DNA damage response signature-based prognostic genes for intrahepatic cholangiocarcinoma: a combined analysis of machine learning and biological experiments

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive subtype of primary liver cancer with insidious onset, early metastasis, and poor prognosis. DNA damage response (DDR) dysfunction is linked to ICC tumorigenesis, progression, and drug resistance. However, the detailed mechanisms remain underexplored. METHODS: DDR activity was quantified by ssGSEA across 6 independent ICC cohorts. DDR risk model was analyzed using 10 machine learning algorithms to construct 101 combined models, and the optimal Lasso + RSF model was adopted by c-index. The core gene SFN was selected for experimental validation. Single‑cell RNA sequencing and spatial transcriptomics were used to explore cellular localization and tissue distribution of signature genes. In vitro assays were performed in ICC cell lines after SFN knockdown to assess cell proliferation and chemosensitivity. RESULTS: The DDR risk model showed stable prognostic performance in training and external validation cohorts. High‑risk patients exhibited poor prognosis, immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutic agents. SFN was specifically enriched in malignant cells. Knockdown of SFN significantly suppressed cell proliferation and colony formation, enhanced sensitivity to cisplatin and gemcitabine, and increased γ‑H2AX expression indicating elevated DNA double‑strand breaks. CONCLUSIONS: We constructed a robust DDR‑based prognostic model for ICC that enables accurate risk stratification and prediction of therapeutic response. SFN serves as a critical driver of chemoresistance and a potential therapeutic target. These results offer novel biomarkers and mechanistic insights to facilitate precision medicine for ICC patients.