Identification and validation of efferocytosis-related biomarkers for the diagnosis of atherosclerosis based on bioinformatics and machine learning.

BACKGROUND: Atherosclerosis is a primary contributor to worldwide morbidity and mortality. Failure to timely clear apoptotic cells can trigger a cascade reaction, where the necrotic core expands until the fibrous cap is ruptured, and atherosclerotic plaques become vulnerable. Efferocytosis is an important method for recognizing and eliminating apoptotic cells. Nevertheless, the specific effect of efferocytosis on atherosclerosis remains uncertain. This study aimed to identify and verify the relevant characteristics of efferocytosis for detecting atherosclerosis. METHODS: The data of gene expression patterns of atherosclerosis were sourced from the Gene Expression Omnibus (GEO) database, and the differential expression analyses of efferocytosis-related genes (EFRGs) were performed between the atherosclerosis samples and the control samples. Subsequently, protein-protein interaction (PPI), correlation analysis, and functional enrichment analysis were performed to reveal the interaction between molecules as well as their pathways. Machine learning (ML) was employed to determine hub genes to construct a clinical prediction model. At the same time, immune infiltration, single-cell transcriptome analysis, and cell experiments were conducted in both atherosclerosis and control samples to provide a reference for the immune cell landscape and the cell heterogeneity under this condition. RESULTS: The study revealed that 14 genes were closely related to efferocytosis in atherosclerosis. Among them, an ML model was used to screen 5 potential diagnostic biomarkers, including tumor necrosis factor (TNF), apolipoprotein E (ApoE), neutrophil cytosolic factor 1 (NCF1), triggering receptor expressed on myeloid cells 2 (TREM2), and chitinase-3 like-protein-1 (CHI3L1). Subsequent external validation indicated that, except for TNF, the other 4 genes were all upregulated. From the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, those 5 genes were all significantly associated with various immune cells. Further single-cell RNA sequencing (scRNA-seq) analysis demonstrated that those 5 genes were selectively upregulated in the macrophages of atherosclerosis lesions, which was supported by mRNA levels in cell experiments. CONCLUSIONS: This study clarified the association between atherosclerosis and efferocytosis, and established an effective diagnostic model. Moreover, potential treatment targets for atherosclerosis were identified, offering new insights into the potential mechanism of atherosclerosis.