Open medicine (Warsaw, Poland) · 2026
Characterization of immune infiltrates, molecular subtypes and signature genes in patients with allergic rhinitis.
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Yang Wenfei, Wang Ruiqing, Peng Shaoping, Liu Xiao
Аннотация
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. In vivo knockdown of ALDH3B2 significantly alleviated AR symptoms.
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 in vivo using an OVA-induced AR mouse model.
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. In vivo knockdown of ALDH3B2 significantly alleviated AR symptoms.
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 in vivo using an OVA-induced AR mouse model.