Abstract：
Objective: The aim of this study was to identify genetic biomarkers and cellular communications associated with severe asthma in microarray data sets and single cell data sets. The potential gene expression levels were verified in a mouse model of asthma.Methods: We identified differentially expressed genes from the microarray datasets (GSE130499 and GSE63142) of severe asthma, and then constructed models to screen the most relevant biomarkers to severe asthma by machine learning algorithms (LASSO and SVM-RFE), with further validation of the results by GSE43696. Single-cell datasets (GSE193816 and GSE227744) were identified for potential biomarker-specific expression and intercellular communication. Finally, The expression levels of potential biomarkers were verified with a mouse model of asthma.Results: The 73 genes were differentially expressed between severe asthma and normal control. LASSO and SVM-RFE recognized three genes BCL3, DDIT4 and S100A14 as biomarkers of severe asthma and had good diagnostic effect. Among them, BCL3 transcript level was down-regulated in severe asthma, while S100A14 and DDIT4 transcript levels were up-regulated. The transcript levels of the three genes were confirmed in the mouse model. Infiltration of neutrophils and mast cells were found to be increased in severe asthma and may be associated with bronchial epithelial cells through BMP and NRG signalingConclusions: We identified three differentially expressed genes (BCL3, DDIT4 and S100A14) of diagnostic significance that may be involved in the development of severe asthma and these gene expressions could be serviced as biomarker of severe asthma and investigating the function roles could bring new insights into the underlying mechanisms.
摘要：
严重哮喘的特点是控制水平差，严重影响患者的生活和预后。然而,潜在的致病机制仍然未知。这里,我们从严重哮喘的微阵列数据集（GSE130499和GSE63142）中鉴定了差异表达基因，然后构建模型，通过机器学习算法（LASSO和SVM-RFE）筛选与严重哮喘最相关的生物标志物，GSE43696对结果进行了进一步验证。3个基因(BCL3、DDIT4和S100A14)被认为是重症哮喘的生物标志物,具有良好的诊断效果。其中,BCL3转录水平在严重哮喘中下调，而S100A14和DDIT4转录水平上调。接下来,我们分析了重症哮喘患者的免疫微环境特征,并鉴定了单细胞数据集(GSE193816和GSE227744)的潜在生物标志物特异性表达和细胞间通讯.在严重哮喘中发现中性粒细胞和肥大细胞的浸润增加，并且可能通过BMP和NRG信号传导与支气管上皮细胞有关。最后,用哮喘小鼠模型验证了潜在生物标志物的表达水平。
