Identification of immune-related biomarkers associated with allergic rhinitis and development of a sample diagnostic model.

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Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including \"cytokine-cytokine receptor interaction\" and \"chemokine signaling pathway.\" Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40901831", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Two datasets were downloaded from NCBI GEO database [13] for analysis: GSE5010 [14], which consists of 38 human blood-extracted CD4 + T cell samples (18 AR [Allergic Rhinitis] patients and 20 from CTRL [Control] subjects), with the detection platform being the GPL10558 Illumina HumanHT-12 V4.0 expression beadchip; and GSE50223, which contains 84 human blood CD4 + T cell samples (42 from AR patients and 42 from CTRL subjects), with the detection platform being the GPL6884 Illumina HumanWG-6 v3.0 expression beadchip.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40901831", "locator": "sections[3] \"2.1 Data set acquisition and batch correction\" paragraph 2", "quote": "Two datasets were downloaded from NCBI GEO database [13] for analysis: GSE5010 [14], which consists of 38 human blood-extracted CD4 + T cell samples (18 AR [Allergic Rhinitis] patients and 20 from CTRL [Control] subjects), with the detection platform being the GPL10558 Illumina HumanHT-12 V4.0 expression beadchip; and GSE50223, which contains 84 human blood CD4 + T cell samples (42 from AR patients and 42 from CTRL subjects), with the detection platform being the GPL6884 Illumina HumanWG-6 v3.0 expression beadchip."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40901831", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively.", "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:40901831"], "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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