Chen Wenlie, Huang Yuanlu, Lei Li, Zhang Rui, Fu Li, Liao Jinwen, Wang Shaohua, Zou Zhenzhuang
The Third People's Hospital of Longgang District, Shenzhen, 518172, China.
Guizhou Medical University, Guiyang, 563000, Guizhou, China.
Genes Genomics. 2025 Aug 18. doi: 10.1007/s13258-025-01665-4.
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.
This study aims to develop a diagnostic model and identify key hypoxia-related biomarkers in childhood asthma based on transcriptomic data analysis.
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.
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.
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.
缺氧是儿童重度哮喘的一个重要表现。早期准确诊断对于提高治疗效果和减轻长期并发症至关重要。本研究旨在利用生物信息学分析来研究儿童哮喘中与缺氧相关的基因（HRGs）。
本研究旨在基于转录组数据分析开发一种诊断模型，并识别儿童哮喘中关键的缺氧相关生物标志物。
使用limma分析从支气管上皮转录组（GSE27011/GSE40732数据集）中鉴定出与缺氧相关的差异表达基因（HRDEGs）。使用LASSO回归开发诊断模型，并通过蛋白质-蛋白质相互作用（PPI）网络鉴定枢纽基因。分别使用ConsensusClusterPlus和CIBERSORTx进行哮喘亚型分类和免疫微环境特征分析。在屋尘螨（HDM）诱导的哮喘小鼠中进行实验验证，证实了候选基因的转录变化。
我们获得了19个HRDEGs和11个模型基因（AHR、AKR1C3、ELP3、GNAL、GZMB、LPP、MAFG、PDGFD、PPP1R12B、SYNE2和TAF15）。回归分析证明了该模型强大的诊断性能。PPI分析确定了10个与哮喘相关的枢纽基因，其中AKR1C3对不同分子亚型显示出高诊断准确性。免疫浸润分析表明枢纽基因与八种免疫细胞类型之间存在显著相关性，包括B细胞、效应T细胞、细胞毒性T细胞、调节性T细胞（Tregs）、单核细胞、肥大细胞、嗜酸性粒细胞和中性粒细胞。
在本研究中，鉴定出了一个与儿童哮喘相关的缺氧相关基因特征。这些发现不仅突出了哮喘潜在的治疗靶点，还为其发病机制提供了新的见解。
