Lactate metabolism-driven tumor heterogeneity and molecular signatures in intrahepatic cholangiocarcinoma

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BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive liver malignancy with limited therapeutic options and poor prognosis. Recent evidence indicates that lactate metabolism (LM) plays a pivotal role in tumor metabolic reprogramming, immune evasion, and disease progression; however, the heterogeneity and regulatory mechanisms of LM activity within ICC remain largely undefined. AIM To systematically characterize LM-driven heterogeneity and its molecular and functional implications in ICC. METHODS Single-cell RNA sequencing and bulk transcriptomic datasets were integrated to characterize LM heterogeneity in ICC. High-dimensional weighted gene co-expression network analysis and multiple machine-learning algorithms (least absolute shrinkage and selection operator, random forest, gradient boosting machine, adaptive best subset selection, and decision tree) were employed to identify LM-associated feature genes. CytoTRACE and CellChat analyses were used to assess differentiation potential and intercellular communication among malignant epithelial subpopulations. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses were performed to elucidate biological functions. A random forest model combined with SHapley Additive exPlanation (SHAP) interpretability analysis identified the most predictive LM-related gene. Functional assays, including quantitative polymerase chain reaction, cell counting kit-8, colony formation, wound-healing, and transwell experiments, were conducted to validate CYC1 in ICC cell lines. RESULTS Malignant ICC cells were stratified into three LM-activity subtypes (high, intermediate, and low) exhibiting distinct transcriptional programs and differentiation trajectories. Twelve LM-associated feature genes GPX3 , CYC1 , NME1 , GSTP1 , MGST1 , ALDH3A1 , TALDO1 , SNRPB , TKT , NAA20 , G6PD , and RPL13A were identified as key molecular markers linked to aggressive phenotypes and poor prognosis. Among them, CYC1 showed the highest predictive accuracy (area under the curve = 0.844) and strongest model contribution (SHAP = 0.091), marking it as the principal LM-related driver gene. Functional experiments confirmed that CYC1 knockdown significantly suppressed ICC cell proliferation, migration, and invasion, validating its oncogenic role in promoting malignant progression. CONCLUSION This integrative single-cell and machine-learning study delineates the molecular heterogeneity of LM in ICC and identifies twelve feature genes linking LM with tumor aggressiveness. These findings provide novel insight into LM-driven oncogenic mechanisms and propose CYC1 and other LM-associated genes as potential biomarkers and therapeutic targets for ICC.

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