Identification of diagnostic signature, molecular subtypes, and potential drugs in allergic rhinitis based on an inflammatory response gene set.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38469312", "kind": "science-record", "revision": 5, "content_hash": "sha256:72b50fa226aecb6e9309a83a1ea9bbc6c192d0f4a9662f4b0e96a578b534e96f", "created_at": "2026-11-01T19:20:25Z", "updated_at": "2026-11-01T22:53:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of diagnostic signature, molecular subtypes, and potential drugs in allergic rhinitis based on an inflammatory response gene set.", "doi": "10.3389/fimmu.2024.1348391", "pmid": "38469312", "authors": ["Jun Dai", "Keyu Xia", "De Huai", "Shuo Li", "Lili Zhou", "Shoufeng Wang", "Li Chen"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Rhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR.\nMethods: We acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes.\nResults: A total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways.\nConclusions: NFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.", "claims": [{"id": "claim-method", "statement": "Using the limma package, we performed a differential expression analysis on the GSE75011 dataset, comparing the HC and AR groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38469312", "locator": "sections[4] \"Identifying differentially expressed genes in patients with AR\" paragraph 2", "quote": "Using the limma package, we performed a differential expression analysis on the GSE75011 dataset, comparing the HC and AR groups."}], "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 authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38469312", "locator": "sections[26] \"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 authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene nomogram based on NFKBIA, HIF1A, MYC, and CCRL2 achieved an AUC of 0.931 for diagnosing allergic rhinitis and was validated using an external dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38469312", "locator": "sections[19] \"Development of the nomogram and evaluation of its diagnostic value\" paragraph 2", "quote": "By analyzing the receiver operating characteristic (ROC) curve, it was observed that the model performed exceptionally well, with a significant area under the curve (AUC) value of 0.931 (Figure 5C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene nomogram based on NFKBIA, HIF1A, MYC, and CCRL2 achieved an AUC of 0.931 for diagnosing allergic rhinitis and was validated using an external dataset.", "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:38469312"], "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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