Background Erectile dysfunction (ED) is a frequent complication following radical prostatectomy, significantly affecting patients' quality of life. Traditional predictive methods often struggle to capture complex nonlinear risk factors. This study aims to develop a machine learning-based model to improve ED risk stratification and guide personalized management. Methods A total of 1,147 prostate cancer patients were analyzed, among whom 285 (24.85%) developed postoperative ED. Univariate and multivariate analyses identified age, smoking history, Gleason score, prostate volume, T-stage, surgical approach, operative time, intraoperative bleeding, and PCT levels as independent risk factors (P < 0.05). Machine learning models, including XGBoost, Random Forest, Support Vector Machine, and k-Nearest Neighbors, were trained for ED risk prediction. Key predictors included advanced age, smoking history, Gleason score ≥ 8, prostate volume ≥ 40 ml, T-stage, laparoscopic-assisted surgery, and prolonged operative duration. Results XGBoost exhibited the highest predictive accuracy (AUC: 0.980 in training; 0.960 in validation), outperforming other models. Calibration curves confirmed strong concordance between predicted and actual probabilities, while decision curve analysis demonstrated superior clinical utility, with XGBoost providing the greatest net benefit. Ten-fold cross-validation indicated stable performance (validation AUC: 0.9127 ± 0.0770; test AUC: 0.9592; accuracy: 0.9111), and external validation confirmed model generalizability (AUC: 0.84). SHAP analysis highlighted key risk contributors, enabling individualized risk assessment and targeted clinical interventions. Conclusion The XGBoost model exhibited superior predictive performance and clinical applicability in assessing ED risk after radical prostatectomy, offering a robust tool for personalized postoperative management.

Cytotoxic T cell antigen-4 (CTLA-4) is reportedly involved in the development of bladder cancer (BC). This research was designed to address the potential link between the +49A/G polymorphism in CTLA-4 gene and BC susceptibility. In total, 355 BC cases and 435 match controls from Chinese Han individuals were included eventually. The PCR-RFLR method was utilized to screen for this polymorphism. The +49A/G polymorphism was shown to increase the risk of BC. Subgroup analyses showed that this polymorphism was linked to an increased susceptibility to BC among individuals aged < 60 years, smokers and drinkers. Additionally, this polymorphism significantly correlated with tumor node metastasis and tumor size (≥3 cm). To sum up, this study reveals that the CTLA-4 +49A/G polymorphism could increase the risk of BC in Chinese Han people. Further large cohort studies with enough sample sizes are urgently warranted to verify the findings of this present study.

Bladder cancer (BCa) is a common urinary tract malignancy with frequent recurrences after initial resection. Submucosal injection of gemcitabine prior to transurethral resection of bladder tumor (TURBT) may prevent recurrence of urothelial cancer. However, the underlying mechanism remains unknown. In the present study, ultra‑performance liquid chromatography Q‑Exactive mass spectrometry was used to profile tissue metabolites from 12 BCa patients. The 48 samples included pre‑ and post‑gemcitabine treatment BCa tissues, as well as adjacent normal tissues. Principal component analysis (PCA) revealed that the metabolic profiles of pre‑gemcitabine BCa tissues differed significantly from those of pre‑gemcitabine normal tissues. A total of 34 significantly altered metabolites were further analyzed. Pathway analysis using MetaboAnalyst identified three metabolic pathways closely associated with BCa, including glutathione, purine and thiamine metabolism, while glutathione metabolism was also identified by the enrichment analysis using MetaboAnalyst. In search of the possible targets of gemcitabine, metabolite profiles were compared between the pre‑gemcitabine normal and post‑gemcitabine BCa tissues. Among the 34 metabolites associated with BCa, the levels of bilirubin and retinal recovered in BCa tissues treated with gemcitabine. When comparing normal bladder tissues with and without gemcitabine treatment, among the 34 metabolites associated with BCa, it was observed that histamine change may be associated with the prevention of relapse, whereas thiamine change may be involved in possible side effects. Therefore, by employing a hypothesis‑free tissue‑based metabolomics study, the present study investigated the metabolic signatures of BCa and found that bilirubin and retinal may be involved in the mechanism underlying the biomolecular action of submucosal injection of gemcitabine in urothelial BCa.