General symptom scores of the mice.

This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. 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.本研究旨在筛选与变应性鼻炎（allergic rhinitis, AR）相关的免疫相关生物标志物，并构建稳健的诊断模型。从NCBI基因表达综合数据库（NCBI GEO）下载了两个数据集GSE5010与GSE50223，分别包含38份和84份血液CD4+T细胞样本。为消除批次效应，本研究采用替代变量分析（surrogate variable analysis, SVA）R包（版本3.38.0）完成数据整合，以供后续分析使用。采用基因集变异分析（Gene Set Variation Analysis, GSVA）R包（版本1.36.3）评估免疫细胞浸润谱。通过加权基因共表达网络分析（Weighted Gene Co-Expression Network Analysis, WGCNA）算法构建基因共表达网络，以筛选疾病相关模块。采用线性模型微阵列分析（linear models for microarray data, limma）R包（版本3.34.7）筛选差异表达基因（differentially expressed genes, DEGs），随后通过DAVID数据库开展功能富集分析。基于STRING数据库构建蛋白质相互作用（protein-protein interaction, PPI）网络，以筛选关键基因。随后采用最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归算法与支持向量机（Support Vector Machine, SVM）方法构建诊断模型，并通过受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估模型的判别能力。共分析28种免疫细胞类型，结果显示变应性鼻炎组与对照组间有8种免疫细胞的浸润水平存在显著差异。通过WGCNA分析，共筛选出3个疾病相关模块，包含4278个候选基因。差异表达分析共得到326个显著差异表达基因，其中257个与WGCNA筛选得到的基因重合。这些基因显著富集于免疫相关通路，包括"细胞因子-细胞因子受体相互作用"及"趋化因子信号通路"。基因集富集分析（Gene Set Enrichment Analysis, GSEA）进一步筛选出12条与疾病风险评分显著相关的KEGG通路。药物筛选共得到24种与关键基因相关的小分子药物。构建了包含RFC4、LYN、IL3、TNFRSF1B及RBBP7共5个基因的诊断模型，该模型在训练集与验证集中的诊断效能分别为0.843与0.739。成功构建变应性鼻炎小鼠模型，并通过实时定量聚合酶链反应（reverse transcription quantitative polymerase chain reaction, RT-qPCR）实验验证了相关基因的表达水平。本研究构建的五基因诊断模型在区分变应性鼻炎患者与健康对照人群时展现出较强的预测能力，在变应性鼻炎的临床诊断以及推动新型诊断与治疗策略开发方面具有潜在应用价值。