多组学分析在尿路上皮癌中识别出三个分子聚类：迈向临床精准的路径

Introduction: Urothelial carcinoma (UC) is a molecularly heterogeneous disease, and transcriptome-based classification systems have given important insights into its biology. The current consensus classifications for UC represent a major step toward biological stratification; however, its prognostic and predictive relevance remains uncertain, limiting use in clinical guidelines. Furthermore, most molecular schemes have been developed either for non-muscle invasive (NMIBC) or muscle-invasive disease (MIBC), relying only on bulk transcriptomic data. This fragmentation hinders comparability across studies and integration with proteomic or single-cell datasets. A simplified molecular framework is therefore needed to capture UC heterogeneity more comprehensively and to support personalized therapeutic approaches. Materials & Methods: Using the TCGA bulk bladder cancer transcriptome dataset, we developed three distinct molecular UC clusters, which were validated using 18 transcriptome, 3 proteome and 33 UC cell line datasets. Making use of in silico predictions, we selected promising treatment strategies, which were further screened in vitro using the IncucyteS3 live-cell imaging system and RNA-sequencing on representative cell lines. Results: Our transcriptomic and proteomic analyses revealed three UC clusters with distinct molecular, biological and clinical features. Each cluster showed specific mRNA and protein expression patterns, metabolic profiles, and driver gene alterations, translating into divergent prognoses and predicted therapeutic sensitivities. Novel approaches, like liquid biopsy-based stratification using ECM-derived urinary peptides or IHC-based profiling of the proposed distinct markers, are currently being investigated. The stroma-rich, high-risk cluster #1 was associated with responsiveness to ferroptosis inducers and PARP inhibition. Cluster #2, which is highly proliferative and immune-infiltrated with basal/squamous traits, showed predicted benefit from cytotoxic agents and inhibition of EGFR or MEK signaling pathways. Cluster #3, dominated by luminal papillary, low-risk tumors with minimal stromal and immune components, appeared susceptible to epigenetic therapies and EGFR/FGFR inhibition. Conclusion: Our new integrative molecular classification scheme provides a practical framework for patient stratification, personalized transcriptome- and proteome-based risk assessment, preclinical research and clinical trial design, including both NMIBC and MIBC.