Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis

• DOI: 10.1007/s00335-025-10111-5
• Publication Date: 2025-02-14T18:19:16Z
• Authors: Runan Jia; Zhiya Li; Yingying Du; Huixian Liu; Ruirui Liang
• Keywords: Identification; Human genetics

Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect provide new avenues for prompt CAD diagnosis. CAD-related information was obtained from Gene Expression Omnibus datasets GSE66360, GSE113079, GSE59421. We identified 995 upregulated 1086 downregulated differentially expressed genes (DEGs) GSE66360. Weighted gene co-expression network analysis revealed a module 503 relevant to CAD. Using clusterProfiler, we 32 PRFs. Eight candidate were protein-protein interaction network. Machine learning algorithms biomarkers that underwent set enrichment analysis, immune cell with CIBERSORT, microRNA (miRNA) prediction using miRWalk database, transcription factor (TF) level predication through ChEA3, drug DGIdb. Cytoscape visualized miRNA -mRNA- TF, miRNA-single nucleotide polymorphism-mRNA, biomarker-drug networks. IL1B, TLR2, FCGR2A, SYK, FCER1G, HCK as biomarkers. The area under curve diagnostic model based on six > 0.7 GSE66360 GSE113079 datasets. differences their biological pathways. CIBERSORT 10 types between control groups. TF-mRNA-miRNA showed has-miR-1207-5p regulates FCER1G expression RUNX1 SPI important TFs. Ninety-five drugs predicted, including aspirin, which influenced ILIB FCERIG. In study, (IL1B, HCK) related phagocytic regulatory factors identified, relationships further studied, providing deeper understanding pathogenesis, diagnosis, potential treatment strategies