Identification of a hypoxia-related gene signature associated with childhood asthma.

Abstract

Background: Hypoxia is a significant manifestation of severe asthma in children. An early and accurate diagnosis is crucial for enhancing treatment outcomes and mitigating long-term complications. This study aims to utilize bioinformatics analysis to investigate hypoxia-related genes (HRGs) in childhood asthma.

Objective: This study aims to develop a diagnostic model and identify key hypoxia-related biomarkers in childhood asthma based on transcriptomic data analysis.

Methods: Hypoxia-related differentially expressed genes (HRDEGs) were identified from bronchial epithelial transcriptomes (GSE27011/GSE40732 datasets) using limma analysis. A diagnostic model was developed using LASSO regression, and hub genes were identified via protein-protein interaction (PPI) networks. Asthma subtyping and immune microenvironment characterization were conducted using ConsensusClusterPlus and CIBERSORTx, respectively. Experimental validation in house dust mite (HDM)-induced asthmatic mice confirmed transcriptional changes in candidate genes.

Results: We obtained 19 HRDEGs and 11 model genes (AHR, AKR1C3, ELP3, GNAL, GZMB, LPP, MAFG, PDGFD, PPP1R12B, SYNE2, and TAF15). Regression analyses demonstrated the model's robust diagnostic performance. PPI analysis identified 10 hub genes associated with asthma, with AKR1C3 showing high diagnostic accuracy for different molecular subtypes. Immune infiltration analysis indicated significant correlations between hub genes and eight immune cell types, including B cells, effector T cells, cytotoxic T cells, regulatory T cells (Tregs), monocytes, mast cells, eosinophils, and neutrophils.

Conclusions: In this study, a hypoxia-related gene signature associated with childhood asthma was identified. These findings not only highlight potential therapeutic targets for asthma but also offer new insights into its pathogenesis.