Work

A novel bone metastasis-related gene signature for predicting prognosis, anti-androgen resistance, and drug choice in prostate cancer

Year: 2025

Type: article

Abstract: Objective: Prostate cancer (PCa) often metastasizes to the bone, posing a significant clinical challenge. This study aims to develop a bone metastasis-related risk model for PCa. Methods: Bone metastasis-related genes (BMRGs) were identified through a combination of differential gene expression analysis and WGCNA using GSE32269 and GSE77930 datasets. Consensus clustering analysis was employed to determine the significance of these genes in molecular subtyping of PCa. LASSO-Cox regression analysis was utilized to construct the bone metastasis-related prognostic gene signature (BMRPS). The predictive performance of BMRPS was assessed using ROC curves, Kaplan-Meier survival curves, and a predictive nomogram. The immune landscape heterogeneity of subgroups was analyzed using CIBERSORT, ESTIMATE, and xCell algorithms. Drug sensitivity and molecular docking analysis were performed to identify drugs associated with BMRPS. Results: Forty-four BMRGs associated with the prognosis of PCa were identified. Consensus clustering revealed the pivotal role of these genes in stratifying PCa into three distinct prognostic clusters. The BMRPS, consisting of 14 BMRGs, demonstrated excellent predictive accuracy for prognosis and served as an independent prognostic factor in PCa. BMRPS effectively predicted the overall survival of bone metastatic PCa and differentiated bone metastasis from other metastatic types. BMRPS showed a close correlation with the immune landscape and immunotherapeutic response biomarkers. Additionally, BMRPS was associated with anti-androgen resistance, and AZD8186 was identified as a potential BMRPS-related drug that holds promise for personalized treatment in PCa. Conclusion: BMRPS facilitates the prediction of prognosis and resistance to anti-androgens in PCa. It also offers insights into the molecular mechanisms of bone metastasis and aids in drug selection for the treatment of PCa.

Source: Journal of bone oncology

DOI: 10.1016/j.jbo.2025.100673