Recurrence is a major problem for prostate cancer patients, thus, identifying prognosis-related markers to evaluate clinical outcomes is essential. Here, we established a fifteen-miRNA-based recurrence-free survival (RFS) predicting signature based on the miRNA expression profile extracted from The Cancer Genome Atlas (TCGA) database by the LASSO Cox regression analysis. The median risk score generated by the signature in both the TCGA training and the external Memorial Sloan-Kettering Cancer Center (MSKCC) validation cohorts was employed and the patients were subclassified into low- and high-risk subgroups. The Kaplan-Meier plot and log-rank analyses showed significant survival differences between low- and high-risk subgroups of patients (TCGA, log-rank P < 0.001 & MSKCC, log-rank P = 0.045). In addition, the receiver operating characteristic curves of both the training and external validation cohorts indicated the good performance of our model. After predicting the downstream genes of these miRNAs, the miRNA-mRNA network was visualized by Cytoscape software. In addition, pathway analyses found that the differences between two groups were mainly enriched on tumor progression and drug resistance-related pathways. Multivariate analyses revealed that the miRNA signature is an independent indicator of RFS prognosis for prostate cancer patients with or without clinicopathological features. In summary, our novel fifteen-miRNA-based prediction signature is a reliable method to evaluate the prognosis of prostate cancer patients.
Keywords: microRNA; prostate cancer; recurrence-free survival.
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