Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets

Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

变应性鼻炎（Allergic Rhinitis, AR）是一种累及上呼吸道的炎症性疾病，近年来全球发病率呈显著攀升趋势。本研究围绕差异表达基因（differentially expressed genes, DEGs）与加权基因共表达网络分析（Weighted Gene Co-Expression Network Analysis, WGCNA）展开系统性探究。在鉴定得到目标基因集后，我们借助基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）完成功能注释与富集分析，随后构建蛋白质相互作用（protein-protein interaction, PPI）网络并应用套索回归（LASSO regression）进行特征筛选。最终通过分子对接模型，阐释了上述关键基因潜在的药物-基因相互作用机制。通过WGCNA与差异表达基因筛选流程，本研究构建PPI网络并筛选出包含CD44、CD69、CD274在内的前20个关键基因。套索回归分析进一步鉴定出STARD5、CST1与CHAC1这3个与AR显著相关的独立风险因子，并搭建得到AUC值超过0.75的预测模型。此外，本研究还筛选得到105种潜在治疗药物，包括氟尿嘧啶、环磷酰胺、多柔比星及氢化可的松，为变应性鼻炎的临床治疗提供了颇具前景的策略。本研究通过整合差异表达基因与WGCNA筛选得到的关键基因，清晰阐明了AR发病过程中的完整基因互作网络，为后续AR的生物标志物与治疗靶点发掘铺平了道路。