Constructing ferroptosis-related competing endogenous RNA networks and exploring potential biomarkers correlated with immune infiltration cells in asthma using combinative bioinformatics strategy

Abstract

Abstract Background Asthma is a common chronic respiratory disease worldwide. Recent studies have revealed the critical effects of the ceRNA network and ferroptosis on patients with asthma. Thus, this study aimed to explore the potential ferroptosis-related ceRNA network, investigate the immune cell infiltration level in asthma through integrated analysis of public asthma microarray datasets, and find suitable diagnostic biomarkers for asthma. Methods First, three asthma-related datasets which were downloaded from the Gene Expression Omnibus (GEO) database were integrated into one pooled dataset after correcting for batch effects. Next, we screened differentially expressed lncRNAs (DElncRNAs) between patients and healthy subjects, constructed a ceRNA network using the StarBase database and screened ferroptosis–related genes from the predicted target mRNAs for Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We also performed Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) on the batch effect-corrected mRNA expression profile. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to screen potential diagnostic biomarkers, and the diagnostic efficacy was assessed using a receiver operating characteristic (ROC) curve. Finally, we determined the proportion of 22 immune cells in patients with asthma using CIBERSORT and investigated the correlation between key RNAs and immune cells. Results We obtained 19 DElncRNAs, of which only LUCAT1 and MIR222HG had corresponding target miRNAs. The differentially expressed ferroptosis-related genes were involved in multiple programmed cell death-related pathways. We also found that the mRNA expression profile was primarily enriched in innate immune system responses. We screened seven candidate diagnostic biomarkers for asthma using LASSO regression (namely, BCL10, CD300E, IER2, MMP13, OAF, TBC1D3, and TMEM151A), among which the area under the curve (AUC) value for CD300E and IER2 were 0.722 and 0.856, respectively. Finally, we revealed the infiltration ratio of different immune cells in asthma and found a correlation between LUCAT1, MIR222HG, CD300E, and IER2 with some immune cells. Conclusion This study explored a potential lncRNA-miRNA-mRNA regulatory network and its underlying diagnostic biomarkers (CD300E and IER2) in asthma and identified the immune cells most associated with them, providing possible diagnostic markers and immunotherapeutic targets for asthma.