Identification of key genes predicting the efficacy of mepolizumab in the treatment of severe eosinophilic asthma

Abstract

Abstract Severe eosinophilic asthma is a treatment-resistant subtype that remains poorly controlled with conventional therapies. According to the latest European Respiratory Society (ERS) guidelines, anti-interleukin-5 (anti-IL-5) biologics such as mepolizumab are recommended for these patients. However, not all individuals respond favorably to mepolizumab. This study aimed to identify gene expression biomarkers predictive of therapeutic response to mepolizumab, facilitating early identification of likely responders. Transcriptomic data (GSE274410) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between responder and non-responder groups were identified. Functional enrichment was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets via gene set enrichment analysis (GSEA). Protein–protein interaction (PPI) networks were constructed using the STRING database, and hub genes were identified with the cytoHubba plugin in Cytoscape. Box plots were used to visualize gene expression patterns. FOS and CXCL3 were significantly downregulated in the responder group. Their predictive value was further assessed using receiver operating characteristic (ROC) curve analysis. These findings suggest that FOS and CXCL3 may serve as promising biomarkers for predicting response to mepolizumab and may aid in stratifying patients with severe eosinophilic asthma for individualized biologic therapy.