# Identification of biomarkers between coronary artery disease and non-alcoholic steatohepatitis: a combination of bioinformatics and machine learning

## TL;DR

This study identifies shared genes and pathways between coronary artery disease and non-alcoholic steatohepatitis, suggesting potential biomarkers and common mechanisms.

## Contribution

The study combines bioinformatics and machine learning to discover novel shared biomarkers and mechanisms between CAD and NASH.

## Key findings

- BATF3, SOCS2, and GPER are key genes with ROC values above 0.7, validated in external datasets.
- Shared genes are enriched in insulin resistance and inflammation pathways.
- The genes are linked to metabolic, inflammatory, and cardiovascular pathways via GSEA and ssGSEA.

## Abstract

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.

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.

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 2,808 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.

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.