Bioinformatics-based study on the regulatory network of lipid metabolism-related genes and mechanisms in coronary heart disease.

Abstract

Coronary heart disease (CHD), is a complex cardiovascular disease driven by atherosclerosis, resulting from a dynamic interplay between dysregulated lipid metabolism and chronic inflammation. This study integrates bioinformatics analysis of GEO datasets with experimental validation to dissect molecular mechanisms underlying CHD pathogenesis. A total of 487 differentially expressed genes (DEGs) were identified (including 295 upregulated and 192 downregulated), with hub genes such as CD36, ALDH2, TNF-α, and IL1B highlighted in lipid handling, oxidative stress, and pro-inflammatory cascades. Weighted gene co-expression network analysis (WGCNA) revealed aberrant activation of lipid metabolism-related modules in CHD patients. KEGG enrichment highlighted their involvement in fatty acid transport, cholesterol homeostasis, NF-κB, and the IL-17 signaling. LASSO regression, applied to the combined datasets, identified SERPINA1, and GLUL as diagnostic biomarkers, with in vitro models supporting their pro-atherogenic roles in oxLDL-induced endothelial injury. Animal experiments further validated these findings: CHD rat models exhibited marked upregulation of SERPINA1, and GLUL in myocardial tissue, paralleled by increased M1 macrophage infiltration. Together, this study delineates the intricate lipid-immune axis in CHD and proposes novel candidate biomarkers and therapeutic targets, underscoring their potential for advancing precision medicine in CHD.