Identification of Metabolic Syndrome-Related miRNA-mRNA Regulatory Networks and Key Genes Based on Bioinformatics Analysis

Abstract

Metabolic syndrome, which affects approximately one-quarter of the world's population, is a combination of multiple traits and is associated with high all-cause mortality, increased cancer risk, and other hazards. It has been shown that the epigenetic functions of miRNAs are closely related to metabolic syndrome, but epigenetic studies have not yet fully elucidated the regulatory network and key genes associated with metabolic syndrome. To perform data analysis and screening of potential differentially expressed target miRNAs, mRNAs and genes based on a bioinformatics approach using a metabolic syndrome mRNA and miRNA gene microarray, leading to further analysis and identification of metabolic syndrome-related miRNA-mRNA regulatory networks and key genes. The miRNA gene set (GSE98896) and mRNA gene set (GSE98895) of peripheral blood samples from patients with metabolic syndrome from the GEO database were screened, and set|logFC|> 1 and adjusted P < 0.05 were used to identify the differentially expressed miRNAs and mRNAs. Differentially expressed miRNA transcription factors were predicted using FunRich software and subjected to GO and KEGG enrichment analysis. Next, biological process enrichment analysis of differentially expressed mRNAs was performed with Metascape. Differentially expressed miRNAs and mRNAs were identified and visualized as miRNA-mRNA regulatory networks based on the complementary pairing principle. Data analysis of genome-wide metabolic syndrome-related mRNAs was performed using the gene set enrichment analysis (GSEA) database. Finally, further WGCNA of the set of genes most closely associated with metabolic syndrome was performed to validate the findings. A total of 217 differentially expressed mRNAs and 158 differentially expressed miRNAs were identified by screening the metabolic syndrome miRNA and mRNA gene sets, and these molecules mainly included transcription factors, such as SP1, SP4, and EGR1, that function in the IL-17 signalling pathway; cytokine-cytokine receptor interaction; proteoglycan syndecan-mediated signalling events; and the glypican pathway, which is involved in the inflammatory response and glucose and lipid metabolism. miR-34C-5P, which was identified by constructing a miRNA-mRNA regulatory network, could regulate DPYSL4 expression to influence insulin β-cells, the inflammatory response and glucose oxidative catabolism. Based on GSEA, metabolic syndrome is known to be closely related to oxidative phosphorylation, DNA repair, neuronal damage, and glycolysis. Finally, RStudio and DAVID were used to perform WGCNA of the gene sets most closely associated with metabolic syndrome, and the results further validated the conclusions. Metabolic syndrome is a common metabolic disease worldwide, and its mechanism of action is closely related to the inflammatory response, glycolipid metabolism, and impaired mitochondrial function. miR-34C-5P can regulate DPYSL4 expression and can be a potential research target. In addition, UQCRQ and NDUFA8 are core genes of oxidative phosphorylation and have also been identified as potential targets for the future treatment of metabolic syndrome.

Keywords: Bioinformatics; GSEA; Metabolic syndrome; mRNA; miRNA.