Identification of Oxidative Stress-Associated Biomarkers in Chronic Obstructive Pulmonary Disease: An Integrated Bioinformatics Analysis.

Purpose: Chronic obstructive pulmonary disease (COPD) is among the three leading causes of death worldwide, with its prevalence, morbidity, and mortality rates increasing annually. Oxidative stress (OS) is a key mechanism in COPD development, making the identification of OS-related biomarkers beneficial for improving its diagnosis and treatment. Methods: The genetic data from patients with COPD and controls were obtained from the Gene Expression Omnibus database to identify OS-related genes (OSRGs). Functional enrichment analysis was conducted using the Kyoto encyclopedia of genes and genomes signaling pathway and gene ontology (GO). Protein-protein interaction networks were constructed to identify the core genes, which were further evaluated using receiver operating characteristic (ROC) curves. Diagnostic models were developed based on the core genes. Besides, the correlation between the expression of the core genes and the immune cells was analyzed using single-sample gene set enrichment analysis. Drug-gene interactions were explored to predict target drugs, and related microribonucleic acid (miRNA) and transcription factors (TFs) were identified using miRNet. Results: In this study, we identified 299 differential genes, including 16 OSRGs. Among these, five core genes-heat shock protein family A (Hsp70) member 1A (HSPA1A), glutamate-cysteine ligase modifier subunit, interleukin-1 beta (IL-1β), intercellular adhesion molecule 1 (ICAM1), and glutamate-cysteine ligase catalytic subunit (GCLC)-were screened and validated using ROC curve analysis. The results of GO enrichment analysis were mainly focused on the OS response, the negative regulation of the exogenous apoptosis signaling pathway, and the regulation of the apoptosis signaling pathway. Additionally, 33 target drugs were predicted, including ofloxacin, cisplatin, and pegolimumab, among others. Meanwhile, the regulatory networks comprising 33 miRNAs related to the core genes and 38 TFs associated with HSPA1A, IL-1β, ICAM1, and GCLC were constructed. A diagnostic model based on the five genes was constructed and validated with an area under the curve of 0.981 (95% confidence interval: 0.941-1.000). Conclusion: This study identifies potential biomarkers for diagnosing COPD, new potential targets, and new directions for drug development and treatment.