Identification of metabolic reprogramming-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39609849", "kind": "science-record", "revision": 4, "content_hash": "sha256:8aa2bb0b4244fc6b977f9d429465167841876f352f29ce2082906da071562da5", "created_at": "2026-11-01T20:29:52Z", "updated_at": "2026-11-01T22:55:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of metabolic reprogramming-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics.", "doi": "10.1186/s13098-024-01531-5", "pmid": "39609849", "authors": ["Hong Chen", "Xiaoxia Su", "Yan Li", "Cui Dang", "Zuojie Luo"], "journal": "Diabetology & metabolic syndrome", "year": 2024, "abstract": "BACKGROUND: Diabetic nephropathy (DN) is a serious complication of diabetes mellitus, marked by progressive renal damage. Recent evidence indicates that metabolic reprogramming is crucial to DN pathogenesis, yet its underlying mechanisms are not well understood. This study aimed to examine how metabolic reprogramming-related genes (MRRGs) are differentially expressed and to explore their potential mechanisms in the development of DN.\nMETHODS: We analyzed the datasets GSE30528 and GSE96804 from the Gene Expression Omnibus (GEO), comprising 50 DN samples and 33 controls. MRRGs were sourced from GeneCards and PubMed. Data preprocessing included batch effect correction using the R package sva, followed by normalization and differential expression analysis with limma (|logFC|> 0.5, adj.p < 0.05). Functional enrichment analyses (GO, KEGG, GSEA) were performed using clusterProfiler. Protein-protein interaction (PPI) networks were constructed via STRING, identifying hub genes through CytoHubba. Regulatory networks (mRNA-TF, mRNA-miRNA) were derived from ChIPBase and StarBase. Validation of hub genes and ROC analysis assessed diagnostic performance. ssGSEA quantified immune cell infiltration.\nRESULTS: Our analysis identified 708 differentially expressed genes (DEGs), including 119 metabolic reprogramming-related DEGs (MRRDEGs). Enrichment analyses revealed significant roles for MRRDEGs in processes such as wound healing and pathways like MAPK signaling. The PPI network identified nine hub genes: FN1, CD44, KDR, EGF, HSPG2, HGF, FGF9, IGF1, and ALB, which exhibited high diagnostic accuracy (AUC 0.7 to 0.9). Notably, FN1 and CD44 showed significant association with renal fibrosis and could serve as potential biomarkers for early diagnosis and therapeutic targets in DN. Immune infiltration analysis showed notable differences in immune cell composition between DN and control samples.\nCONCLUSION: This study identifies hub genes such as FN1 and CD44, with potential diagnostic value in DN. It also reveals immune cell infiltration differences between DN patients and controls, offering insights into disease progression and potential therapeutic targets.", "claims": [{"id": "claim-method", "statement": "We performed differential expression analysis between these groups using the R package limma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39609849", "locator": "sections[4] \"Differential expression analysis of metabolic reprogramming-related genes\" paragraph 2", "quote": "We performed differential expression analysis between these groups using the R package limma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39609849", "locator": "sections[29] \"notes\" paragraph 1", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our analysis identified 708 differentially expressed genes (DEGs), including 119 metabolic reprogramming-related DEGs (MRRDEGs).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39609849", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Our analysis identified 708 differentially expressed genes (DEGs), including 119 metabolic reprogramming-related DEGs (MRRDEGs)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our analysis identified 708 differentially expressed genes (DEGs), including 119 metabolic reprogramming-related DEGs (MRRDEGs).", "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:39609849"], "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. 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