Li Ting, Li Li, Sun Zijuan, Zeng Huijuan, He Guoyong, Tian Zhong, Chen Dong, Li Jun
Department of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
Department of Nephrology, Kunming First People's Hospital, Kunming, 650000, China.
Sci Rep. 2025 Aug 7;15(1):28980. doi: 10.1038/s41598-025-11097-5.
Diabetic nephropathy (DN) is a kidney disease. Mitochondrial and endoplasmic reticulum stress (ERS) significantly contribute to diabetic nephropathy (DN), although the precise mechanisms involved have not yet been fully understood. The objective of this research was to explore the potential of mitochondrial and ERS genes as pivotal genetic elements in individuals with DN and to elucidate their fundamental molecular mechanisms. The datasets GSE30528 and GSE30122 were obtained from the Gene Expression Omnibus (GEO) database. Firstly, differentially expressed genes (DEGs) (DN and control samples) were identified by differential expression analysis. Candidate genes were obtained by intersecting the DEGs with mitochondria and endoplasmic reticulum stress-related genes. The key genes were identified through three machine learning methods, the receiver operating characteristic (ROC) curve analysis and expression validation. Subsequently, a nomogram model for DN was constructed. Moreover, gene set enrichment analysis (GSEA), immune infiltration, molecular regulatory networks of key genes were explored, Later, predicted their drugs. Finally, three key genes (GPX1, PPIF and VDAC1) were identified by expression validation and ROC validation and three key genes were all down-regulated in DN. Meanwhile, RT-qPCR analysis yielded the same results. In addition, the nomogram model of key genes was constructed, and the model had a good prediction effect. GSEA showed that the top 3 most prominent pathways shared by the 3 key genes included oxidative phosphorylation, glutathione metabolism, and ribosome. Immune cells, including gamma-delta T cells, activated mast cells, and M2 macrophages, exhibited differential infiltration between the DN group and the control group. A total of 23 lncRNAs targeting intersecting miRNAs of three key genes. There were 4 drugs associated with the three key genes. In this research, three key genes (GPX1, PPIF and VDAC1) mitochondrial and endoplasmic reticulum stress-related gene in DN were identified, providing a potential theoretical basis for DN treatment. However, this study still has certain limitations. This study only used a single dataset for analysis and validation, so the results of the study may not fully reflect the diversity of DN patients.
糖尿病肾病（DN）是一种肾脏疾病。线粒体和内质网应激（ERS）在糖尿病肾病（DN）的发生发展中起着重要作用，尽管其中的确切机制尚未完全明确。本研究的目的是探索线粒体和ERS相关基因作为DN患者关键遗传因素的可能性，并阐明其基本分子机制。数据集GSE30528和GSE30122来自基因表达综合数据库（GEO）。首先，通过差异表达分析鉴定差异表达基因（DEGs）（DN组和对照组样本）。通过将DEGs与线粒体和内质网应激相关基因进行交叉分析获得候选基因。通过三种机器学习方法、受试者工作特征（ROC）曲线分析和表达验证来鉴定关键基因。随后，构建了DN的列线图模型。此外，还进行了基因集富集分析（GSEA）、免疫浸润分析以及关键基因的分子调控网络分析，随后预测了它们的药物。最后，通过表达验证和ROC验证鉴定出三个关键基因（GPX1、PPIF和VDAC1），且这三个关键基因在DN中均下调。同时，RT-qPCR分析也得到了相同的结果。此外，构建了关键基因的列线图模型，该模型具有良好的预测效果。GSEA显示，这三个关键基因共有的前三大显著通路包括氧化磷酸化、谷胱甘肽代谢和核糖体。免疫细胞，包括γδT细胞、活化肥大细胞和M2巨噬细胞，在DN组和对照组之间表现出差异浸润。共有23个lncRNAs靶向三个关键基因的交叉miRNAs。有4种药物与这三个关键基因相关。本研究鉴定出了糖尿病肾病中与线粒体和内质网应激相关的三个关键基因（GPX1、PPIF和VDAC1），为糖尿病肾病的治疗提供了潜在的理论基础。然而，本研究仍有一定局限性。本研究仅使用了单个数据集进行分析和验证，因此研究结果可能无法完全反映糖尿病肾病患者的多样性。
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