Prognostic Assessment and Analysis of Underlying Biological Mechanisms of Prostate Cancer Based on Estrogen-Related Genes - Zhang, Heng; Fan, Meng-Die; Hu, Yang; Yang, Qing; Jiang, Jia-Wei; Xu, Min (Hormone and Metabolic Research, 2025)
Prostate cancer (PCa) ranks among the most prevalent cancers in men, noted for its high mortality rate and unfavorable prognosis. Estrogen-related genes (ERGs) are significantly associated with the progression of PCa. This investigation aims to comprehensively assess the prognosis of PCa based on ERGs and explore its underlying biological mechanisms. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were conducted to identify prognostic signature genes and build a prognostic model. The model’s predictive performance was assessed using Receiver Operating Characteristic (ROC) curve analysis. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to investigate the underlying molecular mechanisms of PCa. Antitumor drugs with high sensitivity were predicted using the CellMiner database and the pRRophitic package. Additionally, miRNAs targeting the identified signature genes were predicted using the miRNet database. This study identified six ERGs as prognostic biomarkers for PCa: POU4F1, BMP2, PGF, GAS1, GNAZ, and FGF11. The findings indicated that individuals in the low-risk category exhibited improved prognostic results. Notably, PCa progression may be closely linked to the cell adhesion molecule pathway and epigenetic regulation. Additionally, hsa-let-7a-5p and hsa-miR-34a-5p were identified as potential therapeutic regulators for PCa treatment. In conclusion, this research offers novel perspectives into the progression of PCa, providing robust scientific support for the development of personalized treatment strategies for PCa patients.
Keywords: prostate cancer; estrogen-related genes; prognosis; risk model