ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Cancer Cell Biology
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1630708
This article is part of the Research TopicCancer, Metabolism and Kidney Injury: From Molecular Mechanisms to TherapyView all 6 articles
Investigating the Metabolic Reprogramming Mechanisms in Diabetic Nephropathy: A Comprehensive Analysis Using Bioinformatics and Machine Learning
Provisionally accepted
- 1Department of Nephrology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- 2Department of Orthopaedics, Jiujiang University Affiliated Hospital, Jiujiang, China
- 3Department of Nephrology,, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
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Diabetic nephropathy (DN) is a common complication of diabetes, characterized by damage to renal tubules and glomeruli, leading to progressive renal dysfunction. The aim of our study is to explore the key role of metabolic reprogramming (MR) in the pathogenesis of DN.In our study, three transcriptome datasets (GSE30528, GSE30529, and GSE96804) were sourced from the Gene Expression Omnibus (GEO) database. These datasets were integrated for batch effect correction and subsequently subjected to differential expression analysis to identify differentially expressed genes (DEGs) between DN and control samples. The identified DEGs were cross-referenced with genes associated with MR to derive MR associated differentially expressed genes (MRRDEGs). These MRRDEGs underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. To identify key genes and develop diagnostic models, four machine learning algorithms were employed in conjunction with weighted gene co-expression network analysis (WGCNA) and the protein interaction tool CytoHubba. Gene set enrichment analysis (GSEA) and CIBERSORT analysis were conducted on the key genes to assess immune cell infiltration in DN. Additionally, a
Keywords: GEO database, diabetic nephropathy, metabolic reprogramming, bioinformatics, qRT-PCR
Received: 18 May 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 He, Chen, Ye, Wang, Chen and Liu. 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:
Yu Wang, Department of Nephrology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
QinKai Chen, Department of Nephrology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
Siyi Liu, Department of Nephrology,, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
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