Identification of copper-related biomarkers and potential molecule mechanism in diabetic nephropathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36329882", "kind": "science-record", "revision": 4, "content_hash": "sha256:e900d97ed1b1a4ace3c633e8bef21bf569d85d70242cbf4aa7f12533f1f8ad28", "created_at": "2026-11-01T18:15:45Z", "updated_at": "2026-11-01T22:08:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of copper-related biomarkers and potential molecule mechanism in diabetic nephropathy.", "doi": "10.3389/fendo.2022.978601", "pmid": "36329882", "authors": ["Jie Ming", "Si Ri Gu Leng Sana", "Xijin Deng"], "journal": "Frontiers in endocrinology", "year": 2022, "abstract": "Background: Diabetic nephropathy (DN) is a chronic microvascular complication in patients with diabetes mellitus, which is the leading cause of end-stage renal disease. However, the role of copper-related genes (CRGs) in DN development remains unclear.\nMaterials and methods: CRGs were acquired from the GeneCards and NCBI databases. Based on the GSE96804 and GSE111154 datasets from the GEO repository, we identified hub CRGs for DN progression by taking the intersection of differentially expressed CRGs (DECRGs) and genes in the key module from Weighted Gene Co-expression Network Analysis. The Maximal Clique Centrality algorithm was used to identify the key CRGs from hub CRGs. Transcriptional factors (TFs) and microRNAs (miRNAs) targeting hub CRGs were acquired from publicly available databases. The CIBERSORT algorithm was used to perform comparative immune cell infiltration analysis between normal and DN samples.\nResults: Eighty-two DECRGs were identified between normal and DN samples, as were 10 hub CRGs, namely PTGS2, DUSP1, JUN, FOS, S100A8, S100A12, NAIP, CLEC4E, CXCR1, and CXCR2. Thirty-nine TFs and 165 miRNAs potentially targeted these 10 hub CRGs. PTGS2 was identified as the key CRG and FOS as the most significant gene among all of DECRGs. RELA was identified as the hub TF interacting with PTGS2 by taking the intersection of potential TFs from the ChEA and JASPAR public databases. let-7b-5p was identified as the hub miRNA targeting PTGS2 by taking the intersection of miRNAs from the miRwalk, RNA22, RNAInter, TargetMiner, miRTarBase, and ENCORI databases. Similarly, CREB1, E2F1, and RELA were revealed as hub TFs for FOS, and miR-338-3p as the hub miRNA. Finally, compared with those in healthy samples, there are more infiltrating memory B cells, M1 macrophages, M2 macrophages, and resting mast cells and fewer infiltrating activated mast cells and neutrophils in DN samples (all p< 0.05).\nConclusion: The 10 identified hub copper-related genes provide insight into the mechanisms of DN development. It is beneficial to examine and understand the interaction between hub CRGs and potential regulatory molecules in DN. This knowledge may provide a novel theoretical foundation for the development of diagnostic biomarkers and copper-related therapy targets in DN.", "claims": [{"id": "claim-method", "statement": "Based on the GSE96804 and GSE111154 datasets from the GEO repository, we identified hub CRGs for DN progression by taking the intersection of differentially expressed CRGs (DECRGs) and genes in the key module from Weighted Gene Co-expression Network Analysis. The Maximal Clique Centrality algorithm was used to identify the key CRGs from hub CRGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36329882", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Based on the GSE96804 and GSE111154 datasets from the GEO repository, we identified hub CRGs for DN progression by taking the intersection of differentially expressed CRGs (DECRGs) and genes in the key module from Weighted Gene Co-expression Network Analysis. The Maximal Clique Centrality algorithm was used to identify the key CRGs from hub CRGs."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36329882", "locator": "sections[20] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using GSE96804 and GSE111154, the study combined differential-expression analysis with Weighted Gene Co-expression Network Analysis and Maximal Clique Centrality to identify 10 hub copper-related genes in diabetic nephropathy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36329882", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Based on the GSE96804 and GSE111154 datasets from the GEO repository, we identified hub CRGs for DN progression by taking the intersection of differentially expressed CRGs (DECRGs) and genes in the key module from Weighted Gene Co-expression Network Analysis."}, {"asset": "source:pmid:36329882", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Eighty-two DECRGs were identified between normal and DN samples, as were 10 hub CRGs, namely PTGS2, DUSP1, JUN, FOS, S100A8, S100A12, NAIP, CLEC4E, CXCR1, and CXCR2."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using GSE96804 and GSE111154, the study combined differential-expression analysis with Weighted Gene Co-expression Network Analysis and Maximal Clique Centrality to identify 10 hub copper-related genes in diabetic nephropathy.", "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:36329882"], "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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