A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis

Background: Bladder cancer is a common urological associated high significant morbidity and mortality rates. Immunotherapy has emerged as promising treatment option, although response rates vary among patients. Glycosylation been implicated in tumorigenesis immune regulation. However, our current comprehensive understanding of the role glycosylation bladder its clinical implications limited. Methods: We constructed training cohort based on downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 E-MTAB-1803) from Xiangya hospital, GEO ArrayExpress database were obtained used validation cohorts. To identify glycosylation-related genes with prognosis, univariate Cox regression LASSO performed. A proportional hazards model was then to develop risk score model. The performance assessed using Kaplan-Meier survival curves ROC curves, further validated multiple Results: classified patients into two groups gene expression patterns: Cluster 1 2. Prognostic analysis revealed that 2 had poorer outcomes. also showed higher levels cell presence tumor microenvironment increased activation key steps cycle. developed an independent prognostic ( p < 0.001) it construct accurate prediction nomogram. group exhibited infiltration, enrichment scores therapy-related pathways, tendency towards basal subtype. Conversely, low-risk minimal infiltration tended have luminal These findings consistent real-world cohort. Conclusion: This multi-omics these reliably confirmed heterogeneity tumors, predicted efficacy immunotherapy molecular subtypes, optimizing individual decisions.