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.

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.