Bioinformatics analysis of ferroptosis-related gene AKR1C3 as a potential biomarker of asthma and its identification in BEAS-2B cells

Abstract

Ferroptosis is a newly discovered type of cell death and has recently been shown to be associated with asthma. However, the relationship between them at the genetic level has not been elucidated via informatics analysis. In this study, bioinformatics analyses are conducted using asthma and ferroptosis datasets to identify candidate ferroptosis-related genes using the R software. Weighted gene co-expression network analysis is performed to identify co-expressed genes. Protein–protein interaction networks, the Kyoto encyclopedia of genes and genomes, and gene ontology enrichment analysis are used to identify the potential functions of the candidate genes. We experimentally validate the results of our analysis using small interfering RNAs and plasmids to silence and upregulate the expression of the candidate gene in human bronchial epithelial cells (BEAS-2B). The ferroptosis signature levels are examined. Bioinformatics analysis of the asthma dataset GDS4896 shows that the level of the aldo-keto reductase family 1 member C3 (AKR1C3) gene in the peripheral blood of patients with severe therapy-resistant asthma and controlled persistent mild asthma (MA) is significantly upregulated. The AUC values for asthma diagnosis and MA are 0.823 and 0.915, respectively. The diagnostic value of AKR1C3 is verified using the GSE64913 dataset. The gene module of AKR1C3 is evident in MA and functions through redox reactions and metabolic processes. Ferroptosis indicators are downregulated by the overexpression of AKR1C3 and upregulated by silencing AKR1C3. The ferroptosis-related gene AKR1C3 can be used as a diagnostic biomarker for asthma, particularly for MA, and regulates ferroptosis in BEAS-2B cells.