Cuproptosis genes in predicting the occurrence of allergic rhinitis and pharmacological treatment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39913623", "kind": "science-record", "revision": 4, "content_hash": "sha256:cc82bfce133272177fc8056c9b5881a2f2a5d289c3f33e06c468f2598a57e915", "created_at": "2026-11-01T20:30:10Z", "updated_at": "2026-11-01T22:56:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cuproptosis genes in predicting the occurrence of allergic rhinitis and pharmacological treatment.", "doi": "10.1371/journal.pone.0318511", "pmid": "39913623", "authors": ["Ting Yi"], "journal": "PloS one", "year": 2025, "abstract": "BACKGROUND: While drug therapy and allergen immunotherapy are useful for alleviating symptoms of seasonal allergic rhinitis (AR), existing therapeutic options remain limited. Cuproptosis is a novel form of programmed cell death, and its role in allergic rhinitis has not yet been explored. Researching the interaction between cuproptosis and allergic rhinitis will likely pave the way for future treatment of this disease.\nMETHODS: A microarray dataset of AR patients and normal controls (GSE43523) were obtained from the Gene Expression Omnibus (GEO) database for differential gene analysis. Cuproptosis related genes were extracted from the differentially expressed genes (DEGs) to form the AR/cuprotosis-gene set and analyzed by the GO and KEGG databases. Intersection analysis further defined the AR signature genes (AR-sg). Consensus cluster analyses were used to define the AR/cuprotosis-genes into subsets. Finally, AR signature genes were used as targets for drug prediction and molecular docking to identify candidate drugs that may affect SAR.\nRESULTS: Four AR signature genes (MRPS30, CLPX, MRPL13, and MRPL53) were selected by the MCC, EPC, BottleNeck, and Closeness algorithms. Correlation analysis of the AR signature genes and immune genes showed strong interactions; xCell analysis identified multiple immune cell types and supported these cells' importance in the AR pathogenesis. Finally, drug target analysis suggests that 1,5-isoquinolinediol and gefitinib have the potential to become future AR treatments.\nCONCLUSION: Our study analyzed allergic rhinitis and cuproptosis related genes by the bioinformatics approach and predicted 1,5-isoquinolinediol and gefitinib as potentially useful drugs for treating AR patients in the future.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) between controls and AR patients were analyzed using the limma package in R, as previously described [23].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39913623", "locator": "sections[3] \"2.1 Microarray datasets and differential analysis\" paragraph 2", "quote": "Differentially expressed genes (DEGs) between controls and AR patients were analyzed using the limma package in R, as previously described [23]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All relevant data are within the paper and its Supporting Information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39913623", "locator": "sections[26] \"Data Availability\" paragraph 2", "quote": "All relevant data are within the paper and its Supporting Information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Combining all four genes into one variable improved the AUC value to 1.0, indicating that the predictive value of the combined gene signature is high (Fig 3F).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39913623", "locator": "sections[17] \"3.6 Diagnostic efficacy of AR signature genes in predicting AR\" paragraph 2", "quote": "Combining all four genes into one variable improved the AUC value to 1.0, indicating that the predictive value of the combined gene signature is high (Fig 3F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Combining all four genes into one variable improved the AUC value to 1.0, indicating that the predictive value of the combined gene signature is high (Fig 3F).", "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:39913623"], "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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