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.