Prostate cancer (PCa) is a complex disease influenced by many factors, with the genetic contribution for this neoplasia having a great role in its risk. The literature brings an increased number of Genome-Wide Association Studies (GWAS's) that attempt to elucidate the genetic associations with PCa. However, these genome studies have a considerable rate of false-positive data whose results may be biased. Therefore, we aimed to apply Bayesian approaches on significant associations among polymorphisms and PCa from GWAS's data. A literature search was performed for data published before April 20, 2024, whereby two investigators used a specific combination of keywords and Boolean operators in the search ("prostate carcinoma or prostate cancer or PCa" and "polymorphism or genetic variation" and "Genome-Wide Association Study or GWAS"). The records were retrieved, and the data were extracted with further application of two different Bayesian approaches: The False Positive Report Probability (FPRP) and the Bayesian False-Discovery Probability (BFDP), both at the prior probabilities of 10-3 and 10-6. The data were considered as noteworthy at the level of FPRP <0.2 and BFDP <0.8. Besides, in-silico analyses by gene-gene network and gene enrichment were performed to evaluate the role of the noteworthy genes in PCa. As results, 13 GWAS's were included, with 2,520 values for FPRP and 1,368 values for BFDP being obtained. Our study showed an extensive number of gene variations as noteworthy candidate biomarkers for PCa risk, with highlighting for those occurred in the 8q24 locus and in the MSMB, ITGA6, SUN2, FGF10, INCENP, MLPH, and KLK3 genes.
Keywords: Alleles; Genetic polymorphism; Odds Ratio; Prostate neoplasm.
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