To find potential diagnostic biomarkers for patients with bronchial asthma.

this study used bioinformatics analysis to obtain the GSE41861 and GSE64913 bronchial epithelial cell gene expression datasets from the Gene Expression Microarray (GEO) database with bronchial asthma patients and healthy volunteers. The high-throughput microarray data were extracted and differentially expressed genes were screened by websites or software such as GEO2R, omics Bean and STRING. The common differential genes between the two were aligned into a protein-protein interaction (PPI) network to find the key genes. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genomes and Genomes (KEGG) pathway enrichment analysis were also performed in the DAVID website to explore the functional roles of these genes. Finally, ROC curves were applied to analyze the diagnostic value of these genes for bronchial asthma.

The results showed that 11 common differential genes were screened in the GSE41861 and GSE64913 datasets. GO enrichment analysis showed that the differential genes were mainly associated with the activity and regulation of endopeptidase as well as peptidase. KEGG results showed that the above differential genes were involved in the signaling pathways of complement and coagulation cascade. The ROC curves showed that CEACAM5, GRP, SCGB3A1, and KCNA1 were highly accurate in the diagnosis of asthma (AUC values>0.8).

the results of bioinformatics analysis of asthma can provide supporting evidence to explore the potential pathogenesis and key genes of asthma. Among them, CEACAM5, GRP, SCGB3A1, and KCNA1 were correlated with the clinical diagnosis of asthma.