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Article Abstract
Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to establish a novel predictive model based on apoptosis-related gene signature and clinicopathological parameters that can improve risk stratification for BCR and assist in clinical decision-making. Expression data and corresponding clinical information were obtained from four public cohorts, one from The Cancer Genome Atlas (TCGA) dataset and three from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate modules closely correlated to BCR, and univariate and multivariate Cox regression analyses were utilized to build the gene signature. Time-dependent receiver operating curve (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic value. Finally, we analyzed the expression of genes in the signature and validated the results using quantitative real-time PCR (qRT-PCR). The novel gene signature we established exhibited a high prognostic value and was able to act as an independent risk factor for BCR [Training set: < 0.001, hazard ratio (HR) = 7.826; Validation set I: = 0.006, HR = 2.655; Validation set II: = 0.003, HR = 4.175; Validation set III: < 0.001, HR = 3.008]. Nomogram based on the gene signature and clinical parameters was capable of distinguishing high-risk BCR patients. Additionally, functional enrichment analysis showed several enriched pathways and biological processes, which might help reveal the underlying mechanism. The expression results of qRT-PCR were consistent with TCGA results. The apoptosis-related gene signature could serve as a powerful predictor and risk factor for BCR in localized PCa patients after RP.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734189 | PMC |
| http://dx.doi.org/10.3389/fgene.2020.586376 | DOI Listing |
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