A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk.

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures, and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79,194 prostate cancer cases and 61,112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor versus normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor versus normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor versus normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor versus normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology. This article is protected by copyright. All rights reserved.

International journal of cancer. 2021 Sep 14 [Epub ahead of print]

Duo Liu, Jingjing Zhu, Dan Zhou, Emily G Nikas, Nikos T Mitanis, Yanfa Sun, Chong Wu, Nicholas Mancuso, Nancy J Cox, Liang Wang, Stephen J Freedland, Christopher A Haiman, Eric R Gamazon, Jason B Nikas, Lang Wu

Department of Pharmacy, Harbin Medical University Cancer Hospital, Harbin, China., Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA., Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., School of Mathematics, University of Minnesota, Minneapolis, MN, USA., Department of Mathematics, University of the Aegean, Samos, Greece., Department of Statistics, Florida State University, Tallahassee, FL, USA., Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA., Department of Tumor Biology, H. Lee Moffitt Cancer Center, Tampa, FL, USA., Center for Integrated Research in Cancer and Lifestyle, Cedars-Sinai Medical Center, Los Angeles, CA., Research & Development, Genomix Inc., Minneapolis, MN, USA.