Characterization of immune infiltrates, molecular subtypes and signature genes in patients with allergic rhinitis.

Objectives: Allergic rhinitis is a disorder of immune response disease, but to date there still gaps in understanding the molecular characters of allergic rhinitis. This study integrates deconvolution algorithm and machine-learning approach to characterize allergic rhinitis molecular features. Methods: Functional enrichment showed that negative regulation of phosphate metabolic process and Glycolysis/Gluconegenesis pathway were significantly altered after allergic rhinitis onset. The infiltration levels of T cells memory resting, Monocytes and Macrophage M1 wrere significantly changed between allergic rhinitis and control groups. Results: Four allergic rhinitis feature genes were screened via four machine-learning approaches and a diagnostic model was constructed based on the feature genes. The differential expression levels between AR and the control groups were validated using an OVA-induced AR mouse model. Conclusions: Five AR feature genes were obtained with machine-learning algorithms. The AR feature genes were used to construct a diagnostic model. The function of those genes in AR progression was validated