ArticleScientific reports2025
Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Cross-disease biomarker identification reveals shared diagnostic biomarkers for IVDD and NAFLD via bulk and single-cell RNA sequencing.Frontiers in molecular neuroscience · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD. We downloaded CAD and NAFLD datasets from the Gene Expression Omnibus (GEO) database and analyzed the differentially expressed genes (DEGs) by limma. Protein-protein interaction (PPI) network was constructed with common DEGs (co-DEGs), and hub genes were screened by Maximal Clique Centrality (MCC) algorithm. Candidate biomarkers were selected from intersection of three machine learning algorithms. Expression levels, nomogram, the areas under the receiver operating characteristic curve (AUC) of candidate biomarkers were performed. CIBERSORT algorithm was used to assess the immune cell infiltration, and Spearman's correlations tests were used for calculating the correlation of biomarker genes. A total of 554 overlapping DEGs associated with CAD and NAFLD were obtained by analysis of GSE113079 and GSE89632 datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the co-DEGs were significantly enriched in immune effector process, inflammation response and lipid metabolism. The PPI network generated a 1245-edge network, and top 50 genes were selected using the MCC algorithm. The candidate biomarkers were screened from intersection of machine learning in GSE89632, including CEBPA, CXCL2, JUN and FOXO1. The ROC results showed that these four biomarker genes have good diagnostic value for patients with both CAD and NAFLD. Then we explored the immune landscape, immune infiltration and the correlation between biomarker gene expression in CAD and NAFLD samples. In this study, we predict that CEBPA, CXCL2, JUN and FOXO1 can be used to diagnose CAD and NAFLD. Our study provided new insights for potential biomarkers, molecular mechanism and therapeutic targets for both diseases.
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