Integrative multi-cohort analysis identifies urinary microbiota as non-invasive biomarkers of bladder cancer

BACKGROUND: Bladder cancer (BC) is a prevalent urological malignancy where early detection is critical. However, traditional diagnostics like cystoscopy are invasive, and non-invasive methods perform suboptimally, especially in low-grade tumors. Emerging evidence links urinary microbiome dysbiosis to BC, yet its diagnostic potential remains unclear. RESULTS: Reanalyzing 388 public 16S rRNA sequencing data and 65 in-house samples, we identified alterations in microbial composition and microbial-derived metabolism in BC. Six genera exhibited significant differential abundance: Anaerococcus, Fenollaria, and Finegoldia were enriched in BC, while Bacteroides, Muribaculaceae, and Escherichia-Shigella were depleted. Functional analysis indicated microbial metabolic perturbations, particularly in nucleoside/nucleotide metabolism and vitamin biosynthesis. qRT-PCR further validated changes in three nucleoside/nucleotide metabolism-related genes, including purL, adk, and pyrB. Leveraging these findings, we developed a classification model incorporating 20 urinary bacterial biomarkers, achieving an area under the curve (AUC) of 0.86 in the discovery cohort and demonstrating consistent performance in three independent validation cohorts (AUCs = 0.70, 0.75, 0.79). Notably, these biomarkers maintained diagnostic efficacy in low-grade tumors (AUC = 0.80) and exhibited high disease specificity. CONCLUSIONS: This study presents an externally validated urinary microbiota-based diagnostic model for BC, providing both translational biomarkers and novel insights into microbiome-oncogenesis interactions.