanine DNA glycosylase 1 (hOGG1/OGG1) gene (chromosomal location, 3p25.3) consists of 7 exons and encodes a bifunctional DNA glycosylase protein, human 8-oxoguanine DNA glycosylase (OGG1), which exists in two isoforms, α-OGG1 (345 amino acids) and β-OGG1 (424 amino acids) (Shinmura and Yokota 2001; Hung et al. 2005). OGG1 is involved in the repair of 7, 8-dihydro8-oxoguanine or 8-oxoguanine (8-oxoG), a mutagenic lesion which is induced or produced by reactive oxygen species (ROS) and is used as a marker of cellular

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oxidative stress (Kohno et al. 1998). The presence of 8-oxoguanine results in the incorporation of adenine instead of cytosine during DNA replication, which causes GC to TA transversion (Shinmura and Yokota 2001). OGG1 also possesses an intrinsic AP lyase activity, which allows OGG1 enzyme to cleave phosphodiester linkage at the DNA lesion site creating a single-strand break, which is fixed via short-patch BER pathway (Klungland and Bjelland 2007). Several SNPs have been reported in OGG1 gene. Out of these SNPs, the C977G (rs1052133; Ser326Cys) SNP is the most studied in colorectal cancer across different populations worldwide (Sameer et al. 2012; Yoshimura et al. 2003; Le Marchand et al. 2002). A number of meta-analyses studies have reported the association of C977G SNP with an increased risk of colorectal cancer amongst both the Caucasian and Asian populations [OR range ¼ 1.23–1.49] (Wang et al. 2015; Zhang et al. 2014a; Wei et al. 2011). This establishes that the variant allele of OGG1 exhibits a lower DNA repair activity and hence could serve as one of the early biomarkers of CRC.

5.3.3.2 XRCC1 Gene Polymorphisms X-ray repair cross complementing 1 (XRCC1) gene (chromosomal location, 19q13.31), a 33 kb gene, consists of 17 exons and encodes a transcript, 2.2-kb in size which translates into XRCC1 protein containing 633 amino acids (Nissar et al. 2014). XRCC1 protein is involved in repair of single-strand breaks in DNA [singlestrand breaks repair (SSBR) pathway] and plays a critical role in base excision repair (BER) pathway. XRCC1 protein functions as a pivotal scaffolding protein, which participates in repair of single-strand breaks in DNA through stimulation, promotion, and stabilization of various proteins involved in DNA repair at the affected site (Nissar et al. 2014). At the site of DNA repair, XRCC1 protein interacts with various proteins including DNA polymerase beta, poly-(ADP-ribose) polymerase-1 (PARP1), DNA ligase III, OGG1 and APE (Caldecott 2008; Brem and Hall 2005). Several SNPs have been reported in XRCC1 gene. Out of these, three SNPs represented as C580T (rs1799782) SNP, G839A (rs25489) SNP, and A1196G (rs25487) SNP, which affect the coding region have been most extensively studied in CRC. C580T (rs1799782) SNP affects codon 194 and results in substitution of arginine by tryptophan, G839A (rs25489) SNP affects codon 280 and results in substitution of arginine by histidine, and A1196G (rs25487) SNP affects codon 399 and results in substitution of arginine by glutamine. All these non-synonymous SNPs result in structural changes in XRCC1 protein affecting its DNA repair activity and lead to increased frequency of genetic errors, which in turn promotes events, which may lead to carcinogenesis. Two metaanalysis studies have shown that XRCC1 SNPs are not associated with the risk of colorectal cancer risk (Liu et al. 2013; Wang et al. 2010). However, a significant association between XRCC1 C580T SNP and risk of CRC in Han Chinese population under recessive model [OR ¼ 1.32] has been reported by another meta-analysis study (Wang et al. 2016). A significant association between XRCC1 A1196G SNP and CRC risk in both Asian and Caucasian populations has also been reported (Forat-Yazdi et al. 2015).

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5.3.3.3 XRCC3 Gene Polymorphisms X-ray repair cross complementing 3 (XRCC3) gene (chromosomal location, 14q32.33) consists of 10 exons and encodes an important member of RecA/ Rad51-related family of proteins, XRCC3 protein which contains 346 amino acids. XRCC3 protein plays a critical role in repair of double-strand breaks (DSBs) in DNA and maintains chromosomal stability through homologous recombination (HR) (Thacker and Zdzienicka 2004). To carry out its DNA repair and chromosomal stability function, XRCC3 protein interacts with other proteins including RAD51c and XRCC2 and any substantial change in the interaction domain has been reported to negatively affect the function of this protein (Tebbs et al. 1995). Several SNPs have been reported in XRCC3 gene. Out of these, non-synonymous SNP C722T (rs861539, Thr241Met) has been most extensively studied. However, the association of this SNP with the risk of CRC is not conclusive which limits the use of this SNP as a predictive biomarker for this disease. Two meta-analysis studies have reported a significant association between this SNP and risk of colorectal cancer in both Asian and Caucasian populations (Namazi et al. 2015; Wang and Zhang 2013) whereas other two meta-analysis studies have reported no such association (Zhang et al. 2015b; Liu et al. 2013). 5.3.3.4 RAD51 Gene Polymorphisms RAD51 gene (chromosomal location, 15q15.1) consists of 13 exons and encodes RAD51 protein (RAD51 recombinase) which contains 339 amino acids. RAD51 protein plays a critical role in repair of double-strand breaks (DSBs) in DNA through homologous recombination (HR) (Thompson and Schild 2001; Wick et al. 1996). Double-strand breaks (DSBs) severely affect the native structure of DNA, arise due to continued single-strand breaks, and may result from various endogenous or exogenous processes (Kowalska-Loth et al. 1998). To carry out its function, RAD51 protein interacts with various proteins including recombination proteins, DNA damage sensors, tumor suppressor proteins, and cell cycle regulatory proteins and apoptosis regulatory proteins (Essers et al. 2002). Several SNPs have been reported in RAD51 gene out of which G135C (rs1801320) SNP in the 50 -untranslated region (50 -UTR) is the most common SNP (Richardson 2005). In one meta-analysis study, G135C SNP has been associated with a significantly increased risk of colorectal cancer [OR range, 1.21 to 1.62] (Eskandari et al. 2017). Another meta-analysis study has reported a significant association of this SNP with an increased risk of CRC in Caucasian populations but not Asian populations (Zhang et al. 2014b). 5.3.3.5 XPD (ERCC2) Gene Polymorphisms Xeroderma pigmentosum complementary D, XPD (excision repair crosscomplementation group 2, ERCC2) gene (chromosomal location, 19q13.32) consists of 22 exons and encodes XPD (ERCC2) protein, which contains 760 amino acids. XPD protein belongs to Superfamily two (SF2) helicases and constitutes an essential subunit of transcription factor IIH (TFIIH) complex that is involved in gene transcription and DNA repair.

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XPD protein possesses 50 -3’ DNA helicase activity and DNA-dependent ATPase activity and serves as a vital mediator of Nucleotide Excision Repair (NER) pathway (Hoeijimakers et al. 1996; Sung et al. 1993). Several genetic variations including mutations in XPD gene have been reported to negatively affect the normal functioning of Nucleotide Excision Repair (NER) pathway which results from decreased activity of TFIIH complex and lead to defects in transcription, DNA repair, and apoptotic responses and alter genetic susceptibility to various diseases including cancers (Coin et al. 1999; Shen et al. 1998). Out of these genetic variations, two non-synonymous SNPs, G934A (rs1799793; Asp312Asn) SNP and A2251C (rs13181; Lys751Gln) SNP have been studied in many populations. However, various meta-analysis studies have reported no significant association between these SNPs and risk of colorectal cancer (Zhang et al. 2019; Zhang and Mo 2014; Du et al. 2014).

5.3.4

Polymorphisms in Folate Metabolism Genes

Folic acid or folate (vitamin B9) is one of B-group vitamins and structurally consists of three major components, pteridine ring (2-amino-4-hydroxy-pteridine), p-amino benzoic acid, and glutamic acid. Folate plays vital role in regeneration of methionine, synthesis of nucleic acids, and in shuttling, and redox reactions of one-carbon units, which are required for normal cellular metabolism and its regulation (Nazki et al. 2014). Any insufficiency in folate intake or any aberration in folate metabolism has been associated with an increased risk of colorectal cancer (Ose et al. 2018). Folate metabolism (Fig. 5.4) is a complex pathway and involves the coordinated action of at least 30 different enzymes encoded by different genes scattered all over the human genome. Out of these, the eight enzymes critical for its proper functioning include NADPH dependent dihydrofolate reductase (DHFR), C-1-tetrahydrofolate synthase (C1-THF synthetase), 5,10-Methylenetetrahydrofolate reductase (5,10MTHFR), thymidylate synthase (TS), Methionine synthase (MTR), Methionine synthase reductase (MTRR), cystathionine β-synthase (CBS), and cystathionine γ-lyase (CTH) (Nazki et al. 2014). The role of genetic variations in three of the important genes coding for critical enzymes (MTHFR, TS, MTR, and MTRR) in folate metabolism has been extensively reported to modulate risk of colorectal cancer among different population across the world (Coppedè et al. 2019; Ose et al. 2018; Levine et al. 2010; Figueiredo et al. 2013).

5.3.4.1 MTHFR Gene Polymorphisms Methylenetetrahydrofolate reductase (MTHFR) gene, a 19.3 kb gene (chromosomal location, 1p36.22) consists of 11 exons and encodes MTHFR protein, which contains 656 amino acids arranged into multiple domains (Saffroy et al. 2005). MTHFR protein irreversibly converts 5, 10-methyl tetrahydrofolate (5, 10-MTHF) to 5-methyltetrahydrofolate (5-MTHF), the primary circulating form of folate and an important precursor for the synthesis of amino acid, methionine which involves homocysteine methylation. The amino acid, methionine, in turn is required for the

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Fig. 5.4 Schematic diagram showing the metabolism of folate in human body. Important enzymes of the folate pathway are shown in the pink shades and the products in bluish-green rectangles

synthesis of S-adenosyl methionine (SAM), a universal methyl group donor for a large number of methyl-transfer reactions including DNA methylation (Cicek et al. 2004; Sharp and Little 2004; Nazki et al. 2014; Figueiredo et al. 2013). Several SNPs have been reported in MTHFR gene. Out of these, A1298C (rs1801131; Glu429Ala) SNP and C677T (rs1801133; Ala222Val) SNP have been most extensively studied. MTHFR gene A1298C SNP in exon 7 affects codon 429 and results in substitution of glutamate by alanine whereas MTHFR gene C677T SNP in exon 4 affects codon 222 and results in substitution of alanine by valine. Important enzymes of the folate pathway are shown in the blue shades and the products in green rectangles. [Abbreviations used: DHFR (Dihydrofolate reductase); SHMT1 (serine hydroxymethyltransferase 1); B6 (Vitamin B6); MTHFR (methylenetetrahydrofolate reductase); B2 (vitamin B2); TS (thymidylate synthase); MTR (methionine synthase); B12 (vitamin B12); MTRR (methionine synthase reductase); DHF (dihydrofolate); THF (tetrahydrofolate); 5,10-MTHF (5,10-methyl tetrahydrofolate); 5-MTHF (5-methyltetrahydrofolate); dUMP (deoxyuridine monophosphate); dTMP (deoxythymidine monophosphate); 10-formyl THF (10-formyl tetrahydrofolate); SAM (S-adenosyl methionine)]. C677T SNP has been associated with a decreased thermo-stability and reduced activity of MTHFR enzyme, which leads to hyperhomocysteinemia and low serum folate levels (Coppedè et al. 2019; Wang et al. 2017b; Friedman et al. 1999).

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Several studies have reported a significant association of MTHFR C677T SNP with a decreased risk of colorectal cancer. A meta-analysis study reported a significant association between C677T SNP and a decreased risk of colorectal cancer in overall population [(OR, 0.89; 95% Cl, 0.82–0.97)] and Asian population [(OR, 0.82; 95% Cl, 0.69–0.97)] (Sheng et al. 2012). Another meta-analysis study reported a significant association between C677T SNP and a decreased risk of colorectal cancer in Asian population [(OR, 0.80; 95%CI, 0.72–0.89)] and Caucasian population [(OR, 0.84; 95%CI, 0.76–0.93)] in recessive genetic model (Zhou et al. 2012a). A recent meta-analysis has reported that there is no significant association between MTHFR C677T SNP and risk of colorectal cancer (Rai 2015).

5.3.4.2 TYMS/TS Gene Polymorphisms Thymidylate synthase (TS) gene (chromosomal location, 18p11.32) consists of 8 exons and encodes thymidylate synthase protein, which contains 313 amino acids. Thymidylate synthase catalyzes the generation of deoxythymidine monophosphate (dTMP) from deoxyuridine monophosphate (dUMP). This reaction uses 5,10-methylenetetrahydrofolate as a methyl group donor and produces dihydrofolate (DHF) as the by-product, which is used to regenerate tetrahydrofolate (THF) back with the action of dihydrofolate reductase (DHFR) (Nazki et al. 2014). Because of the ability of thymidylate synthase to synthesize thymidine, one of the nucleotides required for DNA synthesis, TS often serves as a drug target for various chemotherapeutic agents, such as 5-fluorouracil (5-FU). Besides, mRNA and protein expression levels of TS serve as important prognostic indicators for several cancers (Jakobsen et al. 2005; Jian et al. 2004; Moertel 1994). A number of different genetic variations in the form of variable number of tandem repeats (VNRTs) have been reported in thymidylate synthase (TS) gene. Out of these, 28-bp tandem repeat (rs34743033) and 1494 ins/del (rs34489327) have been most extensively studied. 28 bp polymorphism present in the 50 -untranslated region (50 -UTR region/enhancer region) of the TS gene is known to affect the translational efficiency of TS mRNA and is present as double-tandem repeat (2R) or a tripletandem repeat (3R) (Coppedè et al. 2019; Figueiredo et al. 2013; Jakobsen et al. 2005; Lecomte et al. 1994). It has been shown that homozygous 3R/3R cells overexpressed TYMS mRNA compared with homozygous 2R/2R cells (Mandola et al. 2003). TYMS 1494 ins/del (rs34489327) gene polymorphism present in 30 -untranslated region (30 -UTR) usually presents as a 6 bp deletion at position 1494 and has been reported to be significantly associated with the low TYMS mRNA expression in colorectal cancers (Lenz et al. 2002). A recent meta-analysis study has reported that TS polymorphism is associated with a decreased risk of colorectal cancer in a dominant genetic model among Caucasian population but exhibited a significant association with an increased risk of gastroesophageal cancer among Asian population (Zhou et al. 2012b). Furthermore, it has also been shown that TS genotyping can be used to predict the toxicity to 5-fluorouracil based chemotherapy, which emphasizes its importance to personalized treatment for patients with CRC (Ose et al. 2018; Lecomte et al. 1994).

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5.3.4.3 MTR Gene Polymorphisms Methionine synthase/5-methyltetrahydrofolate-homocysteine methyltransferase (MTR) gene (chromosomal location, 1q43) consists of 33 exons and encodes methionine synthase (MS) protein, which contains 1265 amino acids. MS catalyzes the synthesis of methionine through the transfer of a methyl group from 5-methyltetrahydrofolate (5-MTHF) to homocysteine using vitamin B12 as a cofactor. The amino acid, methionine, in turn is required for the synthesis of S-adenosyl methionine (SAM), a universal methyl group donor for a large number of methyltransfer reactions including DNA methylation (Coppedè et al. 2019; Levine et al. 2010; Cicek et al. 2004; Sharp and Little 2004; Nazki et al. 2014; Figueiredo et al. 2013). Several SNPs have been reported in MTR gene. Out of these, MTR A2756G (rs1805087; D919G) SNP, which results in substitution of aspartic acid by glycine has been extensively studied (Coppedè et al. 2019; Ding et al. 2013). This SNP leads to decreased enzymatic activity, which results in accumulation of homocysteine and DNA hypomethylation (Zhou et al. 2012b; Levine et al. 2010). Furthermore, 2756GG genotype has been associated with a decreased frequency of CpG island hypermethylation in tumor suppressor genes (Paz et al. 2002). Several studies have evaluated the association of MTR A2756G SNP with risk of colorectal cancer. A meta-analysis study has reported that MTR A2756G SNP is not significantly associated with the risk of colorectal cancer. The same study has, however, reported that through gene–environment interactions, this SNP may be associated with an increased risk of CRC among heavy smokers (OR, 2.06; 95% CI: 1.32–3.20) and heavy drinkers (OR, 2.00, 95% CI: 1.28–3.09) for G allele carriers (Ding et al. 2013). 5.3.4.4 MTRR Gene Polymorphisms Methionine synthase reductase (MTRR) gene (chromosomal location, 5p15.31) consists of 20 exons and encodes 5-methyltetrahydrofolate-homocysteine methyltransferase reductase (methionine synthase reductase, MTRR) protein, which contains 698 amino acids. MTRR protein plays a crucial role in the synthesis of methionine through regeneration of functional methionine synthase (MTR) enzyme (Nazki et al. 2014; Friso and Choi 2005). Several SNPs have been reported in MTRR gene. Out of these, MTRR A66G (rs1801394; I22M) SNP which results in substitution of isoleucine by methionine has been extensively studied (Leclerc et al. 1998). This SNP results in altered MTRR enzymatic activity, which affects DNA methylation and related processes and therefore has a direct impact on cancer susceptibility (Friso and Choi 2005). Under normal conditions, MTR and MTRR enzymes carry out remethylation of almost 40% homocysteine methionine. A study has reported a significant association of MTRR A66G SNP with an increased risk of CRC among Caucasian population (Zhou et al. 2012a). Another study reported that there is no overall significant association between MTRR A66G SNP and CRC risk (OR, 0.96–1.05, P ¼ 0.44). However, a significant association between A allele of this SNP and increased CRC risk was reported among Asian population (OR, 1.13 P ¼ 0.05). The study

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concluded that this SNP is ethnically associated with CRC risk among Asian populations (Pabalan et al. 2015). Several other polymorphisms have been reported in genes involved in processes linked with folate metabolism. These include SNPs in DNA methyltransferase genes [DNMT3A -448A > G (rs1550117) SNP & DNMT3B -149C > T (rs2424913) SNP] and reduced folate carrier gene [SLC19A1/RFC1 80G > A (rs1051266) SNP]. These SNPs have been reported to modulate the risk of various cancers but their association with risk of colorectal cancer is inconclusive. For example, SLC19A1/ RFC1 80G > A (rs1051266) SNP showed no significant association with risk of colorectal cancer in population based evaluation [(OR, 1.00; 95% CI, 0.81–1.23) and clinic based evaluation [(OR, 0.75; 95% CI, 0.44–1.29)] (Figueiredo et al. 2013). Another study has reported that there is no overall significant association between DNMT3B -149C > T (rs2424913) SNP and CRC risk. However, this SNP is associated significantly with an increased risk of colorectal cancer in heterozygote genetic model [GT vs. TT; (OR, 0.50, 95% CI 0.37–0.69); P ¼ 0.00] and dominant genetic model [GG + GT vs. TT; (OR, 0.51, 95% CI, 0.38–0.69); P ¼ 0.00] (Khoram-Abadi et al. 2016).

5.4

Conclusion

Although, the molecular pathogenesis of CRC is well characterized, and the role of major genes involved in its initiation and development are known. However, with the advent of genome-wide association study (GWAS), the role of genetic polymorphisms in driving the carcinogenic mechanisms is becoming more and more elucidated. Therefore, the role played by the SNPs in the low penetrance genes has become more solidified, especially of the ones performing the central role in metabolism, DNA repair, or signal transduction. Furthermore, genetic polymorphisms have also helped in differentiating the geographical and racial links in modulating the susceptibility to CRC. But the influence of additional environmental factors like diet, microbiota, lifestyle, and other geographical factors needs more in-depth studies. The advancement in the next-generation sequencing (NGS) is one of the best modalities which not only provides the better understanding of CRC, its molecular subtypes and molecular pathogenesis but also provides one of the best ways to develop personalized medicine for the management of CRC. NGS can be easily and effectively used for the early diagnosis and prognosis of CRC, identification of novel mutations, identification, and characterization of biomarkers for targeted therapy for treating CRC. NGS coupled with the CRISPR/Cas9 and immunotherapy also holds a promise for effective personalized medicine with minimal side effects.

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6

Role of Genetic Polymorphisms in Breast Cancer Mohammad Rafiq Wani

Abstract

Of all cancers that affect women, the highest number of deaths results from breast cancer (BC). The tumorigenesis of BC is due to either chromosomal instability (CIN) or genetic polymorphisms of various genes involved in varied cellular processes. Numerous single-nucleotide polymorphisms (SNPs) have been identified and associated with an increased risk of BC. Some of these SNPs are considered potential markers of BC and thus are important for early diagnosis and personalized targeted therapy of BC. The present chapter summarizes the SNPs of various genes and their association with breast cancer. Keywords

Breast cancer · Polymorphism · DNA repair · Cyclins · Xenobiotic metabolism

Abbreviations BC BER HER2 HR IDC MMR NER PI3K

Breast cancer Base excision repair Human epidermal growth factor receptor 2 Hormone receptor Invasive ductal carcinoma Mismatch repair Nucleotide excision repair Phosphatidylinositol-3 kinase

M. R. Wani (*) Section of Genetics, Department of Zoology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_6

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SNP TGF VDR

Single-nucleotide polymorphism Transforming growth factor Vitamin D receptor

6.1

Introduction

Of all cancers affecting women, breast cancer (BC) ranks first in mortality rate (Bray et al. 2018). As per WHO, BC killed over half-a-million women in 2018 worldwide. Even though BC is prevalent in developed countries, in recent years, developing countries have also witnessed an increasing trend in the number of BC cases. Breast cancer originates when breast cells divide uncontrollably, forming a small mass or lump initially in the lobules of the breast or in the ducts connecting nipple to lobules. This initial small mass can grow, invade nearby tissues such as lymph nodes, and can even spread to organs outside the breast through blood. Proliferation of fibrous tissue is commonly seen in most cases of breast cancer and the tissues around the proliferation site contract, causing skin dimpling and inward-drawing of the nipple area (Catterall 1995; Bundred and Downey 1996). For a better prognosis and prevention of BC, early diagnosis is required which is possible through genetic testing. BRCA1 and BRCA2 are two genes often tested in breast cancer as it has been confirmed that mutations in these two genes increase the risk of BC. However, these mutations tend to be prevalent in only 0.2–0.3% of the general population, which can go up to 20% in BC patients (Nelson et al. 2013). This low overall prevalence limits their use; so, other potential genetic markers need to be explored. Research studies have confirmed that variants of certain genes resulting from genetic polymorphisms could increase the risk of developing BC; thus, the genetic polymorphisms can be used as potential markers of BC (Wendt and Margolin 2019). Here, we summarize various types of breast cancers, their causes and symptoms, polymorphisms of genes and their association with breast cancer risk.

6.1.1

Types

Depending upon the site of origin in the breast, breast cancer is of the following types: Ductal carcinoma in situ (DCIS) originates inside the milk ducts but does not spread into surrounding breast tissue. It is not-lethal and its presence is an indication that invasive breast cancer can develop in the person later in his life. Invasive ductal carcinoma (IDC): Majority (80%) of BC cases are IDC type. IDC is common in women aged 55 years or older. It begins in the walls of the milk duct and spreads into nearby tissues by breaking through the duct wall and can also spread outside the breast to other body regions.

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Table 6.1 Estimated new breast cancer cases (DCIS and invasive) and deaths in 2019 among US women according to age Age C) SNP is not significantly associated with BC risk (Wu et al. 2011).

6.3.2

Vitamin D Receptor Gene

Vitamin D may confer protection against many cancers (Bikle 2014; Shao et al. 2012). Vitamin D receptor (VDR) is a 48–55 kD protein found in most human cells. VDR protein binds with retinoid X receptor, conformationally changing VDR that allows vitamin D3 to bind at ligand binding domain of VDR. In humans, VDR gene is present on chromosome 12q13–14 and is 75 kb long containing 11 exons. More than 470 SNPs have been identified in VDR gene, including SNPs Fok1 (rs2228570), Bsm1 (rs1544410), Taq1 (rs731236), Apa1 (rs7975232), and Poly A (rs17878969). Various meta-analysis has reported that there exists no significant association between BC risk and VDR gene SNPs (Fok1, Bsm1, Taq1, Apa1, and Poly A) both in mixed races and Caucasian population (Lu et al. 2016; Huang et al. 2014; Xu et al. 2014). However, ff genotype of VDR Fok1 SNP may increase breast cancer risk (Zhang and Song 2014; Wang et al. 2013a, b; Tang et al. 2009).

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6.3.2.1 BRCA2 BRCA2 protein consists of 3418 amino acids and is known for repairing double stranded breaks in DNA by homologous recombination (HR). It acts by regulating another HR protein RAD51 intracellular shuttling and function (Pellegrini et al. 2002). BRCA2 gene is present on chromosome 13q12.3. Various SNPs have been found in this gene including the SNP rs144848 located in exon 10 of the BRCA2 gene (Pharoah et al. 2002). SNP rs144848 (N372H) is an A to C transition that causes an asparagine-to-histidine substitution (N372H), altering the BRCA2 structure, thereby affecting its ability to activate the target genes (Fuks et al. 1998). By altering DNA repair capacity, BRCA2 SNPs may contribute to cancer (Lord and Ashworth 2007). Meta-analysis done by Faramarzi et al. (2018) reported that among various BRCA2 SNPs studied, only BRCA2 rs1799955 SNP (S2414S) was found to be significantly associated with BC. Other meta-analysis reported that BRCA2 N372H SNP (rs144848) might be associated with BC risk (Li et al. 2017a, b; Qiu et al. 2010a, b). 6.3.2.2 BRCA1 BRCA1 is a multifunctional protein that repairs damaged DNA, regulates cell cycle, apoptosis, and transcriptional modulation (Venkitaraman 2014). BRCA1 protein consists of 1863 amino acids and is encoded by BRCA1 gene. Deficient BRCA1 can cause defects in the cell cycle which may lead to cancer development (Deng 2002; Venkitaraman 2002). BRCA1 gene is present on chromosome 17q2. Various polymorphisms have been identified in BRCA1 that have been implicated in various cancers, including breast cancer (Bougie and Weberpals 2011). Xu et al. (2018) in their meta-analysis reported that in Caucasians, BRCA1Gln356Arg SNP (rs1799950) decreased the breast cancer risk and BRCA1Glu1038Gly SNP (rs16941) increased the overall cancer risk. Yang et al. (2019) reported that BRCA1 SNPs rs799917 (Pro871Leu) and rs1799966 (Ser1613Gly) and BC risk showed no association.

6.3.3

SNPs in Transcription Regulating Genes

6.3.3.1 TP53 TP53 (tumor protein p53) is critical in arresting cell cycle and inducing apoptosis when DNA is damaged, thereby maintaining the integrity of the genome (Levine et al. 1991). TP53 is a phosphoprotein of 393 amino acids, having 43.7 kD molecular weight and is highly rich in proline residues (Fig. 6.1). In response to stress, ATM and ATR protein kinases are activated which in turn activate the p53 protein which binds to DNA causing another gene to produce p21 protein. P21 binds to cell-division promoting cdk2 and inhibits it, thus cell cycle is arrested. However, mutated p53 does not bind to DNA or binds ineffectively; as a

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Fig. 6.1 Schematic representation of p53 activity

result p21 stop signal is not produced and cdk2 is free which promotes cell division and in the absence of any inhibitor, cell proliferation continues causing cancer. The p53 gene is present on chromosome 17. It is a tumor suppressor gene and altered/defective p53 is found in nearly 50% of all cancers (Soussi and Wiman 2007) and 30% of breast cancers (Olivier et al. 2006). Numerous SNPs in p53 which affect its function include: intron 3 SNP (rs17878362), intron 4 SNP (rs1794287), and P47S (rs1800371) (Hu et al. 2010; Bellini et al. 2010; Li et al. 2005). The most reported polymorphism in p53 is a G/C variation in exon 4 (SNP72; rs1042522), which causes formation of arginine in place of proline at position 72 (Dahabreh et al. 2013) that markedly affects the primary structure and activity of the protein TP53 (Pim and Banks 2004). Numerous studies have associated p53 Arg72Pro SNP (rs1042522) with BC (Bišof et al. 2010; Surekha et al. 2011; Vijayaraman et al. 2012; Pandrangi et al. 2014). P53 rs17878362 SNP, which is an intron 3 16 bp duplication, and BC are strongly associated (De Vecchi et al. 2008) which is consistent with the results of a meta-analysis (Hu et al. 2010) (Fig. 6.2). In a normal cell, as a ligand binds and activates the growth factor receptor, PI3K is recruited to the membrane through its p85 adaptor subunit, the p110 catalytic subunit is activated which catalyzes formation of secondary messenger PIP3. PIP3 activates serine threonine kinase such as AKT which mediates activates some downstream target proteins while inhibiting the activity of others, resulting in cell survival, proliferation, and protein synthesis. Mutated p110 will continue the cell proliferation resulting in cancer.

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Fig. 6.2 Schematic representation of PI3K signaling pathway

6.3.3.2 PIK3CA PIK3CA belongs to kinases that catalyze the phosphorylation of PIP2 into PIP3. It takes part in cell division, survival, and growth as well as in breast cancer formation and progression (Boyault et al. 2012). PIK3CA gene is present on chromosome 3q26.3. It encodes p110 catalytic subunit of PI3Ks (phosphatidylinositol-3 kinases). SNPs in PIK3CA contribute to various tumors including BC (Pang et al. 2014). One of the common polymorphisms that has been extensively studied in the PIK3CA is rs17849079. This SNP is a silent polymorphism in which C is substituted by T, causing change of codon ACC to ACT, with both codons encoding threonine (Naguib et al. 2011). Various studies have associated PIK3CA rs17849079 SNP with BC (Karakas et al. 2013; Hanafy et al. 2019). 6.3.3.3 ERa and ERb Estrogen, a steroid hormone, serves in breast growth and development of hormone dependent BC (Hammond et al. 2010). It acts through its two receptors: the ERα composed of 595 amino acids is a product of ESR1 gene and ERβ composed of 530 amino acids is encoded by ESR2 gene. ESR1 gene spans over 140 kb of genomic DNA, containing eight exons and located on chromosome 6, whereas ESR2 gene also contains eight exons but is located on chromosome 14. Polymorphisms in both genes can lead to BC in many populations globally (Yu et al. 2011; Jahandoost et al. 2017; Lipphardt et al. 2013; Dehghan et al. 2017; Ghali et al. 2018; Chen et al. 2013). The most characterized SNPs of ESR1, PvuII (rs2234693) and XbaI (rs9340799), have been associated with BC in many populations of the world (Araujo et al. 2011;

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Ramalhinho et al. 2013; Madeira et al. 2014; Chattopadhyay et al. 2014; Chauhan et al. 2019). One meta-analysis concluded that in Caucasian population, ESR1 rs3798577 SNP increased BC risk but ESR1 rs2228480 SNP decreased BC risk (Li et al. 2016). Another meta-analysis associated ESR1 PvuII polymorphism with BC risk in Asians only and not in white populations (Zhang et al. 2015). A metaanalysis concluded that ESR2 rs4986938 SNP may contribute to BC risk (Yu et al. 2011).

6.3.4

SNPs in Xenobiotic Metabolism Genes

Xenobiotics are foreign substances introduced into the body from the environment and are subsequently metabolized in the body by a series of enzymatic actions to facilitate their elimination from the body. A major site for xenobiotic metabolism in the body is the liver and consequently majority of enzymes are present in the liver cells. Some endogenous substances such as eicosanoids and steroid hormones are also modified to facilitate their excretion. As a result of xenobiotic metabolism, the body’s oxidative status is strongly impacted. Huge amounts of ROS are generated in cytochrome P450 mediated reactions which is chiefly responsible for toxicity of many xenobiotics. Xenobiotic metabolism (such as drug metabolism) can be grouped into phase I and phase II. Phase I enzymes activate inactive carcinogens which are subsequently detoxified by phase II enzymes. The role of genetic variations in three of the important genes coding for critical enzymes (CYP1A1, COMT, and GST) in xenobiotic metabolism has been extensively reported to modulate the BC risk in various populations of the world.

6.3.4.1 CYP1A1 CYP1A1 is an enzyme of cytochrome P450 superfamily and takes part in drug metabolism and lipid synthesis. A major phase I enzyme of xenobiotic metabolism, CYP1A1, also known as AHH (aryl hydrocarbon hydroxylase), upon induction by polycyclic aromatic hydrocarbons (PAHs), activates certain pro-carcinogens into active carcinogens; consequently it participates in the development of various cancers in humans (Badal and Delgoda 2014; Go et al. 2015). For example, CYP1A1 participates in conversion of benzo[a] pyrene, a weak carcinogenic, into its epoxide, a more potent carcinogen which induces guanine adducts on DNA, which can cause carcinogenesis, if not repaired. The CYP1A1 gene is located at 15q24.1 and has seven exons and six introns. Several polymorphisms have been reported in CYP1A1 gene out of which CYP1A1 m1 (rs4646903; T3801C) and CYP1A1 m2 (rs1048943; A2455G) have been extensively studied. CYP1A1 m1 is a T to C transition resulting in threonine to cysteine substitution, while m2 in exon 7 leads to an alanine to glycine substitution. The result of these amino substitutions is enhanced enzyme activity (Garte et al. 2001; Schwarz et al. 2001). Meta-analysis has reported no significant association between CYP1A1 m1 SNP or CYP1A1 m2 SNP and BC (Hussain et al. 2018; Yao et al. 2010; Qin et al. 2014).

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However, earlier studies have associated CYP1A1 m1 SNP with BC risk in several populations of the world (Naushad et al. 2011). CYP1A1 m2 SNP is reported to protect Japanese, UK, and US populations from BC (Basham et al. 2001; Miyoshi et al. 2002; Boyapati et al. 2005; Parada et al. 2017). This is consistent with Chen et al. (2007) who reported that East Asian women carrying CYP1A1 m2 alleles have very low risk of BC.

6.3.4.2 GST The glutathione S-transferases (GSTs) are part of phase II of xenobiotic metabolism and act as detoxification enzymes that catalyze the detoxification of phase I enzyme products (such as carcinogens and drugs) by glutathione conjugation, thereby confer protection against cytotoxicity and DNA from damage (Zhong et al. 1993; Zhang et al. 1992; Ryberg et al. 1997). GSTs act by conjugation reactions, making the products such as carcinogens water-soluble which can then be easily excreted out, thereby protecting the body from their toxic effects (Nebert and Dalton 2006; Soya et al. 2007). GSTM1 is located on chromosome 1 and GSTT1 is present on chromosome 22. Both are highly polymorphic and associated with various cancers (McIlwain et al. 2006). Due to frequent homozygous deletions, the corresponding enzymes lose their functional activity including their detoxifying capacity (Mondal et al. 2013). The polymorphisms of GSTT1 and GSTM1 genes exist as null genotypes, which is a result of deletion of both alleles at a single locus and are located in coding region. Various meta-analysis have concluded a significant association of these null polymorphisms with BC (Qiu et al. 2010a; Song et al. 2016; Hussain et al. 2018).

Fig. 6.3 A xenobiotic metabolic pathway

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This is consistent with earlier reports which associated these polymorphisms with BC risk in various populations in the world such as Greek, Brazilian, Indian, Iranian, and Portuguese (Ramalhinho et al. 2011; Kakkoura et al. 2015) (Fig. 6.3). Phase I enzyme CYP1A1 oxidizes estradiol, while phase II enzymes, COMT and GST conjugate the oxidation products with glutathione (GSH), thereby prevent quinone formation which are mutagenic as they can induce adducts in DNA.

6.3.4.3 COMT COMT (catechol-O-methyltransferase) enzyme degrades and inactivates catecholamines (such as neurotransmitters dopamine, epinephrine, and norepinephrine), as well as drugs and substances having a catechol structure such as catechol estrogens (Guldberg and Marsden 1975). The COMT gene is present on chromosome 22q11, encompassing 28 kb of genomic DNA, encoding a 271 amino acid protein COMT of 30 kd molecular weight. One of the widely known polymorphisms reported in this gene is COMT Val158Met SNP (H108L; rs4680), which causes valine to methionine substitution in the protein. This substitution decreases COMT enzymatic activity considerably (Dawling et al. 2001), and due to its low activity, it is referred to as COMT-L in contrast to high activity COMT-H. It has also been reported that COMT-L gene carriers are at higher risk for BC (Goodman et al. 2002). COMT Val158Met polymorphism induces thermolability in COMT protein, thereby hindering its role in estrogen conversion (Dorszewska et al. 2013). Meta-analysis reported that COMT H108L SNP (rs4680) increases breast cancer risk in Indian, Mexican, and Turkish populations (OR ¼ 1.28–1.42, 95% CI ¼ 1.01–1.97) (Hussain et al. 2018), Asian (Rai et al. 2017), and European populations (Ding et al. 2010). However, Mao et al. (2010) in their meta-analysis did not report any significant association between COMT H108L SNP and breast cancer risk.

6.3.5

SNPs in Cell Cycle Regulatory Genes

A vital physiological process of living organisms is the cell cycle. The process of cell cycle is tightly regulated and a disruption in its control can lead to cancer (Karrman et al. 2015). Some vital genes regulating cell cycle and their SNPs related to BC are mentioned below:

6.3.5.1 CCND1 Cyclin D1 (CCND1), a protein of 295 amino acids, governs the G1/S checkpoint of cell cycle. It belongs to cyclin family of proteins, whose members function as regulators of CDK kinases. It interacts with tumor suppressor protein Rb which positively regulates CCDN1 gene. CCND1 gene comprises 13,388 base pairs, 5 exons, and is located on chromosome 11q13.3. CCND1 G870A (rs9344) SNP, located in the exon-4/intron boundary of CCND1 gene, is associated with many cancers (Sameer et al. 2013; Wasson et al.

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2014). CCND1 protein is found to be overexpressed in more than 50% of BC cases (Buckley et al. 1993). Several meta-analysis has reported that CCND1 G870A SNP (rs9344) and BC risk are strongly associated in Indian (Thakur et al. 2018) and Caucasian (Lu et al. 2009; Cui et al. 2012; Soleimani et al. 2017) populations. Akhter et al. (2019) reported in their meta-analysis that AA genotype of the CCND1 G870A SNP and BC risk in Caucasian but not in Asian population are significantly associated.

6.3.5.2 CDKN1B (p27) CDKN1B, also known as p27, functions to inhibit CDKs, thereby preventing cellular proliferation (Hunter 1993). The p27 gene is present on chromosome 12p13. It is a negative regulator of cell cycle as the encoded p27 protein arrests the cell cycle and, therefore, p27 gene is postulated as a tumor suppressor gene (Kawamata et al. 1995). Certain polymorphisms have been identified in p27 gene out of which rs2066827 (V109G) SNP located in codon 109 and rs34330 (
79 C/T) SNP located 50 UTR at nucleotide
79 have been highly investigated and associated with numerous cancers, including breast cancer. Numerous meta-analysis significantly associated p27 rs34330 SNP with BC risk, especially in Asians (Xiang et al. 2013; Cheng et al. 2017). However, p27 V109G SNP has not been found to be associated with BC susceptibility by various metaanalysis (Wei et al. 2012; Jia et al. 2014). 6.3.5.3 ATM ATM is a Ser/Thr protein kinase of 3056 amino acids belonging to PI3/PI4-kinase family. It senses the double-strand breaks in DNA caused due to a variety of DNA damaging radiation or chemical agents and signals downstream (Abraham 2004; Derheimer and Kastan 2010; Shiloh and Ziv 2013). Once activated, ATM initiates cell cycle checkpoint signaling by phosphorylating multiple substrates including p53 and BRCA1 (Kastan and Lim 2000). The human ATM gene, located on chromosome 11q, spans over 150 kb of genomic DNA and comprises 66 exons. Numerous SNPs identified in this gene have been linked to cancer including BC (Lee et al. 2010). Several meta-analysis reported that BC shares a strong association with ATM rs664677 (T/C) SNP and ATM rs189037 (A/G) SNP (Shen et al. 2012; Zhao et al. 2019). 6.3.5.4 Her2 Her2 (human epidermal growth factor receptor 2) is a protein of 1255 amino acids that acts as a receptor in various signaling pathways that regulate cell proliferation and survival (Okines et al. 2011). Her2 gene is present on chromosome 17q12. Her2 Ile655Val SNP which leads to isoleucine (Ile) to valine substitution altering normal Her2 activity is reported to be associated with cancer, including BC (Mojtahedi et al. 2013; Tong et al. 2009).

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Krishna et al. (2018) reported in their meta-analysis that Her2 Ile655Val SNP (rs1136201) is associated with BC risk in many population of the world. Wang et al. (2013a) and Chen et al. (2014) reported significant association between Her2 valine allele in Her2 Ile655Val SNP and BC risk in Caucasian population.

6.3.5.5 TGF-b1 Transforming growth factor-β is a cytokine which plays a role in cell growth, differentiation, and survival and in the development of BC (Derynck et al. 2001; Benson 2004; Massagué 2008; Thiery et al. 2009). TGF-β occurs in three isoforms among which TGF-β1 is universally expressed (Zheng 2009). TGF-β1 gene is present on chromosome 19q13. SNPs reported in this gene include TGF-β1 T869C, TGF-β1*6A, and T869C present in exon 1 and C509T (rs1800469) and 800 G/A located in promoter region. These polymorphisms associate with various cancers including BC (Cebinelli et al. 2016). TGF-β1 T869C SNP (rs1800470) is a cytosine to thymine substitution, resulting in the substitution of proline with leucine. Meta-analysis has reported a significant association of breast cancer with TGF-β1 T29C SNP in Asian (Krishna et al. 2020) and TGF-β1 T869C SNP in Caucasian populations (Qi et al. 2010).

6.3.5.6 pTEN mTOR The pTEN/ mTOR signaling pathway is an important cell signaling pathway involved in cell growth and differentiation. Dysregulation in this pathway can lead to uncontrolled cell cycle and carcinogenesis (Hanahan and Weinberg 2011; Lien et al. 2017). Activation of PI3K/Akt/mTOR pathway results in cancer cell resistance to antitumor therapies (Martini et al. 2014). pTEN dephosphorylates PIP3 resulting in the formation of PIP2, thereby inhibiting AKT signaling pathway (Chung et al. 2009). mTOR activates factors for protein synthesis, thus has a pro-survival effect on cells (Zoncu et al. 2011; Fingar et al. 2004; Laplante and Sabatini 2012; Edinger and Thompson 2002). PTEN and mTOR are both tumor suppressor genes. PTEN gene is present on human chromosome 10q23 and mTOR gene is present on chromosome 1p36.2. SNPs of these genes include mTOR rs2295080 (T > G) in the promoter region, rs2536 (T > C) in the 30 UTR of mTOR, and rs701848 (T > C) in the pTEN 30 UTR region that have been well-studied for their role in carcinogenesis (Zhang et al. 2017; Song et al. 2017). PTEN rs701848 (T > C) and rs2735343 (G > C) SNPs are associated significantly with BC (Li et al. 2017a, b; Chen et al. 2016; Zining et al. 2016). In a study on Chinese population, Zhao et al. (2016) reported that mTOR rs2295080 SNP decreased BC risk and that mTOR rs2536 (T > C) SNP and BC are not associated. Slattery et al. (2012) observed that G allele carriers of mTOR rs1057079 SNP had higher risk of developing BC.

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Conclusion

Numerous studies have provided evidence of the association of SNPs with the BC risk. SNPs can lead to alternative splicing, altered or mutated gene product (protein). Genome-wide association studies (GWASs) have associated about 170 SNPs with breast cancer and about 14% of heredity breast cancer are due to single-nucleotide polymorphisms (SNPs). SNPs can be valuable for accurate diagnosis and prognosis of BC and help in personalized treatment of BC patients. The factors traditionally used to identify the risk of breast cancer such as size or grade of tumor did not take genetic background into account. With the identification of SNPs, it is now known that breast cancer subtypes differ genetically and hence their etiology can be different; this knowledge can help in early and better diagnosis of breast cancer as well as its treatment.

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Genetic Polymorphisms of Essential Immune Pathogenic Response Genes and Risk of Cervical Cancer Saniya Nissar, Aga Syed Sameer, and Mujeeb Zafar Banday

Abstract

The incidence of cervical cancer (CC) shows a geographical variation with the economic status of the country, the countries having lower resources (i.e., those with HDI A (277G > A; rs568408), IVS2T > A (798T > A; rs582054), and 5’UTR T > G (564T > G; rs2243115). All of the four SNPs have been found to affect the IL-12 gene expression ability resulting in defective protein synthesis, and consequently lead to carcinogenesis. In cervical cancer, an early meta-analysis by Zhou et al. (2012a, b) found that IL-12 3’UTR A > C (rs3212227) SNP was significantly associated with the increased overall risk of cancers [OR ¼ 1.14, 95% CI: 1.02–1.27]. Additionally, they also reported that the 3’UTR A > C (rs3212227) SNP was associated with increased risk of cervical and nasopharyngeal cancers in the subgroups of Asians. However, they did not find any association for other SNPs: IL-12 3’UTR G > A (rs568408), IVS2 T > A (rs582054), and 5’UTR T > G (rs2243115). In their metaanalysis Zeng et al. (2017) also reported that IL-12 3’UTR G > A (rs568408) SNP was responsible for the increased cervical cancer risk and overall cancer risk among Caucasians. The meta-analysis by Chang et al. (2015) reported that IL-12 3’UTR +1188 (rs3212227) polymorphism was not associated with risk of CC [OR ¼ 1.09, 95% CI: 0.88 ~ 1.35]. Chen et al. (2012a, b) reported a significant association between IL-12B rs3212227 SNP and overall cancer risk [OR ¼ 1.32, 95% CI ¼ 1.06–1.63], while as in stratified analysis they reported a significant increased risk of CC [OR ¼ 1.34, 95% CI ¼ 1.04–1.73]. 7.3.1.7 HLA Human leukocyte antigen (HLA) genes are a part of 7.6Mbps long major histocompatibility complex (MHC) cluster, mapped at chromosome 6 (6p21.31) in humans and encodes a wide variety of surface proteins that play crucial role in the immune response to pathogens (de Araujo et al. 2009; Horton et al. 2004). HLA comprises a

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Fig. 7.5 Location and organization of the HLA complex on chromosome 6. The class II HLA gene loci on chromosome 6 are designated by three letters: the first (D) which indicates the class of gene, the second letter (M, O, P, Q, or R) represents the family, and the third letter (A or B) for the chain (α or β, respectively)

family of two types of genes—Class I and Class II genes both of which are different in structure as well as in function (Klein and Sato 2000) [Fig. 7.5]. The HLA class I genes code for the α-polypeptide chain of the class I molecule, which has five functional domains: two peptide-binding domains (α1 and α2), one immunoglobulin-like domain (α3), the transmembrane region (α4), and the cytoplasmic domain (α5). The HLA class I β-chain referred to as beta2-microglobulin is a product of distinct gene present on chromosome 15 (Horton et al. 2004; Klein and Sato 2000). Almost all somatic cells are known to have capability to express Class I HLA genes, but the levels of expression do vary in different tissues. Class II HLA genes are translated to α and β polypeptide chains which make up the structure of the class II molecules [Fig. 7.6]. Each of the class II α and β chains consists of four structural domains: the peptide-binding domain (α1 or β1), the immunoglobulin-like domain (α2 or β2), the transmembrane domain, and the cytoplasm facing domain. Class II genes are normally expressed by a subgroup of immune cells that includes B-cells, activated T cells, macrophages, dendritic cells, and thymic epithelial cells (de Araujo et al. 2009; Horton et al. 2004; Klein and Sato 2000). The HLA system is known to play a pivotal role for the host immune response by mediating antigen presentation to the effector cells. There is a huge variability in the structure of the HLA molecules especially within the peptide-binding regions which in turn determine the antigen repertoire capable of binding to each HLA molecule for their display to the T cells for immune responses (de Araujo et al. 2009; Klein and Sato 2000). For the successful and efficient activation of T-cell response the interactions between HLA-peptide complex and the T-cell receptors is a critical step. Thus, the genetic variation in the peptide-binding region of the HLA molecules does affect their ability to bind and present the same to the immune system for the

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Fig. 7.6 Structure of HLA class I and class II molecules

effective response specially to initiate a cell-mediated immune response to HPV infection (de Araujo et al. 2009). Class II genes (DR, DQ, and DP) play a critical role as they are particularly responsible for exposing and presenting the processed viral peptides to the immune system, including that of HPV (Xu et al. 2020; Horton et al. 2004). Numerous molecular and epidemiological studies especially meta-analysis have investigated the relationship of HLA genetic variations and cervical cancer risk (Xu et al. 2020; Brown and Leo 2019; de Araujo Souza et al. 2003; Kamiza et al. 2020; Cheng et al. 2018; Zhang et al. 2015; Wei et al. 2014). Numerous worldwide studies have identified that many HLA alleles like HLA-DPB102:02, DPB103:01, DPB104:02, DPB105:01, DPB113:01, rs9277535 (DPB1), rs4282438 (DPB2), rs3117027 (DPB2), and rs3077 (DPA1) to be significantly associated with the risk of CC (Cheng et al. 2018). Kamiza et al. (2020), in their meta-analysis on 13DRB1 family alleles, reported that in the Asian population, the HLA-DRB1*07:01, DRB1*09, and DRB1*15:02 were associated with an increased risk of CC, while as HLA-DRB1*04:06, DRB1*12:02 and DRB1*13:02 were reported to be associated with decreased risk of CC. However, in Caucasians it was found that HLA-DRB1*04:01, DRB1*04:03, DRB1*11, and DRB1*15:01 were associated with an increased CC risk, while as HLA-DRB1*13, DRB1*13:01, and DRB1*13:02 had a protective effect on cancer risk. Additionally, in the Hispanic or Latin American population, HLA-DRB1*15:01 and DRB1*15:03 increased the risk of CC, whereas DRB1*13 and DRB1*13:02 had a protective effect. Cheng et al. (2018), in their meta-analysis on 11 HLA-DP alleles, reported that HLA-DPB1*03:01 was significantly associated with an increased risk of CC

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[OR ¼ 1.252, 95%CI: 1.116–1.403], while HLA-DPB1*04:02 and HLA-DP rs3117027 G allele were significantly associated with a decreased risk, and HLA-DP rs9277535 G allele was significantly associated with a decreased risk of CC in Asia [OR ¼ 0.802, 95%CI: 0.753–0.855, P ¼ 0.001]. In their meta-analysis to investigate the role of HLA-DQB1 alleles on cervical cancer risk, Zhang et al. (2015) reported that the HLA-DQB1 alleles may contribute to genetic susceptibility to CC. They found that HLADQB1*02, HLADQB1*03, and HLADQB1*0603 decreased the risk of cervical cancer significantly, while as DQB1*05, HLADQB1*0301, and HLADQB1*0402 had a higher risk of CC. Similarly, Zhang et al. (2014a, b) had previously identified HLA DQB1 as the high risk and HLA DQA1 as weaker risk genetic variants for CC susceptibility.

7.3.2

Polymorphisms in Pathogen Response Genes

Toll-like receptors (TLRs) are the pivotal constituents of innate and adaptive immune system which are known to initiate inflammatory response (Akira et al. 2001). TLRs play a significant role in regulating the reactions and mechanisms involved in inflammation and activation of immune responses to foreign pathogens for their elimination. The foreign pathogen can be bacteria, fungi, and viruses as well as cancer debris (Yang et al. 2017; Wang et al. 2008; Werling and Jungi 2003; Akira et al. 2001). TLRs are type I transmembrane proteins that are normally anchored within the cellular bio-membranes, be it extracellular membrane or intracellular organelle membrane of lysosomes or endosomes, characterized by unique leucinerich repeats in the interaction domain. They recognize pathogen-associated molecular patterns (PAMPs) which are the main structural protein motifs present in the pathogenic organisms (Mogensen 2009; Wang et al. 2008). TLRs are known to possess high genomic conservation between humans and mice and around 11 human TLRs and 13 murine TLRs have been characterized so far [Table 7.4], four have been identified to play an important role in the cervical cancer risk—TLR2, TLR3, TLR4, and TLR 9 (Pandey et al. 2019; Mehta et al. Table 7.4 TLRs and their essential ligands TLRs TLR1/2 TLR2 TLR2/6 TLR3/7/8 TLR4 TLR5 TLR9 TLR10 TLR11

Ligands Triacylated lipopeptides Mycoplasma, non-lipopeptidic PAMPs from various pathogens Diacylated lipopeptides Viruses or bacteria mainly in the nucleic acid Lipopolysaccharide (LPS) of gram-negative bacteria Bacterial flagella Unmethylated DNA of bacteria or virus Remain unidentified T. gondii profilin and uropathogenic Escherichia coli

Abbreviations: PAMP pathogen-associated molecular pattern, TLR toll-like receptors

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2017a, b; Zidi et al. 2014). The TLR genes are found throughout the human genome: the genes encoding TLR1 and TLR6 are found in chromosome 4p14, TLR2 and TLR3 have been mapped to 4p31.3-q35, TL4 to 9q32-q33, TLR5 to 1q33.3-q42, TLR7 and TLR8 to Xp22 and TLR9 to 3p21.3 (Akira et al. 2001).

7.3.2.1 TLR2 TLR2 plays a central role in recognizing the foreign pathogens especially grampositive bacteria, listeria, and yeasts. It is necessary for the cellular response to lipopolysaccharide (LPS), peptidoglycan (PGN), mycoplasma lipopeptide macrophage-activating lipopeptide-2 (MALP-2), and lipoarabinomannan (glycolipid of mycobacterium tuberculosis) (Yang et al. 2017; Werling and Jungi 2003; Akira et al. 2001). TLR2 has also been found to play a critical role in detecting the various viral pathogens as well (Mehta et al. 2017a, b; Nischalke et al. 2012). Numerous SNPs of TLR2 have been reported to be correlated with cancer susceptibility including cervical cancer in some genetic studies (Yang et al. 2017; Zidi et al. 2014, 2016; Bi et al. 2014; Kutikhin 2011; El-Omar et al. 2008). In cervical cancer three important SNPs which have been exhaustively studied in various geographical areas of world are: TLR2 + T597C (rs3804099), TLR2 + C1350T (rs3804100) and TLR2G2477A (R753Q, rs5743708) (Pandey et al. 2009, 2019). A recent meta-analysis reported that TLR2 + T597C (rs3804099) was associated with the reduced risk of cancer in general population (Gao et al. 2019). Yang et al. (2013) in their meta-analysis did also report that TLR2 + T597C (rs3804099), TLR2 + C1350T (rs3804100), and TLR2G2477A (R753Q, rs5743708) SNPs were not reported to be associated with cancer risk.

7.3.2.2 TLR3 TLR3 is a unique receptor in the family of toll-like receptors in being able to recognize the double-stranded DNA (ds-DNA) derived from the cytopathic viruses (Mehta et al. 2017a, b; Werling and Jungi 2003). One important genetic polymorphism reported for TLR3 is the non-synonymous SNP—TLR3 C1377T (rs3775290) located within exon 4 of TLR3 gene (4q35.1). This SNP alters the TLR3 ectodomain causing impairment of its functions and hence negatively altering the interaction of ligand with receptor (Sawhney and Visvanathan 2011; Werling and Jungi 2003). The heterozygous CT genotype of TLR3 C1377T SNP is demonstrated to be significantly associated with the chronic HCV infection accentuating its role in viral pathogenesis and evoking of response to viral ds-DNA (Mosaad et al. 2019; Jiang et al. 2008). Zidi et al. (2016) have also reported that TLR3 C1377T SNP to be associated with the modification of risk of CC because of the tobacco usage; however, Pandey et al. (2011) reported contradictory results and suggested only a marginal role of TLR 3 SNP in CC susceptibility.

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7.3.2.3 TLR4 TLR4 mimics the role of TLR2 in recognizing the lipopolysaccharide (LPS) molecules from the gram-negative bacteria and virus particles (Mehta et al. 2017a, b; Long et al. 2014; Werling and Jungi 2003). The TLR4 gene consists of four exons located on the long arm of chromosome 9 (9q33.1). Two important non-synonymous SNPs have been reported—TLR4 A896G (Asp299Gly, rs4986790) and TLR4 C1196T (Thr399Ile, rs4986791), both located on the fourth exon of the TLR4 gene. All of the said SNPs are known to cause a structural modification in the extracellular domain of the TLR4 receptor and have been demonstrated to be associated with the increased susceptibility to viral or bacterial infection (Kutikhin 2011; El-Omar et al. 2008; Lorenz et al. 2001). Recently, Long et al. (2014) demonstrated that TLR4 A896G but not TLR4 C1196T SNP caused an impaired responsiveness of TLR4 to LPS and the corresponding activation of NF-κB. The cells expressing TLR4 with A896G but not C1196T SNP were reported to produce significantly less IL-8 cytokine after its stimulation by LPS. Pandey et al. (2009) reported that TLR4 C1196T polymorphism was associated with stage II CC. Additionally, both TLR4 A896G and TLR4 C1196T SNPs showed a positive association with HPV 16/18 infection, tumor progression and hence thereby with risk of CC (Pandey et al. 2019). TLR 4 together with other TLR gene polymorphisms has also been demonstrated to be involved in the modification of the risk of CC together with the usage of tobacco (Zidi et al. 2016). Chauhan et al. (2019) also did implicate the TLR4 haplotypes with the risk of cervicitis in their meta-analysis. 7.3.2.4 TLR9 TLR9 is a special receptor which can recognize unmethylated CpG areas present in bacteria and viruses. It has been reported that HR-HPV16 derived E6 and E7 proteins are able to directly suppress the immune response by a host cell by amending the TLR9 transcript. These proteins interact with the regulator sequence present within the 2 kb upstream region of TLR9 gene to control its expression (Hassan et al. 2007). Thus, it is hypothesized that genetic variation especially SNPs which affect the promoter region of TLR9 gene may play a role in modulating the risk for CC. The gene coding for TLR9 receptor is mapped to chromosome 3p21.3, a region which is often deleted in various human cancers (Chuang and Ulevitch 2000). It has been reported to possess at least 20 SNPs within its sequence but only two of these have been exhaustively studied in the CC which are -TLR9 -T1486C (rs187084) and G2848A (rs352140) (Chen et al. 2012a, b). In one of the seminal studies, Chen et al. (2012a, b) demonstrated that for TLR9 -T1486C SNP, heterozygous TC genotype is significantly linked with increased CC risk [OR ¼ 1.28, 95% CI ¼ 1.01–1.62] in comparison with homozygous TT genotype. Pandey et al. (2011) in their study had demonstrated that the AA genotype of TLR9 G2848A SNP showed borderline significance with the increased risk for advanced CC stages [OR ¼ 2.63, 95%CI ¼ 0.99–7.01; P ¼ 0.053] (III/IV) in comparison with the early stages (I/II). In contrast, Zidi et al. (2016) reported no

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significant association of TLR9 G2848A SNP with the clinical stages of CC but they did report that this SNP was able to modulate the CC risk associated with the usage of tobacco and the menopausal status among cases together with TLR2, TLR3, TLR4 SNPs. In a recent report by Pandey et al. (2019), it was observed that TLR9 -T1486C SNP had an association with HR-HPV 16/18 infection and both TLR9 -T1486C SNP and TLR9 G2848A SNP were associated with cancer’s early stage. Similar results were also reported by Roszak et al. (2012).

7.3.3

Polymorphisms in Apoptosis Related Genes

Apoptosis is a well-regulated cell suicide mechanism, which is needed for the elimination of the unnecessary and damaged cells in an individual coupled with its critical role in morphogenesis and cellular turnover. The process is tightly controlled by numerous genes for maintenance of the viable functionality of the individual (Vermeulen et al. 2005). Apoptosis, also referred to as the programmed cell death is identified by the typical changes in the morphology of the cell which include blebbing of plasma membrane, shrinkage of cell’s volume, nuclear membrane dissolution, chromatin condensation and fragmentation. Apoptosis process can further be intrinsic or extrinsic depending upon the initiating signals that arise from inside or outside of the cell (Schultz and Harrington 2003) [Fig. 7.7]. The central proteins which play role in the apoptosis are the effector proteins called caspases. Caspases are functionally proteases which belong to a family of cysteine-dependent aspartate-directed proteases that get activated by different ligands to cleave a larger subset of intracellular proteins to mediate cell suicide by apoptosis (Vermeulen et al. 2005). The caspase gene family contains 11 human members which are categorized into three groups. Group 1 includes three caspases: 1, 4, and 5; all of which play role in regulation of inflammation. Group 2 also includes three caspases: 2, 3, and 7; while group 3 includes four caspases (6, 8, 9, and 10), all of which are involved in regulating the actual apoptosis cascade. Caspases which play role in apoptosis are further subclassified into two types based on their mechanism of action a) initiator caspases (8 and 9) or executioner caspases (3, 6, and 7) (McIlwain et al. 2013, 2015). Among the numerous genes which regulate the apoptosis process are the genes for receptors [FAS-FASL, Toll-like receptors (TLRs)], BCL2 gene family, DNA-protein kinase (DNA-PK), cell cycle regulators [e.g., pRb, MDM2, CDKN1], transcription factors (e.g. p53, NF-κB), and cell signaling proteins [e.g., Raf, protein kinase B (PKB)] (Vermeulen et al. 2005; McIlwain et al. 2013, 2015). Genetic variation of the genes involved in the apoptosis mechanism has been explored very exhaustively and many regulatory genes have been found to be associated with the susceptibility to various cancers, because of their capacity in dysregulating the programmed cell death (Vermeulen et al. 2005; Schultz and Harrington 2003). Usually the failure of apoptosis leads to the decreased ability of an individual to eliminate defective cells with damaged DNA which in turn facilitates the accumulation of somatic mutations, and hence contributing to tumor

Fig. 7.7 The two main pathways of apoptosis

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initiation, progression, and metastasis (Evan and Vousden 2001; Lowe and Lin 2000).

7.3.3.1 FAS Fas receptor (also known as CD95 or Apo-1) is the apoptotic death receptor for FasL, is a type I membrane protein which functions as one of the important members of tumor necrosis factor (TNF) receptor superfamily (Nagata 1997). FAS gene is located on chromosome 10q24.1, consists of nine exons and eight introns (Mehta et al. 2017a, b; Tan and Ankathil 2015). The interaction of Fas with its ligand (FasL) is the initial trigger for the activation of signal cascade which eventually leads to apoptosis, via an extrinsic pathway (Nagata 1997). Numerous molecular studies have previously reported the downregulation of Fas expression and/or up-regulation of FasL expression in many human cancers (Rabinowich et al. 1998; CrnogoracJurcevic et al. 2001). While on one side this helps the damaged cell evade the elimination via anti-tumor immune responses and on the other side increases the ability of damaged cells to counterattack the immune system, thereby playing critical roles in carcinogenesis mechanisms (Vermeulen et al. 2005; Evan and Vousden 2001; Lowe and Lin 2000). For the promoter region of FAS gene, two SNPs have been studied extensively because of their association with CC susceptibility, they are FAS -A670G (rs1800682) and -G1377A SNP(rs2234767) (Mehta et al. 2017a, b; Tan and Ankathil 2015; Nunobiki et al. 2011; Lai et al. 2003, 2005). Both of these SNPs have been found to affect the Sp1 and STAT1 transcription factor binding sites resulting in the diminished promoter activity hampering the expression of the gene and consequently leading to decreased FAS expression (Kanemitsu et al. 2002; Nunobiki et al. 2011). FAS -A670G SNP is mapped within the interferon gamma activation sequence (GAS) of the gene that serves as a binding site for STAT1 transcription factor while FAS -G1377A SNP is located within the binding site of SP1 transcription factor (Kanemitsu et al. 2002; Sibley et al. 2003). For the FAS -G670A SNP a varying association with the cancer in general and cervical cancer in particular has been reported (Xu et al. 2014). Xu et al. (2014) reported an association of FAS -G670A SNP with the overall risk of cancer in a homozygous model, same was the report published by Nunobiki et al. (2011) in which association of this SNP was found to be with the CC risk and HPV infection type. Lai et al. (2003) also reported the association of FAS -G670A SNP with the cervical carcinogenesis. However, a meta-analysis by Shen et al. (2013) has reported that FAS -G670A SNP was not associated with the susceptibility to CC for all four models of inheritance [OR varying between 0.92 to 0.97] and for the ethnicity as well (both Caucasians and Asians). Wang et al. (2014) also reported the similar results for the FAS -G1377A SNP in both Asians and Africans. 7.3.3.2 FASL Fas ligand (FasL) is another important part of the Fas signaling of apoptosis. This serves as the ligand for the binding to the Fas receptor and is a product of FASL gene located on chromosome 1q24.3 (Mehta et al. 2017a, b; Tan and Ankathil 2015).

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Two SNPs of FasL gene have been reported to increase the susceptibility to cancers: FasL IVS2nt-124 A > G (rs5030772) and FasL -T844C (rs763110) (Nagata 1997; Lai et al. 2005). Out of these two, FasL -T844C SNP has been studied extensively for its association with CC (Xu et al. 2014). FASL -T844C SNP is located within a presumed binding motif for CAAT/enhancer-binding protein (C/EBPβ); the two iso-forms have different affinities for binding to the C/EBPβ which affects the functional expression of the gene and hence FASL-mediated signaling in lymphocytes (Nagata 1997; Rabinowich et al. 1998). Wang et al. (2014) reported that FasL T844C SNP was not associated with the susceptibility to CC in both Asians and Africans. Similar results were also reported by other two meta-analyses separately (Xu et al. 2014; Zhu et al. 2014).

7.3.3.3 CASP8 As mentioned above, caspases are the main regulative and executive drivers of the apoptosis mechanism. Among all caspases, caspase 8 plays a central role in driving the extrinsic apoptosis pathway. Caspase 8 is the first one to be recruited after the binding of the external FasL (CD95-L) with the Fas receptors on the surface of lymphocytes towards the FAS-associated death domain (FADD) or TNFRassociated death domain (TRADD). Recruitment of caspase 8 results in the dimerization and then its spontaneous activation. Activated caspase 8 then either initiates apoptosis directly by cleaving and thereby activating executioner caspases (3, 6, 7), or activates the intrinsic apoptotic pathway through cleavage of BID to induce efficient cell death. (McIlwain et al. 2013, 2015). Caspase 8 protein is coded by the CASP8 gene, located on chromosome 2q33.1 and consists of 11 exons spanning approximately 30 kb (Grenet et al. 1999). CASP8 gene is one of the highly polymorphic genes and is reported to contain around 500 genetic polymorphisms in its sequence (Cai et al. 2017). Of all the polymorphisms in CASP8 gene, 652 6 N ins/del polymorphism (/CTTACT, rs3834129) is the most studied and reported upon. It is a six-nucleotide insertion/ deletion variant located in the CASP8 promoter region, which has been shown to lead to decreased CASP8 expression (Sun et al. 2007). The decreased expression and hence activity of caspase 8 affect the extrinsic apoptotic pathways drastically which results in the compromised immune surveillance of the tumor cell, thereby affecting carcinogenesis development and progression (Hashemi et al. 2012). In one of the recent meta-analyses, it is reported that CASP8–652 6 N ins/del polymorphism was associated with decreased overall cancer risk including that of CC, both in Asian and Caucasian populations (Cai et al. 2017). Furthermore, another meta-analysis reported CASP8 rs1045485 polymorphism was not associated at all with the CC susceptibility (Hashemi et al. 2020). 7.3.3.4 TP53 The p53 protein is one of the critical tumor suppressors present in the cell, which is responsible for maintaining the integrity and functionality of the genome. p53 protein plays a central role in inducing cell cycle arrest, apoptosis, or DNA damage repair, negatively regulating the cell cycle (Berchuck et al. 1994). p53 plays a crucial

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role in the intrinsic pathway of apoptosis, as it plays a pivotal role of transcriptional activator for numerous pro-apoptotic genes like Bax, Noxa, Puma, and Bid which are expressed in response to the irreparable damage to DNA (Vermeulen et al. 2005; Evan and Vousden 2001; Lowe and Lin 2000). Additionally, p53 is known to induce the transcription of FAS gene and promotes the trafficking of Fas proteins out of the Golgi complex, which does establish that p53 can also modify apoptosis mechanism via the activation of the extrinsic pathway (Bennett et al. 1998). TP53, being a principal tumor suppressor gene is one of the most frequently mutated genes in all types of human cancers (Whibley et al. 2009). It is found on chromosome 17p13.1, is of 19 kb in length, and consists of 11 exons encoding a transcript of 2629 bp and a p53-protein of 393 amino acids (Bojesen and Nordestgaard 2008). As described in the previous chapters, TP53 possesses numerous polymorphisms of which two non-synonymous SNPs are important, because of their ability to result in the change in structure of protein which affects its tumor suppressing capacity. These SNPs are located at codon 47 (P47S; rs1800371) and codon 72 (P72R; rs1042522) of TP53 gene in exon 4 (Pietsch et al. 2006; Murphy 2006). In cervical cancer, as already described above one of the major arms of mechanism of HPV pathogenesis and tumorigenesis is via forced induction of p53 degradation through the activation of E6/E6AP complex, making TP53 a possible candidate gene in cancer (Vermeulen et al. 2005; Evan and Vousden 2001; Lowe and Lin 2000). The TP53 Pro72Arg polymorphism has been envisaged to make p53 protein more vulnerable to E6/E6AP-mediated degradation by about seven times and hence can influence the susceptibility to cervical cancer especially in individuals with homozygous Arg-72 genotype (Storey et al. 1998; Habbous et al. 2012; Sousa et al. 2011). It was reported that Arg variant (Arg72) of p53 protein binds to the highrisk HPV E6 protein with greater efficiency than the Pro variant and there also occurr differences in the affinity between p53 and endogenous transcriptional elements with the Arg variant (Habbous et al. 2012; Sousa et al. 2011). In the past two decades considerable molecular and epidemiological studies have focused on the association of p53 polymorphisms with the risk of CC. In a recent meta-analysis, Habbouset al. (2012) reported that for individuals with HPV infection p53 Arg allele (rs1042522) was significantly associated with the risk of CC [OR: 1.37; 95% CI: 1.15–1.62; P < 0.001]. Zhou et al. (2012a, b) in their meta-analysis found that Pro/Pro genotype was associated with increased risk of CC under the heterozygous model (Pro/Pro vs. Arg/Pro: OR ¼ 1.25, 95%CI: 1.02–1.53, P ¼ 0.005). Furthermore, the subgroup analysis showed that the Pro/Pro genotype and Pro carrier to be significantly associated with increased CC risk among Indian population, but not among Korean, Japanese, and Chinese.

7.3.3.5 MDM2 Mouse double minute-2 homolog (Mdm2) shares the intricate relationship with p53 as both interact dynamically with each other via an autoregulatory loop in which p53 functionality is negatively regulated by Mdm2. MDM2 gene which encodes Mdm2 protein located on chromosome 12q13–15 is itself transcriptionally inducible by p53

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(Ortiz et al. 2018; Saadatzadeh et al. 2017; Bieging et al. 2014). Mdm2 is also known to contain E3 ubiquitin ligase activity via which it degrades the inactivated p53 protein through ubiquitination and proteasomal activity (Saadatzadeh et al. 2017). One of the important genetic polymorphisms that have been reported for the MDM2 gene is the SNP present in its first intron - T309G (rs2279744) at position 309 (Bond et al. 2004). This has been reported to enhance the binding affinity of the Sp1 transcription factor to its consensus sequence in the MDM2 promoter and hence results in the elevated levels of MDM2 in cells eventually decreasing the tumor suppressing activity of p53 protein (Ortiz et al. 2018; Saadatzadeh et al. 2017; Bieging et al. 2014). Numerous meta-analyses on the role of MDM2 T309G SNP in modulation of CC risk have been reported but with conflicting outcomes (Tan and Ankathil 2015; Zhuo et al. 2014; Singhal et al. 2013; Jiang et al. 2011; Nunobiki et al. 2010; Hu et al. 2010). Zhuo et al. (2014) had reported a significant association between MDM2 T309G SNP and CC risk [GG vs TT: OR ¼ 1.31; 95% CI ¼ 0.55–3.13; Dominant model: OR ¼ 1.22; 95% CI ¼ 0.65–2.31; Recessive model: OR ¼ 1.45; 95% CI ¼ 0.79–2.65]. They concluded that homozygous GG alleles of MDM2 T309G SNP might be a risk factor for CC especially among Asians. Zhang et al. (2018) reported a significant association of MDM2 SNP with the risk of gynecological cancers but they didn’t find any notable association between MDM2 T309G SNP and CC risk.

7.3.4

Polymorphisms in Antigen-Processing Genes

The antigen-processing and presentation machinery (APM) is one of the intricate assemblies of multiple molecular species and proteins all of which work together for the generation of antigens to be presented on the surface of antigen-presenting cell in conjunction with the human leucocyte antigen (HLA) for the recognition and hence activation of cytotoxic T lymphocytes (Leone et al. 2013). As already described previously HLA are of two main types: Type I and Type II (Klein and Sato 2000). In order to express the peptides bound to HLA molecules on the surface of antigenpresenting cell, APM functions to perform four main tasks: (1) peptide generation and trimming; (2) peptide transport; (3) assembly of the MHC class I loading complex; and (4) antigen presentation (Leone et al. 2013; Blum et al. 2013). Furthermore, depending upon the source, the peptides derived from proteins degraded by the lysosomal pathway are primarily presented by MHC class II molecules, whereas the peptides generated by the ubiquitin-proteasome pathway are presented by MHC class I molecules (Blum et al. 2013; Yewdell 2001). To function effectively APM itself is made up of many kinds of proteins performing a specific function in the antigen processing. The main components of the APM are: multimeric protein complex proteasome, low molecular-weight proteins (LMP), transporter associated with antigen presentations (TAP), endoplasmic reticulum aminopeptidase associated with antigen presentations (ERAP) (Leone et al. 2013).

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The proteasome is a multimeric protein complex found in both the cytosol and nucleus and does the function of breaking down the polyubiquitinated proteins to the potent antigens. LMP 2 and 7 are involved in generation of peptides from intracellular proteins. The peptides generated within the cytosol are then actively transported into the ER by the TAP. TAP forms a heterodimeric complex of the two halftransporters, TAP1 and TAP2. TAP1 and TAP2 are involved in transporting peptides (8–12 residues) from the cytosol to the endoplasmic reticulum (ER) via its transmembrane pore in the ER membrane whose opening and closing depends on ATP binding and hydrolysis, respectively (Lankat-Buttgereit and Tampé 2002; Schumacher et al. 1994). Once in ER, the peptides undergo further trimming by ERAP1 and ERAP2 to fit into the groove of the MHC class I molecules. ERAP1 is considered a “molecular ruler” because of its substrate preference and ability to trim peptides of 9–16 residues specifically (Chang et al. 2005). In addition many more proteins including tapasin, calnexin, calreticulin, and ERp57 do also contribute to the APM (Blum et al. 2013) [Fig. 7.8]. Numerous genetic polymorphisms affecting various genes encoding APM components have been found to be associated with the susceptibility to CC (Mehta et al. 2007, 2017a, b). The important genes of the APM, which have been studied for the role played by their genetic variation are discussed below.

7.3.4.1 LMP The low-molecular-weight polypeptide (LMP) system plays a pivotal role in the recognition of intracellular infection—a critical part in immunological surveillance in humans via MHC-Class I and cytotoxic T-lymphocyte (CTL) pathway (Leone et al. 2013). The gene coding for both types of LMPs (LMP2/7) is in the MHC class II region of human chromosome 6 (6p21.3) (Monaco 1992). LMP2 and LMP7 make up two critical subunits of proteasome that play a role in the degradation of cytosolic proteins and result in the production of peptides for presentation by HLA system (Blum et al. 2013; Schumacher et al. 1994). Several studies have reported on the genetic polymorphisms of LMP genes especially LMP2 A60G (Arg > His, rs17587) and LMP7 C145A (Gln > Lys, rs2071543) which result in the functional alteration in the proteins resulting in the impaired capacity of APM of the cells. Thus, these genetic variations have been reported to be associated with the development and progression of various cancers (Wu et al. 2017; Ma et al. 2015; Mehta et al. 2007, 2015, 2017a, b; Song et al. 2014; Cao et al. 2006). In a recent meta-analysis, Wu et al. (2017) reported that the SNPs of LMPs (rs17587 and rs2071543) were associated with an increased risk of cancer in the both recessive and homozygote models. However, after stratification of data the risk was found to be significant only for Asian population. Mandal et al. (2017) demonstrated in their report that LMP7 (rs2071543) plays a significant role in increasing cancer risk by two folds [AA vs CC: OR ¼ 2.602, 95% CI ¼ 1.780 to 3.803; p ¼ 0.001] in general and in particular Asian population but not in Caucasians, and hence could be useful as a prognostic marker for early cancer predisposition.

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Fig. 7.8 The antigen processing of MHC class I peptides. Four main steps in the processing involve (1) antigen uptake, (2) processing through proteasome, (3) peptide transport and association with MHC class 1, and (4) expression on surface of cell

7.3.4.2 Tap Transporter associated with antigen presentation (TAP) plays a crucial role in the second stage of antigen processing. This involves the ATP dependent transport of newly generated peptides from the cytosol to the endoplasmic reticulum (ER) for further processing or trimming and subsequent expression or presentation on the surface of the cell (Blum et al. 2013; Schumacher et al. 1994; Engelhard 1994). TAP functions as a heterodimer, which consists of two transmembrane domain-containing subunits, TAP1 and TAP2. TAP1 and TAP2 also possess motif for binding of ATP (Engelhard 1994; Lankat-Buttgereit and Tampé 2002). The genes, which encode TAP1 and TAP2 proteins, are located in the short arm of human chromosome 6 (6p21.32). A number of genetic variations including

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polymorphisms in the form of SNPs have been reported in TAP1 and TAP2 genes. Of these TAP1 1341 (rs1057141) and TAP1 2254 (rs1135216) polymorphisms in TAP1 gene and TAP2 1135 (rs1800454), TAP2 1693 (rs2228396) and TAP2 1993 (rs241447) polymorphisms in TAP2 gene have been extensively studied for their role in modulating the risk of cervical cancer (Praest et al. 2018; Mehta et al. 2017a, b; Natter et al. 2013; Faucz et al. 2000). Einstein et al. (2009) have reported the association of two TAP1 gene SNPs, TAP1 I333V and TAP1 D637G with high-grade CIN, however, no such association has been reported for any TAP2 gene SNPs. Natter et al. (2013) and Kordi Tamandani (2009) have also reported no such association between TAP gene SNPs and the risk of CC.

7.3.4.3 ERAP Endoplasmic reticulum (ER) aminopeptidases (ERAPs) are the third level proteins involved in the antigen-processing machinery (APM). They are located within the lumen of the endoplasmic reticulum where they act upon the peptides entering ER via transporter associated with antigen processing (TAP) and process or trim them to fit MHC class I proteins, which in turn present these antigens on the surface of the cell (Blum et al. 2013; Schumacher et al. 1994; Engelhard 1994). Human ERAPs, ERAP1 and ERAP2 are important members of a subfamily of M1 zinc metalloproteases known as oxytocinase, which catalyze the trimming of the N-terminal regions of peptides to make them fit to be loaded onto the HLA Class I molecules. ERAP1 and ERAP2 are induced by tumor necrosis factor-α (TNF-α) and interferons (IFNs) including INF-γ and exhibit almost 50% homology with each other in their sequences (Saulle et al. 2020; Neefjes et al. 2011). ERAPs function together in a well-coordinated manner and so as not to be redundant there are marked differences in their enzymatic specificities. ERAP1, a type II integral membrane protein, has a cleavage preference for peptides, which contain large hydrophobic amino acids in their C-terminal side and usually acts on peptides, 9–16 amino acids in length, to generate smaller fragments, 8–9 amino acids in length, which is optimal to fit MHC class I proteins (Hattori and Tsujimoto 2013). In comparison, ERAP2 is not a type II integral membrane protein, and has a cleavage preference for peptides, which contain positively charged amino acids, arginine and lysine, in their N-terminal side. ERAP2 usually acts on peptides shorter in length in comparison with what ERAP1 can process (Saulle et al. 2020; Hattori and Tsujimoto 2013; Serwold et al. 2001). However, it has been reported that ERAP1 and ERAP2 interact with each other physically in ER and form a functional heterodimer to modulate each other’s activities (Saveanu et al. 2005). Two separate genes, which are oriented in opposite direction and located on chromosome 5, locus 5q15, encode human ERAPs. ERAP1 gene has a length of 47,379 bp and contains 20 exons. ERAP2 gene has a length of 41,438 bp and contains 19 exons. A number of genetic variations including polymorphisms in the form of SNPs have been reported in both these genes. More than a dozen intronic and exonic variations have been reported in ERAP1 gene. However, only two genetic variations have been reported in ERAP2 gene. Since ERAP1 and ERAP2

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proteins play vital roles in antigen processing and presentation pathways, any genetic variation which affects their functioning is essentially a prelude to various immune linked diseases including infectious diseases and cancers (Saulle et al. 2020; Hattori and Tsujimoto 2013). Yao et al. have reported exhaustively on the role played by several ERAP SNPs in modulating the susceptibility to various autoimmune diseases, infectious diseases, as well as cancer initiation and/or progression. The downregulation of ERAPs has been reported in cervical cancer and this downregulation has been associated with initiation and progression of CC (Compagnone et al. 2019; Stratikos et al. 2014). In a recent study, Liu et al. (2017) investigated the impact of ERAP1 and ERAP2 variants on HCV chronic infection, and found that the ERAP1 rs26618 polymorphism may affect either the enzymatic activity or the structure, leading to alterations in the HCV antigen presentation pathway, and in turn to increased susceptibility to this infection. It was also observed that combination of rs26618-C/rs2248374-A on the two genes also provides a greater risk of susceptibility to HCV chronicity. Paladini et al. (2018) reported that the presence of a G instead of an A at SNP rs75862629 in the ERAP2 gene promoter strongly influences the expression of the two ERAPs with a down-modulation of ERAP2 coupled with a significant higher expression of ERAP1. ERAP1 rs27044 SNP has been reported to modulate CC risk. The variant form of rs27044 SNP has been reported to result in a significantly decreased ERAP1 mRNA expression in comparison with wild form of this SNP (P ¼ 1  103) (Mehta et al. 2007). Another study has reported a significant difference in the frequencies of allelic and genotypic forms of ERAP1 rs26653 SNP, ERAP1 rs27044 SNP, and ERAP2 rs2287988 SNP between the cervical cancer case and control groups. Further, three haplotypes involving these genes have been associated with an increased cervical cancer risk. These include one ERAP1 haplotype, ERAP1-rs27044C-rs30187Trs26618T-rs26653G-rs3734016C haplotype and two ERAP2 haplotypes, ERAP2rs2549782T-rs2548538T-rs2248374G-rs2287988A-rs1056893T, and ERAP2rs2549782G-rs2548538A-rs2248374A-rs2287988G-rs1056893T (Li et al. 2020).

7.4

Conclusion

As discussed in this chapter, several polymorphisms in the low penetrance genes have been reported to be involved in modulating the susceptibility of an individual towards cervical cancer. However, the expression and function of numerous genes is affected by the dynamic gene environment interactions. Therefore, large-scale genome-wide association studies need to be conducted in various populations in order to understand fully their mutual relationships and to devise the susceptibility model for cervical cancer, which encompasses most of the populations of the world, irrespective of their ethnicity or race.

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8

Thyroid Cancer and SNPs Mosin S. Khan and Syed Mudassar

Abstract

Thyroid cancer (TC) is the most common endocrine malignancy especially in women with papillary thyroid carcinoma (PTC) being the most prevalent type of endocrine cancer whose incidence is growing. Despite the favorable outcome and long survival rates of most patients, some tumors display an aggressive behavior and may progress to the highly aggressive and lethal, anaplastic thyroid carcinoma. In recent years, several progresses have been made on the molecular characterization of TC. The large number of single nucleotide polymorphism (SNP) markers available in the public databases makes studies of association and fine mapping of disease loci very practical. In this chapter we have summarized the genetic characterization of TC, giving a particular emphasis to SNPs, their diagnostic importance in the risk stratification of TC and their therapeutic value. Keywords

TC · Genes · PTC · FTC · Polymorphism · HRAS · RET · Cancer genetics

M. S. Khan (*) Department of Biochemistry, Government Medical College Srinagar & Associated Hospitals, Srinagar, Jammu and Kashmir, India Department of Clinical Biochemistry, Sher-I-Kashmir Institute of Medical Sciences (SKIMS), Srinagar, Kashmir, India S. Mudassar Department of Clinical Biochemistry, Sher-I-Kashmir Institute of Medical Sciences (SKIMS), Srinagar, Kashmir, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_8

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Abbreviations AFIP AJCC ATC BTD DNA DTC ERCC2 FTC HCC HRAS HSCR MALT MTC OS PDTC PTC RET RNA TC TP53 TSH TSHR XPD XRCC1 XRCC3

8.1

Armed Forces Institute of Pathology The American Joint Committee on Cancer Anaplastic TC Benign thyroid disease Deoxyribonucleic acid Differentiated thyroid carcinoma Excision repair cross-complementation group 2 Follicular TC Hürthle cell cancer Harvey rat sarcoma Hirschsprung disease Mucosa-associated lymphoid tissue Medullary TC Oxidative stress Poorly differentiated thyroid carcinoma Papillary TC Rearranged during transfection Ribonucleic acid Thyroid cancer Tumor protein 53 Thyroid-stimulating hormone Thyroid-stimulating hormone receptor Xeroderma pigmentosum group D X-ray repair cross-complementing protein 1 X-ray repair cross-complementing protein 3

Introduction

Cancer is a large group of diseases that vary in their age of onset, rate of growth, state of cellular differentiation, diagnostic detectability, invasiveness, metastatic potential, response to treatment, and prognosis. From a molecular and cell biological point of view, however, cancer may be a relatively small number of diseases caused by similar molecular defects in cell function resulting from common types of alterations to a cell’s genes. Cancers (carcinomas) are characterized by their unregulated growth and spread of cells to other parts of the body (Yarbro et al. 2005). Cancer results from an accumulation of genetic alterations in somatic cells. This accumulation of mutations leads to a breakdown of cooperation between cells in a tissue, such that single cells revert to selfish behavior reminiscent of unicellular organisms. Cancer susceptibility genes can be subdivided into three categories (Michor et al. 2004) (1) Gatekeepers, such as oncogenes and tumor suppressor genes, control

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growth and differentiation pathways of the cell; alterations of such genes lead to increased growth rates or decreased death rates as compared with neighboring cells; (2) Caretakers maintain the genomic integrity of the cell; alterations of such genes increase the rate of accumulating further mutations and cause genetic instabilities; and (3) Landscapers regulate the cell’s interaction with the surrounding microenvironment; alterations of such genes contribute to the neoplastic transformation of the cell. The alteration of one gene, however, does not suffice to give rise to full blown cancer. For progression toward malignancy and invasion, further mutational hits are necessary (Knudson 2001). Epigenetic abnormalities are important factors in the etiology of virtually all human cancer types (Goldberg Aaron et al. 2007). DNA methylation is the most widely studied epigenetic abnormality in tumorigenesis. Methylation defects include genome hypomethylation (resulting in epigenetic inactivation of oncogenes and retroelements) and localized aberrant hypermethylation of CpG islands, resulting in transcriptional silencing of many important genes (Ting et al. 2006). Promoter methylation patterns in human cancers show strong specificity with respect to tissue of origin and can be found early in tumorigenesis. DNA polymorphisms are also playing a crucial part in unraveling the genetic basis of tumor formation and progression in cancer. As is true of the human genome as a whole in which over 3.1 million sequence variations have been mapped, which represent 25–35% of the total estimated SNPs (Frazer et al. 2007). Thyroid cancer (TC) is the most common malignancy of the endocrine system. It accounts for approximately 2% of all newly diagnosed cancer cases and majority of endocrine cancer related deaths each year (Sarlis and Benvenga 2004). TC can arise from either follicular or non-follicular thyroid cells. Follicular cancers include Papillary TC (PTC—80%), Follicular TC (FTC–11%), Hürthle cell cancer (HCC—3%), and Anaplastic TC (ATC—2%) (Rebecca et al. 2011). Non-follicular thyroid cancers include Medullary TC (MTC). Genetic alterations in thyroid tumors can be divided into two categories: inheritable (germline) mutations and sporadic (somatic) mutations. Many gene mutations have been studied in thyroid tumors, but only one inheritable genetic mutation and few sporadic mutations are of good significance. The single known inheritable gene mutation associated with thyroid cancer is a point mutation in the RET protooncogene that causes medullary thyroid cancer. The association of a genetic mutation with medullary thyroid cancer (MTC) was first hypothesized in the late 1980s, but was not specifically identified until 1993 (Mulligan et al. 1993). RAS oncogenes (H, N, and K) were the first to be associated with sporadic thyroid cancer. About 30% of all human tumors contain a mutation in a RAS allele, making this one of the most widely mutated human proto-oncogene (McCormick and Wittinghofer 1996). RET/PTC is a chromosomal rearrangement found in papillary thyroid cancer (Santoro et al. 1992). As a result of the rearrangement, a portion of the RET gene is fused to one of several possible partner genes. Two of the most common rearrangement types are RET/PTC1 and RET/PTC3, in which RET is fused to either CCDC6 (also known as H4) or NCOA4 (also known as ELE1 or RFG), respectively

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(Santoro et al. 1994). RET/PTC rearrangements are found on average in 10–20% of adult sporadic papillary carcinomas. Alterations in the TP53 tumor suppressor gene by inactivating point mutations, usually involving exons 5–8, or by deletion result in progressive genome destabilization, additional mutations, and propagation of malignant clones. Among thyroid tumors point mutations of TP53 occur in approximately 60% of ATC and in 25% of PDTC (Ito et al. 1993). The PAX8-PPARγ rearrangement leads to in-frame fusion of exon 7, 8, or 9 of PAX8 on 2q13 with exon 1 of PPARγ on 3p25 (Nikiforov 2004). The PAX8-PPARγ rearrangement is found in follicular thyroid carcinoma and in the follicular variant of PTC, where it occurs in approximately 33% of all tumors (Nikiforov 2004). The most recent and major development in the field of thyroid cancer genetics has been the identification of the BRAF-activating point mutation as the most common molecular defect in PTC (Xing 2005). The BRAF-activating point mutation in thyroid cancer is almost exclusively a thymine-to-adenine transversion at position 1799 (T1799A) in exon 15. This leads to a valine-to-glutamate substitution at residue 600 (V600E) and subsequent constitutive activation of the BRAF kinase (Kumar et al. 2003). Recent studies have reported a prevalence of BRAF mutation in 29% to 83% of PTC, making it the most common oncogene identified in sporadic forms of PTC (Kimura et al. 2003). Besides the mutation hotspots apparently inherited polymorphisms in the RAS sequence especially HRAS was described by many studies (Capon et al. 1983; Castro et al. 2006) to be an important cause of the predisposition to several human cancers including TC. HRAS T81C SNP can indicate an increased risk of skin (KreimerErlacher et al. 2001), oral (Sathyan et al. 2006), bladder (Johne et al. 2003), and gastric cancer (Zhang et al. 2008). Castro et al. have first time investigated HRAS T81C polymorphism in thyroid tumors and have associated it with aneuploidy in follicular tumors of the thyroid. Although HRAS T81C polymorphism does not alter the amino acid sequence of the protein, it may affect the expression of the gene inducing overexpression (Castro et al. 2006). The Rearranged during Transfection (RET) proto-oncogene encodes a membrane tyrosine kinase receptor and is expressed in cells originating in the neural crest (Raymon et al. 2010). However, the RET tyrosine kinase domain may also be expressed in thyroid follicular cells (Giuseppe et al. 2000). Some sporadic and radiation-induced PTCs were found to have somatic RET translocations. The RET rearrangements product results in constitutive activation of the RET tyrosine kinase domain that can lead to PTC carcinogenesis (Tallini and Asa 2001). In addition to these RET rearrangements, the coding sequence of RET can exhibit polymorphisms in exon 2 (A45A; rs1800858), in exon 11 (G691S; rs1799939), in exon 13 (L769L; rs1800861), in exon 14 (S836S; rs1800862), and in exon 15 (S904S; rs1800863) (Elisabeth et al. 2009). RET polymorphisms were associated with the etiology of sporadic Hirschsprung disease (HSCR) and MTC (Lesueur et al. 2002). In papillary thyroid carcinoma, it has recently been suggested that silent RET polymorphisms A45A (rs1800858) in exon 2 and L769L (rs1800861) in exon 13 may represent low-penetrance risk alleles (Lesueur et al. 2002). RET Polymorphic Haplotypes and risk of Differentiated TC were also described by some studies (Tang et al. 2005). The mechanism by which

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the silent polymorphisms may act in the development of PTC may include transcript stability, RNA splicing, and DNA protein binding and protein folding.

8.2

Thyroid Cancer (TC)

Thyroid carcinoma is the most prevalent endocrine malignancy and accounts for 2% of all human cancers. TC typically occurs in thyroid nodules, and is relatively common, occurring in 6% of adult women and 2% of adult men which can be detected by palpation and imaging in a large proportion of adults. Approximately 90% of thyroid malignancies are well-differentiated thyroid carcinomas arising from thyroid follicular epithelial cells, which are classified as papillary or follicular based on histopathological criteria, whereas 3–5% of cancers originate from parafollicular or C cells. Follicular adenoma is a benign tumor that may serve as a precursor for some follicular carcinomas. Even though differentiated thyroid carcinomas are usually curable by the combination of surgery, radioiodine ablation, and thyroidstimulating hormone suppressive therapy, recurrence occurs in 20–40% of patients (Mazzaferri and Massoll 2002). During tumor progression, cellular dedifferentiation occurs in up to 5% of cases and is usually accompanied by more aggressive growth, metastatic spread, and loss of iodide uptake ability, making the tumor resistant to the traditional therapeutic modalities and radioiodine (Alessandro et al. 2010). Knowledge of genetic alterations occurring in TC has rapidly expanded in the past decade. This improved knowledge has provided new insights into TC etiology and has offered novel diagnostic tools and prognostic markers that enable improved and personalized management of patients with thyroid nodules (Nikiforov and Nikiforov 2011).

8.2.1

Classification of Thyroid Tumors (AFIP)

The Classification of Thyroid Tumors is given by the World Health Organization (WHO) (Hedinger et al. 1988) and Armed Forces Institute of Pathology (AFIP) (Rosai et al. 1992) with slight difference. According to AFIP, priority is given to the cell of origin and incorporating, in each cell type, special tumor types and subtypes designated as “variants.” Classification scheme adopted by the Armed Forces Institute of Pathology (AFIP) is discussed below;

8.2.1.1 Primary Tumors Epithelial Tumors Tumors originating from epithelial cells. Tumors of Follicular Cells Tumors originating from epithelial follicular cells.

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Benign Localized tumors with no present features of malignancy. Follicular Adenoma

Follicular adenoma is defined as a benign encapsulated tumor with follicular cell differentiation showing a uniform pattern throughout the confine nodule. The fibrous capsule varies in thickness, but is usually thin. On the basis of microscopic features, several variants have been described, including oncocytic adenoma (Hürthle cell adenoma), adenoma with clear cell change, atypical adenoma, hyalinizing trabecular adenoma, adenoma with bizarre nuclei, and rare types such as adenoma with adipose (adenolipoma) or cartilaginous (adenochondroma) metaplasia (Rosai et al. 1992). Malignant Cancers with metastasis. Differentiated Thyroid Carcinoma (DTC) Follicular Thyroid Carcinoma (FTC)

A malignant epithelial tumor showing evidence of follicular cell differentiation but lacking the diagnostic features of papillary carcinoma. The frequency of follicular carcinoma among thyroid malignancies ranges from 5–10% in non-iodine-deficient areas to 30–40% in iodine-deficient areas (Rosai et al. 1992). Depending on the degree of their invasiveness, follicular carcinomas have been divided into two major categories: minimally invasive or encapsulated (the most common) and widely invasive. The follicular carcinoma, oxyphilic cell type is largely or entirely composed of eosinophilic cells. It should not be referred to as a Hürthle cell carcinoma, a misnomer, or as an oxyphilic carcinoma, an incomplete term. Papillary Thyroid Carcinoma (PTC)

A malignant epithelial tumor showing evidence of follicular cell differentiation, typically with papillary and follicular structures as well as characteristic nuclear changes. Papillary carcinoma is the most common type of TC, comprising approximately 80% of all primary thyroid malignancies (Baloch and LiVolsi 2002). There are several histologic variants of papillary carcinoma, some of which are associated with a more guarded prognosis: Papillary microcarcinoma, encapsulated variant, follicular variant, tall and columnar cell variant, diffuse sclerosing variant, solid variant, clear cell variant, oncocytic variant and oxyphilic variant CribriformMorular Variant. Hürthle Cell Carcinoma (HCC)

A rare malignancy of the thyroid gland accounts for 3–7% of all malignant thyroid tumors (Har-El et al. 1986). It is usually a solitary, well-encapsulated tumor that is composed of greater than 75% Hürthle cells; HCC is a distinct clinical entity. For example, only an estimated 10% of metastases from HCC take up radioiodine (Soh and Clark 1996), compared with 75% of metastases from FTC (Maxon and Smith

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1990). HCC is more often multifocal and bilateral and more frequently involves regional nodes than does FCC. Among well-differentiated thyroid carcinomas, HCC is associated with the highest incidence of distant metastases (Elaine Yutan 2001). Poorly Differentiated Thyroid Carcinoma (PDTC) Poorly differentiated thyroid carcinoma represents a heterogeneous group of malignant neoplasms, with various histologic patterns of growth and different biologic behavior, that lie somewhere between well-differentiated and undifferentiated carcinomas (Volante et al. 2004). Microscopically, most of these tumors show a trabecular, solid, or insular growth pattern. Undifferentiated (Anaplastic) Carcinoma It account for 5–10% of all primary malignant tumors of the thyroid (Rosai et al. 1992). These tumors, usually present in elderly patients (mean age 60–65 years), are rapidly growing, with massive local invasion and early distant metastases, most frequently to lung, adrenals, and bone. Features of undifferentiated (Anaplastic) carcinoma are high mitotic activity, extensive necrosis, and a marked degree of invasiveness within the gland as well as to the extrathyroidal structures (LiVolsi 1990). Tumors of C Cells and Their Variants Medullary Thyroid Carcinoma (MTC)

Medullary thyroid carcinoma is a malignant tumor of the thyroid which shows evidence of C-cell differentiation and usually contains calcitonin. It accounts for up to 10% of all malignant thyroid tumors (Baloch and LiVolsi 2002). The variants of medullary carcinoma are: glandular (composed in part of tubular or follicular structures and may resemble follicular carcinoma), papillary (exhibiting true papillary pattern of growth), small cell (resembling the intermediate variant of small cell carcinoma of the lung), and giant cell (occasionally present or focal areas with giant cell formation). Mixed Follicular Parafollicular Carcinoma Mixed medullary and parafollicular carcinomas are rare neoplasms which show morphologic features of both follicular and C-cell differentiation (Holm et al. 1987). The dual differentiation has also been noted in their metastatic sites. WHO is very strict in defining them as tumors showing both the morphologic features of medullary carcinoma together with immunoreactivity for calcitonin and the morphologic features of follicular carcinoma together with immunoreactivity for thyroglobulin (Hedinger et al. 1988).

8.2.1.2 Thyroid Sarcomas Sarcomas arising in the thyroid are extremely rare. Various microscopic types have been reported in the form of isolated case reports, including fibrosarcoma,

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liposarcoma, leiomyosarcoma, chondrosarcoma, (Andrion et al. 1991) osteosarcoma, and malignant schwannoma (Thompson et al. 1997).

8.2.1.3 Malignant Lymphomas Primary non-Hodgkin lymphomas of the thyroid are now considered to be tumors of mucosa-associated lymphoid tissue (MALT) (Baloch and LiVolsi 2002). They constitute about 8% of all thyroid malignancies (Rosai et al. 1992). Primary thyroid lymphomas have a B cell phenotype and are highly associated with lymphocytic or Hashimotos thyroiditis. Thyroid malignant lymphomas are most common in adult or elderly women, clinically presented in the form of an enlarged thyroid, leading to symptoms of trachealor laryngeal compression when extended outside the gland (Rosai et al. 1992). 8.2.1.4 Secondary Tumors of the Thyroid Although any malignant tumor can metastasize to the thyroid gland, the latter is an infrequent site of tumor metastases. Direct extension into the thyroid may occur in carcinomas of the pharynx, larynx, trachea, and esophagus. Most of these neoplasms are of squamous cell type. Hematogenous metastases to the thyroid, particularly of malignant melanoma, lung, gastrointestinal, breast, and renal cell carcinomas are commonly encountered at autopsy series (Chrisoula 2004).

8.2.2

Staging of Thyroid Carcinoma

The American Joint Committee on Cancer (AJCC) has designated staging for cancers of the thyroid (Jatin and Pablo 2018). Separate stage groupings are recommended for papillary, follicular, medullary, and anaplastic cell types. In addition, within papillary and follicular, separate stage groupings are recommended based on age at diagnosis (20–54 and 55+). The SEER modified eighth edition AJCC staging is given in Tables 8.1 and 8.2.

8.2.3

Risk Factors of Thyroid Carcinoma

8.2.3.1 Gender and Age TC is 2–4 times more frequent in women than men. Women generally exhibit a better prognosis than men who are reported to have a higher malignant progression of nodules. It is rare in patients aged 1 cm but
2 cm in greatest dimension, limited to the thyroid T2 Tumor size >2 cm but
4 cm, limited to the thyroid T3 Tumor size >4 cm, limited to the thyroid or any tumor with minimal extrathyroidal extension (e.g., extension to sternothyroid muscle or perithyroid soft tissues) T4a Moderately advanced disease; tumor of any size extending beyond the thyroid capsule to invade subcutaneous soft tissues, larynx, trachea, esophagus, or recurrent laryngeal nerve T4b Very advanced disease; tumor invades prevertebral fascia or encases carotid artery or mediastinal vessel All anaplastic carcinomas are considered stage IV: T4a Intrathyroidal anaplastic carcinoma T4b Anaplastic carcinoma with gross extrathyroid extension Regional lymph nodes (N) Regional lymph nodes are the central compartment, lateral cervical, and upper mediastinal lymph nodes NX Regional nodes cannot be assessed N0 No regional lymph node metastasis N1 Regional lymph node metastasis N1a Metastases to level VI (pretracheal, paratracheal, and prelaryngeal/Delphian lymph nodes) N1b Metastases to unilateral, bilateral, or contralateral cervical (levels I, II, III, IV, or V) or retropharyngeal or superior mediastinal lymph nodes (level VII) Distant metastasis (M) M0 No distant metastasis is found M1 Distant metastasis is present

North America, Canada, and USA. In the USA, the TC is more frequent in Caucasian descent subjects than Afro-American, Hispanic, Hawaiian, Chinese, or Japanese whose incidence is still twice as high in their countries of origin. All these findings suggest that such differences may be attributable to both environmental (e.g., dietary habits) and genetic factors (Parkin et al. 2003).

8.2.3.3 Previous Exposure to Ionizing Radiation The role of a previous exposure to ionizing radiation in thyroid carcinogenesis has been established since 1950 following the explosion of the atomic bomb in Japan. Exposure to ionizing radiation for external irradiation of the neck increases the incidence of thyroid nodules, either benign or malignant, and palpable nodules are detected in 20–30% of people exposed to radiation (Hanson et al. 1983), as well it happens in pediatric patients undergoing radiation therapy for oncological and hematological malignancies such as lymphoma or leukemia (Pui et al. 2003). The

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Table 8.2 Stage grouping of TC Stage grouping Separate stage groupings are recommended for papillary or follicular (differentiated), medullary, and anaplastic (undifferentiated) carcinoma Papillary and follicular TC (age < 55 years) Stage T N M I Any T Any N M0 II Any T Any N M1 Papillary and follicular; differentiated (age  55 years) Stage T N M I T1 N0 M0 II T2 N0 M0 III T3 N0 M0 IVA T1–3 N1a M0 T4a N1b M0 IVB T4b Any N M0 IVC Any T Any N M1 Anaplastic carcinoma (all anaplastic carcinomas are considered stage IV) Stage T N M IVA T4a Any N M0 IVB T4b Any N M0 IVC Any T Any N M1 Medullary carcinoma (all age groups) Stage T N M I T1 N0 M0 II T2, T3 N0 M0 III T1–T3 N1a M0 IVA T4a N0 M0 T4a N1a M0 T1 N1b M0 T2 N1b M0 T3 N1b M0 T4a N1b M0 T4a N0, N1b M0 T1–T4a N1b M0 IVB T4b Any N M0 IVC Any T Any N M1

minimum latency period between exposure and clinical evidence of thyroid disease has been reported to be at least 4–5 years, reaching the maximum peak 20 years from exposure to decrease thereafter.

8.2.3.4 Age at the Time of Irradiation It represents the main risk factor and after 15–20 years there is no longer an increased risk. In children exposed to doses of 1Gy, the excess risk for TC is equal to 7.7 (Ron

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et al. 1995). Several studies conducted after the Chernobyl nuclear disaster have shown an increased incidence of TC in subjects that at that time were aged between 5 months and 10 years (Leenhardt and Aurengo 2000).

8.2.3.5 Previous History of Benign Thyroid Disease (BTD) People who have certain non-cancerous (benign) thyroid diseases are slightly more likely to develop TC. These include: an enlarged thyroid (goiter), thyroid nodules (adenomas), and inflammation of the thyroid (thyroiditis). Approximately 1 in 5 TCs (20%) occur in people who have had a benign thyroid disease in the past (Farbota et al. 1985). 8.2.3.6 Contribution of Iodine in the Food In areas with iodine deficiency, a higher incidence of thyroid nodules and TC has been observed. However, after correction for the greatest number of nodules, the percentage of TC in thyroid nodules is similar to the one found in areas with normal intake of dietary iodine. In presence of a sufficient iodine intake, more than 80% of cancer consist of PTC; whereas in areas with iodine deficiency follicular and anaplastic figures are more frequently reported (approximately 2–3 times higher than observed in areas with adequate iodine intake) (Belfiore et al. 1992). 8.2.3.7 Body Mass Index Several case–control studies have shown an increased risk of TC in patients with high body mass index (BMI). The risk would be increased by five-fold in obese men and 2 times in obese women (>97 percentile), compared to the risk observed in patients with weight < third percentile. In women (especially in postmenopausal age) a weight gain >14% appears to positively correlate with the onset of TC (Suzuki et al. 2008). 8.2.3.8 Hormonal Factors The female: male incidence ratio has been reported to be different according to the period of life in which TC occurs. In women of child bearing age, this ratio is about 4:1 and is reduced to 1.5:1 in older, prepuberal, and menopause individuals (Franceschi et al. 1999). Thyroid-stimulating hormone (TSH) regulates the growth and function of the thyroid gland as “TSH excess hypothesis” holds good for TC (Ingbar and Woeber 1974). This hypothesis is supported by the observation that growth of some TCs is dependent on TSH secretion so that suppression of TSH release by administration of thyroxin is often an effective treatment for thyroid carcinomas (Crue 1966). The thyroxine-binding globulin level in normal females is 10–20% higher than in males (Gershengorn et al. 1980) and in pregnancy, a 50% increase in the level of thyroxinebinding globulin results in a similar magnitude increase in TSH level (Malkasian and Mayberry 1970). It, therefore, appears likely that TSH levels of non-pregnant normal females will be elevated above the level in males at some point in the menstrual cycle although not necessarily throughout the cycle (Brian et al. 1982).

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An elevated risk was also reported in women who used estrogens for gynecological problems. In some studies higher levels of estrogen receptors (ERs) were found in neoplastic than in normal thyroid tissues (Yane et al. 1994). Classically, ERs are intracellular receptors that serve as transcription factors. The ligand-bound dimer ER can interact with an estrogen-responsive element, resulting in transcriptional activation of the target gene (Carson-Jurica et al. 1990). However, various studies have provided evidence that estrogen may also affect growth factor-dependent signaling pathways (Auricchio et al. 1996). One of the targets of estrogen action is the mitogen activated protein (MAP) kinase (MAPK) whose activity is regulated by growth factors (Migliaccio et al. 1996). 17 ß-Estradiol stimulates cell cycle progression early in G1 phase by induction of cyclin D1 gene expression (Foster and Wimalasena 1996). In different cell lines, the induction of cell growth was found to correlate with increased expression of cyclin D1 protein levels (Diana et al. 2001).

8.2.3.9 Smoking Status Although relatively little is known about the etiology of TC beyond its association with radiation exposure and some previous thyroid disorders (Ron 1996), data is slowly accumulating as to the protective effect of cigarette smoking on this disease. TC has been negatively associated with cigarette smoking in a number of studies, possibly consistent with the greater occurrence of the disease in women than in men (Kreiger and Parkes 2000). There are at least five distinct proposed mechanisms for the effect of tobacco smoke on thyroid function. The first one relates to a smoking related reduction in TSH secretion, as it has long been hypothesized that elevated levels of TSH may increase the risk of TC (Mack et al. 2003). The lower body weight among smokers compared to non-smokers is a second proposed explanation, as increased body weight was associated with a slightly increased TC risk in the abovementioned pooled analysis (Mack et al. 2003). A third possible biological pathway lies in the potential anti-estrogenic effect of cigarette smoke; a role for estrogen in the etiology of TC is hypothesized because of the higher incidence of this cancer in females relative to males (Mack et al. 2003). The fourth is higher levels of thyroxinebinding globulin and testosterone among smokers compared to non-smokers and the fifth is the higher levels of thyrotoxins in tobacco smoke in heavy smokers compared to light and moderate smokers (Konstantinos and Faidon 2004).

8.2.3.10 Oxidative Stress (OS) OS is an imbalance between free radicals and antioxidants in body. In essence, OS represents an imbalance between the production of oxidants and their elimination by anti-oxidative systems in the body. Many studies have linked OS to TC by showing its association with abnormally regulated oxidative or anti-oxidative molecules (Lassoued et al. 2010).

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Genetic Basis of Thyroid Cancer

TC is the most common malignancy of the endocrine system, and a disease whose incidence has been increasing over the past 20 years (Jennifer and Manisha 2009). Thyroid tumors represent an appropriate model for the study of epithelial neoplastic transformation. The roles of somatic mutations, gene rearrangement(s), and level of gene expression in carcinogenesis are now well established. The application of molecular techniques to thyroid tumors has focused particular attention on the role of point mutations activating (or inhibiting) the genes for the TSH receptor (TSHR), RAS, BRAF, Gsp, P53, etc. (Yashimoto et al. 1992), specific rearrangements of the oncogenes RET and TRK (Bounacer et al. 1997), and alterations in the pattern of expression of the oncogene BRAF, MET, etc. (Horacio 1998). The theory of sequential progression of well-differentiated thyroid carcinoma through the spectrum of poorly differentiated to undifferentiated thyroid carcinoma is supported by the presence of pre- or co-existing well-differentiated thyroid carcinoma with less differentiated types, and the common core of genetic loci with identical allelic imbalances in co-existing well-differentiated components (Tetsuo et al. 2006). Figure 8.1 depicts the model of multi-step carcinogenesis of thyroid neoplasms.

8.3.1

Genetic Alterations in Signaling Pathways in TC

The principal cell-surface receptors that are involved in the regulation of thyroid follicular cell growth and function are shown in Fig. 8.2. Alterations in key signaling effectors seem to be the hallmark of distinct forms of thyroid neoplasia.

8.3.1.1 Cyclic AMP (cAMP) Cascade Thyroid-stimulating hormone (TSH) activates the GSα–adenylyl cyclase–cyclic AMP (cAMP) cascade on binding to the TSH receptor (TSHR) (Fig. 8.2), thereby regulating thyroid hormone synthesis and the growth of follicular cells (Kimura et al. 2001). Gain-of-function mutations of TSHR (which is located on chromosome 14q31) and GNAS1 (which encodes GSα and is located on chromosome 20q13), both of which activate cAMP, occur in hyper-functioning thyroid adenomas (Krohn et al. 2005). By contrast, activating mutations of TSHR and GNAS1 in thyroid malignancies are rare—this is consistent with the clinical observation that hyperfunctioning thyroid nodules are unlikely to be malignant (Matsuo et al. 1993). 8.3.1.2 MAP Kinase Signaling Pathway In well-differentiated thyroid carcinoma, which is the most common form of TC, mutations or rearrangements in genes that encode MAPK pathway effectors seem to be required for transformation. Indeed, exclusive, non-overlapping, activating events that involve the genes RET, NTRK1 (neurotrophic tyrosine kinase receptor 1), BRAF, or RAS are detectable in nearly 70% of all cases (Xing 2005). Gain-of-function mutations of RET are involved in sporadic and familial C-cell-derived medullary

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Fig. 8.1 Model of multi-step carcinogenesis of thyroid neoplasms. The proposed model of thyroid carcinogenesis is based on general concepts and specific pathways. (a) Risk factors, such as exposure to radiation, induce genomic instability through direct and indirect mechanisms, resulting in early genetic alterations (b) Scheme of step-wise dedifferentiation of follicular cell-derived TC along with genetic alterations

thyroid carcinoma, including multiple endocrine neoplasia 2A (MEN2A), MEN2B, and familial medullary thyroid carcinoma. By contrast, chimeric oncogenes, designated RET/PTC, are implicated in the development of papillary carcinoma (Tallini and Asa 2001) (Fig. 8.2). Mutations in BRAF were the most recently identified MAPK effector in TC. BRAF mutation provides an alternative route for the aberrant activation of ERK signaling that is implicated in the tumorigenesis of several human cancers—

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Fig. 8.2 Cell signaling pathways in follicular cells. TSHR signaling operate through cAMP as secondary messenger; MAP kinase signaling operates through RAS family and BRAF

for example, melanoma and colon carcinoma (Davies et al. 2002). BRAF V600E is the most common alteration in sporadic papillary carcinoma (Xing et al., 2005). Instead, a chromosomal rearrangement (AKAP9–BRAF) that represents yet another form of paracentric inversion has been identified in radiation-associated TC (Ciampi et al. 2005). Unlike other solid neoplasms, RAS, a member of MAP kinase pathway, is the least prominent participant in thyroid carcinogenesis. Three RAS protooncogenes (NRAS, HRAS, and KRAS) are implicated in human tumorigenesis (Downward 2003). Mutations involving codon 61 of HRAS and NRAS have been reported with variable frequency in thyroid neoplasms (Lemoine et al. 1989). RAS mutations are more common in iodine-deficient than iodine-sufficient areas and in lesions with follicular architecture (including follicular carcinoma and follicular variant papillary thyroid carcinoma) than in typical papillary thyroid carcinoma (Zhu et al. 2003).

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Genetic Polymorphisms in Thyroid Cancer

One of the most important tools underlying the revolution in medical genetics is the ability to visualize sequence differences directly in DNA. When studied in the context of a population, these differences in DNA sequences are called polymorphisms; they may occur in coding regions (exons) or noncoding regions of genes. The ability to visualize thousands of DNA polymorphisms has made possible family studies for tracking genes of medical importance. Classically, polymorphisms represent sequence variations, which are present in the general population, and confer no deleterious effects. However, as the human genome project evolved and molecular epidemiological studies were performed, it became clear that some “polymorphisms” were not entirely harmless. This technique has located and identified genes for many disorders with a clear pattern of Mendelian inheritance, such as cystic fibrosis, the inherited muscular dystrophies, and neurodegenerative disorders such as Huntington’s disease. Methods that exploit genetic polymorphism will also be essential for finding genes that predispose people to more common conditions in which inheritance patterns are complex, such as diabetes, atherosclerosis, and hypertension. DNA polymorphisms are also playing a crucial part in unraveling the genetic basis of tumor formation and progression in cancer. They provide markers for the loss of specific chromosomal segments during the evolution of a tumor. DNA polymorphisms have already been crucial in the identification of genes important for susceptibility to common forms of cancer, such as colon cancer, as well as susceptibility to less common childhood tumors, such as retinoblastoma and Wilms’ tumor (David et al. 1995). As is true of the human genome as a whole–in which over 3.1 million sequence variations have been mapped, which represent 25–35% of the total estimated SNPs (The International HapMap Consortium 2003; Frazer et al. 2007). Differentiated TC is characterized by a strong hereditability, and individual susceptibility is likely due to genetic factors modulating the environmental risk. Identification of genetic polymorphisms is important for understanding the potential mechanisms involved in thyroid carcinogenesis. In TC many single nucleotide polymorphisms have been reported in different genes and functional analysis of many single nucleotide polymorphisms have been carried out. It has been reported that these DNA polymorphisms in various genes predispose a person to higher risk of TC and also has a marked effect on various clinicopathological characteristics of TC patients (David et al. 1995).

8.4.1

HRAS (Harvey Rat Sarcoma)

In mammalian cells, the ras protooncogenes consist of three highly homologous members, KRAS, HRAS, and NRAS. Each gene has the capacity to become oncogenic as a consequence of a somatic mutation (Esteban et al. 2001). The word Ras comes from a contraction of Rat sarcoma, the tumor where the first gene of the

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family was identified, as part of the genome of a retrovirus isolated from a carcinogenesis protocol. It is located at 11p15.5, which is the short (p) arm of chromosome 11 at position 15.5 spanning base pairs 532,242 to 535,576.

8.4.1.1 Structure and Function of HRAS The HRAS protooncogenes encode a family of evolutionarily conserved, 21-kDa intracellular guanosine triphosphate (GTP) binding proteins (189 amino acids in length) that are integral for cellular signal transduction and that ultimately regulate cellular differentiation, proliferation, and function in a wide diversity of cell types (Scott 2002) called HRAS. Through a process known as signal transduction, the HRAS protein relays signals from outside the cell to the cell’s nucleus. These signals instruct the cell to grow or divide. The HRAS protein is a GTPase, which means it converts a molecule called GTP into another molecule called GDP. The H-Ras protein acts like a switch, and it is turned on and off by GTP and GDP molecules. To transmit signals, the protein must be turned on by attaching (binding) to a molecule of GTP. The HRAS protein is turned off (inactivated) when it converts GTP to GDP. When the protein is bound to GDP, it does not relay signals to the cell’s nucleus. Mutations at affected codons result in oncogenic proteins containing single amino acid substitutions at those corresponding positions. In a functional sense, such ras proteins are aberrant as they become constitutively GTP-bound due to a loss both in intrinsic GTPase activity and responsiveness to GAPs (GTPaseactivating proteins), which ordinarily help to facilitate the hydrolysis or dephosphorylation of GTP to GDP. Hence, ras oncoproteins in the activated form appear to be in a constitutively “on” state or signal transmitting mode, which has been proposed to represent an early event in the initiation of the neoplastic process. This is most notable, for example, in the pathogenesis and progression of human colorectal and pancreatic adenocarcinoma. Mutations of ras have been found in, and associated with, benign adenomas/polyps and pancreatic intraductal lesions (PILs) of colorectal and pancreatic tumorigenesis, respectively, suggesting that the mutation precedes the development of malignancy (Scott 2002). 8.4.1.2 Reported SNPs in HRAS Gene Besides the mutation hotspots, inherited polymorphisms in the HRAS sequence were described (Sol-Church et al. 2006; Sanyal et al. 2004). A single nucleotide polymorphism at HRAS cDNA position T81C (rs12628), originally described by Catela et al., was shown to be associated with the risk of human cancers (Catela et al. 2009). It is present in codon 27 of exon 1 of HRAS, which is located in a wobble base position. To the author’s knowledge, three relative meta-analyses were performed previously. In 2008, Zhang et al. performed a meta-analysis of four case–control studies with 388 cases and 391 controls and found that HRAS rs12628 seems to be associated with the susceptibility to cancers, including bladder, thyroid, and oral cancer (Zhang et al. 2008). In 2012, Traczyk, M. et al. assessed the association between HRAS rs12628 and urinary bladder cancer in the Polish patients, and conducted another meta-analysis of eight case–control studies (Traczyk et al. 2012). Their meta-analysis results showed that CC genotype of HRAS rs12628

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is likely to have implications in the overall cancer risk (Traczyk et al. 2012). In 2013, Pandith et al. evaluated the relationship between HRAS rs12628 and urinary bladder cancer in ethnic Kashmiri population, and also performed a meta-analysis with five case–control studies, which suggested that HRAS rs12628 may act as a kind of risk factor for cancer (Pandith et al. 2013). Another polymorphic site that could be involved in the development of cancer disease is a hexanucleotide repeat (Kotsinas et al. 2001).

8.4.1.3 HRAS T81C Gene Polymorphism in TC Follicular tumors of the thyroid are often aneuploid (Castro et al. 2006). Although the presence of aneuploidy is not by itself an indicator of malignancy (Castro et al. 2006), aneuploid carcinomas tend to carry a worse prognosis than diploid (or neardiploid) carcinomas of the thyroid (Schelfhout et al. 1990). Castro et al. have first time investigated HRAS T81C polymorphism in thyroid tumors and has associated it with aneuploidy in follicular tumors of the thyroid (Castro et al. 2006). Although HRAS T81C polymorphism does not alter the amino acid sequence of the protein, it may affect the expression of the gene inducing overexpression. It is possible that it is linked to another polymorphic locus inside regulatory intronic region. HRAS T81C SNP has been found to moderately increase TC risk with variant alleles implicated more in follicular thyroid tumors. Interestingly, it is also observed that HRAS T81C variant allele shows reduced risk for the smokers with TC (Mosin et al. 2013).

8.4.2

RET (Rearranged During Transfection)

The RET gene is located on chromosome 10q11.2 near the centromere and includes 21 exons. Takahashi et al. first identified RET in 1985 as a proto-oncogene that can undergo activation by cytogenic rearrangement (Takahashi et al. 1985). Three years later, the RET gene was cloned by the same investigators (Takahashi et al. 1988). The RET gene encodes a plasma membrane-bound tyrosine kinase enzyme, the RET receptor, which is expressed by neuroendocrine and neural cells, including thyroid C cells, adrenal medullary cells, parasympathetic, sympathetic, and colonic ganglia, cells of the urogenital tract, and parathyroid cells derived from branchial arches (Tsuzuki et al. 1995).

8.4.2.1 Structure and Biology of RET Receptor The RET protein consists of an N-terminal signal peptide, an extracellular region (four cadherin-like repeats, a calcium binding site, and a cysteine-rich domain), a transmembrane domain, and two intracellular tyrosine kinase domains. The extracellular cadherin-like domains are important for cell–cell signaling, whereas the cysteine-rich extracellular domain is important for receptor dimerization. The C-terminal tail of RET shows three different splicing variants that produce three protein isoforms with 9 (RET9; short isoform), 43 (RET43; middle isoform), or 51 (RET51; long isoform) distinct amino acids at their C termini (Myers et al. 1995)

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Fig. 8.3 The RET protein, its functional domains, ligands, and co-receptors. Left, functional domains of the three RET isoforms. Right, canonical (unbroken lines), and noncanonical (broken lines) interactions of the RET ligands GDNF, neurturin (NRTN), persephin (PSPN), and artemin (ARTN) with their GFRa co-receptors. Lipid rafts are depicted as a purple box in the plasma membrane

(Fig. 8.3). To date, four ligands for the RET receptor have been identified (Treanor et al. 1996). These ligands are the glial cell line-derived neurotrophic factor (GDNF), neurturin, artemin, and persephin (Milbrandt et al. 1998). Various RET mutations identified and correlated with disease phenotype are shown in Fig. 8.4.

8.4.2.2 Polymorphisms and Haplotypes in RET Not only do high penetrant germline RET mutations have a key role in disease development, but also RET polymorphisms exist that are believed to be genetic modifiers and might be associated with an increased relative risk for the development of disorders derived from neural crest cells. RET polymorphisms have a relatively strong association with various disease phenotypes. The most frequent RET polymorphisms; the nonsynonymous variant G691S (G2071A) in exon 11 and the synonymous variants L767L (T2307G) in exon 13, S836S (C2508T) in exon 14, and S904S (C2712G) in exon 15, are being referred

R

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EN

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M -F

R A SC 2 H EN M

-

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Tyrosine kinasedomain

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Fig. 8.4 Germline missense mutations in RET associated with MEN2 and Hirschsprung disease. Shown are the structure of the RET mRNA and protein. The codons mutated, the associated clinical entities, and the location of these mutations in relation to the exons and structural domains are indicated

Loss of function

Mutations all over the gene

H

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to as disease modifiers owing to the observation that they were found more often in patients with sporadic MTC and DTC (“cases”) than controls [104] (Andreas et al. 2012). These polymorphisms might interact with other genetic variants or with disease-associated germline mutations, modulating the disease phenotype or age of onset. Because polymorphisms are comparatively common in the population, they could bestow a much higher attributable risk on the general population as compared with rare mutations in high-penetrance disease susceptibility genes such as RET. It has been suggested that the RET polymorphisms G691S and S904S have a modifier effect on the age of onset of MEN2A (Robledo et al. 2003). Several RET polymorphisms have been described in association with sporadic MTC. Both G691S and S904S had been previously associated with sporadic MTC and MEN2A (Elisei et al. 2004). A low-penetrance RET haplotype comprising the wild-type allele at IVS1–126 and IVS1–1463 and a 16-basepair intron-1 deletion of these SNPs is strongly associated with and over-represented in sporadic pheochromocytoma (McWhinney et al. 2003). Several groups have characterized candidate disease-associated polymorphisms in Hirschsprung disease (Garcia-Barcelo et al. 2005). Two groups have described two closely located SNPs, rs2435357 and rs2506004, in intron 1 as disease-causing candidates on the basis of association studies, functional assays, and comparative genomics (Ivan et al. 1997). Linkage between G691S and S904S has been suggested previously (Borrego et al. 1999).

8.4.2.3 RET Polymorphisms and Haplotypes in TC In recent years, it has been shown that there are some interconnections between follicular and parafollicular-type C cells. The microenvironment provided by MTC cells has the capacity to stimulate the proliferation of follicular cells, resulting in hyperplastic and adenomatous follicles, and as suggested recently, the latter can ultimately acquire a fully developed neoplastic phenotype (either follicular or papillary). The opposite situation has also been described: C-cell hyperplasia was recognized in some patients with Hashimoto thyroiditis as well as in thyroid adjacent to follicular and papillary neoplasms (Tsui-Pierchala et al. 2002). Giuseppe et al in their combination of in vivo and in vitro results have shown that the thyroid follicular component can express a functional RET receptor, which may be activated in the presence of specific ligands in the thyroid microenvironment (Giuseppe et al. 2000). Because C cells express the RET receptor, the concept that RET ligands are present in this microenvironment is highly plausible. They have reported presence of RET mRNA in follicular cell-derived TCs which may be activated in the presence of specific ligands in the thyroid microenvironment (Giuseppe et al. 2000). So the role of RET gene polymorphism in differentiated TCs came into being. Some variants within RET could represent low penetrant alleles for the PTC phenotype. A study conducted by Lesueur et al. found the strongest association with PTC with A45A (G135A) polymorphism in exon 2 and L769L (T2307G) in exon 13 (Lesueur et al. 2002). The seven most frequent haplotypes have been described by Borrego et al. in controls of Spanish and German origin (Borrego et al. 2000). Distribution of some of these haplotypes differed between cases and controls. The G G C C haplotype is over-represented in both Italian and French sporadic PTC and this over-

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representation is significant when all four populations are pooled (OR 1.68, 95% CI 1.04–2.71). The G G C C haplotype includes the G allele of exon 2 and the G allele of exon 13, the two alleles that are associated with an increasing risk in the single locus analysis. Lesueur et al. observed the strongest association with DTC for polymorphisms A432A in exon 7 and S836S in exon 14. Nevertheless, the magnitude of the observed effect between the RET SNPs and DTC was modest (Lesueur et al. 2002). SNP S904S gives rise to less frequent codons, so ribosome stalling can happen. In the case of SNP L679L where the codon with higher codon usage appears, the sheet may not finish creating the structure when the helix appears (Nakamura et al. 2007). As a consequence, there is not enough space to create the appropriate structure. This can lead to changes in kinase activity and/or specificity and, as a result, influence disease symptoms. These postulated mechanisms are not mutually exclusive (Maria et al. 2010). The L769L (T > G) variant reduces the energy of the wild type by 17% and the mutant Y791F by 7%, leading the authors to conclude that the L769L polymorphism reduces the MFE of small RET mRNA (Lucieli et al. 2012). The S904S polymorphism does not lead to an amino acid alteration and because of its co-segregation with G691S, the results obtained with S904S could be interpreted as a founder effect without influence as genetic modifier, according to a role of RET S904S as a linked neutral polymorphism, and the main putative modifier would be the amino acid sequence variant G691S (Robledo et al. 2003). The G691S SNP is synonymous; it has been explained that the G691S variant occurs in the cytoplasmic tail of the RET amidst transmembrane region and the first tyrosine kinase domain close to the residue Y687. To explain the G691S polymorphism exerting an effect in PTC without activation by RET rearrangement, two scenarios are possible. Firstly, although RET is not generally believed to be expressed in thyroid follicular cells (Fabien et al. 1992), it is expressed in the parafollicular C cells and hence might influence the microenvironment of the follicular cells. Alternatively, it may be the case that wild-type proto-RET is in fact expressed in follicular cell tumors as well as parafollicular C cells, as has been suggested in several recent studies (Fluge et al. 2001). As a matter of fact, the two amino acids, glycine in the wild-type RET protein and serine in the polymorphic RET variant, confer different electrochemical and conformational structures to the RET protein, and consequently influence the processing, folding, subcellular localization, or function of the protein. It has been shown that polymorphic sequence variants can lead to production of different amounts of mRNA (Leviev et al. 1997). So, single nucleotide polymorphisms may confer a lot of changes in RET proto-oncogene at mRNA level or at protein level that deregulates the MAP kinase or Akt pathway, hence predisposing a person to TC. It may be suggested that the GGT > AGT polymorphism causes the creation of a cryptic splice donor, splice acceptor, or splice enhancer, therefore leading to an altered protein that may contribute to the development of C-cell hyperplasia (Fitze et al. 1999).

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TP53 (Tumor Protein 53)

The human TP53 gene spans about 20 kb of DNA and localized to the short arm of chromosome 17 (17p13). This gene is composed of 11 exons, the first of which is noncoding and localized 8  10 kb away from exons 2 through 11 (Benchimol et al. 1985).

8.4.3.1 Structure and Function of P53 Protein The human p53 protein consists of 393 amino acids and contains four major functional domains. At the N terminus is a transcriptional activation domain (amino acids 1  42) and within the central part of p53 is the sequence-specific DNA-binding domain (amino acids 102  292). The C-terminal portion contains an oligomerization domain (amino acids 323  356) and a regulatory domain (amino acids 360  393) (Pierre and Evelyne 1999). 8.4.3.2 Polymorphisms That Alter the Coding Sequence of p53 Protein The Serine 47 Polymorphism The SNP P47S changes an evolutionarily conserved proline residue of p53 to serine. Initially identified by Felley-Bosco et al., the S47 polymorphic variant is very rare, with an allele frequency under 5% in African Americans and undetectable in Caucasians. S47 form of p53 did not affect the growth suppression activity of this protein as compared to wild-type p53 (P47, or wt) (Felley-Bosco et al. 1993). The Codon 72 (Arg72Pro) Polymorphism and Its Impact on Cancer Risk This common SNP (Arg72Pro; rs1042522) results in a non-conservative change of an arginine (R72) to a proline (P72) at amino acid 72 that results in a structural change of the protein giving rise to variants of distinct electrophoretic mobility (Matlashewski et al. 1987). This polymorphism occurs in a proline-rich region of p53, which is known to be important for the growth suppression and apoptotic functions of this protein (Sakamuro et al. 1997). Beckman and co-workers were the first to demonstrate a significant difference in the allelic distribution of the R72 and P72 variants. They first noted a significant difference in the P72 allele frequency between a Nigerian population (African Black) and a Swedish population (Western Europe), which were 17% and 63%, respectively; in contrast, they did not note any differences between populations living on the same geographical latitude (Beckman et al. 1994). One of the first studies demonstrated a correlation between the codon 72 polymorphism of TP53 and the risk to cervical cancer wherein they reported that women with the R72 variant of p53 had a seven-fold increased risk to develop cervical cancer (Storey et al. 1998). Several groups have reported an association between the R72 TP53 variant (binds and inactivates p73 better) and increased risk for epithelial cancer, including gastric cancer (Shen et al. 2004) and cancer of the breast (Ohayon et al. 2005), ovary (Pegoraro et al. 2002), esophagus (Kawaguchi et al. 2000), skin (De Oliveira et al. 2004), lung (Wu et al. 2002), bladder (Soulitzis et al. 2002), prostate (Henner et al.

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2001), and larynx (Sourvinos et al. 2001). In other studies, however, authors have found the opposite correlation, instead demonstrating an association between the P72 (lesser apoptotic) variant and increased risk for other cancer types, including cancer of the thyroid (Granja et al. 2004), nasopharynx (Tiwawech et al. 2003), prostate (Suzuki et al. 2003), skin (Chen et al. 2003a, b), urogenital region (Kuroda et al. 2003), and lung (Zhang et al. 2003). Still other groups have failed to demonstrate any association between codon 72 variants of p53 and cancer risk (Pietsch et al. 2006).

8.4.3.3 TP53 Gene Polymorphisms in TC Genetic polymorphisms are reported to be an important cause of the predisposition to several human cancers including thyroid cancer. The TP53 Arg72Pro (CGC > CCC; rs1042522) polymorphism of exon 4 (SNP identification number 1042522) has been suggested to play a role in several different cancer types (Matlashewski et al. 1987). These two variant protein forms may behave differently (Dumont et al. 2003). To date, only few studies have investigated the association of codon 72 polymorphism in thyroid tumors (Boltze et al. 2002). They have examined Caucasian thyroid carcinoma patients, Brazilian population, and Turkish cohort and concluded that the presence of the proline variant was a potential risk factor for the induction of thyroid carcinoma and was also associated with a relatively poorer prognosis. Combined impact of homozygous proline and heterozygous proline/ arginine at codon 72 of TP53 may be a potential risk factor for TC as reported by the majority of investigations (Yao-Yuan and Chich-Sheng 2006). Although some previous studies had found only Pro/Pro genotype as a risk factor for both benign and malignant TCs (Granja et al. 2004) the main factor accounting for such a discrepancy may be a difference in the genetic back-ground of individuals. The Arg/Arg genotype has been reported to induce apoptosis more effectively than the Pro/Pro genotype (Dumont et al. 2003) which may be due to enhanced mitochondrial localization of p53 protein in cells with the Arg/Arg genotype (Dumont et al. 2003). In contrast, the Pro/Pro genotype appears to induce a higher level of G1 arrest than the Arg/Arg genotype probably due to altered binding affinity to p73 (Pietsch et al. 2006). There is a strong association between TC and TP53 Arg72Pro polymorphism and the TP53 Pro72 homozygotes and heterozygotes are related with higher susceptibility of TC (Mosin et al. 2015).

8.4.4

XRCC1 (X-Ray Repair Cross-Complementing Protein 1)

This gene is located at 19q13.31, which is the long (q) arm of chromosome 19 at position 13.31. It extends from base pairs 43,543,311 to 43,575,527 on chromosome 19. The protein encoded by this gene is involved in the efficient repair of DNA single-strand breaks formed by exposure to ionizing radiation and alkylating agents. This protein interacts with DNA ligase III, polymerase beta, and poly (ADP-ribose) polymerase to participate in the base excision repair pathway (Keith 2019; Nissar et al. 2014).

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8.4.4.1 XRCC1 Protein Structure The human gene that encodes XRCC1 was cloned nearly 30 years ago but experimental analysis of this fascinating protein is still unveiling new insights into the DNA damage response. XRCC1 is a molecular scaffold protein that interacts with multiple enzymatic components of DNA single-strand break repair (SSBR) including DNA kinase, DNA phosphatase, DNA polymerase, DNA deadenylase, and DNA ligase activities that collectively are capable of accelerating the repair of a broad range of DNA single-strand breaks (SSBs) (Thompson et al. 1990). Human XRCC1 is 633 amino acids in length (Thompson et al. 1990) and is comprised of a number of protein/molecular interaction domains, and as such is classified as a molecular scaffold protein (Thompson and West 2000) (Fig. 8.5). Consistent with this idea, biophysical analyses suggest that the molecule is asymmetric with an elongated cigar-shaped structure and axial ratio of >7 (Mani et al. 2004). XRCC1 is comprised of an N-terminal domain (NTD) of ~160 amino acids that interacts with Pol β (Caldecott et al. 1996), a central BRCT domain of ~90 amino acids that interacts with PARP1 (Masson et al. 1998), PARP2 (Schreiber et al. 2002), poly (ADP-ribose) (Li et al. 2013), and DNA (Mok et al. 2019), and a C-terminal BRCT domain of ~100 amino acids that binds DNA ligase III (Caldecott et al. 1994) (Fig. 8.5). These three domains are separated by two linker domains that contain a nuclear localization signal and phosphorylation-independent binding site for PNKP (linker 1) (Breslin et al. 2017), and a phosphorylation-dependent binding site for PNKP (Loizou et al. 2004), APTX (Luo et al. 2004), and APLF (linker 2) (Bekker-Jensen

Fig. 8.5 XRCC1 protein domains and interactions. A cartoon depicting the position of XRCC1 protein domains and their molecular interactions. Note that only directly interacting partners of XRCC1 are shown. Verified interactions are shown above the XRCC1 cartoon and less wellestablished and/or weaker interactions are summarized below. XRCC1 is comprised of (left to right); (1) an amino terminal domain (NTD) that binds directly to POLβ and APEH; (2) a linker region containing a SUMOylation site (K176) that binds TDG, an RIR-like motif that is part of a “low-affinity” interaction site for PNKP (see text), and a nuclear localization signal (NLS); (3) a central BRCT domain (BRCT1) that interacts with poly(ADP-ribose) (green wavy lines, shown here on PARP1/2) and DNA; (4) a linker region containing three clusters of CK2 phosphorylation sites (red asterisks), one of which (located at residues 518–523) interacts with PNKP, APTX, and APLF; (5) a C-terminal BRCT domain (BRCT2) that interacts with LIG3α

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et al. 2007). XRCC1 reportedly also binds a number of additional proteins, the interactions of which are less defined. A summary of these interactions and their molecular roles during SSBR is presented in Fig. 8.5.

8.4.4.2 Reported SNPs in the XRCC1 Gene Although numerous validated SNPs in XRCC1 gene have been identified in the dbSNP database (http://www.ncbi.nlm.nih.gov/SNP), only three of which are most widely investigated including Arg194Trp (rs1799782) on exon 6, Arg280His (rs25489) on exon 9, and Arg399Gln (rs25487) on exon 10 (Shen et al. 1998). These XRCC1 polymorphisms may affect DNA repair capacity by changing interactions between XRCC1 protein and other proteins in BER pathway. A large number of studies have focused on the relationship between XRCC1 polymorphisms and development of cancer in humans (Tae et al. 2004). 8.4.4.3 TC and XRCC1 Polymorphisms Over the past decade, several epidemiological studies have reported the association regarding XRCC1 polymorphisms and TC risk (Zhu et al. 2004; Chiang et al. 2008). However, the results are to some extent divergent, but nevertheless intriguing. Previously, two studies by Akulevich et al. (2009) and Ho et al. (2009), respectively, reported that Arg399Gln (rs25487) polymorphism was associated with decreased risk of DTC and PTC, whereas Arg399Gln variant genotype carriers presented an increased risk of PTC in a Chinese study (Zhu et al. 2004). However, more studies did not support an association between Arg399Gln polymorphism and thyroid cancer risk (Chiang et al. 2008). Furthermore, Ho et al. (2009) reported that Arg194Trp variant homozygote genotype was associated with increased risk of DTC, in agreement with the conclusion by Chiang et al. (2008). In contrast, it was reported that the heterozygous genotype was significantly associated with a decreased risk of PTC in a Korean population (Ryu et al. 2011). And other studies did not reveal statistically significant association regarding this polymorphism and thyroid cancer (Fard-Esfahani et al. 2011; Santos et al. 2012). As for Arg280His polymorphism, Garcia-Quispes et al. (2011) addressed the variant genotype showed a highly increased risk for DTC among Caucasian, while two studies by Akulevich et al. (2009) and Fard-Esfahani et al. (2011) found similar trends toward having DTC, but statistical significance was not attained. Moreover, another two studies suggested that Arg280His heterozygous genotype might provide protective effects against the risk of thyroid cancer among Asians, but this also did not reach statistical significance (Chiang et al. 2008). The discrepancies across these studies motivated the present meta-analysis. More importantly, many systematic reviews and meta-analyses have addressed the association of XRCC1 polymorphisms with various cancers (Dai et al. 2009), but have not evaluated the association between these polymorphisms and TC. In other words, this is the first meta-analysis undertaken so far of the largest and most comprehensive assessment for the relationship between XRCC1 polymorphisms and the susceptibility to thyroid cancer. Our meta-analysis did not

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show any significant association between these three polymorphisms (Arg399Gln, Arg194Trp, and Arg280His) and the risk of DTC in the total population for all genetic models. Interestingly, in the subgroup analysis by ethnicity, the results indicated that Arg280His polymorphism was associated with a significantly increased risk of DTC among Caucasians under dominant genetic model, additive genetic model, and heterozygote comparison, whereas heterozygote Arg/His genotype might provide protective effects in Asians against the risk of DTC. We also detected that individuals harboring variant allele of Arg399Gln polymorphism might have a decreased risk of DTC in mixed population, but not in Caucasians or Asians. In contrast, the Arg194Trp variant allele carriers might have an increased risk of DTC in mixed population, but not in Caucasians or Asians (Yi et al. 2013).

8.4.5

XRCC3 (X-Ray Repair Cross-Complementing Protein 3) Gene

Located at 14q32.33, which is the long (q) arm of chromosome 14 at position 32.33 and spans base pairs 103,697,611 to 103,715,486 on chromosome 14. This gene is involved in the homologous recombination repair (HRR) pathway of doublestranded DNA, thought to repair chromosomal fragmentation, translocations, and deletions (Kurumizaka et al. 2001).

8.4.5.1 Structure and Function of XRCC3 Protein XRCC3 is one of the central proteins playing an important role in HRR pathway. The XRCC3 protein is one of five paralogs of RAD51, including RAD51B (RAD51L1), RAD51C (RAD51L2), RAD51D (RAD51L3), XRCC2, and XRCC3. They each share about 25% amino acid sequence identity with RAD51 and each other. XRCC3 interacts and stabilizes RAD51 and involves in HRR for DNA DSBs and cross-link repair in mammalian cells (Kurumizaka et al. 2001). XRCC3 is a paralog of RAD51, and similar to RAD51, it is essential for genetic stability (Brenneman et al. 2002). Part of the RAD21 paralog protein complex (CX3) acts in the BRCA1-BRCA2-dependent HR pathway. Upon DNA damage, CX3 acts downstream of RAD51 recruitment; the complex binds predominantly to the intersection of the four duplex arms of the Holliday junction (HJ) and to junctions of replication forks. Involved in HJ resolution and thus in processing HR intermediates late in the DNA repair process; the function may be linked to the CX3 complex and seems to involve GEN1 during mitotic cell cycle progression. Part of a PALB2scaffolded HR complex containing BRCA2 and RAD51C and which is thought to play a role in DNA repair by HR. Plays a role in regulating mitochondrial DNA copy number under conditions of oxidative stress in the presence of RAD51 and RAD51C (Romana et al. 2017). 8.4.5.2 Polymorphisms in XRCC3 Gene The Thr241Met (18067C/T, rs861539) is the most common polymorphism of XRCC3, which substitutes at codon 241 in exon 7, with a C to T transition (Matullo et al. 2001). Previous studies were performed to investigate the relationship between

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the XRCC3 Thr241Met polymorphism and ovarian cancer risk (Auranen et al. 2005). XRCC3 Thr241Met polymorphism has also been studied in TC wherein they found homozygous polymorphic genotype (18067TT) to be associated with a borderline or statistically significant increased risk for DTC (P ¼ 0.05). The homozygous XRCC3 18067TT polymorphic genotype was also found to be associated with a borderline increased risk for benign thyroid disease (P ¼ 0.052) and this finding was relatively stable after multivariate adjustment (P ¼ 0.078) (Winsey et al. 2000; Matullo et al. 2001). XRCC3 Thr241Met is the most extensively studied genetic variant of XRCC3 (Shen et al. 2004) and evidence indicates that the Thr/Met variant resides in the adenosine triphosphate-binding domain of XRCC3, the only domain with known functional activity (Manuguerra et al. 2006). This fact suggests that the change caused by the XRCC3 Thr241Met polymorphism is associated with other proteins, especially RAD51 (Werbrouck et al. 2009). In earlier studies, rs1799794 (intron, A > G) polymorphism in XRCC3 has been associated with increased risk of overall cancer (Han et al. 2006), breast cancer (He et al. 2012), bladder cancer (Matullo et al. 2005). Finally, rs1799796, an intronic A > G transition in the XRCC3, was studied in many cancers but the results are contradictory even in patients suffering from the same type of cancer in different population. A study from the British population showed that rs1799796 decreases the risk of breast cancer (Kuschel et al. 2002) while another study from Belgium reported an increased risk associated with this SNP in BRCA1 and BRCA2 carriers (Vral et al. 2011). More recently, a significant association with ovarian cancer was confirmed in a meta-analysis involving Caucasian, Asian, and African populations (Yuan et al. 2014). On the other hand, studies on ovarian cancer from the British population (Auranen et al. 2005), lung cancer in Danish population (Jacobsen et al. 2004), bladder and breast cancer in American population (Wu et al. 2006; Han et al. 2004), and prostate and urinary bladder cancer in Indians failed to show any association with this SNP (Mandal et al. 2010).

8.4.5.3 XRCC3 Polymorphisms and TC XRCC3 Thr241Met polymorphism (18067C/T, rs861539) has also been studied in TC wherein they found homozygous polymorphic genotype (18067TT) to be associated with a borderline or statistically significant increased risk for DTC (P34 ¼ 0.05) (Erich et al. 2005). Previous studies have reported significant association of Thr241Met (rs861539) polymorphism with TC risk (Sturgis et al. 2005; Bastos et al. 2009). Nevertheless, many studies have found no evident correlation between XRCC3 polymorphisms and thyroid cancer (Siraj et al. 2008; Akulevich et al. 2009). Variant alleles of XRCC3 polymorphisms (rs1799794, rs861539) were found to increase the risk of TC; however, GG genotype frequency of rs1799796 polymorphism was found to decrease the TC risk. 30 UTR location of rs1799794 SNP can affect polyadenylation and alterations in regulatory protein–mRNA and miRNA– mRNA interactions. It can cause disease directly, modifying the severity of the disease phenotype, or be linked with disease susceptibility (Skeeles et al. 2013). Fourth studied SNP of XRCC3, rs1799794 (A > G), is located in the 50 UTR region

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and significant association of this SNP was observed with increased risk of TC (Romana et al. 2017). The underlying mechanism of its function may be related to its alteration in local DNA secondary structure or functional motif, thereby affecting the binding affinities of the relevant transcription factors (Xu et al. 2012).

8.4.6

The Xeroderma Pigmentosum Group D (XPD) Gene

Also known as excision repair cross-complementation group 2 (ERCC2) gene and encodes for a 2.3-kb mRNA containing 22 exons and 21 introns. Located at 19q13.32, which is the long (q) arm of chromosome 19 at position 13.32 spanning the base pairs 45,349,837 to 45,370,647 on chromosome 19. The ERCC2 gene provides instructions for making a protein called XPD (Benhamou and Sarasin 2002).

8.4.6.1 XPD: Structure and Function XPD (also known as ERCC2) is a helicase protein of 761 amino acids with a molecular weight of 86.9 kDa (Benhamou and Sarasin 2002; Sameer and Nissar 2018). XPD is one of the two pivotal ATPase/Helicase of the core unit of THIIF molecular assembly. It seems to form a bridge between the TFIIH core complex and the CAK module—which otherwise also exists as a free trimeric complex with its own distinct functions (Cameroni et al. 2010). XPD belongs to an ATP-dependent 50 -30 superfamily 2 (SF2) helicases, which are characterized by seven “helicase motifs” (walker motif I, Ia, II, III, IV, V, and VI) constituted of highly conserved amino acid sequences (Oksenych and Coin 2010). Interestingly, the XPD protein also constitutes a 4Fe4S (FeS) cluster that has been demonstrated to be essential for its helicase activity. Because of this cluster XPD becomes a founding member of a family of related SF2 helicases (Rudolf et al. 2010). SF2 family helicases also comprise various important family members like bacterial DinG (damage-inducible G) and the eukaryotic XPD paralogs FancJ (Fanconi’s anemia complementation group J), RTEL (regular of telomere length), and Chl1 (chromatid cohesion in yeast) (Wolski et al. 2010; Sameer and Nissar 2018). The exact function of the FeS cluster is not known but a number of explanations as to its role have been given like—a purely structural role and providing stabilization to the FeS domain; direct interaction with the damaged DNA substrate and acting as a damage sensor and acting as a regulatory center for XPD helicase (Houten et al. 2016). Furthermore, XPD serves as the authenticator of the DNA lesion initially sensed by XPC-HR23B which preludes the binding of TFIIH at the site of lesion (Oksenych and Coin 2010; Coin et al. 2007). The opening of the DNA duplex at the site of lesion requires the dual ATPase function of both XPB and XPD, but the helicase activity of XPD plays a critical role in the opening of the DNA. The biochemical data vividly demonstrated that mutations in the motif I (containing ATPase activity) of either XPB or XPD inhibit the formation of DNA bubble at the lesion site but the mutations in the motif III and IV (containing helicase activities) of XPB impair its functionality but do not inhibit NER in vivo (Coin et al. 1998).

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However, some specific mutations in both XPB and XPD can completely prevent opening and dual incision of the DNA lesions site in NER (Chen et al. 2003a, b). Additionally, it has also been demonstrated that binding of N-terminal p44 subunit with XPD stimulates its helicase activity by almost ten-fold. Furthermore, mutations in the C-terminal domain (CTD) of XPD prevent the interactions with p44 resulting not only in decrease in the overall TFIIH helicase activity but also modulate TFIIH composition and contributing to further transcription defects (Coin et al. 1999). XPD has also been demonstrated to control the cell cycle via its interaction with CAK domain of the TFIIH complex. Downregulation of XPD as happens at the beginning of the mitoses initiates the disengagement of CAK module from TFIIH complex and its eventual role as regulator of cell cycle independent of TFIIH core complex (Chen et al. 2003a, b).

8.4.6.2 The XPD Gene and Its SNPs Apart from point mutations that cause disease, SNPs in the XPD sequence are found among the general population with a frequency highly variable between 1% and 90% of BCa cases are categorized as urothelial carcinomas (UCs) (Guo and Czerniak 2019; Saginala et al. 2020; Yazbek-Hanna and Jain 2020; Zhu et al. 2020), ~5% of histological variants of BCa belongs to squamous cell carcinoma (SCC), while adenocarcinoma, neuroendocrine tumors, sarcoma, and metastases constitute the minor cases of BCa (in toto ~5% of cases) (Saginala et al. 2020; Sjödahl et al. 2012; Yazbek-Hanna and Jain 2020). The BCas based on clinical and histopathological demonstrations of the initial invasive tumor on the depth of urinary bladder wall, are divided into non-muscular (muscle) invasive BCa (NMIBC) and muscular (muscle) invasive BCa (MIBC) (Jalanko et al. 2020). Seventy percent of BCa cases are belonging to NMIBC and the left 30% belongs to MIBC cases (Saginala et al. 2020; Tian et al. 2020; Yazbek-Hanna and Jain 2020). Normally in 50% of patients with NMIBC, the BCa is recognized as low grade; while the BCa in the majority of patients with MIBC or metastatic tumors is recognized as high grade (Kamat et al. 2016). The urinary bladder tumors are morphologically classified into three different groups of solid, mixed, and papillary types. In major cases, the UCs involve the papillary type (Kamat et al. 2016; LOPEZ 2020). Due to this fact, urothelium encompasses an inherent plasticity which may lead to occurrence of a wide range of histologic variants and histologic differentiation in UCs (LOPEZ 2020) (Table 9.3). According to the eighth edition of American Joint Committee on Cancer (AJCC), the staging of urinary BCa is represented for urinary bladder nodes, urinary bladder tumors, and urinary bladder metastases. In this regard, the represented staging of BCa for nodes, tumors, and metastases is, respectively, as follows: N in five levels of NX, N0, N1, N2, and N3; T in 11 levels of Tx, T0, Ta (NMIBC), Tis (NMIBC), T1 (NMIBC), T2 (MIBC), T2a (MIBC), T2b (MIBC), T3 (MIBC), T3a (MIBC), T3b (MIBC), T4 (MIBC), T4a (MIBC), and T4b (MIBC); M in four levels of M0, M1, M1a, and M1b (Cornejo et al. 2020; Ohadian Moghadam and Nowroozi 2019). In 1973 the World Health Organization (WHO) started to represent grading and classification system regarding tumors of urinary system. Due to this fact, the papillary urothelial lesions are classified into three grades of G1, G2, and G3. The minimal abnormalities of tumors in papillary UCs (no atypia) are shown by G1. The G2 grade includes a wide range of non-invasive UCs which can be confusing and G3 involves the maximum atypia with maximally constitutional disorders. The G2 grade has some complications and limitations. In 1998, the WHO/International Society of Urological Pathology (ISUP) represented a new classification involving categories of papilloma, papillary urothelial neoplasm of low malignant potential (PUNLMP), low-grade (LG) and high-grade (HG) tumors in association with carcinomas.

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Table 9.3 The fourth edition of the WHO classification of non-invasive urothelial lesions and invasive urothelial tumors Non-invasive urothelial lesions Urothelial carcinoma in situ (CIS)

Papillary urothelial carcinoma, low grade Papillary urothelial carcinoma, high grade Papillary urothelial neoplasm of low malignant potential (PUNLMP) Urothelial papilloma Inverted urothelial papilloma Urothelial proliferation of uncertain malignant potential (hyperplasia) Urothelial dysplasia

Invasive urothelial tumors Infiltrating urothelial carcinoma with divergent differentiation (e.g., glandular, squamous, and trophoblastic differentiations) Nested including large nested

References (Humphrey et al. 2016; Lopez-Beltran et al. 2019; Wang and McKenney 2019)

Microcystic Micropapillary

Lymphoepithelioma-like Plasmocytoid/signet ring cell/ diffuse Sarcomatoid

Giant cell Poorly differentiated Lipid rich Clear cell (glycogen rich cell) Tumors of maüllerian type Tumors arising in a bladder diverticulum

Fig. 9.1 The grading systems of UC done by the WHO in the years of 1973 (involving grades 1–3) and 2016 (involving grades 1–2). The PUNLMP depicts papillary urothelial neoplasms of low malignant potential. The low- and high-grades imply NILGC and NIHGC which, respectively, depict: non-invasive low-grade papillary UC and non-invasive high-grade papillary UC

Interestingly, the mentioned 1998 grading system was reused in WHO 2004 and 2016 classifications too. The WHO classifications for non-invasive and invasive urothelial tumors are shown in Table 9.3. Moreover, the classifications of UCs

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achieved by WHO in 1973 and 2016 are compared in Fig. 9.1 (Compérat et al. 2019; Humphrey et al. 2016; Kamat et al. 2016; Wang and McKenney 2019).

9.5

Urinary Bladder Cancer: Classification and Grading

As BCa is a heterogeneous cancer with different pathological and molecular parameters, a number of classification systems have been represented which involve traditional and molecular subtyping systems. The BCa traditional classification system usually involves pathological items. As we know, the pathological grading and staging and the BCa progression and recurrence are versatile in different patients. Besides, all the risks in association with the all types of BCas including MIBCs and NMIBCs are not predictable by traditional system. In this regard, the recurrency and progression risks may be predictable for NMIBCs but not for MIBCs. These parameters determine how the patients’ should be monitored and treated; while the pathological and biological reactions differ from person-to-person. Traditional system is supported by the European Organisation for Research and Treatment of Cancer (EORTC) (http://www.eortc.be/tools/bladdercalculator/default. htm). It is important to know that, the pathological characteristics do not reveal the genetical and molecular properties. By the advanced technologies of sequencing and omics (such as epigenomics, genomics, metabolomics, proteomics, transcriptomics, etc.) the accuracy rate of BCa diagnosis and treatment has been raised up (Loras et al. 2019; Sylvester et al. 2006; Zhu et al. 2020). Therefore, since the year of 2012 different molecular subtyping systems have been conducted by scientists to have accurate and effective results in association with diagnosing and treatment of BCa cases (Zhu et al. 2020). Due to this fact, in 2012 Sjödahl et al. from Lund University, Sweden, performed a study based on gene expression profiles belonging to tumor cases of patients with BCa. The gene expression profiles involve cell adhesion genes, cell cycle genes, cytokeratins, and receptor tyrosine kinases genes. In this regard, five determined molecular subtypes of urothelial cell carcinoma including genomically unstable, infiltrated class of tumors, SCC-like, urobasal A and urobasal B were defined (Table 9.4). The molecular subtyping system done by Sjödahl et al. is known as an early subtyping which includes both types of MIBC and NMIBC (Sjödahl et al. 2012; Zhu et al. 2020). In 2014, three groups of molecular subtypes regarding MIBC including UNC (Damrauer et al. 2014), MDA (Choi et al. 2014), and TCGA (Cancer Genome Atlas Research Network 2014) were represented, separately (Zhu et al. 2020). The UNC subtyping system is based on high-grade BCa of MIBC. In this regard, the related subtypes are divided into two groups of basal-like and luminal (Table 9.4) (Damrauer et al. 2014; Zhu et al. 2020). The MD Anderson as another molecular subtyping system (based on whole genome mRNA expression profiling) for MIBC was achieved in the MD Anderson Cancer center by a scientific group in 2014 (Choi et al. 2014; Zhu et al. 2020). Throughout this methodology, three subtypes including basal, luminal, and p53-like were recognized for MIBC subtyping system (Table 9.4) (Choi et al. 2014; Zhu et al. 2020).

Highgrade MIBC

MIBC

MIBC

UNC 2014 (University of North Carolina)

MDA 2014 (MD Anderson)

TCGA 2014 (The Cancer Genome Atlas)

TCGA 2017 (The Cancer Genome Atlas)

Target BCas Early BC subtyping for MIBC and NMIBC

Subtyping system Lund 2012

criterion Gene expression profiles belonging to tumor cases of patients with BCa Expression of gene set predictor of BASE47 Whole genome mRNA expression profiling Genetic expression signature

Luminal

Cluster I (luminal)

Luminal

Luminal

Luminalinfiltrated

Cluster II (luminalinfiltrated)

p53-like

Basal-like

Molecular subtypes Urobasal Genomically A unstable

Basalsquamous

Cluster III (basalsquamous)

Basal

Infiltrated class of tumors

Table 9.4 A comparison between the main BCas molecular subtyping systems from 2012

Neural

Cluster IV (basal)

Urobasal B

Luminalpapillary

SCC-like

(Cancer Genome Atlas Research Network 2014; Zhu et al. 2020) (Robertson et al. 2017; Zhu et al. 2020)

(Damrauer et al. 2014; Zhu et al. 2020) (Choi et al. 2014; Zhu et al. 2020)

References (Sjödahl et al. 2012; Zhu et al. 2020)

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Gene expression

Gene expression

Gene expression and transcriptome

Gene expression

MIBC

High-risk NMIBCs

MIBC and NMIBC

MIBC

GSC 2017 (Genomic subtyping classifier) EAU 2018 (European Association of Urology) guideline BOLD 2018 (BLCA (Bladder Cancer) Subtypes Of Large metacohort Database) NAC 2018 (NeoAdjuvant Chemotherapy)

Transcriptome sequencing analysis

NMIBC

UROMOL 2016

CC1basal

Neurallike

Good

Claudinlow

Class 1

CC2-luminal

Luminal-like

Moderate

Basal

Class 3

CC3immune

Papillarylike

Poor

Luminalinfiltrated

CC4scar-like

HER2like

Luminal

Class 2

Squamouscell carcinomalike

Mesenchymallike

(Seiler et al. 2019; Zhu et al. 2020)

(Tan et al. 2019; Zhu et al. 2020)

(van Kessel et al. 2018; Zhu et al. 2020)

(Hedegaard et al. 2016; Zhu et al. 2020) (Seiler et al. 2017; Zhu et al. 2020)

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The Cancer Genome Atlas (TCGA) subtyping system was performed in 2014 for the first time and was supported by two US research centers of The National Cancer Institute (TNCI) and The National Human Genome Institute (TNHGI). By the TCGA—version 2014—four MIBCs molecular subtypes including Cluster I, Cluster II, Cluster III, and Cluster IV were detected (Table 9.4) (Cancer Genome Atlas Research Network 2014; Zhu et al. 2020). Again, the TCGA subtyping system was performed in 2017 as a multiple TCGA analytical platform. The TCGA—version 2017—identified five molecular subtypes including luminal, luminal-infiltrated, basal-squamous, luminal-papillary, and neuronal (Table 9.4) (Robertson et al. 2017). UROMOL 2016 is an NMIBC subtyping system with three molecular subtypes of Class 1, Class 3, and Class 2 (Table 9.4). The UROMOL subtyping system is based on a transcriptome sequencing analysis which targets a huge number of genes which are involved in different biological processes. Hence, the Class 1 involves LG-tumors, Class 3 includes tumors with phenotypic characteristics like CIS, and Class 2 represents the HG-malignant tumors (Hedegaard et al. 2016; Zhu et al. 2020). The single-sample genomic subtyping classifier (GSC) is another molecular subtyping system for MIBC with four target subtypes of basal, claudin-low, luminal, and luminal-infiltrated which was performed in 2017 (Table 9.4). The GSC subtyping system was invented to predict the prognosis and response to neoadjuvant cisplatin-based chemotherapy (NAC) based on gene expression (Seiler et al. 2017; Zhu et al. 2020). Another NMBIC subtyping system is European Association of Urology (EAU) guideline for NMIBC which was achieved by Van Kessel et al. This subtyping system targets EAU high-risk NMIBCs and a reclassification was done for the related molecular subtypes including good, moderate, and poor (Table 9.4) (van Kessel et al. 2018; Zhu et al. 2020). The BOLD subtyping system which involves both of MIBC and NMIBC was performed by Tan et al. to present the related molecular subtypes based on transcriptomic properties and gene expression. Six subtypes of HER2-like, luminal-like, neural-like, papillary-like, mesenchymal-like (MES-like), and squamous cell carcinoma-like (SCC-like) were recognized (Table 9.4) (Tan et al. 2019; Zhu et al. 2020). Seiler et al. run a subtyping system based on adjuvant chemotherapy with four molecular subtypes of CC1-basal, CC2-luminal, CC3-immune, and CC4-scar-like (Table 9.4) (Seiler et al. 2019). The gene profiling of NMIBC and MIBC is shown in Table 9.4.

9.6

Toll-Like Receptors and Signaling Pathways

TLRs—the magic molecules of type I transmembrane glycoprotein pertain to the largest pathogen recognition receptors (PRRs) family which have pivotal role in immunity, pathology, and physiology (Fig. 9.2). The TLR molecules bind to three types of ligands involving exterior molecules of pathogen-associated molecular

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Fig. 9.2 Tumor biology diagram, the increase or decrease of TLRs may lead to turning-on or turning-off the anti-tumoral activities or tumorigenesis; the double-edged sword characteristics of TLRs

patterns (PAMPs), exterior synthetic molecules of xenobiotic-associated molecular patterns (XAMPs), and interior molecules of damage/danger-associated molecular patterns (DAMPs) (Angrini et al. 2020; Behzadi and Behzadi 2016; Behzadi 2020; Javaid and Choi 2020; Pradere et al. 2014; Saghazadeh and Rezaei 2020; Wang et al. 2020). There are 13 recognized TLRs up to now. Humans encompass TLRs 1–10 and mice possess TLRs 1–9 and 11–13. Indeed, there are 10 and 12 functional types of TLRs in human and mouse immune systems, respectively. However, the TLRs of 11, 12, and 13 are protein products of TLR11, TLR12, and TLR13 pseudogenes in mouse (Angrini et al. 2020; Behzadi and Behzadi 2016; Dajon et al. 2017; Saghazadeh and Rezaei 2020).

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TLRs are expressed on a wide range of immune and non-immune cells in which results in production of different immune compounds such as cytokines, interleukins (ILs), interferons (IFNs), etc. Moreover, the TLRs are expressed on the surface of different tumor cells and regulate the activity and metabolism of tumor growth directly and indirectly in versatile types of cancers such as breast and prostate cancers. Therefore, the TLRs have a pivotal role in both progression and treatment of different cancers (Fig. 9.2) (Behzadi 2020; Behzadi et al. 2016, 2019; Boozari et al. 2019; Huang et al. 2018; Javaid and Choi 2020; Ohadian Moghadam and Nowroozi 2019; Pradere et al. 2014; Saghazadeh and Rezaei 2020). In this regard, immune cells comprising dendritic cells (DCs), macrophages (MΦs), B-cells, T-cell subsets, mast cells, neutrophils and non-immune cells including parenchymal-, endothelial-, and epithelial cells and fibroblasts express TLRs. Interestingly, TLR molecules may lead to maturate DCs. TLR glycoproteins are molecules that act as bridges which connect the innate into the adaptive immune system (Behzadi and Behzadi 2016; Boozari et al. 2019; Dajon et al. 2017; Javaid and Choi 2020; Nissar et al. 2017; Pradere et al. 2014). Based on amino acid sequencing, the TLR molecules expressed in human body are categorized into five families of TLRs 2 (involving TLR members of 1, 2, 6, 10), 3 (TLR3), 4 (TLR4), 5 (TLR5), and 9 (comprising TLR members of 7, 8, and 9). The extracellular TLR molecules of 1, 2, 5, 6, and 10 are expressed on the surface of the cells (plasma membrane), while the intracellular TLR molecules of 3, 7, 8, and 9 are expressed within the intracellular organelles such as endosomes, lysosomes, and endoplasmic reticulum (ER). Due to this fact TLR 4 molecule, a bi-functional glycoprotein is expressed as extracellular and intracellular molecule (Angrini et al. 2020; Behzadi and Behzadi 2016; Behzadi et al. 2019; Javaid and Choi 2020; Saghazadeh and Rezaei 2020). TLRs are structurally consisted of three domains including an IL-1 receptor (IL-1R)-like (Toll/IL-1R (TIR)) cytoplasmic (intracellular) domain (a conserved and homolog signaling domain located in C-terminal), an extracellular domain with leucine rich repeat (LRR) motifs (comprising 24 amino acids) which is situated in N-terminal and mediates the linkage of TLR molecule with the related ligands and a transmembrane domain. The LRR motifs are responsible for the horseshoe-shaped structure of TLR molecules via construction of an α-helix and a β-strand conformation which are separated by a loop (Fig. 9.3). The TLR horseshoe structure normally happens after TLR-ligand connection. Moreover, the extracellular domain of TLR molecule is able to link to its suitable ligand (e.g., PAMPs, DAMPs, and XAMPS) via some co-receptor molecules of CD14, CD36, and myeloid differentiation factor 2 (MD-2) (Behzadi and Behzadi 2016; Javaid and Choi 2020; Kawasaki and Kawai 2014; Medvedev 2013; Ohto et al. 2012; Saghazadeh and Rezaei 2020; Wang et al. 2020). By recognition of the related ligand, the spatial structure of TLR molecule transfigures to have a successful linkage with the ligand. In this regard, hetero(TLR2/1, TLR2/6, TLR2/10) or homodimers (TLR3/3, TLR4/4, TLR5/5) and reorientation (TLRs 7, 8, and 9) occur and the TIR domains are triggered to interact with the related intracellular adaptors including five downstream signaling adaptor

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Fig. 9.3 The horseshoe structure of TL4. α-helices and β-strands are separated by a loop (4G8A PDB file) (Ohto et al. 2012)

proteins (such as myeloid differentiation primary response protein 88 (MyD88), sterile α- and armadillo motif-containing protein (SARM), TIR domain-containing adaptor inducing IFN-β (TRIF/TCAM1)), TIR domain-containing adaptor protein (TIRAP/Mal), and TRIF-related adaptor molecule (TRAM/TICAM2). Due to this fact, the TLRs (excluding TLR3 and endosomal TLR4) activate the signaling pathway of canonical MyD88-dependent. The TRIF dependent signaling pathway which is a non-canonical one induces nuclear translocation of transcription factors. Then the IFN regulatory factors of 3 and 7 (IRF3 and IRF7) lead to produce type 1 IFN as the main product. The TRIF dependent signaling pathway is inhibited by the SARM in human body. The activation of MyD88-dependent signaling pathway leads to induction of a number of proteins including nuclear translocation of transcription factors, nuclear factor (NF)-κB, mitogen-activated protein kinases (MAPKs), adaptor protein-1 (AP-1), and cyclic Adenosine Mono Phosphate (cAMP) response element binding protein (CREB). The activation of this signaling pathway results in expression of the genes belonging to pro-inflammatory cytokines. The TLRs of 3 and endosomal 4 activate TRIF signaling pathway; moreover, the extracelluar TLR4 is able to induce the canonical pathways of MyD88 and TIRAP dependent, while the intracellular TLR4 triggers the non-canonical pathways of TRAM and TRIF (Fig. 9.4) (Angrini et al. 2020; Ayala-Cuellar et al. 2019; Basith et al. 2011, 2012; Ernst et al. 2019; Gajdács and Behzadi 2020; Javaid and Choi 2020; Kawasaki and Kawai 2014; O'neill et al. 2013; Saghazadeh and Rezaei 2020).

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Fig. 9.4 The MyD88-dependent and TRIF-dependent pathways

In normal and healthy individuals, the TLRs of urothelial cells are produced against uropathogenic microorganisms including uropathogenic Escherichia coli (UPEC), uropathogenic Klebsiella pneumoniae (UPKP), uropathogenic Proteus mirabilis (UPPM) and uropathogenic Candida albicans (UPCA) as common uropathogens. Hence, in the presence of uropathogens (and their related components) the urinary tract TLRs involving TLR2 (the related ligands are bacterial LPS and fungal zymosan), TLR4 (the related ligands are bacterial LPS and Type I and P fimbriae), and TLR5 (the related ligand is flagellin) induce the MyD88dependent signaling pathway to produce transcription factors of NF-κB (for TLR2), NF-κB, IRF3, and IRF7 (for TLR4), and NF-κB (for TLR5). This signaling pathway results in inflammatory cytokines (Fig. 9.4). In parallel with this feature, the urinary tract TLRs may act against tumor cells in which may lead to maturation of antigen presenting cells (APCs). In follow, APCs produce and secrete different cytokines including IL-6, IL-12, tumor necrosis factor-α (TNF-α), and INFs. These cytokines trigger natural killer cells (NK cells) and cytotoxic T lymphocytes (CTLs) to remove tumor cells. However, in people with oncogenic predisposing factor(s) the

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expression of TLRs on tumor cells upregulates the expression of pro-angiogenic factors (e.g., Transforming growth factor-β (TGF-β) and vascular endothelial growth factor (VEFG)), other growth factors, pro-inflammatory cytokines and compounds (e.g., IL-6, IL-12, and nitric oxide), and anti-apoptotic proteins (it is reported that those tumor cells which go through apoptotic process are able to disseminate DAMPs which are relating to tumor progression). This process may lead to evoke more immune cells which results in tumor progression and metastasis. Hence, TLR agonists are able to support the presence of malignant cells and even promote their resistance against chemotherapy; however, the TLR ligands have suppressive effect on tumor growth. In addition, there are four types of factors which affect the expression of TLR molecules including animal factor (age, breed, immunity condition, sex, and species), external factors (such as diet, light, and temperature), genetic factors (copy number variants, SNPs, DNA duplication, DNA deletion, promoter sequences), and internal factors (e.g., apoptosis, cell cycle, epithelial-mesenchymal transition (EMT), inflammation, and migration) (Behzadi and Behzadi 2016; Behzadi et al. 2019, 2020; Ohadian Moghadam and Nowroozi 2019; Sarshar et al. 2020; Vidya et al. 2018).

9.7

MyD88-Dependent Pathway

Throughout MyD-dependent pathway, the MyD88 adaptor together with IL-1receptor associated kinases (IRAKs) constructs a complex known as Myddosome (Fig. 9.4). In parallel with Myddosome complex formation, the IRAK1 become activated (by IRAK4) and then autophosphorylated which lead to TRAF6 and TAK1 activation. The activation of TRAF6 and TGF-activated kinase 1 (TAK1) is the result of k63-linked polyubiquitination process on them via IRAK1. Finally, activation of TAK1 results in activation of two pathways of MAPKs and the inhibitor of kappa light polypeptide gene enhancer in B-cells kinase (IKK)-NF-κB (the IKK complex involves three compartments of IKKα (catalytic structure), IKKβ (catalytic structure), and the regulatory structure of IKKγ (NF-kappa B Essential Modulator) (NEMO)). The activated MAPKs (or MAPK family members including extracellular signal-regulated kinase (ERK) 1/2, c-Jun N-terminal kinase (JNK) and p38) trigger the AP-1 s (AP-1 family transcription factors) activity to modulate the production of inflammatory cytokines. And in IKK-NF-κB pathway, the NF-κB is transferred into the nucleus which results in pro-inflammatory responses (Fig. 9.4). The chronic inflammations occurred by TLRs of 4, 7, 8, and 9 signaling pathway have pro-tumor effects on cancer cells. In this regard, the activation of TLR4 throughout Myd88dependent pathway results in tumor formation and metastasis. Although, TLRs of 4, 7, 8, and 9 support tumor cells by escaping them from immune system, apoptotic resistant and immunosuppressive abilities, TLRs of 3 and 5 signaling pathways activate the anti-tumor T-cell reactions (Basith et al. 2012; Gajdács and Behzadi 2020; Javaid and Choi 2020; Kawasaki and Kawai 2014; Sughra et al. 2010; Vidya et al. 2018).

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MyD88-Independent (TRIF-Dependent) Pathway

The TRIF-dependent pathway is mediated by TRIF adaptor and this pathway is continued throughout activated molecules of TRAFs 6 and 3. TRAF6 employs the kinase molecule of RIP-1 which in follow leads to activation of the cascade of TAK1 complex, NF-κB, MAPKs, AP-1 s, and inflammatory responses, respectively. Simultaneously, the TRAF3 activates the IKK kinases of TBK1 and IKKi to phosphorylate IRF3. This process leads to IRF3 dimerization and translocation into the nucleus. Thereafter, the IFN-I genes are triggered to express the type I IFN protein in the presence of IRF3 dimers. The pellino family E3 ubiquitin ligases including Pellino-1 employ RIP-1 to activate NF-κB pathway through TRIFdependent pathway. On the other hand, Pellino-1 links to the transcription factor of Deformed Epidermal Autoregulatory Factor-1 (DEAF-1) to modulate the linkage of IRF3 to promoter of IFN-β (Fig. 9.4). The NF-κB has pivotal role in regulation of apoptosis, cell proliferation and transcription (about 200 genes which express growth and inflammatory responses) processes (Basith et al. 2012; Gajdács and Behzadi 2020; Javaid and Choi 2020; Kawasaki and Kawai 2014; Wu and Sheng 2015).

9.9

Toll-Like Receptors and Trafficking

TLR molecules are totally biosynthesized within the ER and then are transferred into the Golgi apparatus for necessary glycosylation. Thereafter, they translocate to their right positions within the cell via the general chaperone protein of gp96. Indeed, gp96 is known as an ER-associated paralog of Hsp90 that transfers a wide range of molecules comprising integrins, several TLRs (including TLR1, TLR2, TLR4, TLR5, TLR7, and TLR9), etc., to their natural situations and places. The protein associated with TLR4 (PRAT4A) is another ER resident molecule which contributes in transferring TLR molecules of 1, 2, 4, 7, and 9 from ER to their places on cell membrane and within the endosomes. In this regard, TLRs 1, 2, 4 are transferred upon the cell membrane, while the TLR9 is carried into the endosome. The Unc-93 homolog B1 (Unc-93B1) protein is a multispan transmembrane molecule which participates in transmission of intracellular TLRs of 3, 7, 8, and 9 into the endosomes to mediate the signaling processes. The exaggerated activity of TLR7 is controlled by Unc-93B1 via application of TLR9. Now it is known that, the consequence of the absence of gp96 protein is the lack of TLR molecules of 1, 2, 3, 4, 5, and 7 on one hand and misfolding of TLR9 on the other hand. Hence, there are a huge number of proteins and molecules which co-work as an unite orchestrate to harmonize the position, structure, and function of TLRs (Kawasaki and Kawai 2014; Medvedev 2013; Vijay 2018).

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Toll-Like Receptors and Urinary Bladder Cancer

The TLR molecules have been recognized as double-edged sword. On one hand, TLRs act as anti-tumor molecules in healthy people; on the other hand, the expressed TLRs in tumor cells act as tumorigenic molecules (Angrini et al. 2020; Javaid and Choi 2020; Ohadian Moghadam and Nowroozi 2019). Depending on the stage and the type of cancer, the expression of TLRs in tumor cells differs in different individuals (Angrini et al. 2020; Li et al. 2014a). The immune system in urinary bladder acts in particular pathway. The non-specific and physical defense system of urinary bladder depends on production of natural antimicrobial agents comprising cathelicidin and β-defensin, a significant layer of mucin which acts as a strong barrier against infectious agents, strong urine flow during micturition, and the immunosuppressive milieu. Because of immunosuppressive condition within urinary bladder tumor microenvironment, the urinary bladder tumor is able to neutralize the activity of tumor-infiltrating lymphocytes (TILs) which leads to tumor maintenance. Simultaneously, high expression of regulatory T cells (Tregs) and cytokines like TGF-β and IL-10 (known as Th1 inhibitory cytokines) supports the development and progression of urinary bladder tumors. Due to this fact, the expression of TLR molecules within tumor cells activates the relating signaling pathways in which a versatile of proteins and cytokines are induced and finally results in tumor survival, invasion, development, progression, and metastasis. Hence, it seems that the TLR signaling pathways in tumors reprogram the tumor metabolism and growth and reduce the anti-tumor immune responses (Angrini et al. 2020; deLeeuw et al. 2012; Ingersoll and Albert 2013; Ohadian Moghadam and Nowroozi 2019). According to previous studies, expression of TLRs and induction of their related signaling pathways may lead to localization of NF-κB within the cell’s nucleus. The NF-κB is recognized as tumor promoter which supports the transcription of cell proliferation genes such as BCL-2, BCL-XL, c-Myb, c-Myc, cyclin D1, cyclin D2, and cyclooxygenase 2 (COX-2) (Ohadian Moghadam and Nowroozi 2019). Previous reports indicate that in NMIBCs, the expression of the majority of TLRs including 2, 3, 4 (extracellular TLR), 4 (intracellular TLR), 5, 7, and 9 is reduced while in MIBCs only the expression of TLR9 is decreased. Interestingly, the TLR4 has a dual function which can support the progression or suppression of urinary bladder tumors. This property supports us to use TLRs as suitable agonists against BCas (Angrini et al. 2020; Ayari et al. 2011; Li et al. 2017; Ohadian Moghadam and Nowroozi 2019).

9.11

Single Nucleotide Polymorphism in Toll-Like Receptors and Urinary Bladder Cancer

TLRs as the key immune molecules have pivotal role in both innate and adaptive immune responses. They are involved in angiogenesis, cell proliferation, apoptosis, cell survival, and tissue remodeling and repair mechanisms. Hence, the occurrence

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of SNPs may lead to imbalanced consequences in cytokines secretion, production, and harmonic orchestrates of different mechanisms against infectious diseases, cancers, and inflammations. As mentioned before, SNPs can be seen in different types of genes which may lead to different cancers such as BCas. In this regard, the occurrence of SNPs in the genes encoding TLR molecules and the related proteins and molecules involved in TLR signaling pathways is recognized as an important genetic predisposing factor for the cancer risk. Up to date, the occurrence of SNPs in TLRs genes and their importance in prevalence of BCas have not been studied so well. Different SNPs in association with TLR genes—in a wide range of cancers including prostate cancer, lymphoma, breast cancer, nasopharyngeal cancer cervical cancer, etc.,—have been investigated and a mass of SNPs have been recognized. Interestingly, the outcome of the occurrence of SNPs in TLR genes is very different; from changing in protein structure and receptor activity to predisposing for different types of cancers with versatile frequencies. The occurrence of SNPs results in different consequences involving amino acid substitution, alteration of proteins activities and functions, affecting splicing process and changes in enhancer sequences and structures and stability of the mRNA molecules, changes in transcription factor binding motifs, altering the efficacy of enhancers and repressors, interference in translation process by changing the structure of initial codons, adaptor activities, etc. (Afsharimoghaddam et al. 2016; Angrini et al. 2020; Gomaz et al. 2012; Kutikhin 2011).

9.11.1 TLR1 9.11.1.1 Structure and Function The molecules of TLR1 and 2 and then TLRs 3, 4, and 5 are known as the first recognized human TLRs (hTLRs). The TLR 1 as an extracellular TLR is able to heterodimerize with TLRs of 2 and 10, respectively, to construct the heterodimers of TLR1/TLR2 and TLR1/TLR10. TLR1—as well as TLR2, TLR6, and TLR10— belongs to TLR2 family and its gene is located on chromosomal position of 4p14 within the cluster gene of TLR10-TLR1-TLR6. TLR1 molecules link to Triacyl lipoproteins and are expressed by innate immune cells such as eosinophils, macrophages (MΦs), monocytes, myeloid DCs, natural killer (NK) cells, neutrophils, plasmocytoid DCs, and adaptive immune cells like B cells. The non-immune cells including endothelial- and epithelial cells express TLR1 molecules, too (Table 9.2). (Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Huang et al. 2018; Saghazadeh and Rezaei 2020; Wang et al. 2020). The bioinformatic investigations have been revealed the presence of hydrophobic pockets within the structure of TLRs of 1 and 2 which enable them to bind to the agonists such as Pam3CSK4 (a triacylated lipopeptide) and CU-T12–9. Pam3CSK4 links to TLR1 and TLR2 via an amide bound lipid chain and two ester-bound lipid chains, respectively, which leads to heterodimerization of TLR1/TLR2 and

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activation of the related signaling pathway. The agonist of CU-T12–9 links to TLR1 throughout hydrogen bonds while the linkage between CU-T12-9 and TLR1/TLR2 is hydrophobic. The latter supports the formation of TLR1/TLR2 heterodimers. The linkage of the agonist of SMU-Z1 (with high specificity and low toxicity) with TLR1/TLR2 heterodimer leads to activation of NF-κB pathway. The SMU-Z1 activates the anti-tumor signaling pathway of TLR1/TLR2 against leukemia. The CU-CPT22 is known as the TLR1/TLR2 antagonist which competes with Pam3CSK4 to link to TLR1/TLR2 heterodimer to inactivate it. Expression of TLR1 genes increases in multiple myeloma cells (Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi et al. 2019; Wang et al. 2020).

9.11.1.2 TLR1 SNPs and Their Role The TLR genes of 1, 6, and 10 create a unite gene cluster of TLR10-TLR1-TLR6 which located on 4p14 position of the genomic chromosome (Table 9.2). The minor allele of TLR1-rs7696175 (C > T) is placed at the adjacent of TLR10-TLR1-TLR6 (TLR1-TLR6) gene promoters. The occurrence of this SNP may increase the risk of breast cancer. Moreover, several SNPs in TLR1 gene are reported which may lead to formation of T1805G (rs5743618) (leading to colon cancer) and rs4833095 (leading to gastric cancer in patients infected by Helicobacter pylori (H. pylori). -7202A/G (rs5743551), -6399C/T (rs5743556), and -833C/T (rs5743604) are the recognized TLR1 gene SNPs which are in association prostate cancer. The SNP of rs4833103 (C > A) in the neighborhood of TLR1 gene reduces the risk of tumor development regarding non-Hodgkin’s lymphoma (NHL) (Gomaz et al. 2012; Khan et al. 2016; Semlali et al. 2018; Yeyeodu et al. 2013). 9.11.1.3 TLR1 SNP in Urinary Bladder Cancer No meta-analysis was recognized regarding TLR1 gene SNP contributing in BCa.

9.11.2 TLR2 9.11.2.1 Structure and Function TLR1 like TLR2 belongs to TLR2 family and its gene is situated on chromosomal position of 4q32. The TLR2 molecules are able to join whether to each other as homodimers of TLR2/TLR2 or to bind to TLRs of 1, 6, and 10, respectively, to construct the heterodimers of TLR1/TLR2, TLR2/TLR6, and TLR2/TLR10. The construction of homodimers of TLR2/TLR2 and the heterodimers of TLR1/TLR2, TLR2/TLR6, and TLR2/TLR10 indicate the close relationship among these TLR molecules as the members of the TLR2 family. TLR2 molecules link to Lipoproteins, Glycolipids, bacterial Peptidoglycans, fungal Zymosan, etc. The TLR1/TLR2 heterodimers are able to distinguish bacterial peptidoglycan, fatty acids (saturated), and triacylated lipoproteins. Furthermore, TLR2 molecules are located on the cell surface and are expressed by the innate immune cells comprising MΦs, mast cells, monocytes, myeloid DCs, NK cells and neutrophils, and the adaptive immune cells of B and T. TLR2 gene is

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also expressed by non-immune cells like endothelial- and epithelial cells (Table 9.2). The TLR2 signaling pathway is able to increase the proliferation activity and survival of the cancerous cells in gastric cancer in which may lead to enhance the process of tumor metastasis. The expression of TLR2 gene increases in colon, ovarian, oral squamous cell, laryngeal, and intestinal adenocarcinomas (AyalaCuellar et al. 2019; Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Huang et al. 2018; Saghazadeh and Rezaei 2020; Wang et al. 2020).

9.11.2.2 TLR2 SNPs and Their Role A deletion of 22 bps in promoter region belonging to TLR2 gene from 196 to 174 is recognized as an important TLR2 gene polymorphism which is in association with breast, cervical, colon, gallbladder, prostate, colorectal, gastric, and hepatocellular (with the background of hepatitis C virus infection) cancers. However, some studies indicate that, this polymorphism in TLR2 gene reduces the risk of gastric cancer. The TLR2ins/del SNP contributes to gastric cancer risk. The TLR2 SNPs including N199N (rs3804099) and S450S (rs3804100) predispose individuals for gastric, breast, hepatocellular (N199N rs3804099 C/T and rs3804100 C/T), and papillary thyroid cancer. Moreover, the C allele of the S450S (rs3804100) SNP in TLR2 gene is associated with marginal zone lymphoma (MZL) and in particular mucosaassociated lymphoid tissue (MALT) lymphoma. This association is probably the reason of the unsuitable TLR2 immune responses, against pathogenic microorganisms (comprising Borrelia burgdurferi, Campylobacter jejuni, Chlamydia psittaci, H. pylori and hepatitis C virus) which results in MALT lymphoma in carriers of C allele of the rs3804100 SNP in TLR2 gene. The SNPs of rs5743704, rs5743708, and rs7656411 in TLR2 are associated with colon and hepatocellular carcinomas, respectively. The short and long GT-microsatellite polymorphism is associated with increase in NF-κB activation, overexpression of inflammatory mediators participated in tumorigenesis promotion and colorectal cancer. The short alleles of GT-microsatellite have significant relationship with reduction of TLR2 gene promoter activity and expression (Gomaz et al. 2012; Khan et al. 2016; Kutikhin 2011; Medvedev 2013; Ranjbar et al. 2017; Semlali et al. 2018; Vijay 2018). 9.11.2.3 TLR2 SNP in Urinary Bladder Cancer In a case study done by Singh et al. in which they showed a significant association between TLR2 allele (ID + DD))196 to 174 del(polymorphism and the BCa risk in a population of 200 individuals (Singh et al. 2013).

9.11.3 TLR3 9.11.3.1 Structure and Function TLR3 as an intracellular molecule belongs to TLR3 family and its gene is located on chromosomal position of 4q35. TLR3 molecules are expressed by innate immune

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cells like myeloid and plasmacytoid DCs and NK cells and the non-immune cells comprising endothelial- and epithelial cells (Table 9.2). The homodimer of TLR3 is known as an active viral sensor which in parallel with recognition of viral molecules of double stranded RNA (dsRNA) activates the apoptosis mechanism (Fig. 9.2), the pro-inflammatory and type I IFN pathways (Fig. 9.4). The TLR3 molecules are going to be effective targets regarding anti-viral, anti-tumor, and cancer immunotherapies. The antagonist of CU-CPT4a can be useful in this regard. The ligand of poly-inosinic-poly-cytidylic (Poly (I:C)) (a synthetic dsRNA molecule) directly triggers the TLR-positive process of tumor cell apoptosis or supports the activities of tumor-infiltrating innate and tumor specific T cells. Due to this fact, the activation of protein kinases is responsible for prevention of proliferation and increase of apoptosis process within cancerous cells in prostate cancer. In contrast, activation of TLR3 via its ligand of poly-adenylic-poly-uridylic acid (Poly (A:U)) (a synthetic dsRNA molecule) supports cancerous cells by reprogramming their metabolic pathway in head and neck carcinomas which leads to progression in tumor growth. The TLR3 gene expression raises up in colon, laryngeal, cervical, papillary thyroid, oral, nasopharyngeal, hepatocellular, rectal, lung, breast, prostate, esophageal squamous cell, urinary bladder, and ovarian cancers (Ayala-Cuellar et al. 2019; Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Cheng et al. 2014; Dajon et al. 2017; Huang et al. 2018; Javaid and Choi 2020; Saghazadeh and Rezaei 2020; Wang et al. 2020; Yeyeodu et al. 2013).

9.11.3.2 TLR3 SNPs and Their Role Some SNPs in TLR3 gene have been recognized. The TLR3 gene SNP of rs10025405 G allele is in association with breast and colorectal cancers. In this regard, the TLR3 gene SNP of rs10025405 G allele has a five-fold reduction effect on breast cancer occurrence. The SNP of rs5743312 in TLR3 gene is correlated with breast, melanoma, and oral cancers. The SNPs of rs11721827 and rs3775292 in TLR3 gene are in association with rectal and colon cancers, respectively. The polymorphisms in TLR3 gene including +1234C/T (+1234CT and +1234TT) and +1377 C > T (rs3775290) are associated with hepatocellular, urinary bladder, prostate, breast, and cervical cancers, respectively. The result of a study has revealed that the 11381G/C variant in 3’UTR region of TLR3 gene has association with cervical cancer. The SNP of 829A > C increases the risk of nasopharyngeal cancer. Furthermore, the TLR3 SNPs of rs3775291 (relating to breast, colon, oral, melanoma, hepatocellular, and nasopharyngeal cancers), rs5743305 (an intronic SNP) (relating to breast and hepatocellular cancers), rs5743312 (an intronic SNP) (relating to melanoma, breast, and oral cancers) have significant association with increasing the risk of cancers (Cheng et al. 2014; Medvedev 2013; Semlali et al. 2018; Yeyeodu et al. 2013). 9.11.3.3 TLR3 SNP in Urinary Bladder Cancer In a meta-analysis study done by Cheng et al., the association between TLR3 gene SNP of rs377529 and the risk of BCa occurrence was recognized. The sample size included 200 patients (Cheng et al. 2014).

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9.11.4 TLR4 9.11.4.1 Structure and Function Among different TLRs, TLR4 is known as a particular TLR molecule because of its bi-functional activity as extra- and intracellular TLR and application of two different signaling pathways of MyD88-dependent and TRIF-dependent (Fig. 9.4). Moreover, the activity of TLR4 depends on the co-receptor of MD-2 as an accessory protein (Figs. 9.3 and 9.4). TLR belongs to TLR4 family and its gene is located on chromosomal position of 9q32–33. The molecules of TLR4 are expressed by innate immune cells of eosinophils, MΦs, mast cells, monocytes, myeloid and plasmacytoid DCs and neutrophils and non-immune cells of endothelial-, epithelial-, and smooth muscle cells (Table 9.2). The expression of TLR4 gene within the urinary tract area occurs by urothelial cells in both of kidney and urinary bladder. Formation of the hexameric structure of (TLR4-MD-2-LPS)2 (Gram-negative bacterial lipopolysaccharide) triggers the TLR4 signaling pathway (Figs. 9.2 and 9.4). The TLR4 ligand of LPS supports the tumor development and survival. Furthermore, XAMPs like cocaine, methamphetamine, and morphine (a common substance for treating pain both in acute and chronic forms) trend to link to MD-2 which may lead to TLR4 dimerization and neuro-immune signaling activation in central nervous system (CNS). This property of TLR4 shows its importance in treatment of drug addiction. TLR4 activation and upregulation may have correlation with tumor relapse, chemo-resistance, and metastasis in colorectal, pancreatic, laryngeal, ovarian, breast, prostate, esophageal squamous cell, adrenocortical, colon, head and neck squamous cell, urinary bladder, and lung cancers (Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Huang et al. 2018; Saghazadeh and Rezaei 2020; Wang et al. 2020). 9.11.4.2 TLR4 SNPs and Their Role In previous investigations, it was recognized that the SNPs of rs10759931 and D299G (rs4986790) in exon region of TLR4 gene are in correlation with breast cancer while the SNPs of rs2770150 and rs10759932 in promoter region of TLR4 gene have no association with breast cancer; however, the C allele of this SNP has protective effect on gastric cancer. Moreover, the SNP of T393I (rs4986791) in TLR4 gene with CT genotype has higher relation with lung cancer rather than the other genotypes of the mentioned SNP and also is associated with gastric cancer. The +3725G/C polymorphism increases the susceptibility of individuals to breast cancer. The SNPs of rs10759931 and rs11536898 in TLR4 gene have correlation with colon cancer with no limitations among patients while the SNP of rs2770150 in TLR4 gene is associated with colon cancer in women patients with >50 years old. The SNP of rs1927911(C/T) TT genotype in TLR4 gene and the SNP of rs6973569 in IL-17 gene have synergistic effect on each other regarding colorectal cancer. It has been reported that, the low incidence of the SNPs of D299G (rs4986790) and T393I (rs4986791) in TLR4 gene is associated with ovarian cancer in female patients. The AG genotype and G allele of T393I (rs4986791) in TLR4 gene is related to gastric cancer. The C allele and CC genotype of rs11536889 in TLR4 gene is also

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positively associated with gastric cancer. G allele a/w of D299G (rs4986790) in TLR4 gene is associated with tumor development in distal part of stomach and the GG and AG genotypes of this SNP are positively associated with colorectal cancer. The polymorphisms of +896AA/G and +1196C/T in TLR4 gene are correlated with gastric cancer. Moreover, the T399I polymorphisms in TLR4 gene are related to occurrence of cervical, prostate, and nasopharyngeal cancers. The nasopharyngeal carcinoma is the result of increase in expression of IL-1α, IL-10, and TNF-α and the process of tumorigenesis. Another investigation has revealed that the 11381G/C variant in 3’UTR region of TLR4 gene is in association with prostate cancer. The GG genotype of rs1156858, G/A SNP and TC genotype of rs1927911 are positively associated with developing prostate cancer (Gomaz et al. 2012; Medvedev 2013; Pandey et al. 2018; Semlali et al. 2018; Theodoropoulos et al. 2012).

9.11.4.3 TLR4 SNP in Urinary Bladder Cancer The SNPs in the gene of TLR4 may lead to BCa. In this regard, C allele, GC and CC genotypes of the 729G/C (rs11536865) and +3725G/C and GC and CC genotypes and C allele of rs11536889 SNPs in TLR4 are significantly in association with BCa (Table 9.2) (Pandey et al. 2018; Shen et al. 2013, 2015). In a meta-analysis study done by Zhu et al. showed that there is no association between 196 to 174 del polymorphism in TLR2 gene and BCa. Moreover, their results indicated that the SNPs of rs11536889, rs1927911, rs1927914, rs2149356, rs4986790, and rs4986791 in TLR4 gene increase the risk of different cancers (Zhu et al. 2013). Singh et al. in their investigation indicated that there is no significant correlation between the SNP of T399I (rs4986791) in TLR4 gene and BCa (Singh et al. 2013).

9.11.5 TLR5 9.11.5.1 Structure and Function TLR5 molecule belongs to TLR5 family and its locus is located on 1q33.3 position on chromosome (Table 9.2). Bacterial flagellin is the inducer of TLR5 in urothelial cells of urinary bladder and kidneys (Fig. 9.2). However, the expression of TLR5 is high in urinary bladder and low in kidneys. The TLR5 molecules are expressed by the innate immune cells of MΦs, monocytes, myeloid and plasmacytoid DCs, neutrophils and NK cells, the adaptive immune cells of T, and the non-immune cells of endothelial- and epithelial cells (Table 9.2). The TLR5 resembling TLR2 signaling pathway is able to increase the proliferation activity and survival of the cancerous cells in gastric cancer which may lead to enhance the process of tumor metastasis. In contrast, the reported results from previous studies regarding breast cancer show that, the signaling pathway of TLR5 downregulates the expression of some cyclins of E2, B1, and D2 which may lead to prevent the proliferation of tumor cells. The expression of TLR5 increases in intestinal adenocarcinoma, ovarian, gastric, cervical, breast, and colon cancers and reduces in esophageal adenocarcinoma (Ayala-Cuellar et al. 2019; Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi

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et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Gomaz et al. 2012; Huang et al. 2018; Javaid and Choi 2020; Ohadian Moghadam and Nowroozi 2019; Saghazadeh and Rezaei 2020; Wang et al. 2020).

9.11.5.2 TLR5 SNPs and Their Role The SNP of rs5744168 T allele in TLR5 gene has correlation with sporadic breast cancer. The SNPs of rs2072493 (A > G) and 5744174 (T > C) CC genotype are associated with colorectal and colon cancers. In addition, the SNP of F616L (rs5744174) (TC + CC) in TLR5 gene has significant correlation with H. pylori and gastric cancer (Gomaz et al. 2012; Medvedev 2013; Ranjbar et al. 2017; Semlali et al. 2018). 9.11.5.3 TLR5 SNP in Urinary Bladder Cancer No meta-analysis was found regarding the association of TLR5 gene and BCa.

9.11.6 TLR6 9.11.6.1 Structure and Function The TLR6 like TLR1, TLR2, and TLR10 belongs to TLR2 family and its gene is located on chromosomal position of 4p14. The TLR6 gene together with TLR1 and TLR10 genes forms a gene cluster of TLR10-TLR1-TLR6 on 4p14 position of genomic chromosome. A functional TLR6 can only be recognized in the form of heterodimers of TLR2/TLR6 and extracellular-TLR4/TLR6 (in Alzheimer’s disease). The TLR6 links to Diacyl lipoprotein as its specific ligand (Fig. 9.2). TLR6 molecules are expressed by innate immune cells of MΦs, mast cells, monocytes, myeloid and plasmacytoid DCs, neutrophils and NK cells, adaptive immune cells of B, and non-immune cells of endothelial- and epithelial cells (Table 9.2). The extracellular heterodimer of TLR2/TLR6 resembling TLR1/TLR2 heterodimer has m spatial configuration. It has been recognized that, the truncated transmembrane peptide belonging to TLR2 as a novel antagonist against TLR2/TLR6 heterodimer is able to interact with TLR6, specifically. Oxidized lipoproteins (with low density) and fibers of amyloid-β peptides induce inflammatory signaling pathway throughout the heterodimers of TLR4/TLR6 (TLR4/TLR6/CD36). The transmembrane domains belonging to TLRs of 4 and 6 play vital role in formation of TLR4/TLR6 heterodimers. Interestingly, the truncated transmembrane domains pertaining to TLRs of 4 and 6 and related derivatives are recognized as antagonists against formation of TLR4 and TLR6 dimers (Behzadi and Behzadi 2016; Boozari et al. 2019; Dajon et al. 2017; Godfroy III et al. 2012; Gomaz et al. 2012; Saghazadeh and Rezaei 2020; Shmuel-Galia et al. 2017; Stewart et al. 2010; Wang et al. 2020; Zeng et al. 2017). Bioinformatic studies indicate that TLR2/TLR6 heterodimer in contrast to TLR1/ TLR2 heterodimer has no hydrophobic channel; because the TLR6 misses hydrophobic binding pocket. Due to this fact, the agonists of CU-T12-9 and Pam3CSK4 and the antagonist of CU-CPT22 cannot be linked to TLR2/TLR6 to activate it. In

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accordance with previous studies, the TLR2 plays an axial role in recognition of PAMPs and DAMPs (e.g., high mobility group box 1 (HMGB 1)) in heterodimers of TLR1/TLR2 and TLR2/TLR6. Interestingly, the TLRs of 1 and 6 have ~66% similarity in their amino acid sequences; however, the similarity of the amino acid sequences constructing the TLRs 1 and 6 active binding sites is low which may lead to different configurations in the related active binding sites (Behzadi and Behzadi 2016; Behzadi et al. 2019; Wang et al. 2020).

9.11.6.2 TLR6 SNPs and Their Role The people with homo-/heterozygotes for A1401G SNP in promoter of TLR6 gene are susceptible to prostate cancer while those with SNP of C744T in their TLR6 gene are protected against asthma. Moreover, the SNP of V427A, 1280 T > C (rs5743815) in TLR6 gene is associated with NHL (Gomaz et al. 2012; Kutikhin 2011; Medvedev 2013; Ranjbar et al. 2017; Semlali et al. 2018; Vijay 2018). 9.11.6.3 TLR6 SNP in Urinary Bladder Cancer No meta-analysis was found regarding the association of TLR6 gene and BCa.

9.11.7 TLR7 9.11.7.1 Structure and Function TLR7, a TLR member belonging to TLR9 family with chromosomal position of Xp22.3 which resembling TLR4 has two types of extra- and intracellular structures (Table 9.2). TLR7 links to its ligand molecule of ssRNA (Fig. 9.2). The main cells which express TLR7 molecules for the most are recognized as B cells (adaptive immune cells) and the innate immune cells of eosinophils, MΦs, monocytes, neutrophils and plasmacytoid DCs. The non-immune cells of endothelial cells express the molecules of TLR7, too (Table 9.2). The molecule of TLR7 is very close to TLR8 in both chromosomal location and function. TLRs 7 and 8 both recognize intracellular microorganisms’ nucleosides and nucleotides (in the form of single stranded RNA (ssRNA)) (Fig. 9.2) (e.g., viral ssRNAs of HIV) and possess two active binding sites (Fig. 9.5). The first binding site in both TLRs of 7 and 8 is conserved and links to small-molecule ligands. However, the second binding site of TLR7 differs from the TLR8. The second active binding site in TLR7 links to ssRNA and has synergistic effect on the first active binding site. The activation of TLR7 results in inflammatory responses and type I IFN production (Fig. 9.4). Hence, the activation of TLR7 signaling pathway participates in both of anti-tumor and antiviral therapeutics. Now, we know the anti-cancer positive effects of TLR7 antagonist like imiquimod. The expression of TLR7 increases in multiple myeloma cells, esophageal squamous cell, lung, and colorectal cancers (Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Chi et al. 2017; Dajon et al. 2017; Huang et al. 2018; Saghazadeh and Rezaei 2020; Wang et al. 2020).

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Fig. 9.5 The structure of hTLR8 heterodimer in combination of ssRNA. The two active binding sites are shown. The first site which binds to small molecules like uridine and the second site binds to short oligonucleotides like UG (4R08 PDB file) (Tanji et al. 2015)

9.11.7.2 TLR7 SNPs and Their Role The SNP of rs3853839 (G/C) in TLR7 gene has correlation with metastatic colon cancer chemotherapy via etuximab as a predictive biomarker (Okazaki et al. 2017; Semlali et al. 2018). 9.11.7.3 TLR7 SNP in Urinary Bladder Cancer No meta-analysis was found regarding the association of TLR7 gene and BCa.

9.11.8 TLR8 9.11.8.1 Structure and Function TLR8 belongs to TLR9 family members which its locus is situated on genomic chromosome with position of Xp22. TLR8, as an intracellular molecule is expressed mostly by the innate immune cells of MΦs, mast cells, monocytes, myeloid and plasmacytoid DCs and neutrophils, the adaptive immune cells of T and Tregs, and

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non-immune cells of endothelial- and epithelial cells (Table 9.2). As mentioned before, TLR8 encompasses two active binding sites including a conserved one which links to small-molecule ligands (e.g., Uridine) and the other which connects to short oligonucleotides (e.g., UG). The TLRs of 7 and 8 have high similarities in their sequences and functions (Fig. 9.5). TLR8 recognizes microbial (bacterial and viral) ssRNA molecules (Fig. 9.2) and in particular those ssRNAs enriched by uridine and guanosine molecules. The hTLR8 signaling pathway is able to reverse the suppressive activity of tumor-derived CD4+, CD8+, and γδ Treg cells, directly. In accordance with recent studies, the activated TLR8 signaling pathway is able to inhibit the production of cAMP molecules in tumor cells and block the conversion of naïve and tumor specific T cells into senescent cells which has been triggered by tumor formation. This process increases the anti-tumor immunity in in vivo condition. Furthermore, the signaling pathway of hTLR8 suppresses metabolism in tumor cells in which may lead to downregulation of cAMP amounts in senescent T cells and tumor cells. Hence, it seems that the signaling pathway of hTLR8 within Treg cells is able to regulate the metabolism of Treg cells in which results in inhibition of Treg-suppressive function. The hTLR8 expression increases in colorectal and lung cancers (Basith et al. 2012; Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Huang et al. 2018; Saghazadeh and Rezaei 2020; Tanji et al. 2015; Vasilakos and Tomai 2013; Wang et al. 2020).

9.11.8.2 TLR8 SNPs and Their Role No TLR8 SNP was detected as a pivotal agent in cancers. 9.11.8.3 TLR8 SNP in Urinary Bladder Cancer No meta-analysis was found regarding the association of TLR8 gene and BCa.

9.11.9 TLR9 9.11.9.1 Structure and Function TLR9, the main member of the TLR9 family with the locus position of 3p21.3 on genomic chromosome (Table 9.2) is expressed in the presence of microbial unmethylated cytosine-phosphate-guanine (CpG) DNA (Fig. 9.2) belonging to bacteria, fungi, protozoa, and viruses. The TLR of 9 is expressed by innate immune cells of eosinophils, MΦs, monocytes, myeloid and plasmacytoid DCs and neutrophils, the adaptive immune cells of B, and the non-immune cells, e.g. endothelial- and epithelial cells (Table 9.2). The anti-cancer positive effects of TLR9 antagonist like CpG have been recognized. The previous studies indicate that high expression of TLR9 gene may support the process of tumor proliferation in patients with oral squamous cell carcinoma. The expression of TLR9 gene increases in pancreatic, multiple myeloma cells, breast, prostate, esophageal squamous cells, lung, cervical, renal, ovarian, and gastric cancers (Ayala-Cuellar et al. 2019; Basith et al. 2012; Behzadi and Behzadi

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2016; Behzadi et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Huang et al. 2018; Saghazadeh and Rezaei 2020; Wang et al. 2020).

9.11.9.2 TLR9 SNPs and Their Role The SNPs of rs352140 and rs187084 (C/T) in TLR9 gene are associated with breast and colon cancers (in women), respectively. Besides, the SNPs of rs352139 (A/G) and rs352144 (A/C) in promoter region of TLR9 gene have correlation with colorectal cancer. In an investigation, it has been shown that the SNPs of 11381G/C variant in 3’UTR region, 1486 T/C rs187084, rs5743836 and P545P 2848 A > G rs352140 in TLR9 gene are associated with cervical cancer. Moreover, the SNPs of 1237 T > C (rs5743836) and P545P 2848 A > G (rs352140) in TLR9 gene are in correlation with Hodgkin’s lymphoma (Gomaz et al. 2012; Medvedev 2013; Semlali et al. 2018). 9.11.9.3 TLR9 SNP in Urinary Bladder Cancer In a meta-analysis study done by Zhang et al. shows that the SNP of rs352140 in TLR9 gene contributes in cancer development (Zhang et al. 2013). In another metaanalysis done by Wan et al. the SNP of rs187084 in TLR9 gene raises up the risk of different cancers and in particular cervical cancer while the SNPs of rs352140 and rs5743836 in TLR9 gene have preventive effect on cancer development in alimentary tract and breast cancers (Wan et al. 2014). Singh et al. showed in their study that there is no association with the SNP of G2848A in TLR9 gene and the BCa (Singh et al. 2013).

9.11.10 TLR10 9.11.10.1 Structure and Function The TLR10 is known as a member of TLR2 family which its gene is located within the cluster gene of TLR10-TLR1-TLR6 on chromosomal position of 4p14. It links to its ligands of Diacyl- and Triacyl lipoproteins. The molecules of TLR10 are expressed by both of innate (e.g., eosinophils, MΦs, monocytes, neutrophils and myeloid and plasmacytoid DCs) and adaptive immune cells (e.g., B, T cells and Tregs) and also by the non-immune cells of endothelial- and epithelial cells. The TLR10 is able to homodimerize with itself (TLR10/TLR10) and heterodimerize with its phylogenetic related molecules of TLR1 (TLR1/TLR10) and TLR2 (TLR2/ TLR10) (Table 9.2). The TLR10 has an anti-inflammatory role which makes it unique among TLR molecules. Mourits et al. have shown that the extra- and intracellular TLR10 molecules are expressed within human CD45+ CD14+ monocytes. The heterodimer of TLR1/TLR10 and homodimer of TLR10/TLR10 are able to bind with diacetylated lipoproteins and the heterodimer of TLR2/TLR10 links to triacetylated lipoproteins. Due to this fact, it seems that the TLR10 molecules resembling TLRs 1 and 2 encompass hydrophobic binding site (Behzadi and Behzadi 2016; Behzadi et al. 2019; Boozari et al. 2019; Dajon et al. 2017; Mourits et al. 2020; Saghazadeh and Rezaei 2020; Wang et al. 2020).

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9.11.10.2 TLR10 SNPs and Their Role According to previous report, the T allele SNP of rs7696175 in TLR10 gene has significant relation with breast cancer (Kutikhin 2011). Kim et al. have shown that the SNP of M326T (rs11466653) in TLR10 gene is an effective biomarker for tumor growth in papillary thyroid cancer (Kim et al. 2013). 9.11.10.3 TLR10 SNP in Urinary Bladder Cancer No meta-analysis was found regarding the association of TLR10 gene and BCa.

9.12

Conclusion

Despite the use of different scientific disciplines including genomics, transcriptomics, metabolomics, etc., there are many questions regarding the role of versatile SNPs in different genes of TLRs and their association with BCa. There is a long way to complete our knowledge regarding different SNPs in TLRs genes which may lead to BCa. To date, there are just a few numbers of recognized SNPs in TLR genes and in particular TLR4 gene which are positively associated with BCa. We are in the beginning of this way. Acknowledgements I have special thanks to Prof. Syed Sameer, the editor of the present book for his sincere guidance, help, and support.

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Genetic Polymorphism and Their Role in Lung Cancer

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Sheikh M. Shaffi

Abstract

Lung cancer is the leading cause of cancer related mortality all over the world, partly owing to its late discovery and early progression. Despite the recent advances made in the understanding of molecular basis of the lung cancer, there has not been much improvement in the overall survival of patients. Although smoking is considered to be the major risk factor for lung cancer, less than 15% of lifetime smokers have been found to develop the disease, suggesting there might be inter-individual differences which may contribute to risk modification. Besides, chemical and environmental factors, genetic variations have been implicated in the susceptibility of the disease. One of the most common types of genetic variation is the SNPs. Many of these SNPs have been identified in major pathways involved in development and progression of lung cancer like carcinogen metabolizing and detoxifying pathways, immune response/inflammatory pathways, cell cycle regulatory pathways, etc. The SNPs not only affect the transcription of genes and structure and function of the proteins, but also have a potential role in diagnosis, prognosis, and even treatment selection and overall survival of the patients. In this chapter we will discuss role of some of the commonly studied SNPs involved in the major lung cancer pathways. Keywords

Lung cancer · SNP · Xenobiotic · DNA repair · Inflammation

S. M. Shaffi (*) Department of Biochemistry, Government Medical College, Anantnag, Jammu and Kashmir, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. S. Sameer et al. (eds.), Genetic Polymorphism and Cancer Susceptibility, https://doi.org/10.1007/978-981-33-6699-2_10

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Abbreviations COMT COX-2 CYP ERCC ETS GSTs IFN- γ IL MDM2 NATs NNKs NSCLC PAHs SCC SCLC SNP TLR TNF-α

10.1

Catechol-O-methyltransferase Cyclo-oxygenase-2 Cytochrome P450 Excision repair cross complementing Environmental tobacco smoke Glutathione S-transferases Interferon gamma Interleukin Murine double minute 2 N-acetyl transferases 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone Non-small cell lung cancer Polycyclic aromatic hydrocarbons Squamous cell carcinoma Small cell lung cancers Single nucleotide polymorphism Toll-like receptors Tumor necrosis factor alpha

Introduction

Cancer is the most feared and complex medical condition. Cancer can be defined as an autonomous growth that is unresponsive to normal growth factors and antigrowth signals. Cancer cells progressively loose the differentiated characteristics and functions of tissue of origin and revert to growth and antigenic properties which are characteristic of fetal cells. A number of genetic and epigenetic changes have been observed in cancer cells. Among these which are primary or secondary is still a matter of investigation. Nonetheless, these changes have been explored to understand the mechanism of carcinogenesis as well as in establishing their diagnostic, prognostic, or therapeutic potential. Moreover, Human Genome Project has revealed 99.9% genome similarity among individuals. This finding has led to the hypothesis that the 0.1% genetic difference may help in determining the susceptibility of individuals to particular diseases; predicting the prognosis of disease as well as the response to the therapeutic treatment. Since SNP is the most commonly observed genetic variation among individuals, a lot of studies have been conducted to establish their role in the management of diseases like cancer. In this chapter we will discuss the role of some important SNPs that have been studied in the genes involved in the important carcinogenic pathways of lung cancer.

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321

Lung Cancer and Its Types

Lung cancer is the leading cause of mortality among cancers, besides being the most fearful human malignancy owing to its late discovery and early progression. The term lung cancer is used for tumors arising from the respiratory epithelium (bronchi, bronchioles, and alveoli). Almost 95% of primary lung cancers are epithelial in origin and are categorized on the basis of histological appearance into Non-Small Cell Lung Cancer (NSCLC) and Small Cell Lung Cancers (SCLC) (ZochbauerMuller et al. 2002). SCLC which accounts for 15% of primary lung cancers is characterized histologically by small hyperchromatic cells with almost no visible cytoplasm, absent nucleoli, and variable degrees of necrosis. SCLC may be distinguished from NSCLC by the presence of neuroendocrine markers including CD56, neural cell adhesion molecule (NCAM), synaptophysin, and chromogranin. Small cell carcinomas are clinically aggressive with early metastasis and are usually centrally located. Although they respond well to chemotherapy but at the time of diagnosis they are often at advanced stages and so the patients have a poor prognosis (Midthun and Jett 1996). The median survival of patients with limited-stage disease is reported to be 15–18 months only. NSCLC accounts for 85% of primary lung cancers. Histologic subtypes of NSCLC as defined by WHO and IASLC include squamous cell carcinomas, adenocarcinoma, large cell carcinoma, adenosquamous carcinoma, sarcomatoid carcinomas, neuroendocrine tumors, and unclassified tumors. Squamous cell carcinomas are characterized histologically by cytokeratin and intracellular bridges. They are usually centrally located endobronchial masses that may present with hemoptysis, postobstructive pneumonia, or lobar collapse and generally metastasize late in the disease course (Patz 2000). Adenocarcinomas, the predominant histologic patterns of lung adenocarcinoma include acinar, solid, papillary, and bronchioloalveolar. Adenocarcinoma cells express cytokeratins 7 and 20. These tumors are often found in patients with underlying lung disease and metastasize early (Travis et al. 1995). Large cell carcinomas are poorly differentiated. These tumors are large peripheral masses associated with early metastases (Travis et al. 1995). The overall prognosis for NSCLC is dismal with 5-year survival generally reported below 10% (for all patients).

10.3

Risk Factors of Lung Cancer

10.3.1 Smoking The link between smoking and lung cancer is beyond dispute. Tobacco smoke consists of a complex mixture of over 4000 different chemical, more than 40 of which have been described as carcinogens in animals by the International Agency for Research on Cancer (IARC) (Fig. 10.1). Polycyclic aromatic hydrocarbons have

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Constituents of smoke

Concentration/cigarette 15-40mg

Particulate matter

10-23mg

CO

100-600μg

Nitrogen oxides

20-50μg

Benzene Nitoso compounds

0-200ng

Vinyl chloride

1.3-16ng

Benzene(a) anthacene

20-70ng

4-aminobiphenyl

2.4-4.6ng

2-naphthylamine

1.7-22ng 20-40ng

Benzo(a)pyrene Adopted from; IARC database

Fig. 10.1 Amount of some common mutagens present in cigarette

been shown to induce lung cancer in animals (Woltebeek et al. 1995). Most studies have demonstrated that the risk of lung cancer is related to exposure of tobacco smoke that is the number of cigarettes smoked and duration of exposure (IARC 1986; USPHS 1989), with the duration of smoking being stronger factor than number of cigarettes smoked per day (Doll and Peto 1978).

10.3.2 Second Hand Smoke/Environmental Tobacco Smoke (ETS) ETS/second hand smoke is the exhaled smoke of the smokers and the sidestream smoke released during smoking. The sidestream smoke is gaseous in nature consisting of some carcinogens in higher concentration like benzo(a)pyrene, nitrosamine and 210Po, etc. (Lam and Du 1988). Being lighter it can penetrate into the peripheral parts of lung causing particularly adenocarcinoma, among non-smokers (Wynder and Goodman 1983). ETS is classified as a class A carcinogen and is found to be responsible for around 20% of lung cancers in non-smokers (USEPA 1992).

10.3.3 Air Pollution 10.3.3.1 Indoor Pollution The fumes arising during cooking have been described as a significant risk factor among non-smokers for lung cancer (Metayer et al. 2002; Wang et al. 1996a, b). The fumes from vegetable oils have been found to contain known mutagens like benzo (a)pyrene, benzo(a) anthracene, and dibenz (a,h) anthracene (Chiang et al. 1999). Besides volatile substances produced under similar conditions from vegetable oils

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have been found to be genotoxic (Wu et al. 1999), making the link between the fumes from cooking oil and risk of lung cancer more clear. The smoke produced from burning of coal which is used for cooking and sometimes for heating purposes during winter time has been included in the list of risk factors especially for non-smokers. A study by Mumford et al. (1987), showed a strong correlation between indoor air benzo(a)pyrene concentration and high lung cancer mortality rates particularly from adenocarcinoma in women. Household coal burning has also been found to be a strong risk factor for lung cancer in other studies (Wang et al. 1996a, b). Various studies have been conducted to evaluate the risk if any associated with smoke produced from incense burning and lung cancer but no such effect was observed (Koo et al. 1996). Other indoor pollutions, such as exposure to smoke produced from kerosene stove cooking, etc., have also been studied but no significant effect was observed (Koo et al. 1984).

10.3.3.2 Outdoor Pollution In order to assess the effect of air pollution on the risk of lung cancer various studies have been conducted but none showed any significant association (Speizer et al. 1994). Although the air particularly in urban areas has been found to contain known carcinogens, like benzo(a)pyrene, benzene, dimethyl nitrosamine, and even radon, arsenic, asbestos, cadmium, chromium, etc. Also diesel exhaust has been included in a list of probable carcinogen by the IARC (IARC 1989).

10.3.4 Occupational Exposure Among different risk factors for lung cancer, occupational exposure has also been included and found to account for about 5–20% of all lung cancers particularly in smokers (Samet and Lerchen 1984). Asbestos exposure is one such occupational risk factor which has been associated with increased risk in mine workers (Stayner et al. 1996). Similarly silica which has been included by the IARC as carcinogen for humans has been studied by various groups to see if there is any association with lung cancer. But there have been conflicting results, while some studies suggested a positive correlation Nakagawa et al. (2000) and Wang et al. (1996a, b) and others did not find its role in causing lung cancer independently (Fu et al. 1994; Chan et al. 2000). Among other occupational exposures increased risk of lung cancer has been observed in textile workers although statistically insignificant (Udwadia et al. 2000) and also in benzene exposed workers increase in deaths due to lung cancer has been observed (Hayes et al. 1996).

10.3.5 Diet Vegetables and fruits have been proved to be good for health. Various studies have been conducted to see if there is any protective effect of taking higher intake of

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vegetable and fruits particularly beta carotene and vitamin A against lung cancer risk, but not a single study has been able to prove it (Alpha Tocopherol Group 1994; Omenn et al. 1996). Preserved foods form a large part of the diet in various cities containing N-nitroso compounds or their precursors with mutagenicity. Again some interesting results have been observed like Miso (preserved, fermented soybean paste) soup and pickles (excluding salted fish) were found to be associated with an increased risk, and frequent intake of soybeans and tofu (soybean curd) a decreased risk of lung cancer, particularly SCC (Wakai et al. 1999), was found.

10.3.6 Infections Whether tuberculosis leads to lung cancer or not has been studied by various groups and could not reach to any conclusion, although a study had shown a significant increase in lung cancer risk among TB patients independent of smoking (Zheng et al. 1987).

10.3.7 Gender Differences Studies have suggested that women are more susceptible than men to smoking induced lung cancer (Zang and Wynder 1996). Even some studies have shown that lower mean pack-years of smoking for women diagnosed with lung cancer compared with men (Risch et al. 1993). The reasons for gender differences in susceptibility to lung cancer are not clear, but endogenous and exogenous estrogens have been implicated (Taioli and Wynder 1994).

10.4

Polymorphism and Lung Cancer

One of the most common genetic variation which leads to inter-individual difference is the single nucleotide polymorphism. A SNP is a difference in a single nucleotide in DNA between two individuals. Since these nucleotide variations are inherited from parents, it may give rise to homozygous or heterozygous base variations in an individual. It has been found that SNPs occur almost every 1000 nucleotides in a genome which roughly means 4–5 million SNPs in an individual. SNPs can be present in any gene so it can affect its function. Although most of the SNPs have no effect on the function of a gene but some of them have proved to be responsible for varied response to susceptibility to certain diseases, detoxification of xenobiotics, etc.

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10.4.1 Polymorphism in Major Biological Pathways for Lung Carcinogenesis Smoking is the major cause of lung cancer (Subramanian and Govindan 2007), but still less than 10–15% of lifetime smokers develop the disease, suggesting there are some inter-individual that may play a role in pathogenesis of lung cancer (Wu et al. 2004). One such variation is the presence of SNPs in some of the genes involved in detoxification of tobacco smoke, repairing of DNA damage caused by smoking and also suppression of mutations caused by tobacco smoking (Sugimura et al. 2011). Many of them are also present in the stress response genes and the genes involved in immune function. Although the significance of many of these polymorphisms has been studied; however, many more remain undiscovered. Here we will discuss the major pathways where polymorphism of genes has been found to modify the risk of lung cancer.

10.4.1.1 Xenobiotic Pathway Tobacco smoke carcinogens, like PAHs (Benzo(a)pyrene), NNKs, etc. are substances that are foreign to the human body, i.e. xenobiotics. Most of these substances are metabolized in liver in two steps phase I and phase II (Fig. 10.2). In phase I these xenobiotics are hydroxylated and made polar, while in phase II they are conjugated and made more water soluble in order to remove them from body. Cytochrome P450 genes code a number of enzymes which constitute phase I system of body (Sim and Ingelman-Sundberg 2010). CYP genes like CYP1A1 and CYP1B1 transform benzopyrene, a tobacco carcinogen into an epoxide, B(a)P-7,8 dihydroepoxide (Pratt et al. 2011). Both of these are induced by tobacco smoke and also have been found to be overexpressed in lung cancer (Kim et al. 2004). A study has

Fig. 10.2 Xenobiotic metabolism pathway. Adapted from Tim Vickers, http://en.wikipedia.org/ wiki/File:Xenobiotic_metabolism.png

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shown a positive correlation between CYP1A1 expression and PAH-DNA adduct formation in smokers (Mollerup et al. 2006). Besides, another study has detected a strong correlation between adduct levels and higher expression of CYP3A5, another enzyme involved in detoxification of PAHs (Piipari et al. 2000). Similarly CYP2A6 an enzyme involved in detoxification of NNKs has been reported to affect the smoking addiction behavior (Bloom et al. 2011). Several epidemiological studies were conducted to assess the correlation between SNPs in CYP genes and lung cancer, as some SNPs have been found to affect the activity of these enzymes. Chen et al. (2011) conducted a meta-analysis to understand the effect of MspI and Ile462Val polymorphisms in the CYP1A1 gene in association with lung cancer and it was concluded that both these SNPs were associated with increased lung cancer risk, especially among East Asians. Another study by Xun et al. (2011), who investigated the role of SNPs in CYP1B1 like Leu432Val, Ala119Ser, Arg48Gly, and Asn453Ser found that these SNPs were associated with lung cancer risk with higher risk in homozygous condition like 40% higher risk in 432 Val/Val, twice more risk in 119Ser/Ser carriers that 119 Ala/Ala carriers and four times higher risk was found in those with 48Gly/Gly genotype than 48 Arg/Arg genotype carriers. Furthermore, they found that Asn453Ser SNP was associated with increased risk in female sex. GSTs (glutathione S-transferases) and NATs (N-acetyl transferases) belong to phase II enzymes and their role in xenobiotic metabolism has been also studied in relation to lung cancer risk as they are also highly polymorphic in nature as that of CYPs (Brigelius-Flohe and Kipp 2009). Among various SNPs in GSTs, Met139Ile (GSTT) and Ala114Val (GSTP1) SNPs have been found to be strongly associated with increased lung cancer risk (Zienolddiny et al. 2006). NAT1 and NAT2 enzymes metabolize aryl and heterocyclic enzymes by acetylation reaction. It has been found that there exists a polymorphism in these NAT genes and result in three variants slow, intermediate, and fast acetylators (Agundez 2008). Various studies were conducted to understand the effect of NAT variants on the risk of lung cancer and it was concluded that fast acetylators of NAT were associated with increased lung cancer risk (McKay et al. 2008). Tobacco smoke has also been found to contain reactive species of oxygen and nitrogen which have a demonstrated role in pathogenesis of lung cancer. Several mechanisms are present in the body to deal with these oxidants like superoxide dismutase, myeloperoxidase, catechol-O-methyltransferase (COMT) and microsomal EPHX1, etc., and they also exist in polymorphic variants (Tilak et al. 2011). There have been studies to understand the role of the variants of antioxidants in relation to lung cancer. Several SNPs were studied and among them Val158Met in the COMT, His139 Arg in the EPHX, and Val16 Ala in the SOD2 have been found to be involved in modifying the risk of lung cancer (Kiyohara et al. 2006).

10.4.1.2 DNA Damage Repair Pathway DNA is continuously subjected to various chemical, physical, and biological insults causing damage to it, which if remain unrepaired may lead to mutations, rearrangements, or chromosomal aberrations (Ataian and Krebs 2006). To prevent

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Fig. 10.3 DNA repair pathways in mammals. Adapted from Harper’s Illustrated Biochemistry 30th Edition

the damage to DNA and maintain genomic integrity, cells have developed specific damage repair pathways (Mannuss et al. 2011). There are five main pathways each dealing with a specific damage, including nucleotide excision repair, mismatch repair, base excision repair, homologous recombination, and nonhomologous end joining pathways (Ataian and Krebs 2006) (Fig. 10.3). Depending on the intensity and severity of damage, cells utilize specific repair pathways like in case of tobacco smoke induced DNA adducts (PAH-DNA) cells use nucleotide excision repair pathway which consists of a series of proteins (Rouillon and White 2011) and the commonly studied in cancers are excision repair cross complementing (ERCC), XPA, XPC, ERCC1, ERCC2/XPD, ERCC4/XPF, and ERCC5/XPG (Kiyohara et al. 2010; Kiyohara and Yoshimasu 2007). Similarly in order to repair large double strand breaks cells utilize homologous recombination and nonhomologous end joining pathways which also consists of several proteins like RAD51, ataxia telangiectasia-mutated, and x-ray repair cross complementing (XRCC) proteins (Mladenov and Iliakis 2011). Base excision repair pathway is involved to repair the apurinic/apyrimidinic sites like 7,8-dihydro-8-oxoguanine (8-oxo-G), created by reactive oxygen/nitrogen species (Kryston et al. 2011). Besides, some alkylating agents cause addition of alkyl groups to nucleotide bases, for example, O6-methylguanine (O6-meG) formation. Such damages are

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repaired by O6-meG-DNA methyltransferase protein (MGMT) (Christmann et al. 2011). It has been found that individuals with a lower DNA repair capacity are prone to develop diseases including lung cancer (Shen et al. 2003). Since there are several proteins involved in each of the repair pathways and many of them are polymorphic in nature various epidemiological studies have been conducted to see the effect of specific SNPs in genes involved in repair pathways and their effect on the risk of developing cancers. A meta-analysis by Vineis et al. (2009), has concluded that several polymorphisms in APEX/APE Ile64Val (base excision repair), XRCC1 Arg194Trp and Arg399Gln (double strand repair), ERCC1 Asn118Asn, ERCC2 Lys751Gln, ERCC5 His64His and His1104Asp, XPA G23A 4 (nucelotide excision repair), MGMT Leu84Phe (alkylation damage), and OGG1 Ser326Cys (base excision repair) could affect the risk of lung cancer. Another study has shown that polymorphisms in ERCC1, ERCC2 could affect the survival and response of drugs used in NSCLC (Olaussen et al. 2006). Polymorphism in XRCC4 has also been studied and found to increase the risk of lung cancer (Yu et al. 2011). OGG1 which is involved in BER and repairs 8-oxo-G which otherwise could lead to G to T mutations has been studied by various groups and found that a SNP at Ser326Cys increases the risk of lung cancer as the Cys allele has lower repair ability than the Ser allele and this increased risk is more in case of smokers. In case of enzymes involved in double strand break repair pathway a SNP in XRCC2 at 188 position has been found to increase the lung cancer risk almost three times more in heterozygous condition Arg188His than in individuals with Arg188Arg genotype. ATR (ataxia telangiectasia and Rad3-related) another protein involved in activating damage sensing proteins like P53 by phosphorylating them plays an important role damage repair. A study has confirmed that a SNP in ATRThr211Met could decrease the risk of lung cancer (Zienolddiny et al. 2006). Several SNPs of MGMT gene have been studied in association with lung cancer like Lys178Arg, Ile142Val, and Leu 84Phe. In case of Lys178Arg and Ile142Val it has been found to increase the risk of lung cancer especially adenocarcinoma type, as higher levels of PAH-adducts have been detected in these variants. Similarly Qiu et al. (2014) conducted a meta-analysis and concluded that Leu84Phe SNP shows increased risk of lung cancer in Caucasians but not in Asians.

10.4.1.3 TP53 Pathway P53 acts an emmergency brake to maintain the genomic integrity by sensing the DNA damage and inducing cell cycle arrest to allow DNA damage repair and apoptosis in cells were damage cannot be repaired (Levine and Oren 2009). P53 is regulated by MDM2, also called HDM2 which is itself induced by P53, by ubiquitin mediated degradation (Bond et al. 2005, 2006). Several SNPs have been identified in TP53 but most of them have no role in cancer particularly those in introns and synonymous in nature. Although a SNP in intron3 a 16 bp insertion has been found to increase the risk of some cancers (Costa et al. 2008; Boldrini et al. 2008) and also a synonymous SNP at 36 codon has been shown to reduce the TP53 mRNA levels and decreasing its ability to induce apoptosis (Candeias et al. 2008). Only two SNPs

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(Pro47Ser and Arg72Pro) have been studied in TP53 which have a demonstrated role in modifying the risk of cancers including lung cancer. Pro47Ser Located at codon 47 of transactivation domain this SNP has been studied by various groups to find its association with the risk of cancer. But it has only been reported in African origins at the rate of 5% and not so far in any other population (Felley-Bosco et al. 1993). This SNP is important with respect to apoptotic ability of P53 as Ser46 which is phosphorylated by p38 and homeodomain-interacting protein kinase 2 (HIPK2) enhances the transcription of apoptosis related genes and so affects the P53 mediated apoptosis, requires a Pro residue adjacent to it (Kruse and Gu 2008). So replacement of Pro by Ser at codon 47 will affect the ability of P53 to induce apoptosis and so increased risk of cancer (Kurihara et al. 2007). Arg72Pro The SNP at codon72 is the most commonly studied among different SNPs in TP53 gene. It has been found from the comparative analysis in primates that Pro 72 variant is the ancestral one but the Arg72 variant happens to be at a higher frequency in some populations (Puente et al. 2006), besides, it has also been noted that the Pro 72 variant is found at higher frequency in populations living near to equator and have suggested that it might play a role in protection from UV induced effects or some environmental factors (Beckman et al. 1994; Sjalander et al. 1996). Although it is the most well studied polymorphism but the results have been inconsistent especially from individual case control studies. Earlier meta-analysis as the one by Van Heemst et al. (2005), suggested a weak cancer risk in those with homozygous Pro72 variant. A recent meta-analysis by Chao et al. (2013) also suggested that TP53 codon72 may weakly modify the risk of lung cancer in particular for adenocarcinoma in non-smokers. Another meta-analysis has concluded that Pro 72 variant is predominantly found in Asian lung cancer patients, so it might suggest that it is the risk factor for lung cancer in Asians, although no statistically significant difference was observed (Wang et al. 2013). In case of MDM2, a negative regulator of P53, various polymorphisms have been studied but only SNP that has got attention is the T309G in the first intron of the MDM2 gene. Interestingly the G allele has been found to lead to higher transcription of MDM2 in females possibly because of female hormones (Bond et al. 2004, 2006). Various epidemiological studies have tried to found the link between SNP309 and lung cancer but majority has not seen any association between them (Hu et al. 2006; Pine et al. 2006). Although a meta-analysis by Wo et al. (2011) concluded that G allele carriers have increased lung cancer compared to T allele carriers.

10.4.1.4 Inflammatory Pathways Tobacco smoking exposure has been one of the several factors responsible for inducing inflammatory response in lungs (Balkwill and Coussens 2004). There have been several evidences that chronic inflammation increases the risk of lung cancer, as inflammation leads to recruitment of immune cells which secrete

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Fig. 10.4 Interactions between tumor cells and infiltrating immune cells in the tumor microenvironment. Adapted from Tumor-related interleukins: old validated targets for new anti-cancer drug development by SarraSetrerrahmane and Hanmei Xu

cytokines and also invite other cells so disturbing the tissue homeostasis (Brenner et al. 2011) (Fig. 10.4). Smoking causes irritation of lung and the cells respond by producing proinflammatory cytokines like interleukin 1-beta (IL-1β), IL-6, IL-8, tumor necrosis factor alpha (TNF-α), and cyclo-oxygenase-2 (COX-2). It is now clear that cancer cells not only produce cytokines but also over express their receptors to avoid immune response. There has been growing evidence of cytokines being involved in cell growth, differentiation, angiogenesis, and inhibition of apoptosis in tumor cells (Pollard 2004). Since cytokines have diverse roles, various studies have been conducted to study the effect of their polymorphic variants in cancers. SNPs in the genes of inflammatory pathways have been associated with change in their expression levels and so could contribute to the pathogenesis of cancers. Various epidemiological studies have shown that SNPs in interleukin genes may predispose individuals to lung cancer. Some of commonly studied interleukins in lung cancers are discussed in the following paragraphs:

10.4.1.5 IL-1b and IL1RN IL-1 is an important proinflammatory cytokine existing in two forms IL-1α and IL-1β. While alpha form can be located in both nuclear as well as cytosolic compartments, beta form is found in cytosolic compartment (Rider et al. 2011). Out of these forms of IL-1, beta form is more potent in nature with respect to its proinflammatory role and has also been found to have a role in growth and invasion of almost all tumor cells (Wu and Zhou 2009). Various studies have been performed to see the role of SNPs in the IL-1 βgene such as T-31C, C-511 T, T-3893G, and C-1464G in relation to lung cancer and most of them have a strong correlation between these SNPs and expression with NSCLC (Zienolddiny et al. 2004; Landvik et al. 2009). Also haplotype analyses of these SNPs showed that G3893A, G1464C, C511T, and T31C SNPs formed a specific haplotype in NSCLC cases and were found to be associated with higher expression of IL-1 β in the lung (Landvik et al. 2009). Besides studies also confirmed that C

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allele of the SNP -C511T disrupted the TATA-box and so affected its transcription with higher transcriptional activity (Lind et al. 2007). Some studies have studied the effect of 86 bp VNTR polymorphism of the IL1RN (interleukin 1- β receptor antagonist) gene and have found increased risk of lung cancer with 2 alleles of IL1RN compared to those with 1 allele particularly in those having some kind of inflammatory condition like atopy, asthma, etc., with no history of smoking (Wei et al. 2011).

10.4.1.6 IL 4 IL-4 is one of the most important cytokine involved in proliferation and survival of lymphocytes and is synthesized by various immune cells including activated T cells. There have been various studies which found that IL-4 is over expressed by many tumors along with its receptor including lung cancer, besides promoting survival and proliferation of tumor cells (Sarra and Hanmei 2017). Various polymorphisms of IL-4 have been studied, the most common ones are T-1099G, C-589 T, and C-33 T in the promoter region of IL-4. The results with respect to their association have not been consistent possibly because of difference in ethnicity and the sample size. However, studies have shown that T allele of both C-589 T and C-33 T SNPs lead to increased IL-4 transcription by increasing the binding of transcription factors to its promoter region and might have protective role against cancers (Rosenwasser et al. 1995; Nakashima et al. 2002). Also a study by Scarel-Caminaga et al. (2002), has also found that C allele of C-589 T SNP is associated with decreased expression of IL-4. 10.4.1.7 IL-6 IL-6 is another important cytokine involved in maturation of plasma cells and proliferation and differentiation of cytotoxic T cells (Kishimoto 1989). Besides studies have also observed higher levels of IL-6 in cancers including lung cancer and have been associated with poor survival. Because of its role in the pathogenesis of cancers several targeted therapies have been used like tocilizumab, an anti-IL-6 receptor antibody which suppressed progression of tumor and angiogenesis (Hara et al. 2016). In IL-6 two SNPs G-174C and G-634C have been studied by various groups in association with lung cancer. G-174C has been found to be strongly associated with the risk of lung cancer particularly squamous cell carcinoma type and also has been found to be associated with higher levels of this interleukin (Van den Borst et al. 2011; He et al. 2009). As far as IL-6 G-634C SNP is concerned number of studies have seen that the G allele variant at this position increased the risk of lung cancer, more so in patients with asthma, atopy, or other inflammatory problem and having no history of smoking (Lim et al. 2011). The findings have also been confirmed by Bai et al. (2013). 10.4.1.8 IL-8 IL-8 is a major inflammatory response mediator cytokine released early in response to some injury by cells like macrophages (Hedges et al. 2000). IL-8 has been found

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to be involved in cell proliferation, tissue remodeling, and angiogenesis (Waugh and Wilson 2008). Various studies have been conducted to understand the role of polymorphisms in IL-8 in cancers, but so far only one SNP T-251A has been found to have some kind of association. As Gao et al. (2010) conducted a meta-analysis on T-251A SNP of IL-8 and concluded that this polymorphism is a susceptibility marker for lung cancer. Since this SNP affects the expression of this interleukin another study has shown that it decreases the risk of lung cancer especially in females (Zienolddiny and Skaug 2012).

10.4.1.9 IL-10 IL-10 is an anti-inflammatory cytokine synthesized by macrophages and T cells (Howard et al. 1992). Several studies have been conducted on IL-10 polymorphisms to see if they have any effect on the risk of lung cancer. SNPs studied included A592C, T -819C, and A-1082G but the results have been inconsistent because of sample size, ethnic difference, etc. A study on south Indian population has concluded that –1082G/G variant was significantly associated with the risk of NSCLC especially in smokers (Vidyullatha et al. 2016). However, recently a meta-analysis carried out by Jamal et al. (2019) concluded that SNP A592C might be the risk factor for lung cancer but not T -819C and A-1082G SNPs. 10.4.1.10 TNF-Alpha TNF-α has a complex role in cancer progression with its high and low levels having opposite roles. Several studies have been conducted to study the role of its polymorphisms particularly G-238A, G-308A, and G488A in different cancers. In one study SNPs G-308 A and G488 A have been found to be associated with the susceptibility of lung cancer (Mekinian et al. 2011). In another study by Shih et al. (2016), it was found that G238A, G-308A had a strong association with lung cancer susceptibility, as the AA/GA genotype at 308 SNP showed increased lung cancer and A allele was found to have a tumor promotive effect, while AA/GA genotype at 238 SNP showed decreased lung cancer risk and it was found that A allele at this SNP had a protective role. A study on Indian population also studied TNF-α 308 SNP in lung cancer patients but did not find any association (Vidyullatha et al. 2016). 10.4.1.11 IFN Gama IFN-γ is secreted by various immune response cells activated by antigen stimulation. It has been reported to inhibit tumor angiogenesis (Hanahan and Folkman 1996) and also have been found to exert anti-proliferative and anti-metabolic effects on tumors (Chen et al. 2000). Various groups have studied the polymorphisms in IFN- γ and their association with the risk of cancers. Some studies have shown the association between IFN-γ 874 T/A polymorphism and cancer and suggested that T allele might be a protective genotype in cancer as T allele produces a high level of IFN-γ by preferential binding of nuclear factor kappa-B (NF-kB) to T allele increasing its transcription (Rossouw et al. 2003). Yet a recent meta-analysis showed no association between IFN-γ 874 T/A polymorphism and cancer susceptibility (Ge et al.

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2014). Another meta-analysis on leukemia patients confirmed that no significant association between IFN-γ 874A/T polymorphism and leukemia risk (Wu et al. 2016). A study on Indian population also showed that IFN- γ 874 A/T polymorphism was found to be significantly associated with NSCLC risk especially in smokers (Vidyullatha et al. 2016). A polymorphism in IFN-γ receptor switch at position-56 (T to C) in the IFN- γ R1 promoter leads to a lower expression of the receptor and so reduced IFN-γ-mediated immunity against intracellular microbes by at least partially blocking the pathways which activate macrophages or process antigens (Cooke et al. 2006). Addressing the importance of polymorphisms of IFN- γ R1 at position-56 with susceptibility to mycobacterial infection, a meta-analysis showed that IFN- γ R1 T-56C polymorphism is possibly associated with increased TB risk in Africans, but not in Asian or Caucasians. Another study also showed that the 56 CC genotypes of IFN-g receptor1 were significantly associated with NTM infection (Poopak et al. 2017).

10.4.1.12 COX-2 Cyclooxygenase-2 (COX-2) converts arachidonic acid to prostaglandins. Contrary to COX-1, which is ubiquitous and regulates normal physiologic function, COX-2 is induced by inflammatory stimuli, growth factors, mitogenic substances, and oncogenes (Williams et al. 1999). In addition to its role in the inflammatory response, it may play an important role in carcinogenesis. Potential mechanisms of carcinogenesis include dysregulation of cell growth, inhibition of apoptosis, interference with immune surveillance, and angiogenesis stimulation (Gridelli et al. 2002). It has been observed that certain cancers overexpress the COX2 (Wolff et al. 1998), and also the activity of COX2 has been found to be affected by certain polymorphisms (Tan et al. 2007). Several SNPs have been studied in COX2, one of them is A 1195G, which has been found to influence the expression of COX-2, while the A allele showed higher transcription rate than G allele (Zhang et al. 2005). Another SNP, G 765C, has been reported to lead to a lower expression of COX-2 when C allele is present at this SNP (Kristinsson et al. 2009). Another SNP at 3 UTR (8473) has been studied in association with lung cancer and found to show increased risk of NSCLC (Campa et al. 2004). 10.4.1.13 TLR4 Toll-like receptors (TLRs) are known to act sensors of innate response to invaders which lead to activation of various transcription factors like NF-κB to induce expression of inflammatory genes to counter the invading organisms. Till date there have more than 10 types of TLRs identified each with a specific role (Abdelhabib et al. 2018). Although various polymorphisms have been studied in TLRs in association with susceptibility to cancers (Misch and Hawn 2008) but only TLR4 polymorphism has been found to show some association with lung cancer. In one study by Kurt et al. (2016) two SNPs at 896 and 1196 nucleotide position (rs4986790 and rs4986791), respectively, were studied and found that a higher risk of lung cancer with CT allele was present at 1196 position compared to CC allele but did not find any association with SNP at 896 position. Another study investigated the

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role of TLR4 rs4986790 SNP in response to chemotherapy in non-small cell lung cancer and concluded that loss of function of TLR4 alleles did not affect the overall survival in non-small cell lung cancer (NSCLC) patients (Vacchelli et al. 2012).

10.5

Conclusion

In conclusion, a number of SNPs in the pathways described above have been found to be associated with the risk of lung cancer, but none of these SNPs has yet been brought to clinical use. To put a SNP into clinical practice, the statistical association must be strong enough and must show uniformity among different populations, which is lacking in lung cancer. Researchers have shifted the association studies from single SNPs to genome wide association studies but the results so far have not been encouraging. Keeping in view the limited success of SNPs or haplotype to act as biomarker, it can be argued that there is a need to focus more on driver mutations in different cancers. It can be speculated that SNPs being common genetic variation have no specific role in predisposing the individuals to a particular type of cancer.

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