Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy

• Published in: Journal of translational medicine Vol. 21; no. 1; pp. 490 - 17
• Main Authors: Yan, Meng, Li, Wenwen, Wei, Rui, Li, Shuwen, Liu, Yan, Huang, Yuqian, Zhang, Yunye, Lu, Zihao, Lu, Qian
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 21.07.2023; BioMed Central; BMC
• Subjects: Analysis; Apoptosis; Care and treatment; Cell death; Clustering; Correlation analysis; Cytokines; Datasets; Diabetes; Diabetes mellitus; Diabetic nephropathies; Diabetic nephropathy; Diagnosis; Drug delivery; Drugs; Drug–gene prediction; Enrichment analysis; Gene expression; Genetic aspects; Genomes; Health aspects; Immune response; Immunosuppressive agents; Inflammation; Kidney diseases; Lasso regression; Microvasculature; Nephropathy; Oxidative stress; Pathogenesis; Prediction models; Protein-protein interactions; Pyroptosis; Regression analysis; Signal transduction; China; Diabetic nephropathy; Pyroptosis; Enrichment analysis; Lasso regression; Drug–gene prediction
• DOI: 10.1186/s12967-023-04350-w
• ISSN: 1479-5876

Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN. DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein-protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes. A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes. We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN.