Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

• Published in: PLoS computational biology 2024-05, Vol.20 (5), p.e1012113
• Main Authors: Wang, Meng, Yan, Xinyue, Dong, Yanan, Li, Xiaoqin, Gao, Bin
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; B cells; Biological analysis; Biology and Life Sciences; Biomarkers, Tumor - genetics; Cancer; Cancer therapies; Carcinoma, Hepatocellular - classification; Carcinoma, Hepatocellular - genetics; Care and treatment; Cell cycle; Classification; Computational Biology - methods; Computer and Information Sciences; Copy number; Development and progression; DNA Copy Number Variations - genetics; DNA methylation; Drug therapy; Epigenetics; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic - genetics; Genes; Genetic aspects; Genomics; Genomics - methods; Health aspects; Hepatocellular carcinoma; Hepatoma; Heterogeneity; Humans; Liver cancer; Liver Neoplasms - classification; Liver Neoplasms - genetics; Machine Learning; Medical prognosis; Medicine and Health Sciences; Metabolism; Molecular genetics; Molecular modelling; Multiomics; Mutation; Mutation - genetics; Pathogenesis; Performance prediction; Precision Medicine - methods; Prognosis; Proteins; Technology application; Temsirolimus; Therapeutic targets; Transcriptomics
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012113

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.