Copyright
©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
Lactate metabolism-driven tumor heterogeneity and molecular signatures in intrahepatic cholangiocarcinoma
An-Ke Wu, Jun-Yi Li, Kai Zhang, Martin Meng, Xue Wang, Yue Liu, Peng Xie, Wei-Qi Rong, Fan Wu, Hong-Guang Wang, Xuan Meng, Jian-Xiong Wu
An-Ke Wu, Kai Zhang, Yue Liu, Peng Xie, Wei-Qi Rong, Fan Wu, Hong-Guang Wang, Xuan Meng, Jian-Xiong Wu, Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Jun-Yi Li, State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Martin Meng, Xue Wang, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, United States
Co-corresponding authors: Xuan Meng and Jian-Xiong Wu.
Author contributions: Wu AK designed and performed the research, analyzed the data, and drafted the manuscript; Li JY and Zhang K contributed to data analysis and figure preparation; Meng M and Wang X assisted in and part of the experimental validation and revision of the manuscript; Liu Y and Xie P assisted in data collection; Rong WQ and Wu F participated in supervision and guidance of study; Wang HG and Meng X obtained funding, supervised the study and provided critical revision of the manuscript; Wu JX conceived the study and provided overall guidance.
Supported by the National Natural Science Foundation of China, No. 82272963 and No. 82473496; Natural Science Foundation of Beijing Municipal, No. 4222058; and Shenzhen Major Scientific and Technological Project, No. KJZD20230923114615031.
Institutional review board statement: This study does not involve any human participants, human samples, or identifiable personal data. All analyses were conducted using publicly accessible datasets through computational methods and in vitro experiments. Therefore, ethical approval statements are not applicable to this manuscript.
Institutional animal care and use committee statement: This study does not involve any animal experiments. All analyses were conducted using publicly accessible datasets through computational methods and in vitro experiments.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Jian-Xiong Wu, MD, Professor, Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan South Lane, Beijing 100021, China.
dr_wujx@163.com
Received: September 9, 2025
Revised: October 22, 2025
Accepted: December 15, 2025
Published online: February 21, 2026
Processing time: 150 Days and 15.3 Hours
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.
Core Tip: This study integrates single-cell and bulk transcriptomic data with machine learning to uncover lactate metabolism-driven heterogeneity in intrahepatic cholangiocarcinoma (ICC). Twelve lactate metabolism-related genes were identified as prognostic biomarkers, and functional validation of CYC1 revealed its role in promoting tumor invasion. These findings provide new insights into ICC progression and offer promising targets for diagnosis and therapy.
