Bioinformatics-based identification of mirdametinib as a potential therapeutic target for idiopathic pulmonary fibrosis associated with endoplasmic reticulum stress.

The molecular link between endoplasmic reticulum stress (ERS) and idiopathic pulmonary fibrosis (IPF) remains elusive. Our study aimed to uncover core mechanisms and new therapeutic targets for IPF. By analyzing gene expression profiles from the Gene Expression Omnibus (GEO) database, we identified 1519 differentially expressed genes (DEGs) and 11 ERS-related genes (ERSRGs) diagnostic for IPF. Using weighted gene co-expression network analysis (WGCNA) and differential expression analysis, key genes linked to IPF were pinpointed. CIBERSORT was used to assess immune cell infiltration, while the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore biological mechanisms. In three GEO datasets (GSE150910, GSE92592, and GSE124685), the receiver operating characteristic (ROC) curve analysis showed area under the ROC curve (AUC) > 0.7 for all ERSRGs. The Connectivity Map (CMap) database was used to predict small molecules modulating IPF signatures. The molecular docking energies of mirdametinib with protein targets ranged from - 5.1643 to - 8.0154 kcal/mol, while those of linsitinib ranged from - 5.6031 to - 7.902 kcal/mol. Molecular docking and animal experiments were performed to validate the therapeutic potential of identified compounds, with mirdametinib showing specific effects in a murine bleomycin-induced pulmonary fibrosis model. In vitro experiments indicated that mirdametinib may alleviate pulmonary fibrosis by reducing ERS via the PI3K/Akt/mTOR pathway. Our findings highlight 11 ERSRGs as predictors of IPF and demonstrate the feasibility of bioinformatics in drug discovery for IPF treatment.