Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40950408", "kind": "science-record", "revision": 3, "content_hash": "sha256:5d720d6a5dec3f1779ef588f92a1181cad0b54844daabd4d2299ee7afcb0ab84", "created_at": "2026-11-01T20:31:02Z", "updated_at": "2026-11-01T22:14:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.", "doi": "10.3389/fcell.2025.1630708", "pmid": "40950408", "authors": ["Shan He", "Yi Wei Chen", "Jian Ye", "Yu Wang", "Qin Kai Chen", "Si Yi Liu"], "journal": "Frontiers in cell and developmental biology", "year": 2025, "abstract": "Background: 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.\nMethods: 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).\nResults: 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 CXCR2, NAMPT, and CUEDC2, aligning with bioinformatics results.\nConclusion: Through bioinformatics analysis, a total of six potential MRRDEGs were identified, among which CUEDC2, NAMPT, CXCR2 could serve as potential biomarkers.", "claims": [{"id": "claim-method", "statement": "The reliability of the model predictions was then assessed using ROC curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40950408", "locator": "sections[8] \"2.6 Diagnostic model construction and assessment\" paragraph 2", "quote": "The reliability of the model predictions was then assessed using ROC curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40950408", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CXCR2 showed the highest AUC of 0.846 (Figure 6B), while the other genes had AUCs of 0.704, 0.8, 0.689, 0.77, and 0.704 (Figures 6B,C).", "kind": "quantitative", "target": {"value": 0.846, "unit": "auc", "tolerance": null, "source": "CXCR2 showed the highest AUC of 0.846 (Figure 6B), while the other genes had AUCs of 0.704, 0.8, 0.689, 0.77, and 0.704 (Figures 6B,C)."}, "evidence": [{"asset": "source:pmid:40950408", "locator": "sections[19] \"3.5 Diagnostic model construction and evaluation\" paragraph 2", "quote": "CXCR2 showed the highest AUC of 0.846 (Figure 6B), while the other genes had AUCs of 0.704, 0.8, 0.689, 0.77, and 0.704 (Figures 6B,C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "CXCR2 showed the highest AUC of 0.846 (Figure 6B), while the other genes had AUCs of 0.704, 0.8, 0.689, 0.77, and 0.704 (Figures 6B,C).", "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:40950408"], "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:GSE30528"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_mann_whitney", "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:GSE30528"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_mann_whitney"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40950408", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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