Seven basement membrane-specific expressed genes are considered potential biomarkers for the diagnosis and treatment of diabetic nephropathy

Aims Diabetic nephropathy (DN) is a diabetes-related chronic vasculitis. DN diminishes kidney function over time and, of course, leads to end stage renal disease in people (ESRD). In spite of the advances in diagnostic and treatment methods for DN, DN continues to impose a significant physical and psychological burden on patients, severely impacting their quality of life, making the hunt for novel therapeutic targets necessary. Methods The Gene Expression Omnibus (GEO) microarray datasets GSE1009, GSE30122, GSE142153, and GSE96804 were downloaded to identify differentially expressed genes (DEGs) in kidney tissues from patients in the DN group and normal controls. These three datasets were examined for genes associated with basement membranes (BMs) with differential gene expression. The target genes were then subjected to gene ontology (GO) annotation and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis. BM-related genes underwent PPI network analysis and screening of the top 10 hub genes, along with immune infiltration analysis and column line graph model development. Finally, we conducted DN therapeutic medication prediction and the creation of something like a miRNA network for genetic markers with BMs. Results Seven candidate BM-related genes (COL4A1, COL4A2, COL6A2, COL6A3, FN1, ITGQ4, and LAMB1) with acceptable helps the healthcare were discovered. Enrichment analysis of diabetes-related genes event occurred the role of biological processes including extracellular matrix organization, extracellular structural organization, and collagen-containing extracellular matrix, as well as the PI3K-Akt signaling pathway and the AGE-RAGE signaling pathway, in diabetic complications. These genes may also be associated in immune cells and autoimmune activities, such as Macrophages and MHC class I, in order to impact the immune process in DN. In the meanwhile, based on these seven BM-related genes, we discovered that Ginsenoside Rh1 was very significant for drug targeting. Conclusions This research identified seven BM-related genes as possible diagnostic and therapeutic biomarkers for DN. Analysis of inflammatory infiltration indicated that these genes may be important in inflammatory processes through Macrophages and MHC class I, hence impacting the course and development of DN illness. The development of a correlated column line graph model for it also shown excellent predictive capabilities. In addition, we have found pharmaceuticals, such as Ginsenoside Rh1, that may provide fresh insights into the personalized management of patients with DN.