Single-cell RNA sequencing identifies potential critical prognostic biomarkers in bladder carcinoma

To identify genetic markers that can predict the prognosis of bladder carcinoma (BC) patients using single-cell RNA sequencing (scRNA-seq). The RNA-seq data for BC were obtained from the GSA-Human database (HRA000212). Quality control and normalization were performed using the Seurat package, followed by identification of highly variable genes. Cell clustering and annotation were based on these genes. Functional enrichment analyses (GO and KEGG) were conducted, along with cell-cell communication and trajectory analysis. Prognostic modeling was performed using LASSO-Cox regression, with Receiver Operating Characteristic (ROC) analysis for risk group evaluation. Gene expression validation was carried out via RT-qPCR and western blotting in normal (SV-HUC-1) and BC cell lines (T24, J82, EJ, UM-UC-3, 5637, RT112). A total of 473 upregulated genes and 106 downregulated genes were identified in BC samples. These differentially expressed genes (DEGs) were significantly enriched in apoptosis-related signaling pathways and IL-17 signaling pathway. Cell-cell communication analysis suggested that the CXCL2/MIF-CXCR2 signaling pathway may mediate interactions between epithelial cells and fibroblasts. By integrating bulk RNA sequencing data, we identified 49 genes that are associated with the prognosis of BC. Using the LASSO-Cox regression model, 17 prognostic genes were selected. Stratification of patients into high- and low-risk groups based on these genes allowed for effective survival prediction, supported by the ROC curve analysis. Univariate and multivariate Cox regression analyses further confirmed that the risk score was an independent predictor of overall survival. Furthermore, IGFBP5, KRT14 and SERPINF1 were found to be linked to poor survival outcomes in BC, with their expression levels notably elevated in BC cell lines compared to a normal bladder cell line. This study identified potential prognostic marker genes for BC, offering valuable insights into the prediction of patient survival outcomes.