ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Diabetes: Molecular Mechanisms
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1654401
Comprehensive Bioinformatics and In Vivo Validation Reveal Key Molecular Drivers of Diabetic Nephropathy Progression
Provisionally accepted- 1Hebei Medical University, Shijiazhuang, China
- 2The Second Hospital of Hebei Medical University, Shijiazhuang, China
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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, This is a provisional file, not the final typeset article 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.
Keywords: diabetic nephropathy, Bioinformatics analysis, key genes, Inflammation, machine learning
Received: 26 Jun 2025; Accepted: 19 Aug 2025.
Copyright: © 2025 Yang, He, Liu, Mu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yakun Yang, Hebei Medical University, Shijiazhuang, China
Shuo Wang, Hebei Medical University, Shijiazhuang, China
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