Single-cell Analysis Highlights Anti-apoptotic Subpopulation Promoting Malignant Progression and Predicting Prognosis in Bladder Cancer.

Abstract

Backgrounds: Bladder cancer (BLCA) has a high degree of intratumor heterogeneity, which significantly affects patient prognosis. We performed single-cell analysis of BLCA tumors and organoids to elucidate the underlying mechanisms.

Methods: Single-cell RNA sequencing (scRNA-seq) data of BLCA samples were analyzed using Seurat, harmony, and infercnv for quality control, batch correction, and identification of malignant epithelial cells. Gene set enrichment analysis (GSEA), cell trajectory analysis, cell cycle analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis explored the functional heterogeneity between malignant epithelial cell subpopulations. Cellchat was used to infer intercellular communication patterns. Co-expression analysis identified co-expression modules of the anti-apoptotic subpopulation. A prognostic model was constructed using hub genes and Cox regression, and nomogram analysis was performed. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to predict immunotherapy response.

Results: Organoids recapitulated the cellular and mutational landscape of the parent tumor. BLCA progression was characterized by mesenchymal features, epithelial-mesenchymal transition (EMT), immune microenvironment remodeling, and metabolic reprograming. An anti-apoptotic tumor subpopulation was identified, characterized by aberrant gene expression, transcriptional instability, and a high mutational burden. Key regulators of this subpopulation included CEBPB, EGR1, ELF3, and EZH2. This subpopulation interacted with immune and stromal cells through signaling pathways such as FGF, CXCL, and VEGF to promote tumor progression. Myofibroblast cancer-associated fibroblasts (mCAFs) and inflammatory cancer-associated fibroblasts (iCAFs) differentially contributed to metastasis. Protein-protein interaction (PPI) network analysis identified functional modules related to apoptosis, proliferation, and metabolism in the anti-apoptotic subpopulation. A 5-gene risk model was developed to predict patient prognosis, which was significantly associated with immune checkpoint gene expression, suggesting potential implications for immunotherapy.

Conclusions: We identified a distinct anti-apoptotic tumor subpopulation as a key driver of tumor progression with prognostic significance, laying the foundation for the development of new therapeutic strategies to improve patient outcomes.