Identification of immune-related genes in diagnosing hypercholesterolemia with myocardial infarction through bioinformatics analysis

Background Increasing evidence suggests that familial hypercholesterolemia (FHC) exacerbates myocardial infarction (MI). This study aimed to identify possible candidate biomarkers for patients with FHC and MI. Methods The data were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using Limma, while module genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA) in GSE48060. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, protein-protein interaction (PPI) network and CIBERSORT methods were performed to explore the intersection genes. A receiver operating characteristic (ROC) curve were employed to evaluate the diagnostic effectiveness, with validation conducted using datasets GSE61144 and RT-qPCR. Results The FHC datasets included 656 DEGs, while there were 956 DEGs and 90 module genes in MI datasets. There were 49 overlapping DEGs between FHC and MI, which were associated with immune functions. Additionally, immune infiltration analysis revealed disturbances in immune cell populations. There were 13 candiate hub genes were screen after PPI network analysis. MCEMP1 were identified as the real hub genes after the intersection of the candiate hub genes and module genes in FHC and MI. ROC curve analysis indicated high diagnostic ability of MCEMP1 to detect MI in GSE61144 datasets. In addition, RT-qPCR was used to detect MCEMP1 expression in ApoE-/- mice, and the results were consistent with the bioinformatics analysis. Conclusion MCEMP1 were identified and provided new insights into the diagnosis and treatment of FHC with MI.