Monteiro André Victor Oliveira, Dos Santos Naum Neves da Costa, da Silva Jonatan Pinho Rodrigues, Brasileiro Samuel Arcebispo, Botelho Juliana Campos, Sobreira Luis Eduardo Rodrigues, Leal Alessandro Luiz Araújo Bentes, Pereira Adenilson Leão, de Oliveira Ana Carolina Alves, Monteiro José Rogério Souza, da Silva Felipe Rodolfo Pereira
Medicine College, Altamira University Campus, Federal University of Para, Altamira, PA, Brazil; Laboratory of Genetics and Medicine-Based Evidence, Altamira University Campus, Federal University of Para, Altamira, PA, Brazil.
Medicine College, Altamira University Campus, Federal University of Para, Altamira, PA, Brazil.
Urol Oncol. 2025 Apr;43(4):270.e19-270.e28. doi: 10.1016/j.urolonc.2024.10.023. Epub 2024 Nov 26.
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 and 10. 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.
前列腺癌（PCa）是一种受多种因素影响的复杂疾病，该肿瘤形成中的遗传因素在其发病风险中起着重要作用。文献中出现了越来越多的全基因组关联研究（GWAS），试图阐明与PCa的遗传关联。然而，这些基因组研究存在相当比例的假阳性数据，其结果可能存在偏差。因此，我们旨在将贝叶斯方法应用于GWAS数据中多态性与PCa之间的显著关联。我们对2024年4月20日前发表的数据进行了文献检索，两名研究人员在检索中使用了特定的关键词和布尔运算符组合（“前列腺癌或前列腺癌或PCa”以及“多态性或基因变异”以及“全基因组关联研究或GWAS”）。检索记录并提取数据，进一步应用两种不同的贝叶斯方法：假阳性报告概率（FPRP）和贝叶斯假发现概率（BFDP），两者的先验概率均为10和10。当FPRP<0.2且BFDP<0.8时，数据被视为值得关注。此外，通过基因-基因网络和基因富集进行了计算机分析，以评估这些值得关注的基因在PCa中的作用。结果纳入了13项GWAS，获得了2520个FPRP值和1368个BFDP值。我们的研究表明，大量基因变异是PCa风险的值得关注的候选生物标志物，尤其突出的是发生在8q24位点以及MSMB、ITGA6、SUN2、FGF10、INCENP、MLPH和KLK3基因中的变异。
