Prostate cancer (PCa) is the second most common malignancy in men, but its exact pathogenetic mechanisms remain unclear. This study explores the effect of enhancer RNAs (eRNAs) in PCa. Firstly, we screened eRNAs and eRNA -driven genes from The Cancer Genome Atlas (TCGA) database, which are related to the disease-free survival (DFS) of PCa patients;. screening methods included bootstrapping, Kaplan–Meier (KM) survival analysis, and Pearson correlation analysis. Then, a risk score model was established using multivariate Cox analysis, and the results were validated in three independent cohorts. Finally, we explored the function of eRNA-driven genes through enrichment analysis and analyzed drug sensitivity on datasets from the Genomics of Drug Sensitivity in Cancer database. We constructed and validated a robust prognostic gene signature involving three eRNA-driven genes namely MAPK15, ZNF467, and MC1R. Moreover, we evaluated the function of eRNA-driven genes associated with tumor microenvironment (TME) and tumor mutational burden (TMB), and identified remarkable differences in drug sensitivity between high- and low-risk groups. This study identified a prognostic gene signature, which provides new insights into the role of eRNAs and eRNA-driven genes while assisting clinicians to determine the prognosis and appropriate treatment options for patients with PCa.

Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent type of kidney cancer. This study aimed to establish a nomogram to predict ccRCC prognosis.Methods: By integrating DNA methylation (DNAm) data and gene expression profiles of ccRCC obtained from The Cancer Genome Atlas (TCGA), DNAm-driven genes were identified by differential and correlation analyses. Next, risk genes were selected by multiple algorithms (univariate Cox and Kaplan-Meier survival analyses) and various databases (TCGA, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and The Human Protein Atlas (HPA)). A risk score model was established by multivariate Cox analyses. ConsensusPathDB and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functions of the selected genes. After comprehensively evaluating the clinical data, we established and assessed a dynamic nomogram available on a webserver.Results: In total, 220 differentially expressed DNAm-driven genes were identified, and five-gene signature (EPB41L4B, HHLA2, IFI16, CMTM3, and XAF1) was related to overall survival (OS). Next, we integrated the DNAm-driven genes into the prognostic risk score model and found that age, histologic grade, pathological stage, and risk level were correlated with OS in ccRCC patients. Based on these variables, a dynamic nomogram was established to predict the ccRCC prognosis. Finally, Functional enrichment analysis showed that the functions of these genes were relevant to immune reactions.Conclusions: We identified a 5 DNAm-driven gene signature whose altered status was highly correlated with ccRCC patient OS. We constructed a dynamic nomogram to provide individualized survival predictions for ccRCC patients.