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

Abstract

BackgroundSevere asthma is de ned as a persistent increase in airway in ammation despite the use of systemic glucocorticoids, targeted biologic therapies. Early prediction of severe asthma is challenging due to the lack of valuable biomarkers. The aim of this study was to identify crucial differentially expressed genes (DEGs) associated with severe asthma through approaches of bioinformatics analysis. MethodsThree datasets GSE130499, GSE43142 and GSE43696 were derived from the GEO expression database. Two datasets (GSE130499 and GSE43142) were merged, and batch effects were removed by using the "SVA" package. Afterwards, the differentially expressed genes (DEGs) were analyzed with the "limma" package. Next, DEGs were functionally enriched and pathway analyzed in the online analysis website DAVID, then DEGs were analyzed again by two machine learning algorithms (LASSO and SVM-RFE) to obtain the candidate biomarkers, and the diagnostic validity of the biomarkers was assessed using subject operating characteristic (ROC) curves, and nally the results were further validated through the GSE43696. ResultsTotal of 73 gene differential expression genes were identi ed in severe asthma and normal control. After screening with two machine learning algorithms, LASSO and SVM-RFE, three genes (BCL3, DDIT4 and S100A14) were recognized as biomarkers of 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. ConclusionsIn this study, we identi ed three differentially expressed genes (BCL3, DDIT4 and S100A14) of diagnostic signi cance that may be involved in the development of severe asthma and proposed new insights into the underlying mechanisms.