RPA3 and RFC3 as biomarkers for myocardial infarction diagnosis under computed tomography angiography

Computed tomography angiography (CTA) technology holds significant value in the diagnosis and management of acute myocardial infarction. The severity of myocardial infarction lies in its potential to cause ischemic death of myocardial tissue, severely impacting cardiac function, and even posing a threat to life. However, the molecular mechanisms underlying acute myocardial infarction diagnosed by CTA remain unclear. We utilized CTA technology for coronary artery imaging, collecting whole blood samples from confirmed myocardial infarction patients and control patients. The "limma" R package was used for probe summarization and background correction of GSE229044. Weighted gene co-expression network analysis (WGCNA) was performed to identify significant module. gene set enrichment analysis and Metascape database provides comprehensive gene list annotation and analysis resources, with visualization export capabilities. Additionally, we calculated the top 10 genes using 3 algorithms (Maximal Clique Centrality, Density of Maximum Neighborhood Component, EcCentricity) and obtained their intersection, visualizing and exporting the core gene list after visualization. In order to explore the further function of hub genes, comparative toxicogenomics database Analysis and Immune Infiltration Analysis were implied. Through R software, we ultimately identified 827 differentially expressed genes. According to the gene ontology analysis, they were mainly enriched in processes such as organic acid metabolic processes, chromosomal centromeric regions, and oxidoreductase activity. In the Kyoto encyclopedia of genes and genomes analysis, they were primarily concentrated in metabolic pathways, the P53 signaling pathway, and the PPAR signaling pathway. Ultimately, we obtained 5 core genes (PCNA, RPA3, RPA1, RFC2, RFC3). Core genes (RPA3, RFC3) were highly expressed in myocardial infarction samples and lowly expressed in normal samples. Furthermore, we analyzed the correlation between infiltrating immune cells and obtained a co-expression pattern diagram of immune cell components, indicating that when NK_cells_activated expression was high, T_cells_CD4_naive expression was also high. A highly positive correlation between NK_cells_activated and T_cells_CD4_naive expression levels may affect the progression of myocardial infarction. The RPA3 and RFC3 genes may serve as core biomarkers for myocardial infarction diagnosed by CTA technology.