ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1573621
Identification of biomarkers between coronary artery disease and nonalcoholic steatohepatitis: A combination of bioinformatics and machine learning
Provisionally accepted
- Zhejiang Chinese Medical University, Hangzhou, China
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Background: Non-alcoholic steatohepatitis (NASH) commonly complicates coronary artery disease (CAD), yet the interaction mechanism remains unclear. Our research seeks to investigate the common mechanisms and key signature genes between CAD and NASH. Methods: RNA sequence information for CAD and NASH was screened from the GEO database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene analysis identified key genes, followed by functional enrichment analysis of these shared genes. Three machine learning methods-LASSO, random forest, and SVM-RFE-were used to identify signature genes. Gene set enrichment analysis (GSEA) was then performed to explore potential mechanisms associated with the signature genes. In addition, single-sample gene set enrichment analysis (ssGSEA) evaluated immune infiltration in CAD and NASH and its correlation with the signature genes. Results: WGCNA has revealed two key modules for CAD and NASH. The intersection of the CAD modules and their differential genes narrowed the key genes down to 2808 shared genes. Finally, 44 shared genes were selected for both CAD and NASH. Kyoto Encyclopedia of Genes and Genomes analysis showed that these genes were primarily enriched in insulin resistance and inflammation pathways. Machine learning identified the signature genes BATF3, SOCS2, and GPER, all with ROC values above 0.7, validated in external datasets. GSEA revealed that these genes act through common mechanisms in CAD and NASH, regulating metabolic, inflammatory, and cardiovascular pathways. In addition, ssGSEA suggested their involvement in immune cell infiltration. Conclusion: BATF3, SOCS2, and GPER have emerged as promising gene candidates that may serve as biomarkers or potential therapeutic targets for CAD combined with NASH, linked to the regulation of metabolic, inflammatory, and cardiovascular pathways. We also identified insulin resistance and inflammation pathways as common mechanisms underlying both diseases.
Keywords: Coronary Artery Disease, non-alcoholic steatohepatitis, machine learning, WGCNA, bioinformatics
Received: 10 Feb 2025; Accepted: 09 Jul 2025.
Copyright: © 2025 Lin, Song and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaohong Li, Zhejiang Chinese Medical University, Hangzhou, China
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