[Transcriptomics study of coronary slow flow disease and verification of differentially expressed genes].

ABSTRACT

Objective: To determine the differentially expressed genes between patients with coronary slow flow (SCF) and healthy controls, as well as to define the signal pathways and protein interactions related to the pathogenesis of the disease. Methods: A total of 43 hospitalized SCF patients in the year of 2018 and 43 healthy subjects, who underwent health checkup in the same year, were enrolled in this study. General data were obtained, blood samples were collected to determine the related indicators of glucose metabolism, lipid metabolism and kidney metabolism. RNA was extracted from blood monocytes, and the differential gene expression profiles were investigated by RNA-Seq. GO function annotation, KEGG pathway enrichment, protein interaction network analysis (PPI) and phenotype analysis were performed. The levels of related cytokines were detected by ELISA, and qPCR was used to verify differentially expressed genes of the two groups. Results: In the SCF group, there were 27 (62.79%) males and 16 females (37.21%), the average age was (54.3±8.8) years. In the control group, there were 29 males (67.44%) and 14 females (31.56%), the average age was (57.2±8.3) years. The percent of smoking history, abnormal fasting blood glucose, abnormal blood lipid levels and body mass index were significantly higher in the SCF group than in the control group (all P<0.01). There were 117 differentially expressed genes between SCF patients and healthy controls, of which 73 were up-regulated and 44 were down-regulated. Biological function analysis of the differentially expressed genes showed that these genes were mainly related to antigen processing and presentation, cell phagocytosis, immunoglobulin, intestinal immune network, Th1 and Th2 cell differentiation and Th17 cell differentiation pathways. The expression levels of inflammatory cytokines IL-6, IL-10, tumor necrosis factor-α and interferon-γ were significantly higher in SCF patients than in healthy controls (all P<0.05). Among the top 12 genes with the most significant differences between the two groups, qPCR analysis indicated consistent results with the transcriptome results in 11 out of 12 genes. PPI analysis showed that FPR2 and THBS3 proteins were at the core of the entire protein interaction network. Conclusion: Genes such as FPR2 and THBS3 may play important roles in the pathogenesis of SCF through immune-related pathways such as antigen processing and presentation and Th17 cell differentiation.