Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics analysis.

BACKGROUND

Bladder cancer (BLCA) continues to be a significant cause of cancer mortality in the urinary tract, with therapeutic resistance representing a major barrier to improving patient outcomes. Within the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) are pivotal drivers of BLCA progression, contributing to immune evasion and therapy resistance. This study leverages single-cell analysis to delineate CAF subclusters and explore the immune characteristics of CAFs-based BLCA classification.

MATERIALS AND METHODS

Signal-cell RNA sequencing (scRNA-seq) datasets were used to identify CAF subpopulations in BLCA, and bulk RNA-seq datasets were used to construct CAFs-based BLCA classification. Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.

RESULTS

This study identified CAFs closely associated with BLCA development based on scRNA-seq datasets. Through further systematic clustering and functional analysis of CAFs, we successfully identified 10 distinct CAF sub-clusters, including PSCA+ Pericyte, ISG15+ Pericyte, ACTA2+ Smooth muscle cell (SMC), ACTG2+ SMC, CCL21+ inflammatory Pericyte, CD74+ apCAF, STMN1+ pCAF, CXCL14+ mCAF, APOD+ iCAF, CFD+ iCAF. The study identified four pCAFs-based BLCA distinct subtypes with different molecular, functional, and immunologic characteristics. C3 exhibited an immune-rich subtype accompanied by poor clinical prognosis, cell death pathway enrichment, higher expression of MHC molecules and co-stimulatory/co-inhibitory molecules. Conversely, C4 subtype has a smaller number of patients and an optimal prognosis, associated with lower levels of cell death pathway enrichment, lower frequency of tumor mutations, and an "immune desert" TME. C1 is mainly enriched in metabolism-related pathways, and C2 is mainly enriched in the activation of genome instability pathways, accompanied by more frequent mutations and higher Atezolizumab response. Furthermore, this study identified potential target genes or prognostic markers for each subtype.

CONCLUSION

Various heterogeneous CAF subgroups exist in BLCA, which is closely associated with the development of BLCA. This study identified a promising platform for understanding heterogeneity of CAFs-based BLCA subtypes, providing novel insights into the intricate molecular mechanisms of BLCA. Potential target genes for each subtype provide a basis for diagnosis and screening of BLCA patients.