Resumen de RNA-Seq Analysis of ceRNA-Related Networks in the Regulatory Metabolic Pathway of Mice with Diabetic Nephropathy Subjected to Empagliflozin Intervention
Ting Wu, Zhiqin Zhang, Haowen Huang, Xiaoyan Wu
Objective: We conducted bioinformatics analysis of the gene chip data of empagliflozin for diabetic nephropathy (DN). The differentially expressed genes (DEGs) between DN and control mice and between DN and DN treated with empagliflozin (DNE) mice were screened to explore the related metabolic pathogenesis and predict the potential competing endogenous RNA (ceRNA)-related networks’ metabolic mechanism of the empagliflozin effect on DN.
Methods: The intersection of DEGs in mice between the control and DN groups and between the DN and DNE groups was selected. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were performed, and the metabolic items involving the most genes in the coregulation were considered. A protein-interaction network was constructed with the STRING website. Cytoscape software and its plug-ins were utilised to analyse the hotspot differential genes. The noncoding RNAs in which the differential genes may play a role were obtained from the miRanda, miRDB, and TargetScan databases to establish network diagrams.
Results: Analysis of the diabetes and control groups showed that 424 genes were upregulated and 354 were downregulated. In the analysis of DEGs between the DN and diabetic groups, the comparison between the diabetic and empagliflozin groups showed that 430 genes were upregulated and 84 were downregulated. The co-downregulated enrichment results were primarily reflected in various metabolic disorders, including glucose metabolism, lipid metabolism, amino acid metabolism, and others. The co-upregulated genes were associated with the inflammatory response, apoptosis, and cell senescence. This finding indicated that empagliflozin may inhibit the progression of diabetic nephropathy by inhibiting inflammation, apoptosis, and senescence. The key genes and related mechanisms of noncoding RNA were determined through Cytoscape analysis and the prediction of common DEGs in metabolic items.
Conclusions: The analysis of DEGs and key core genes in this study enhanced our understanding of the effect of empagliflozin on the pathogenesis of DN and provided more potential gene targets and application ideas for DN treatment.
