Analysis of infiltrating immune cells and identification of related biomarkers in patients with refractory asthma

Abstract

Abstract Background Patients with refractory asthma (RA) have an enhanced risk of asthma-related symptoms, mortality, and exacerbations.RA is related to innate immune deficiency. Thus, the aim of this study was to identify immune-related diagnostic genes involved in RA. Methods The limma R package was used to identify differentially expressed genes (DEGs) between RA and healthy control groups of induced sputum samples. The ClusterProfiler R package was used to carry out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the DEGs. Single-sample gene set enrichment analysis (ssGSEA), was used to calculate the relative infiltration of 28 immune cell types. Weighted gene co-expression network analysis (WGCNA) was used to identify the modules that were correlated with the differentially infiltrated immune cells. Protein–protein interaction (PPI) network analysis was used to identify the hub genes of the network, and diagnostic genes were identified from among these hub genes by creating a diagnostic logistic regression model.The miRNA–diagnostic gene and transcription factor (TF)–diagnostic gene networks were constructed to explain the regulatory mechanisms of the diagnostic genes in RA. Results 1844 DEGs (452 up-regulated and 1392 down-regulated) were identified between the RA and control groups. ssGSEA revealed that 17 immune cell types were significantly different between the RA and control samples. WGCNA identified two modules that were correlated with the differentially infiltrated immune cells. Next, 386 genes were identified as DEIRGs. Thereafter, the top 10 hub genes (CS, ATP5L, NDUFA4, ATP5B, COX5B, NDUFAB1, ATP5G1, NDUFA9, PARK7, and RPL8) in the PPI network, based on degree value, were identified. Next, we constructed a logistic regression model to accurately distinguish the RA and control groups; P < 0.05 for three of the genes (CS, ATP5L, and NDUFA4), which might be useful as RA diagnostic genes. Finally, the regulatory mechanisms of the diagnostic genes were explored based on the miRNA–diagnostic gene and TF–diagnostic gene networks. Conclusion The study identified CS, ATP5L, and NDUFA4 as RA diagnostic genes. These genes may serve as therapeutic targets for RA patients.