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Correlation between Genomic Variants and Worldwide Epidemiology of Prostate Cancer.

Giovana Miranda VieiraLaura Patrícia Albarello GellenDiana Feio da Veiga Borges LealLucas Favacho PastanaLui Wallacy Morikawa Souza VinagreVitória Teixeira AquinoMarianne Rodrigues FernandesPaulo Pimentel de AssumpçãoRommel Mario Rodríguez BurbanoSidney Emanuel Batista Dos SantosNey Pereira Carneiro Dos Santos
Published in: Genes (2022)
Prostate cancer (PCa) incidence and mortality vary across territories and populations. This can be explained by the genetic factor of this disease. This article aims to correlate the epidemiological data, worldwide incidence, and mortality of PCa with single-nucleotide polymorphisms (SNPs) associated with the susceptibility and severity of this neoplasm in different populations. Eighty-four genetic variants associated with prostate cancer susceptibility were selected from the literature through genome association studies (GWAS). Allele frequencies were obtained from the 1000 Genomes Project, and epidemiological data were obtained from Surveillance, Epidemiology, and End Results (SEER). The PCa incidence, mortality rates, and allele frequencies of variants were evaluated by Pearson's correlation. Our study demonstrated that 12 SNPs (rs2961144, rs1048169, rs7000448, rs4430796, rs2066827, rs12500426, rs6983267, rs11649743, rs2075110, rs114798100, rs855723, and rs2075109) were correlated with epidemiological data in different ethnic groups. Ten SNPs (rs2961144, rs1048169, rs7000448, rs4430796, rs2066827, rs12500426, rs11649743, rs2075110, rs114798100, and rs2075109) were positively correlated with the mortality rate. Seven SNPs (rs1048169, rs2961144, rs7000448, rs4430796, rs2066827, rs12500426, and rs114798100) were positively correlated with incidence. Positive correlations of incidence and mortality rates were more frequent in the African population. The genetic variants investigated here are likely to predispose to PCa and could play a role in its progression and aggressiveness. This genetic study demonstrated here is promising for implementing personalized strategies to screen for prostate cancer in diverse populations.
Keyphrases
  • prostate cancer
  • risk factors
  • systematic review
  • type diabetes
  • cardiovascular events
  • machine learning
  • dna methylation
  • low grade