Identification of Crucial Genes Associated with Ferroptosis in COPD via Comprehensive Bioinformatics Analysis and the Relevant Experimental Validation

Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by partially reversible airway obstruction, with high mortality and disability rates. Smoking is the primary risk factor for COPD. Ferroptosis is a novel form of cell death characterized by iron-mediated lipid peroxidation induced by reactive oxygen species (ROS) generated in the Fenton reaction. Recent studies have shown that ferroptosis in airway epithelial cells may be involved in and mediate the pathogenesis of COPD. This study aimed to identify and validate key genes associated with ferroptosis in COPD via bioinformatics methods. Methods: Four microarray datasets (GSE10006, GSE20257, GSE11906, and GSE11784) were downloaded from the GEO database. Differential gene expression analysis was conducted separately for each dataset via the limma package in R, resulting in a set of 132 overlapping differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was employed to identify key gene modules associated with COPD. String analysis, Cytoscape, functional enrichment analysis, and construction of protein‒protein interaction (PPI) networkswere utilized to identify hub genes.We subsequently generated a receiver operating characteristic (ROC) curve to predict the risk of COPD occurrence. Concurrently, we conducted differential expression analysis of ferroptosis-related genes across three datasets and identified ferroptosis-related hub genes (FRHGs) that overlapped with pivotal genes related to ferroptosis. These FRHGs were validated via the GSE11784 dataset, followed by validation via in vitro cell experiments (westernblotting, quantitative PCR). Finally, we analyzed immune cell infiltration and performed consistent clustering analysis on the basis of gene set enrichment analysis (GSEA) scores. Results: We identified four potential hub genes associated with ferroptosis in COPD (NQO1, AKR1C3, GPX2, and CBR1), identifying new therapeutic targets for clinical treatment and diagnosis. Additionally, on the basis of these four FRHGs, we found that acetaminophen and glycidamide were highly relevant drug targets. Conclusion: This study identified 4 FRHGs as potential biomarkers for COPD diagnosis and treatment. We predict COPD occurrence through bioinformatics analysis and various machine learning algorithms. Moreover, cell experiments revealed significant upregulation trends of the FRHGs identified in this study in COPD disease models, suggesting new avenues for clinical diagnosis and treatment strategies.