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Background <p>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.</p> Methods <p>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.</p> Results <p>Following the identification of 219 lactate-associated DEGs, we constructed and validated prognostic five-gene signature (<i>PABPC1</i>, <i>CCNA2</i>, <i>MKI67</i>, <i>RACGAP1</i>, and <i>CSRP1</i>) 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 <i>PBRM1</i> and <i>BAP1</i>, respectively. Additionally, drug sensitivity analysis suggested increased sensitivity to drugs like Akt and Raf inhibitors in high-risk group.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Hongyu Xu,
  • Weijie Xiong,
  • Jingli Zhang,
  • Hanzhi Yu

摘要

Background

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.

Methods

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.

Results

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.

Conclusion

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.