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 develop a nomogram prediction model and an online risk calculator,and to predict the continence of patients after robot-assisted laparoscopic radical prostatectomy(RARP).Methods·A total of 604 prostate cancer patients who underwent RARP and had preoperative prostate magnetic resonance imaging at the Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine from September 2022 to December 2024 were analyzed and included.All patients were randomly resampled and divided into a training set(n=420)and a validation set(n=184)at a ratio of 7∶3.The patients'continence was followed up every month from the first month after the operation.The least absolute shrinkage and selection operator(LASSO)model was applied to screen the features.A Logistic multivariate regression analysis was used to establish a prediction model integrating the features selected from the LASSO analysis.The receiver operator characteristic(ROC)curve was drawn to predict the recovery of continence in patients after RARP,and the areas under the curve were compared by the DeLong test to evaluate the discrimination of the model.Calibration curves and decision curve analysis(DCA)were used to evaluate the calibration and clinical utility the model.Results·According to the postoperative continence follow-up data of the patients,the continence rate of the patients at 3 months after the operation was 58.28%(352/604).The length of the membranous urethra,the thickness of the right levator ani muscle,and blood loss were identified as independent predictors of early postoperative(3-month)incontinence by Logistic multivariate regression analysis of the training set.The area under the ROC curve was calculated as 0.976(0.954,0.998)for the training set and 0.977(0.945,1.000)for the validation set,demonstrating good discrimination of this model.No significant difference between the ROC curves of the training set and the validation set was confirmed by the DeLong test(P=0.949).A good goodness of fit of this model was demonstrated by the Hosmer-Lemeshow test(P=0.179).The clinical utility of the nomogram prediction model was indicated by the DCA plot.This nomogram prediction model was incorporated into an online calculator(https://yitingd.shinyapps.io/DynNomapp).Conclusion·This study developed and validated a nomogram prediction model that can effectively predict the early continence of patients after RARP.The length of the membranous urethra,the thickness of the right levator ani muscle,and the intraoperative blood loss are significant independent predictors of early postoperative incontinence.

Objective·To develop a nomogram prediction model and an online risk calculator,and to predict the continence of patients after robot-assisted laparoscopic radical prostatectomy(RARP).Methods·A total of 604 prostate cancer patients who underwent RARP and had preoperative prostate magnetic resonance imaging at the Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine from September 2022 to December 2024 were analyzed and included.All patients were randomly resampled and divided into a training set(n=420)and a validation set(n=184)at a ratio of 7∶3.The patients'continence was followed up every month from the first month after the operation.The least absolute shrinkage and selection operator(LASSO)model was applied to screen the features.A Logistic multivariate regression analysis was used to establish a prediction model integrating the features selected from the LASSO analysis.The receiver operator characteristic(ROC)curve was drawn to predict the recovery of continence in patients after RARP,and the areas under the curve were compared by the DeLong test to evaluate the discrimination of the model.Calibration curves and decision curve analysis(DCA)were used to evaluate the calibration and clinical utility the model.Results·According to the postoperative continence follow-up data of the patients,the continence rate of the patients at 3 months after the operation was 58.28%(352/604).The length of the membranous urethra,the thickness of the right levator ani muscle,and blood loss were identified as independent predictors of early postoperative(3-month)incontinence by Logistic multivariate regression analysis of the training set.The area under the ROC curve was calculated as 0.976(0.954,0.998)for the training set and 0.977(0.945,1.000)for the validation set,demonstrating good discrimination of this model.No significant difference between the ROC curves of the training set and the validation set was confirmed by the DeLong test(P=0.949).A good goodness of fit of this model was demonstrated by the Hosmer-Lemeshow test(P=0.179).The clinical utility of the nomogram prediction model was indicated by the DCA plot.This nomogram prediction model was incorporated into an online calculator(https://yitingd.shinyapps.io/DynNomapp).Conclusion·This study developed and validated a nomogram prediction model that can effectively predict the early continence of patients after RARP.The length of the membranous urethra,the thickness of the right levator ani muscle,and the intraoperative blood loss are significant independent predictors of early postoperative incontinence.

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.