In Silico Integrative Multi-omics Analysis Reveals Microbiome-host Interaction Networks and Prognostic Microbial Signatures in Bladder Urothelial Carcinoma

Abstract

Bladder urothelial carcinoma (BLCA) is a molecularly heterogeneous malignancy with substantial unmet needs in risk stratification and therapeutic optimization. While the urinary microbiome has emerged as a critical modulator of cancer biology, its systems-level integration with host genomic, transcriptomic, and immune multi-omics data remains poorly characterized. We performed an integrative in silico analysis of 412 muscle-invasive bladder cancers from The Cancer Genome Atlas (TCGA-BLCA), combining curated microbial abundance profiles with host transcriptomic, epigenomic, mutational, immune deconvolution, and clinical survival data. Differential abundance analysis, Spearman correlation networks, Gene Set Enrichment Analysis, and machine-learning-based prognostic modeling were employed to identify microbe-host interaction landscapes and evaluate clinical translational potential. We identified profound microbial dysbiosis in tumor tissues, with Paenibacillus (31.1-fold enrichment, P = 2.56 × 10-6) and Prevotella (19.0-fold enrichment, P = 2.60 × 10-3) dominating the tumor microenvironment, while commensal genera, including Lactobacillus, Arthrobacter, and Gemella, were significantly depleted. Paenibacillus exhibited strong negative correlations with oncogenic drivers MYC (Spearman Correlation Coefficient (SCC) = -0.506), ESR1 (SCC = -0.491), and AR (SCC = -0.458), suggesting tumor-suppressive mechanisms through metabolic and immune modulation. Conversely, Prevotella demonstrated bidirectional modulation of host genes, implicating pro-inflammatory and epithelial-mesenchymal transition pathways. Multi-omics integration revealed that microbial signatures stratified TCGA molecular subtypes, immune phenotypes, and clinical outcomes. A microbiome-informed prognostic model achieved superior predictive accuracy (AUC = 0.847) compared to clinical variables alone, with validation across four independent cohorts (combined HR = 0.65, 95% CI: 0.52-0.81, P < 0.001). This study establishes a comprehensive framework for microbiome-host interactions in BLCA, identifying Paenibacillus and Prevotella as opposing microbial orchestrators of tumor biology. These findings advance bladder cancer microbiome research from descriptive taxonomy toward the development of mechanistic, clinically actionable biomarkers for precision oncology.