A Meta-Analysis of Association Between One-Carbon Metabolism Gene Polymorphisms and Risk of Prostate Cancer

A Meta-Analysis of Association Between One-Carbon Metabolism Gene Polymorphisms and Risk of Prostate Cancer by Mahmood Tazari MSc, Carleton University, 2010 MSc, University of Guelph, 2006 BSc, Azad University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Health Walden University March 2015 Abstract Prostate cancer is the most common cancer among men. The purpose of this quantitative, meta-analysis study was to examine one-carbon metabolism gene polymorphisms in a group of genes to determine their association with prostate cancer risk. The genetic epidemiology theory provided the framework for the study. The data collected were from published articles. From over 2,800 individual studies, 20 articles were retained for results and data abstraction, following the title, abstract screen, and full text screening in the second phase. The data were analyzed by a meta-analysis statistical method, combining the results from selected studies to estimate the overall association. According to study results by the adjusted p-values of fixed model, there was a significant association between decreased risk of prostate cancer and the variant of Allele T, Genotype TT, and the recessive model of C667T polymorphism. In the random model, the adjusted p-values show a significant association between decreased risk of prostate cancer, the variant of Genotype TT, and recessive model. There was an increased risk of prostate cancer in A1298C polymorphism by adjusted p-value on the variant of Genotype AC, in the fixed model. This study leads to positive social change by providing information on an optimization surveillance strategy to ensure valid screening test for prostate disease reporting. Future studies with a greater number of samples are needed, including gene-gene and gene-environment interaction to verify study results.Prostate cancer is the most common cancer among men. The purpose of this quantitative, meta-analysis study was to examine one-carbon metabolism gene polymorphisms in a group of genes to determine their association with prostate cancer risk. The genetic epidemiology theory provided the framework for the study. The data collected were from published articles. From over 2,800 individual studies, 20 articles were retained for results and data abstraction, following the title, abstract screen, and full text screening in the second phase. The data were analyzed by a meta-analysis statistical method, combining the results from selected studies to estimate the overall association. According to study results by the adjusted p-values of fixed model, there was a significant association between decreased risk of prostate cancer and the variant of Allele T, Genotype TT, and the recessive model of C667T polymorphism. In the random model, the adjusted p-values show a significant association between decreased risk of prostate cancer, the variant of Genotype TT, and recessive model. There was an increased risk of prostate cancer in A1298C polymorphism by adjusted p-value on the variant of Genotype AC, in the fixed model. This study leads to positive social change by providing information on an optimization surveillance strategy to ensure valid screening test for prostate disease reporting. Future studies with a greater number of samples are needed, including gene-gene and gene-environment interaction to verify study results. A Meta-Analysis of Association Between One-Carbon Metabolism Gene Polymorphisms and Risk of Prostate Cancer by Mahmood Tazari MSc, Carleton University, 2010 MSc, University of Guelph, 2006 BSc, Azad University, 1992 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Public Health Walden University March 2015 Dedication I do dedicate this study to the people who are involved in research to make a positive social change in our community, especially those who are in health sciences and work hard to make our world healthy. I do respect everyone who helped me to make this study happen and will help me to bring the results to the practice. Acknowledgments I would like to express my appreciation and thanks to my advisory committee, Dr. Xianbin Li, Committee Chairperson & Methodology Expert with his valuable advice, Dr. Peter Anderson, Committee Member & Content Expert who have been a tremendous mentor for me, and Dr. Michael Fumio Furukawa the committee URR for his valuable comments. They encourage and motivate me in my long way education. I do thank staff and faculty members at Walden University for their supports. I do appreciate Dr. Tammy Root for her support. I would like to appreciate Dr. Julian Little for his encouragement and taught in the research opportunity that he gave me in the field of genetic studies. Table of