Luo Yu, Deng Xiaoqi, Wei Chengcheng, Liu Zhangcheng, Song Liangdong, Han Kun, Li Yunfan, Zhang Jindong, Su Shuai, Wang Delin
Department of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Department of Nephrology, Zigong Fourth People's Hospital, Zigong, Sichuan Province 643000, China.
J Bone Oncol. 2025 Mar 20;52:100673. doi: 10.1016/j.jbo.2025.100673. eCollection 2025 Jun.
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.
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.
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.
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.
前列腺癌（PCa）常转移至骨骼，带来重大临床挑战。本研究旨在构建PCa骨转移相关风险模型。
通过使用GSE32269和GSE77930数据集，结合差异基因表达分析和加权基因共表达网络分析（WGCNA）来鉴定骨转移相关基因（BMRGs）。采用一致性聚类分析确定这些基因在PCa分子亚型中的意义。利用LASSO - Cox回归分析构建骨转移相关预后基因特征（BMRPS）。使用ROC曲线、Kaplan - Meier生存曲线和预测列线图评估BMRPS的预测性能。使用CIBERSORT、ESTIMATE和xCell算法分析亚组的免疫景观异质性。进行药物敏感性和分子对接分析以鉴定与BMRPS相关的药物。
鉴定出44个与PCa预后相关的BMRGs。一致性聚类揭示了这些基因在将PCa分为三个不同预后簇中的关键作用。由14个BMRGs组成的BMRPS对预后具有出色的预测准确性，并作为PCa的独立预后因素。BMRPS有效预测了骨转移性PCa的总生存期，并区分了骨转移与其他转移类型。BMRPS与免疫景观和免疫治疗反应生物标志物密切相关。此外，BMRPS与抗雄激素耐药性相关，且AZD8186被鉴定为一种潜在的与BMRPS相关的药物，有望用于PCa的个性化治疗。
BMRPS有助于预测PCa的预后和抗雄激素耐药性。它还为骨转移的分子机制提供了见解，并有助于PCa治疗的药物选择。
