scRNA-seq reveals epithelial heterogeneity in bladder cancer and establishes a cancer stem cell prognostic model

Background Bladder cancer (BLCA) is a common malignancy with increasing incidence globally. Epithelial cells play crucial roles in tumor development and metastasis. Single-cell RNA sequencing (scRNA-seq) enables investigation of cellular heterogeneity. This study aims to analyze epithelial cell heterogeneity in BLCA, identify biomarkers, and develop prognostic models using machine learning to explore their clinical significance. Methods We integrated scRNA-seq and bulk RNA-seq data from TCGA and GEO databases, including muscle-invasive bladder cancer (MIBC), non-muscle-invasive bladder cancer (NMIBC), and adjacent tissue samples. Data processing included Seurat clustering, Harmony batch correction, copy number variation analysis, KEGG enrichment analysis, CellChat intercellular communication analysis, and Monocle3 trajectory analysis. A cancer stem cell-related prognostic index (CSCRPI) was constructed using four machine learning algorithms based on cancer stem cell (CSC) subpopulation marker genes. Results We identified 39 clusters, 9 cell types, and 11 epithelial cell subtypes, including a cancer stem cell subpopulation (Epi9_CSC). Epi9_CSC was highly enriched in MIBC and promoted invasion and recurrence. Pathway analysis revealed that Epi9_CSC secretes collagen proteins that interact with integrin and CD44 receptors, activating downstream signaling pathways including Focal adhesion, PI3K-Akt, NF-κB, and MAPK pathways. Drug sensitivity analysis identified AZD1208, IAP inhibitors, and Nutlin-3a as potential therapeutic agents targeting Epi9_CSC. The CSCRPI scoring system, based on 5 key feature genes from Epi9_CSC, accurately predicted BLCA patient prognosis and provided clinical guidance. Conclusions This study identified Epi9_CSC as a highly invasive cancer stem cell subpopulation that drives BLCA malignancy through extracellular matrix remodeling and multiple oncogenic pathway activation. The CSCRPI system offers valuable prognostic insights and potential therapeutic targets for precision medicine in BLCA treatment.