Department of Pharmacy, Harbin Medical University Cancer Hospital, Harbin, China.
Division of Cancer Epidemiology, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
Int J Cancer. 2022 Jan 1;150(1):80-90. doi: 10.1002/ijc.33808. Epub 2021 Sep 25.
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 vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs 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 vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs 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.
前列腺癌风险的大部分遗传率仍然未知。全转录组关联研究与整体水平的验证相结合,有助于确定潜在在前列腺癌发展中起作用的候选基因。利用来自基因型组织表达项目的数据,我们构建了遗传模型,使用 PrediXcan 统计框架(分子特征联合检验的一种修改版本)预测正常前列腺组织的基因表达,该框架用于预测正常前列腺组织的基因表达。我们将这些预测模型应用于 79194 例前列腺癌病例和 61112 例对照的遗传数据中,以研究遗传决定的基因表达与前列腺癌风险之间的关联。我们关注相关基因,比较它们在前列腺肿瘤与正常前列腺组织中的表达,比较位于这些基因座的 CpG 位点在前列腺肿瘤与正常组织中的甲基化情况,并通过分析癌症基因组图谱 (TCGA) 数据评估差异表达基因的表达与相应 CpG 位点的甲基化之间的相关性。我们确定了 573 个与前列腺癌风险相关的基因,在错误发现率 (FDR) ≤ 0.05 时,包括 451 个新基因和 122 个以前报道的基因。在 573 个基因中,有 152 个在前列腺肿瘤与正常组织样本之间表现出差异表达。在 57 个基因的基因座中,有 151 个 CpG 位点在前列腺肿瘤与正常组织样本之间表现出差异甲基化。在这些 CpG 位点中,有 20 个与 11 个相应基因的表达相关。在本次 TWAS 中,我们确定了前列腺癌风险的新候选易感基因,为前列腺癌遗传学和生物学提供了新的见解。