Identification and validation of three potential biomarkers and immune microenvironment for in severe asthma in microarray and single-cell datasets

<b>Objective:</b> 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.<b>Methods:</b> 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.<b>Results:</b> 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 signaling<b>Conclusions:</b> 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

<b>研究目的：</b>本研究旨在从微阵列数据集与单细胞数据集内筛选与重症哮喘相关的遗传生物标志物及细胞通讯机制，并在哮喘小鼠模型中验证潜在基因的表达水平。<b>研究方法：</b>本研究首先从重症哮喘的微阵列数据集（GSE130499与GSE63142）中筛选差异表达基因，随后通过机器学习算法——最小绝对收缩和选择算子（LASSO）与支持向量机递归特征消除（SVM-RFE）——构建模型，筛选与重症哮喘关联性最强的生物标志物，并借助数据集GSE43696对筛选结果开展进一步验证。此外，本研究利用单细胞数据集（GSE193816与GSE227744）分析潜在生物标志物的特异性表达模式与细胞间通讯情况。最终，通过哮喘小鼠模型验证潜在生物标志物的表达水平。<b>研究结果：</b>重症哮喘组与正常对照组间共筛选出73个差异表达基因。通过LASSO与SVM-RFE分析，最终确定BCL3、DDIT4及S100A14三个基因为重症哮喘的生物标志物，三者均具备良好的诊断效能。其中，BCL3的转录水平在重症哮喘中呈下调趋势，而S100A14与DDIT4的转录水平则显著上调。上述三个基因的转录水平在哮喘小鼠模型中得到了验证。本研究同时发现，重症哮喘患者体内中性粒细胞与肥大细胞的浸润水平显著升高，该现象可能通过骨形态发生蛋白（BMP）与神经调节蛋白（NRG）信号通路与支气管上皮细胞产生相互作用。<b>研究结论：</b>本研究筛选出三个具备诊断价值的差异表达基因（BCL3、DDIT4及S100A14），它们可能参与重症哮喘的发病进程；上述基因的表达水平可作为重症哮喘的生物标志物，对其功能机制的深入研究可为阐明重症哮喘的潜在发病机理提供全新的研究视角。