Bioinformatics led discovery of biomarkers related to immune infiltration in diabetes nephropathy

BACKGROUND: The leading cause of end-stage renal disease is diabetic nephropathy (DN). A key factor in DN is immune cell infiltration (ICI). It has been shown that immune-related genes play a significant role in inflammation and immune cell recruitment. However, neither the underlying mechanisms nor immune-related biomarkers have been identified in DNs. Using bioinformatics, this study investigated biomarkers associated with immunity in DN. METHODS: Using bioinformatic methods, this study aimed to identify biomarkers and immune infiltration associated with DN. Gene expression profiles (GSE30528, GSE47183, and GSE104948) were selected from the Gene Expression Omnibus database. First, we identified 23 differentially expressed immune-related genes and 7 signature genes, LYZ, CCL5, ALB, IGF1, CXCL2, NR4A2, and RBP4. Subsequently, protein–protein interaction networks were created, and functional enrichment analysis and genome enrichment analysis were performed using the gene ontology and Kyoto Encyclopedia of Genes and Genome databases. In the R software, the ConsensusClusterPlus package identified 2 different immune modes (cluster A and cluster B) following the consistent clustering method. The infiltration of immune cells between the 2 clusters was analyzed by applying the CIBERSORT method. And preliminarily verified the characteristic genes through in vitro experiments. RESULTS: In this study, the samples of diabetes nephropathy were classified based on immune related genes, and the Hub genes LYZ, CCL5, ALB, IGF1, CXCL2, NR4A2 and RBP4 related to immune infiltration of diabetes nephropathy were obtained through the analysis of gene expression differences between different subtypes. CONCLUSIONS: This study was based on bioinformatics technology to analyze the biomarkers of immune related genes in diabetes nephropathy. To analyze the pathogenesis of diabetes nephropathy at the RNA level, and ultimately provide guidance for disease diagnosis, treatment, and prognosis.