Integrative single-cell and machine-learning analysis identifies ac4C-related S100A13 as a causal risk gene in cholangiocarcinoma

BACKGROUND: N4-acetylcytidine (ac4C) is an emerging epitranscriptomic modification that regulates mRNA stability and translation, yet its biological and clinical relevance in cholangiocarcinoma (CCA) remains unclear. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) and multi-cohort bulk transcriptomic data to systematically profile ac4C-related genes (acRGs) in CCA. Using UCell scoring and CellChat analysis, we assessed ac4C activity and intercellular communication within the tumor microenvironment (TME). A comprehensive machine learning framework combining 117 model algorithms was implemented to construct an ac4C-related gene signature (acRGS) for prognostic prediction. Immune contexture, causal inference, and in vivo validation were subsequently performed to elucidate functional relevance. RESULTS: Single-cell analysis revealed that malignant and myeloid populations exhibited the highest ac4C activity and denser ligand-receptor crosstalk. The derived 11-gene acRGS robustly stratified patients into prognostic groups across independent cohorts. High-risk tumors showed elevated checkpoint expression but markedly reduced immune and stromal infiltration, indicating an immune-exhausted yet poorly inflamed TME. Mendelian randomization identified S100A13 and ASPH as causal ac4C-linked risk genes for CCA. Functional experiments confirmed that S100A13 overexpression significantly enhanced tumor growth and proliferation in vivo. CONCLUSIONS: Our integrative framework delineates the transcriptional and immunological consequences of ac4C modification in CCA and identifies S100A13 as a novel ac4C-associated oncogene. The acRGS provides a clinically relevant tool for prognostic assessment and mechanistic insight into RNA acetylation-driven tumor progression.