Wu Junchao, Chen Ziqi, Wu Wentian, Qin Jiaxuan, Zhong Rongfang, Meng Jialin, Yin Yu, Guo Peng, Fan Song
Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Institute of Urology, Anhui Medical University, Hefei, China.
J Cell Mol Med. 2025 Aug;29(16):e70806. doi: 10.1111/jcmm.70806.
Prostate cancer (PCa) is an extremely heterogeneous cancer and is highly prevalent in the older male population. Since intra-tumour heterogeneity (ITH) commonly results in PCa chemotherapy resistance and recurrence, it is critical to explore its effects on tumour behaviour. Prognostic genes related to ITH were identified, and a signature was constructed using Cox regression analyses and multiple machine learning algorithms. Single-cell RNA sequencing data extracted from PCa and CRPC samples were analysed via sub-clustering, pseudotime, cell communication and drug sensitivity approaches to elucidate their function. The oncogenic potential of hub genes was confirmed by immunohistochemistry and cell proliferation assays. An 11-gene signature underlying a prostate cancer meta-program (PCMP) was generated by selecting an optimal combination of machine learning methods. Survival assays and multivariate Cox regression analyses conducted in multiple cohorts revealed the superior prognostic value of the PCMP signature. Functional enrichment analyses indicated that it dysregulates the cell cycle. Using trajectory and cell-cell communication analyses, we illustrated that PCMP genes exert oncogenic effects by enhancing the proliferation and oxidative phosphorylation of epithelial cells. Intra-cellular assays also demonstrated that CENPA and CKS1B had promising malignant potential. In summary, our research not only establishes the association between the PCMP signature and reveals its malignant characteristics, but also deepens our understanding of the mechanisms underlying PCa progression and ITH. It holds promise for the development of targeted therapeutic interventions, thereby offering clinical benefits to patients.
前列腺癌（PCa）是一种极具异质性的癌症，在老年男性群体中高度流行。由于肿瘤内异质性（ITH）通常会导致PCa化疗耐药和复发，因此探索其对肿瘤行为的影响至关重要。我们鉴定了与ITH相关的预后基因，并使用Cox回归分析和多种机器学习算法构建了一个特征标签。通过亚聚类、伪时间、细胞通讯和药物敏感性分析方法，对从PCa和CRPC样本中提取的单细胞RNA测序数据进行分析，以阐明其功能。通过免疫组织化学和细胞增殖试验证实了枢纽基因的致癌潜力。通过选择机器学习方法的最佳组合，生成了一个基于前列腺癌元程序（PCMP）的11基因特征标签。在多个队列中进行的生存分析和多变量Cox回归分析显示，PCMP特征标签具有卓越的预后价值。功能富集分析表明，它会失调细胞周期。通过轨迹和细胞间通讯分析，我们阐明了PCMP基因通过增强上皮细胞的增殖和氧化磷酸化发挥致癌作用。细胞内试验还表明，CENPA和CKS1B具有潜在的恶性潜能。总之，我们的研究不仅建立了PCMP特征标签之间的关联并揭示了其恶性特征，还加深了我们对PCa进展和ITH潜在机制的理解。它为开发靶向治疗干预措施带来了希望，从而为患者提供临床益处。
