Yan Miao, Lei Yan, Huixia Cao, Xiaojing Jiao, Fengmin Shao
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Important gene modules were identified by weighted gene Coexpression network analysis (WGCNA) clustering. Next, we obtained key genes by intersecting DEGs, important gene modules and MRGs. The ROC curve was employed to assess the sensitivity and specificity of the diagnostic indicators for DN. Finally, the expression of key genes was assessed in the in vitro DN model and the mechanisms of key gene were investigated.</p><p><strong>Results: </strong>A total of 343 DEGs were identified, with functional analysis revealing a primary focus on metabolic biological processes. A sum of 752 important module genes was ascertained. PDK4, ECH1, and ETFB were selected as key genes. Then, the expression level and specificity of key genes were verified by the GSE104954 dataset, which confirmed the high diagnostic value of PDK4 and ECH1 (AUC>0.9). Finally, the q-PCR, flow cytometry, and Western blot results indicated that key genes were significantly decreased in high glucose induced HK-2 cells. ECH1 could promote fatty acid oxidation and inhibit cell apoptosis, oxidative stress, and inflammation.</p><p><strong>Conclusion: </strong>This study identified biomarkers related to mitochondrial metabolism in DN, providing new insights and directions for the diagnosis and treatment of DN.</p>","PeriodicalId":11116,"journal":{"name":"Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy","volume":"18 ","pages":"1087-1098"},"PeriodicalIF":2.8000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995922/pdf/","citationCount":"0","resultStr":"{\"title\":\"The Mitochondrial Metabolism Gene ECH1 Was Identified as a Novel Biomarker for Diabetic Nephropathy: Using Bioinformatics Analysis and Experimental Confirmation.\",\"authors\":\"Yan Miao, Lei Yan, Huixia Cao, Xiaojing Jiao, Fengmin Shao\",\"doi\":\"10.2147/DMSO.S494644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Diabetic nephropathy (DN) is a major cause of kidney failure, and its incidence is increasing worldwide. 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引用次数: 0
摘要
背景：糖尿病肾病（DN）是肾衰竭的主要原因，其发病率在世界范围内呈上升趋势。已有研究表明，线粒体功能障碍可能与DN的发病机制有关。本研究旨在探索可能影响DN诊断和治疗的与线粒体代谢相关的新型生物标志物。方法：使用Gene Expression Omnibus （GEO）数据库和MitoCarta3.0数据库分别下载DN数据集和线粒体代谢相关基因（MRGs）。使用“limma”R包鉴定差异表达基因（DEGs），并通过基因本体（GO）和京都基因与基因组百科全书（KEGG）进行功能分析。通过加权基因共表达网络分析（WGCNA）聚类识别出重要的基因模块。接下来，我们通过交叉deg、重要基因模块和mrg得到关键基因。采用ROC曲线评价DN诊断指标的敏感性和特异性。最后，在体外DN模型中评估关键基因的表达，并探讨关键基因的作用机制。结果：共鉴定出343个deg，功能分析揭示了代谢生物学过程的主要重点。确定了752个重要模块基因。选择PDK4、ECH1和ETFB作为关键基因。然后，通过GSE104954数据集验证关键基因的表达水平和特异性，证实PDK4和ECH1具有较高的诊断价值（AUC>0.9）。最后，q-PCR、流式细胞术和Western blot结果显示，高糖诱导的HK-2细胞中关键基因明显减少。ECH1能促进脂肪酸氧化，抑制细胞凋亡、氧化应激和炎症反应。结论：本研究确定了DN中与线粒体代谢相关的生物标志物，为DN的诊断和治疗提供了新的见解和方向。
The Mitochondrial Metabolism Gene ECH1 Was Identified as a Novel Biomarker for Diabetic Nephropathy: Using Bioinformatics Analysis and Experimental Confirmation.
Background: Diabetic nephropathy (DN) is a major cause of kidney failure, and its incidence is increasing worldwide. Existing studies have shown that mitochondrial dysfunction is potentially related to the pathogenesis of DN. This study aims to explore novel biomarkers related to mitochondrial metabolism that may affect the diagnosis and treatment of DN.
Methods: The Gene Expression Omnibus (GEO) database and MitoCarta3.0 database were used to download the DN datasets and mitochondrial metabolism-related genes (MRGs), respectively. Differentially expressed genes (DEGs) were identified using the "limma" R package, and their functional analysis was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Important gene modules were identified by weighted gene Coexpression network analysis (WGCNA) clustering. Next, we obtained key genes by intersecting DEGs, important gene modules and MRGs. The ROC curve was employed to assess the sensitivity and specificity of the diagnostic indicators for DN. Finally, the expression of key genes was assessed in the in vitro DN model and the mechanisms of key gene were investigated.
Results: A total of 343 DEGs were identified, with functional analysis revealing a primary focus on metabolic biological processes. A sum of 752 important module genes was ascertained. PDK4, ECH1, and ETFB were selected as key genes. Then, the expression level and specificity of key genes were verified by the GSE104954 dataset, which confirmed the high diagnostic value of PDK4 and ECH1 (AUC>0.9). Finally, the q-PCR, flow cytometry, and Western blot results indicated that key genes were significantly decreased in high glucose induced HK-2 cells. ECH1 could promote fatty acid oxidation and inhibit cell apoptosis, oxidative stress, and inflammation.
Conclusion: This study identified biomarkers related to mitochondrial metabolism in DN, providing new insights and directions for the diagnosis and treatment of DN.
期刊介绍：
An international, peer-reviewed, open access, online journal. The journal is committed to the rapid publication of the latest laboratory and clinical findings in the fields of diabetes, metabolic syndrome and obesity research. Original research, review, case reports, hypothesis formation, expert opinion and commentaries are all considered for publication.
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