Comprehensive bioinformatics and

Background: Diabetic nephropathy is a leading cause of end-stage renal disease worldwide, characterized by progressive glomerulosclerosis, chronic inflammation, and extracellular matrix (ECM) accumulation. Despite advances in clinical management, the underlying molecular mechanisms remain incompletely understood, and reliable biomarkers for early diagnosis and targeted therapy are still lacking. Methods: To identify candidate molecular genes associated with DN, we conducted an integrative bioinformatics analysis combining transcriptomic profiling, weighted gene co-expression network analysis, protein-protein interaction network construction, and machine learning-based feature selection. The biological relevance of candidate genes was validated using human renal biopsy specimens and two diabetic mouse models. Gene set enrichment analysis was performed to uncover associated functional pathways. Results: Four genes-COL1A2, CD163, FN1, and CCL2-were consistently upregulated in both human and murine DN samples. These genes are closely associated with immune activation, ECM remodeling, and chronic inflammation. GSEA revealed their significant enrichment in pathways such as NOD-like receptor signaling, ECM-receptor interaction, and T/B cell receptor signaling, highlighting their potential roles in DN pathogenesis. Experimental validation confirmed elevated expression of these genes at both mRNA and protein levels. Conclusion: Our study identifies COL1A2, CD163, FN1, and CCL2 as key molecular signatures involved in the immunoinflammatory and fibrotic progression of diabetic nephropathy. These genes hold promise as potential biomarkers and therapeutic targets, offering novel insights into the molecular mechanisms and clinical management of DN.