Tracing the molecular landscape of diabetic nephropathy: Insights from machine learning and experiment verification.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40470717", "kind": "science-record", "revision": 3, "content_hash": "sha256:0ec6a92f9fe6b0e3a53a8d15cd6ae0c42b9a491a53caece2cfaf637696fa8339", "created_at": "2026-11-01T20:30:35Z", "updated_at": "2026-11-01T22:14:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Tracing the molecular landscape of diabetic nephropathy: Insights from machine learning and experiment verification.", "doi": "10.1111/jdi.70026", "pmid": "40470717", "authors": ["Shahad W Kattan", "Ahmed M Basri", "Mohammad H Alhashmi", "Amany I Almars", "Reem Hasaballah Alhasani", "Ifat Alsharif", "Ikhlas A Sindi", "Ahmad H Mufti", "Iman S Abumansour", "Nasser A Elhawary", "Aishah Abdullah Qahtani", "Hailah M Almohaimeed"], "journal": "Journal of diabetes investigation", "year": 2025, "abstract": "OBJECTIVE: Diabetes is a chronic disease resulting from insufficient insulin secretion or impaired insulin function. Diabetic nephropathy (DN) is one of the most common complications of diabetes and a leading cause of end-stage renal disease. Early diagnosis of DN is crucial for timely intervention and effective disease management.\nMETHODS: Gene expression profiles GSE142025 and GSE220226 were retrieved from the GEO database and combined into a metadata cohort, while GSE189007 was obtained as an independent validation dataset. Differentially expressed genes (DEGs) were identified in 46 glomerular samples from DN patients and 31 control samples. Gene Ontology (GO) and Disease Ontology (DO) enrichment analyses, gene set enrichment analysis (GSEA), least absolute shrinkage and selection operator (LASSO) regression, support vector machine-recursive feature elimination (SVM-RFE) analysis, and area under the curve (AUC) calculations were performed.\nRESULTS: A total of 109 DEGs were identified. Among them, DUSP1, EGR1, FPR1, G6PC, GDF15, LOX, LPL, PRKAR2B, PTGDS, and TPPP3 were selected as potential diagnostic biomarkers for DN. These biomarkers exhibited a positive correlation with immune cell infiltration. Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN. This study provides new insights into the early diagnosis, pathogenesis, and molecular mechanisms of DN.", "claims": [{"id": "claim-method", "statement": "DEGs were screened using the least absolute shrinkage and selection operator (LASSO) regression model for variable selection and regularization, reducing overfitting and improving predictive accuracy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40470717", "locator": "sections[6] \"Selection of candidate biomarkers via machine learning\" paragraph 2", "quote": "DEGs were screened using the least absolute shrinkage and selection operator (LASSO) regression model for variable selection and regularization, reducing overfitting and improving predictive accuracy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40470717", "locator": "sections[3] \"Acquisition of gene expression data\" paragraph 2", "quote": "The DN Glomeruli and Control datasets, GSE142025 31 and GSE220226 32 , were downloaded from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40470717", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN."}, {"asset": "source:pmid:40470717", "locator": "sections[17] \"Diagnostic value of the identified biomarkers\" paragraph 2", "quote": "LOX (AUC = 0.926)"}, {"asset": "source:pmid:40470717", "locator": "sections[17] \"Diagnostic value of the identified biomarkers\" paragraph 2", "quote": "LOX (AUC = 1.000)"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Experimental validation identified LOX as the most promising novel diagnostic biomarker for DN.", "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:40470717"], "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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