Cuproptosis-related gene signatures define the immune microenvironment in diabetic nephropathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40460381", "kind": "science-record", "revision": 3, "content_hash": "sha256:469d83ccf6e24858848d90efe3abb945a4cdc01ce5f00bfb13d49f1540c93b3f", "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": "Cuproptosis-related gene signatures define the immune microenvironment in diabetic nephropathy.", "doi": "10.1371/journal.pone.0321636", "pmid": "40460381", "authors": ["Hongmin Luo", "Yuxuan Cao", "Liping Guo", "Hui Li", "Yingying Yuan", "Fan Lu"], "journal": "PloS one", "year": 2025, "abstract": "BACKGROUND: Cuproptosis may be a new clue to illustrate the pathogenesis of the disease. There was no study focused on the relationship between the cuproptosis genes and diabetic nephropathy (DN). This study aimed to reveal the relationship between cuproptosis genes and the immune microenvironment in DN and distinguish different phenotypes to describe disease heterogeneity through consensus clustering based on cuproptosis genes.\nMETHODS: We downloaded RNA sequencing data sets of DN glomerular and normal renal tissue samples (GSE142025, GSE30528, and GSE96804) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between DN and control samples were screened. Immune cell subtype infiltration and immune score were figured out via different algorithms. Consensus clustering was performed by Ward's method to determine different phenotypes of DN. Key genes between phenotypes were identified via a machine-learning algorithm. Logistic regression analysis was applied to establish a nomogram for assessing the disease risk of DN. The role of related genes was verified by cell experiments.\nRESULTS: In DN samples, NOD-like receptor thermal protein domain associated protein 3(NLRP3) and cyclin-dependent kinase inhibitor 2A Gene(CDKN2A) were positively correlated to immune score. Nuclear factor erythroid 2-related factor 2(NFE2L2), Lipoic Acid Synthetase(LIAS), Lipoyltransferase 1(LIPT1), Dihydrolipoamide dehydrogenase(DLD), Dihydrolipoamide Branched Chain Transacylase E2(DBT) and Dihydrolipoamide S-Succinyltransferase(DLST) were negatively correlated to immune score. Via Consensus clustering based on cuproptosis genes, the DN samples were divided into cluster C1 and cluster C2. Cluster C1 was characterized by low cuproptosis gene expression, high immune cell subtype infiltration, and high enrichment of immune-related pathways. Cluster C2 was on the contrary. Dicarbonyl/l-xylulose reductase (DCXR) and heat-responsive protein 12 (HRSP12) were key genes related to clinical traits and immune microenvironment, negatively correlated with most immune cell subtypes. The nomogram constructed based on DCXR and HRSP12 showed good efficiency for DN diagnosis.\nCONCLUSION: Immune microenvironment imbalance and metabolic disorders may lead to the occurrence of DN. Cuproptosis genes, with the ability to regulate the immune microenvironment and metabolism, can be used for disease clustering to describe the heterogeneity and characterize the immune microenvironment. HRSP12 and DCXR, as key genes related to disease phenotypes and immune microenvironment characteristics, were jointly constructed as nomograms for DN diagnosis with high accuracy and reliability. HRSP12 and DCXR may be potential biological markers and renal protective factors.", "claims": [{"id": "claim-method", "statement": "Consensus clustering was performed by Ward’s method to determine different phenotypes of DN.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40460381", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Consensus clustering was performed by Ward’s method to determine different phenotypes of DN."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All relevant data are within the paper and Supporting Information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40460381", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "All relevant data are within the paper and Supporting Information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Consensus clustering based on cuproptosis genes divided DN samples into clusters C1 and C2.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40460381", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Via Consensus clustering based on cuproptosis genes, the DN samples were divided into cluster C1 and cluster C2."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Consensus clustering based on cuproptosis genes divided DN samples into clusters C1 and C2.", "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:40460381"], "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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