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◆ Discover Oncology2026-08-01· Intrahepatic Cholangiocarcinoma

Construction of a prognostic risk model for lactylation-associated genes in intrahepatic cholangiocarcinoma

Hongyu Xu, Weijie Xiong, Jingli Zhang, Hanzhi Yu

原始摘要(英文原文)· Original abstract
Intrahepatic cholangiocarcinoma (ICC) carries a poor prognosis, and its development is closely associated with metabolic reprogramming. Lactylation, as a key post-translational modification linking cellular metabolism and functional regulation, remains of unclear clinical significance and prognostic value in ICC. Based on TCGA-ICC data, we identified lactate-associated differentially expressed genes and constructed a multi-gene prognostic model using LASSO-Cox regression. The robustness of the model was validated across Kaplan-Meier survival analysis, ROC curves and nomograms. Subsequently, we systematically compared the tumor immune microenvironment, mutation of signature genes, and potential drug sensitivities between high- and low-risk groups. Following the identification of 219 lactate-associated DEGs, we constructed and validated prognostic five-gene signature ( PABPC1 , CCNA2 , MKI67 , RACGAP1 , and CSRP1 ) for ICC that stratified patients into high- and low-risk groups. High risk scores were strongly correlated with poor patient survival. Moreover, regulatory T cells were significantly lower, while PD L1 showed a non-significant decreasing trend in the high-risk group. GSEA revealed that pathways related to tumor proliferation, metastasis, and glycolysis were enriched in the high-risk group. Somatic mutation analysis demonstrated that the high- and low-risk groups were linked to high-frequency mutations in PBRM1 and BAP1 , respectively. Additionally, drug sensitivity analysis suggested increased sensitivity to drugs like Akt and Raf inhibitors in high-risk group. We constructed and validated a novel lactylation-associated prognostic model for ICC. Furthermore, risk score from the model identified two patient subtypes with distinct biological characteristics and therapeutic responses, providing hypothesis-generating insights for refined patient stratification and potential therapeutic decision-making in ICC.
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