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

Abstract

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.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage

“…This work can complement findings from other large transcriptome-wide association studies that have also identified potential prostate cancer susceptibility loci, but lack detail about treatment exposure and clinical outcome. 31,32 Among participants with identified EHR data evidence of RP and biopsy, clinical-practice databased algorithms reliably identified treatment timing and sequence, achieving 97.9% concordance for the timing of RP and 86.0% concordance for diagnosis among participants who had GC calculated from a biopsy specimen. Moreover, through pharmacy records and claims, we were able to quantify the duration of systemic therapy and account for time-dependent exposures to systemic therapy, which can be leveraged for larger-scale pharmacogenomic study in prostate cancer in the future.…”

“…Furthermore, by including the majority of participants undergoing GC testing in the clinical-practice setting, the participants included in this linkage are demographically representative of those tested in the contemporary era. This work can complement findings from other large transcriptome-wide association studies that have also identified potential prostate cancer susceptibility loci, but lack detail about treatment exposure and clinical outcome …”

“…This work can complement findings from other large transcriptome-wide association studies that have also identified potential prostate cancer susceptibility loci, but lack detail about treatment exposure and clinical outcome. 31 , 32 …”

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

“…To date, genomewide association studies (GWAS) have identified several hundred common genetic risk loci for each of three prevalent cancer types: breast, colorectal, and prostate [5][6][7][8] , and several dozen risk loci have been identified for other cancers, such as cancer of lung, pancreas, and ovarian [9][10][11][12][13] . Previous research, including our work, has identified hundreds of putative cancer susceptible genes potentially regulated by these risk variants, using methods such as expression quantitative trait loci (eQTL) analysis [8][9][10][11][12][14][15][16][17][18][19][20] and transcriptome-wide association studies (TWAS) 7,19,[21][22][23][24][25][26][27][28][29] . However, most dysregulated gene expression has not been thoroughly investigated at the protein level.…”

A microRNA Transcriptome-wide Association Study of Prostate Cancer Risk

“…Follow-up expression quantitative trait locus (eQTL) studies have begun to explore putative regulatory targets of PrCa risk SNPs ( Thibodeau et al, 2015 ; DeRycke et al, 2019 ). Recent methodological advancements in colocalization of disease-associated SNPs and eQTLs have yielded powerful transcriptome-wide association study (TWAS) approaches to disease-gene discovery, which have been highly successfully in translating single-SNP GWAS hits into gene-level associations with PrCa risk ( Mancuso et al, 2018 ; Liu et al, 2022 ).…”