Prognostic Assessment and Analysis of Underlying Biological
                    Mechanisms of Prostate Cancer Based on Estrogen-Related Genes

Heng Zhang; Meng-Die Fan; Yang Hu; Qing Yang; Jia-Wei Jiang; Min Xu

doi:10.1055/a-2548-1568

Hormone and Metabolic Research, Table of Contents

Horm Metab Res 2025; 57(04): 273-285
DOI: 10.1055/a-2548-1568

Original Article: Endocrine Care

Prognostic Assessment and Analysis of Underlying Biological Mechanisms of Prostate Cancer Based on Estrogen-Related Genes

Heng Zhang

¹Urology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

,

Meng-Die Fan

²Dental Department, Hubei Medical College Affiliated Shiyan People’s Hospital, Hubei, China

,

Yang Hu

¹Urology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

,

Qing Yang

¹Urology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

,

Jia-Wei Jiang

¹Urology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

,

Min Xu

¹Urology Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

› Author Affiliations

Abstract

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

Full Text

References

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Supplementary Material

Supplementary Material