Allergic rhinitis (AR), which presents symptoms like sneezing and a runny nose, is categorized as an upper respiratory condition of type 2. Recent progress in comprehending AR has revealed the significant role played by type 2 cytokines, specifically interleukin (IL)-13, IL-4, and IL-5. These cytokines are released by helper T cells 2 (Th2) and innate lymphoid cells (ILC2s). ILC2s have the ability to interact with various immune cells and are essential in promoting both type 2 immune response and tissue repair, contributing to normal homeostatic functions within the body. This article presents a summary of the latest advancements in comprehending the activity of ILC2s, with particular emphasis on their potential role involvement in AR. It explores how they collaborate with Th2 cells to exacerbate nasal inflammation and interact with regulatory T cells (Tregs) to counteract the suppressive role mediated by Tregs during allergic inflammation. The significance of ILC2s in allergen-specific therapy is highlighted. A comprehensive understanding of ILC2s biology establishes a robust foundation for unraveling the pathogenesis of AR and devising innovative therapeutic approaches for its management.

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.