Identification of a prognostic biomarker predicting biochemical recurrence and construction of a novel nomogram for prostate cancer.

Abstract

Biochemical recurrence (BCR) is common in prostate cancer (PCa), but its prediction is based predominantly on clinicopathological characteristics with low accuracy. We intend to identify a potential prognostic biomarker related to the BCR and construct a nomogram for improving the risk stratification of PCa patients. The transcriptome and clinical data of PCa patients were obtained from TCGA and GEO databases. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to screen out differentially expressed genes (DEGs) related to the BCR of PCa. Cox regression analysis was further applied to screen out DEGs related to BCR-free survival (BFS). Time-dependent receiver operating curve (ROC) analysis and Kaplan-Meier (K-M) survival analysis were conducted to assess the prognostic value. Then, a prognostic nomogram was established and evaluated. The clinicopathological correlation analysis, GSEA analysis, and immune analysis were used to explore the biological and clinical significance of the biomarker. Finally, the qRT-PCR, western blotting, and immunohistochemistry (IHC) were conducted to validate the expression of the biomarker. BIRC5 was identified to be the potential prognostic biomarker. The clinical correlation analysis and K-M survival analysis found that the BIRC5 mRNA expression was positively associated with disease progression and negatively associated with the BFS rate. Time-dependent ROC curves verified its accurate prediction performance. The GSEA and immune analysis suggested that the BIRC5 was related to immunity. A nomogram with an accurate prediction for BFS of PCa patients was constructed. qRT-PCR, western blotting, and IHC results validated the expression level of BIRC5 in PCa cells and tissues. Our study identified BIRC5 as a potential prognostic biomarker related to BCR of PCa and constructed an efficacy nomogram for predicting BFS to assist clinical decision-making.