Integrated bioinformatics analysis and in vivo validation of potential immune-related genes linked to diabetic nephropathy.

Background: Diabetic nephropathy (DN) is a common microvascular complication of diabetes mellitus and the main cause of chronic renal failure. This study explored the potential immunomodulation-related genes (IRGs) in DN using bioinformatics.

Methods: IRGs were identified using GeneCards, and differentially expressed genes were identified using the GSE99339, GSE96804, and GSE30122 datasets. We conducted enrichment analyses using Gene Ontology, gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes to identify the associated signaling pathways. Prognostic models were constructed using Least Absolute Shrinkage and Selection Operator regression. The predictive power of IRGs was evaluated using receiver operating characteristic (ROC) curves. Furthermore, we utilized ssGSEA to determine the relative abundance of immune cell infiltration. The expression of five significant IRGs was further validated using immunohistochemistry (IHC) in individuals with DN and real-time PCR (RT-PCR) in animal experiments.

Results: In total, 17 immunomodulation-related differentially expressed genes were identified, which were enriched in immune-associated pathways and inflammation. GSEA unveiled substantial enrichments in metabolic irregularities and the structural composition of the extracellular matrix. ROC analysis results revealed that the diagnostic efficacy of and was comparatively high. Notably, we identified potential IRGs for DN, including , , and , using IHC and RT-PCR.

Conclusions: and are potential IRGs in patients with DN. Our findings may offer a theoretical basis for developing more focused and innovative immunotherapy approaches for patients with DN.