Yuan Yuyun, Zhu Honghua, Huang Sihong, Zhang Yantao, Shen Yiyun
Department of Pediatrics, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, 201999, China.
Department of Medical Imaging, Shanghai Seventh People's Hospital, Shanghai, 200137, China.
Heliyon. 2024 Feb 2;10(4):e25735. doi: 10.1016/j.heliyon.2024.e25735. eCollection 2024 Feb 29.
Allergic asthma is driven by an antigen-specific immune response. This study aimed to identify immune-related differentially expressed genes in childhood asthma and establish a classification diagnostic model based on these genes.
GSE65204 and GSE19187 were downloaded and served as training set and validation set. The immune cell composition was evaluated with ssGSEA algorithm based on the immune-related gene set. Modules that significantly related to the asthma were selected by WGCNA algorithm. The immune-related differentially expressed genes (DE-IRGs) were screened, the protein-protein interaction network and diagnostic model of DE-IRGs was constructed. The pathway and immune correlation analysis of hub DE-IRGs was analyzed.
Eight immune cell types exhibited varying levels of abundance between the asthma and control groups. A total of 112 differentially expressed immune-related genes (DE-IRGs) was identified. Through the application of four ranking methods (MCC, MNC, DEGREE, and EPC), 17 hub DE-IRGs with overlapping significance were further selected. Subsequently, 8 optimized were identified using univariate logistic regression analysis and the LASSO regression algorithm, based on which a robust diagnostic model was constructed. Notably, TNF and CD40LG emerged as direct participants in asthma-related signaling pathways, displaying a positive correlation with the immune cell types of immature B cells, activated B cells, activated CD8 T cells, activated CD4 T cells, and myeloid-derived suppressor cells.
The diagnostic model constructed using the DE-IRGs (CCL5, CCR5, CD40LG, CD8A, IL2RB, PDCD1, TNF, and ZAP70) exhibited high and specific diagnostic value for childhood asthma. The diagnostic model may contribute to the diagnosis of childhood asthma.
过敏性哮喘由抗原特异性免疫反应驱动。本研究旨在识别儿童哮喘中免疫相关的差异表达基因，并基于这些基因建立分类诊断模型。
下载GSE65204和GSE19187作为训练集和验证集。基于免疫相关基因集，采用ssGSEA算法评估免疫细胞组成。通过WGCNA算法选择与哮喘显著相关的模块。筛选免疫相关差异表达基因（DE-IRGs），构建DE-IRGs的蛋白质-蛋白质相互作用网络和诊断模型。对核心DE-IRGs进行通路和免疫相关性分析。
哮喘组和对照组之间8种免疫细胞类型表现出不同程度的丰度差异。共鉴定出112个差异表达的免疫相关基因（DE-IRGs）。通过应用四种排序方法（MCC、MNC、DEGREE和EPC），进一步选择了17个具有重叠显著性的核心DE-IRGs。随后，使用单因素逻辑回归分析和LASSO回归算法鉴定出8个优化基因，据此构建了一个稳健的诊断模型。值得注意的是，TNF和CD40LG是哮喘相关信号通路的直接参与者，与未成熟B细胞、活化B细胞、活化CD8 T细胞、活化CD4 T细胞和髓系来源抑制细胞的免疫细胞类型呈正相关。
使用DE-IRGs（CCL5、CCR5、CD40LG、CD8A、IL2RB、PDCD1、TNF和ZAP70）构建的诊断模型对儿童哮喘具有较高的诊断价值和特异性。该诊断模型可能有助于儿童哮喘的诊断。
