Screening and regulatory mechanism exploration of M1 macrophage polarization and efferocytosis-related biomarkers in coronary heart disease

Background Macrophage polarization and efferocytosis have been implicated in CHD. However, the underlying mechanisms remain elusive. This study aimed to identify CHD-associated biomarkers using transcriptomic data. Methods This study examined 74 efferocytosis-related genes (ERGs) and 17 M1 macrophage polarization-related genes (MRGs) across two CHD-relevant datasets, GSE113079 and GSE42148. Differential expression analysis was performed separately on each dataset to identify differentially expressed genes (DEGs1 and DEGs2). The intersection of upregulated and downregulated genes from both sets was then used to define the final DEGs. Subsequently, MRG and ERG scores were calculated within the GSE113079 dataset, followed by weighted gene co-expression network analysis (WGCNA) to identify key module genes. The overlap between these module genes and the DEGs yielded candidate biomarkers, which were further evaluated through machine learning, receiver operating characteristic (ROC) curve analysis, and expression profiling. These biomarkers were subsequently leveraged to explore immune infiltration patterns and to construct a molecular regulatory network. To further validate their expression, quantitative reverse transcriptase PCR (qRT-PCR) was performed on clinical CHD samples, confirming the relevance and expression patterns of these biomarkers in the disease. Results A total of 93 DEGs were identified by intersecting the upregulated and downregulated genes from DEGs1 and DEGs2. WGCNA of the MRG and ERG scores identified 15,936 key module genes in the GSE113079 dataset. Machine learning and ROC analysis highlighted four biomarkers: C5orf58, CTAG1A, ZNF180, and IL13RA1. Among these, C5orf58, and ZNF180 were downregulated in CHD cases, while CTAG1A and IL13RA1 was upregulated. qRT-PCR results validated these findings for C5orf58, CTAG1A, ZNF180, and IL13RA1 showed inconsistent expression trends. Immune infiltration analysis indicated IL13RA1 all had a positive correlation with M0 macrophage, while had a negative correlation with. NK cells activated. The molecular regulatory network displayed that GATA2 and YY1 could regulate CTAG1A and ZNF180. Conclusions These results suggest that C5orf58, CTAG1A, ZNF180, and IL13RA1 serve as biomarkers linking M1 macrophage polarization and efferocytosis to CHD, providing valuable insights for CHD diagnosis and therapeutic strategies.