Unveiling diabetic nephropathy: a novel diagnostic model through single-cell sequencing and co-expression analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38968594", "kind": "science-record", "revision": 3, "content_hash": "sha256:0358a305dfe1fc3f19e423a2a389deba3a62d1b0a1b9c28b3dd58b837769c0c1", "created_at": "2026-11-01T20:29:02Z", "updated_at": "2026-11-01T22:12:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unveiling diabetic nephropathy: a novel diagnostic model through single-cell sequencing and co-expression analysis.", "doi": "10.18632/aging.205982", "pmid": "38968594", "authors": ["Guoyi Wang", "Jinwen Zhao", "Min Zhou", "Haiyuan Lu", "Fei Mao"], "journal": "Aging", "year": 2024, "abstract": "BACKGROUND: Diabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet its significance in DN remains unexplored.\nMETHODS: This study utilized single-cell sequencing data GSE131882 from GEO database combined with bulk transcriptome sequencing data GSE30122, GSE30528 and GSE30529 to investigate disulfidptosis in DN. Single-cell sequencing analysis was performed on samples from DN patients and healthy controls, focusing on cell heterogeneity and communication. Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were employed to identify disulfidptosis-related gene sets and pathways. A diagnostic model was constructed using machine learning techniques based on identified genes, and immunocorrelation analysis was conducted to explore the relationship between key genes and immune cells. PCR validation was performed on blood samples from DN patients and healthy controls.\nRESULTS: The study revealed significant disulfidptosis heterogeneity and cell communication differences in DN. Specific targets related to disulfidptosis were identified, providing insights into the pathogenesis of DN. The diagnostic model demonstrated high accuracy in distinguishing DN from healthy samples across multiple datasets. Immunocorrelation analysis highlighted the complex interactions between immune cells and key disulfidptosis-related genes. PCR validation supported the differential expression of model genes VEGFA, MAGI2, THSD7A and ANKRD28 in DN.\nCONCLUSION: This research advances our understanding of DN by highlighting the role of disulfidptosis and identifying potential biomarkers for early detection and personalized treatment.", "claims": [{"id": "claim-method", "statement": "Compared with other models, “KNN” method has the highest accuracy and the largest AUC area.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38968594", "locator": "sections[18] \"Construction of a diagnostic model\" paragraph 2", "quote": "Compared with other models, “KNN” method has the highest accuracy and the largest AUC area."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The single-cell dataset GSE131882 associated with diabetic nephropathy was downloaded from the GEO database(https://www.ncbi.nlm.nih.gov/geo/) [16, 17]. The dataset included 3 diabetic nephropathy samples and 3 normal control samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38968594", "locator": "sections[3] \"Single-cell sequencing data download and processing\" paragraph 2", "quote": "The single-cell dataset GSE131882 associated with diabetic nephropathy was downloaded from the GEO database(https://www.ncbi.nlm.nih.gov/geo/) [16, 17]. The dataset included 3 diabetic nephropathy samples and 3 normal control samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The KNN diagnostic model performed well in the training cohort GSE30122 and verification cohorts GSE30528 and GSE30529, with AUC greater than 0.9.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38968594", "locator": "sections[18] \"Construction of a diagnostic model\" paragraph 2", "quote": "As shown in Figure 5D–5F, this model performs well in training cohort GSE30122 and verification cohorts GSE30528 and GSE30529, with AUC greater than 0.9."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The KNN diagnostic model performed well in the training cohort GSE30122 and verification cohorts GSE30528 and GSE30529, with AUC greater than 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:38968594"], "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:GSE131882"], "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:GSE131882"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38968594", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205982", "sha256": "sha256:2bf26f7d21859a9fdc4bcca5806fc917141faf0afc115c59cde884dba04a8501", "bytes": 32380, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.205982", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11272118", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE131882", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131882", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE131882", "version": null, "note": "The single-cell dataset GSE131882 associated with diabetic nephropathy was downloaded from the GEO database(https://www.ncbi.nlm.nih.gov/geo/) [16, 17]. 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PCR validation was performed on blood samples from DN patients and healthy controls. RESULTS: T", "METHODS PCR experimental verification Blood samples from 6 DN patients and 6 healthy controls were co"], "order_basis": "paper_text_order:1079", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["anjing, China). Finally, AceQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) was util", "China) was utilized to conduct quantitative real-time PCR (qRT-PCR). 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