Single-cell transcriptomics identifies SOCS3+ exhausted T cells as a biomarker facilitating clear cell renal cell carcinoma progression.

Clear cell renal cell carcinoma (ccRCC), a prevalent renal malignancy with limited early symptoms, demonstrates immunotherapy sensitivity in advanced stages. This study investigates how exhausted T cells drive ccRCC progression through single-cell transcriptomics. Re-analysis of a single-cell transcriptomic dataset was performed through dimensionality reduction and clustering to annotate cells into distinct populations, followed by comprehensive characterization of cellular composition and subset-specific features. Prognostically relevant genes were identified via differential gene expression (DGE) analysis, and a nomogram prediction model was constructed using Cox regression analysis, with validation through Kaplan-Meier (KM) survival curves and risk score plots. Functional annotation of SOCS3+ exhausted T cells was achieved via gene ontology (GO) and reactome pathway enrichment analyses. Cell-cell communication networks involving SOCS3+ exhausted T cells were delineated using ligand-receptor interaction profiling. Single-cell transcriptomic data were annotated into nine distinct cellular populations, among which SOCS3+ exhausted T cells demonstrated significant prognostic relevance. A nomogram prediction model incorporating SOCS3 and N4BP1 effectively stratified patients into low- and high-risk groups with superior prognostic predictive power compared to conventional parameters. Cell-cell communication analysis revealed that SOCS3+ exhausted T cells interact with myeloid cells through the MIF signaling pathway. Integrated single-cell transcriptomic analysis demonstrates that SOCS3+ exhausted T cells promote tumor resistance to cytotoxic killing and serve as a robust prognostic biomarker in ccRCC patients.