Machine learning-based identification of core regulatory genes in hepatocellular carcinoma: insights from lactylation modification and liver regeneration-related genes
This work uniquely establishes lactylation as a metabolic-epigenetic bridge linking physiological regenerative pathways to oncogenesis. By leveraging liver regeneration models and machine learning, we propose the identified gene panel as dual-purpose biomarkers for HCC diagnosis and therapeutic targeting, offering new insights into the metabolic-epigenetic regulation of HCC.
Authors
- Cheng‐Yu Chen (ORCID: https://orcid.org/0000-0003-0428-4373)
- Yashan Wang (ORCID: https://orcid.org/0000-0003-2487-3829)
- Mingzheng Hu (ORCID: https://orcid.org/0000-0003-1734-294X)
- Yingying Hou (ORCID: https://orcid.org/0009-0004-3248-2363)
- Yi Li (ORCID: https://orcid.org/0000-0002-9976-518X)
- Xiang Li (ORCID: https://orcid.org/0009-0008-1868-0039)
- Yang Yu (ORCID: https://orcid.org/0000-0001-7283-9663)
- Rongchun Xing
- Yunxi Fu
Institutions
- China Three Gorges University (CN)
- Yichang Central People's Hospital (CN)
- Qiqihar Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-14
- DOI
- https://doi.org/10.3389/fonc.2025.1683704
- Primary Topic
- Cancer, Hypoxia, and Metabolism
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Three Gorges University
- Natural Science Foundation of Hubei Province