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Detalle de: Prognostic role of genetic biomarkers in clinical progression of prostate cancer

Title Prognostic role of genetic biomarkers in clinical progression of prostate cancer
Abstract

The aim of this study was to analyze the use of 12 single-nucleotide polymorphisms in genes ELAC2, RNASEL and MSR1 as biomarkers for prostate cancer (PCa) detection and progression, as well as perform a genetic classification of high-risk patients. A cohort of 451 men (235 patients and 216 controls) was studied. We calculated means of regression analysis using clinical values (stage, prostate-specific antigen, Gleason score and progression) in patients and controls at the basal stage and after a follow-up of 72 months. Significantly different allele frequencies between patients and controls were observed for rs1904577 and rs918 (MSR1 gene) and for rs17552022 and rs5030739 (ELAC2). We found evidence of increased risk for PCa in rs486907 and rs2127565 in variants AA and CC, respectively. In addition, rs627928 (TT-GT), rs486907 (AG) and rs3747531 (CG-CC) were associated with low tumor aggressiveness. Some had a weak linkage, such as rs1904577 and rs2127565, rs4792311 and rs17552022, and rs1904577 and rs918. Our study provides the proof-of-principle that some of the genetic variants (such as rs486907, rs627928 and rs2127565) in genes RNASEL, MSR1 and ELAC2 can be used as predictors of aggressiveness and progression of PCa. In the future, clinical use of these biomarkers, in combination with current ones, could potentially reduce the rate of unnecessary biopsies and specific treatments.

Apa Reference Alvarez-Cubero, M.J., Martinez-Gonzalez, L.J., Saiz, M., Carmona-Saez, P., Alvarez, J.C., Pascual-Geler, M., Lorente, J.A. & Cozar, J.M. (2015). Prognostic role of genetic biomarkers in clinical progression of prostate cancer. Experimental & Molecular Medicine, 47(8), e176-e176. 10.1038/emm.2015.43
Doi 10.1038/emm.2015.43
Publication Date 7 Aug 2015
Publication Type Article
Researcher Pedro Carmona-Saez
Ontology Multivariate statistics, Clinical data, Dimensionality reduction
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