A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.

摘要: Background and Objective After radical prostatectomy (RP), prostate cancer (PCa) patients may experience biochemical recurrence (BCR) clinical recurrence, which remains a dominant issue in PCa treatment. The purpose of this study was to identify protein-coding gene classifier associated with microRNA (miRNA)-mediated regulation provide comprehensive prognostic index predict after RP. Methods Candidate classifiers were constructed using two machine-learning algorithms (a least absolute shrinkage selector operation [LASSO]-based decision tree-based classifier) based on discovery cohort (n = 156) from Cancer Genome Atlas (TCGA) database. selecting the LASSO-based prediction accuracy, both an internal validation 333) external 100) used examined survival analysis, time-dependent receiver operating characteristic (ROC) curve univariate multivariate Cox proportional hazards regression analyses. Functional enrichment analysis co-expressed genes carried out explore underlying moleculer mechanisms included classifier. Results We three-gene that FAM72B, GNE, TRIM46, we identified four upstream miRNAs (hsa-miR-133a-3p, hsa-miR-222-3p, hsa-miR-1301-3p, hsa-miR-30c-2-3p). exhibited remarkable ability (area under [AUC] 0.927) distinguish high low Gleason scores cohort. Furthermore, it significantly (p < 0.0001, log rank statistic 20.7, AUC 0.733) could serve as independent factor recurrence-free (hazard ratio: 1.708, 95% CI: 1.180-2.472, p 0.001). Additionally, predictor BCR according BCR-free 0.0338, 4.51). Conclusions miRNA-mediated novel biomarker for