Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment
Open accessAbstract
The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.
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Proteogenomic studies in hepatocellular carcinoma have identified three molecular classes: immune-hot with favorable outcomes, proliferative and TP53-enriched with angiogenic signaling and poorer outcomes, and CTNNB1-enriched with mTOR-pathway activation.
“In hepatocellular carcinoma, for example, proteogenomic studies integrating whole-genome sequencing, RNA sequencing, and quantitative proteomics have identified three molecular classes: immune-“hot” with favorable outcomes, proliferative and TP53-enriched with angiogenic signaling and poorer outcomes, and CTNNB1-enriched with mTOR-pathway activation [88,89,90].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [88]
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- Authors
- Wang, M., Yan, X., Dong, Y., Li, X., & Gao, B.
- Affiliations
- Beijing University of Technology (CN)
- Corresponding author
- Li, Xiaoqin
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-05-10
- Volume
- 20
- Issue
- 5
- Pages
- e1012113
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC11230636
- OpenAlex
- W4396789799
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- 78
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- Funders
- National Key Research and Development Program of China (2022YFC2704804)
- National Natural Science Foundation of China (61931013)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- hepatocellular carcinoma, molecular subtypes, driver genes, multi-omics integration, tumor heterogeneity, prognostic model, TTK, therapeutic target, copy number variation, epigenomics, single-cell data, precision medicine
- MeSH terms
- Machine Learning, Multiomics, Algorithms, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Mutation, Prognosis, Biomarkers, Tumor, Gene Expression Regulation, Neoplastic, Computational Biology, Gene Expression Profiling, Genomics, DNA Copy Number Variations, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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