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pmid:40230799

The Mitochondrial Metabolism Gene ECH1 Was Identified as a Novel Biomarker for Diabetic Nephropathy: Using Bioinformatics Analysis and Experimental Confirmation.

Yan Miao · Lei Yan · Huixia Cao · Xiaojing Jiao · Fengmin Shao

Diabetes, metabolic syndrome and obesity : targets and therapy · 2025 doi:10.2147/DMSO.S494644

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
The Mitochondrial Metabolism Gene ECH1 Was Identified as a Novel Biomarker for Diabetic Nephropathy: Using Bioinformatics Analysis and Experimental Confirmation.
doi
10.2147/DMSO.S494644
pmid
40230799
authors array · 5 items
[0]
Yan Miao
[1]
Lei Yan
[2]
Huixia Cao
[3]
Xiaojing Jiao
[4]
Fengmin Shao
journal
Diabetes, metabolic syndrome and obesity : targets and therapy
year
2025
abstract
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.
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The diagnostic performance of candidate genes was evaluated using the Area Under the Curve (AUC) from ROC curve analysis in GSE96804 and GSE104954, employing the “pROC” R package to generate the curves.
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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).
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PMID 40230799 full text
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PDK4 and ECH1 (AUC>0.9). Finally, the q-PCR, flow cytometry, and Western blot results indicated that key
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.9). Finally, the q-PCR, flow cytometry, and Western blot results indicated that key genes were signif
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romote fatty acid oxidation and inhibit cell apoptosis, oxidative stress, and inflammation. Conclus
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e and Treatment HK-2 cells (ATCC®, USA) were cultured in RPMI-1640 medium (Procell, China) supplement
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The diagnostic performance of candidate genes was evaluated using the Area Under the Curve (AUC) from ROC curve analysis in GSE96804 and GSE104954, employing the “pROC” R package to generate the curves.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40230799", "kind": "science-record", "revision": 4, "content_hash": "sha256:d6e1829f1c1491b7d5c603649bf738ba878ce183e5adcc2c4c88229521a2266b", "created_at": "2026-11-01T20:30:23Z", "updated_at": "2026-11-01T22:56:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Mitochondrial Metabolism Gene ECH1 Was Identified as a Novel Biomarker for Diabetic Nephropathy: Using Bioinformatics Analysis and Experimental Confirmation.", "doi": "10.2147/DMSO.S494644", "pmid": "40230799", "authors": ["Yan Miao", "Lei Yan", "Huixia Cao", "Xiaojing Jiao", "Fengmin Shao"], "journal": "Diabetes, metabolic syndrome and obesity : targets and therapy", "year": 2025, "abstract": "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.\nMethods: 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.\nResults: 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.\nConclusion: This study identified biomarkers related to mitochondrial metabolism in DN, providing new insights and directions for the diagnosis and treatment of DN.", "claims": [{"id": "claim-method", "statement": "The diagnostic performance of candidate genes was evaluated using the Area Under the Curve (AUC) from ROC curve analysis in GSE96804 and GSE104954, employing the “pROC” R package to generate the curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40230799", "locator": "sections[6] \"Assessment of Key Genes Diagnostic Value\" paragraph 2", "quote": "The diagnostic performance of candidate genes was evaluated using the Area Under the Curve (AUC) from ROC curve analysis in GSE96804 and GSE104954, employing the “pROC” R package to generate the curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The study datasets GSE96804 and GSE104954 are available for download in the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40230799", "locator": "sections[28] \"Data Sharing Statement\" paragraph 2", "quote": "The study datasets GSE96804 and GSE104954 are available for download in the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "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).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40230799", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "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)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "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).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40230799"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE104954"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE104954"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40230799", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/DMSO.S494644", "sha256": 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