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Peer-reviewedCC BY 4.0Open Life Sciences

Characterization of lactylation-related genes for predictive model development and molecular subtyping in HBV-associated hepatocellular carcinoma

  • Jinlian Li1
  • Xipeng Yan1
  • Xinwei Wang1
  • He Xie2
  • Jujun Sun2
  • Qunying Wu3
  • Limin Chen1,2,4
4 affiliations
  1. 1The Joint Laboratory on Transfusion-Transmitted Diseases (TTDs) Between Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Nanning Blood Center, Nanning Blood Center, Nanning, China
  2. 2The Hospital of Xidian Group, Xi’an, China
  3. 3School of Intelligent Medicine and Biotechnology, Guilin Medical University, Guilin, China
  4. 4Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, China
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Abstract

Chronic hepatitis B virus (HBV) infection is the leading risk factor for hepatocellular carcinoma (HCC). Lactylation-related genes (LRGs) participate in HCC progression, yet their roles in HBV-associated HCC (HBV-HCC) remain largely unclear. In this study, we screened differentially expressed LRGs (DE-LRGs) from HBV-HCC transcriptomic data using weighted gene co-expression network analysis (WGCNA) and built a high-precision random forest prediction model. Three hub genes (RACGAP1, STMN1, ILF2) were verified to be overexpressed in HBV-HCC tissues across multiple datasets and quantitative real-time polymerase chain reaction (qPCR), and their high expression correlated with unfavorable overall survival and distinct immune infiltration. A three-gene nomogram was constructed in the GSE47197 cohort, with an area under the curve (AUC) of 0.928 validated by calibration curves, decision curve analysis (DCA) and receiver operating characteristic (ROC) curves. External validation in GSE121248 reached an AUC of 0.965, verifying its robust predictive power. Moreover, consensus clustering divided HBV-HCC into two LRG-based molecular subtypes, where Cluster 2 had worse prognosis. Collectively, this study establishes an LRG-based predictive model and identifies two molecular subtypes for HBV-HCC, providing reliable and promising biomarker candidates for HBV-HCC risk prediction and individualized immunotherapy.

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Cite

BibTeXRIS
BibTeX
@article{li2026characterization,
  title = {{Characterization of lactylation-related genes for predictive model development and molecular subtyping in HBV-associated hepatocellular carcinoma}},
  author = {Jinlian Li and Xipeng Yan and Xinwei Wang and He Xie and Jujun Sun and Qunying Wu and Limin Chen},
  journal = {Open Life Sciences},
  year = {2026},
  month = nov,
  doi = {10.1515/biol-2025-1394},
  url = {https://doi.org/10.1515/biol-2025-1394},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Characterization of lactylation-related genes for predictive model development and molecular subtyping in HBV-associated hepatocellular carcinoma
AU  - Jinlian Li
AU  - Xipeng Yan
AU  - Xinwei Wang
AU  - He Xie
AU  - Jujun Sun
AU  - Qunying Wu
AU  - Limin Chen
PY  - 2026
DA  - 2026/11/06
JO  - Open Life Sciences
DO  - 10.1515/biol-2025-1394
UR  - https://doi.org/10.1515/biol-2025-1394
AB  - Chronic hepatitis B virus (HBV) infection is the leading risk factor for hepatocellular carcinoma (HCC). Lactylation-related genes (LRGs) participate in HCC progression, yet their roles in HBV-associated HCC (HBV-HCC) remain largely unclear. In this study, we screened differentially expressed LRGs (DE-LRGs) from HBV-HCC transcriptomic data using weighted gene co-expression network analysis (WGCNA) and built a high-precision random forest prediction model. Three hub genes (RACGAP1, STMN1, ILF2) were verified to be overexpressed in HBV-HCC tissues across multiple datasets and quantitative real-time polymerase chain reaction (qPCR), and their high expression correlated with unfavorable overall survival and distinct immune infiltration. A three-gene nomogram was constructed in the GSE47197 cohort, with an area under the curve (AUC) of 0.928 validated by calibration curves, decision curve analysis (DCA) and receiver operating characteristic (ROC) curves. External validation in GSE121248 reached an AUC of 0.965, verifying its robust predictive power. Moreover, consensus clustering divided HBV-HCC into two LRG-based molecular subtypes, where Cluster 2 had worse prognosis. Collectively, this study establishes an LRG-based predictive model and identifies two molecular subtypes for HBV-HCC, providing reliable and promising biomarker candidates for HBV-HCC risk prediction and individualized immunotherapy.
SN  - 2391-5412
ER  -