He Shan, Chen Yi Wei, Ye Jian, Wang Yu, Chen Qin Kai, Liu Si Yi
Department of Nephrology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Department of Orthopaedics, Jiujiang University Affiliated Hospital, Jiujiang, China.
Front Cell Dev Biol. 2025 Aug 29;13:1630708. doi: 10.3389/fcell.2025.1630708. eCollection 2025.
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 competitive endogenous RNA (ceRNA) network was constructed using the key genes. Finally, the expression levels of core genes in human samples were validated through quantitative real-time PCR (qRT-PCR).
We identified 256 MRRDEGs, highlighting metabolic and inflammatory pathways in DN. KEGG analysis linked these genes to the MAPK signaling pathway, suggesting its key role in DN. Six key genes were pinpointed using WGCNA, PPI, and machine learning, with their diagnostic value confirmed by ROC analysis. CIBERSORT revealed a strong link between these genes and immune cell infiltration, indicating the immune response's role in DN. GSEA showed these genes' involvement in inflammatory and metabolic processes. A ceRNA network was predicted to clarify gene regulation. qRT-PCR confirmed the expression patterns of , and , aligning with bioinformatics results.
Through bioinformatics analysis, a total of six potential MRRDEGs were identified, among which , , could serve as potential biomarkers.
糖尿病肾病（DN）是糖尿病常见的并发症，其特征是肾小管和肾小球受损，导致进行性肾功能障碍。本研究的目的是探讨代谢重编程（MR）在DN发病机制中的关键作用。
在本研究中，三个转录组数据集（GSE30528、GSE30529和GSE96804）来源于基因表达综合数据库（GEO）。对这些数据集进行整合以校正批次效应，随后进行差异表达分析，以鉴定DN样本与对照样本之间的差异表达基因（DEG）。将鉴定出的DEG与与MR相关的基因进行交叉参考，以获得与MR相关的差异表达基因（MRRDEG）。对这些MRRDEG进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。为了鉴定关键基因并建立诊断模型，结合加权基因共表达网络分析（WGCNA）和蛋白质相互作用工具CytoHubba，采用了四种机器学习算法。对关键基因进行基因集富集分析（GSEA）和CIBERSORT分析，以评估DN中的免疫细胞浸润情况。此外，使用关键基因构建竞争性内源性RNA（ceRNA）网络。最后，通过定量实时PCR（qRT-PCR）验证人类样本中核心基因的表达水平。
我们鉴定出256个MRRDEG，突出了DN中的代谢和炎症途径。KEGG分析将这些基因与MAPK信号通路联系起来，表明其在DN中的关键作用。使用WGCNA、PPI和机器学习确定了六个关键基因，其诊断价值通过ROC分析得到证实。CIBERSORT揭示了这些基因与免疫细胞浸润之间的紧密联系，表明免疫反应在DN中的作用。GSEA显示这些基因参与炎症和代谢过程。预测了一个ceRNA网络以阐明基因调控。qRT-PCR证实了 、 和 的表达模式，与生物信息学结果一致。
通过生物信息学分析，共鉴定出六个潜在的MRRDEG，其中 、 、 可作为潜在的生物标志物。
