目的·探讨M2巨噬细胞相关基因在前列腺癌（prostate cancer，PCa）中的预后评估价值，以便更准确地预测患者预后并实现个性化治疗。方法·从TCGA数据库下载PCa的普通转录组测序（RNA sequencing，RNA-seq）数据，从GEO数据库获取PCa的单细胞RNA测序（single-cell RNA sequencing，scRNA-seq）数据。通过CIBERSORTx算法评估TCGA样本中的免疫浸润情况，使用FindMarkers功能识别scRNA-seq数据中的差异基因并鉴定免疫细胞亚型，通过基因集富集分析（Gene Set Enrichment Analysis，GSEA）和CellChat算法探究M2巨噬细胞参与通路以及与周围细胞的相互作用情况。最后筛选出M2巨噬细胞特征基因，通过单变量Cox回归和LASSO分析构建PCa预后模型，并基于该模型进行患者临床病理特征分析、免疫抑制与耐药性分析、药物敏感性分析。结果·分析TCGA样本发现，M2巨噬细胞高度浸润的PCa患者显著表现出更低的无进展生存期（progression-free survival，PFS）。分析scRNA-seq发现，肿瘤微环境中的细胞可以分为多个亚群，M2巨噬细胞可以与肿瘤微环境中多种免疫细胞相互作用，促进免疫抑制性微环境的形成，进而发挥促肿瘤关键作用。基于此构建了PCa风险评分模型，模型中包含TREM2、OTOA、SIGLEC1和PLXDC1 4个基因，在测试集与验证集中均表现出良好的预测性能。高风险评分患者的肿瘤微环境呈现免疫抑制特征，并伴有雄激素受体（androgen receptor，AR）信号通路活性降低，同时他们表现出更差的临床分期与病理分级水平，进而导致更差的预后结果。基于药物预测与药物敏感性分析，最终筛选出6种治疗药物对高风险评分患者疗效更佳。结论·基于PCa肿瘤微环境中M2巨噬细胞相关基因构建了一个风险预后模型，为PCa的精准治疗提供了理论基础。

Objective To explore the prognostic value of M2 macrophage-related genes in prostate cancer (PCa), aiming to predict tumor prognosis more accurately and enable personalized treatment. Methods ·RNA sequencing (RNA-seq) data of PCa were downloaded from The Cancer Genome Atlas (TCGA) database, and single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database. The immune infiltration of TCGA samples was assessed using the CIBERSORTx algorithm. Differential genes in scRNA-seq data were identified using the FindMarkers function, and immune cell subtypes were characterized. M2 macrophage-related pathways and interactions with surrounding cells were explored through Gene Set Enrichment Analysis (GSEA) and the CellChat algorithm. M2 macrophage signature genes were selected to construct a prognostic model for PCa using univariate Cox and LASSO analyses. Based on the risk model, clinical characteristics, immune suppression, drug resistance, and drug sensitivity analyses were conducted. Results ·In TCGA samples, patients with high M2 macrophage infiltration exhibited significantly lower progression-free survival (PFS). scRNA-seq analysis identified multiple subpopulations of tumor microenvironment (TME) cells. M2 macrophages interacted with various immune cells in TME, contributing to an immunosuppressive microenvironment and playing a key role in tumor promotion. Based on these findings, a PCa risk model was developed, incorporating TREM2, OTOA, SIGLEC1, and PLXDC1, which showed robust predictive performance in both training and validation cohorts. Patients with higher risk scores demonstrated a more immunosuppressive TME, decreased androgen receptor (AR) signaling activity, and worse clinical characteristics, leading to poorer outcomes. Drug prediction and sensitivity analyses identified six potential therapeutic agents that may offer improved efficacy for patients with higher risk scores. Conclusion ·A prognostic model based on M2 macrophage-related genes in the TME has been constructed, providing a theoretical foundation for precision treatment in PCa.