Identification and validation of oxidative stress-related genes in patients with atrial fibrillation

Abstract

Background: Atrial fibrillation (AF) significantly elevates the risk of ischemic stroke.The upsurge in cardiovascular diseases associated with aging is, in part, attributed to oxidative stress.The objective of this research was to discover key genes associated with oxidative stress (OSGs) that could be used as biomarkers for diagnosing AF using bioinformatics analysis. Methods: Utilizing the AmiGO 2 database, cellular OSGs were identified.The AF patient datasets GSE115574 and GSE79768 were obtained from the Gene Expression Omnibus (GEO) database. GSE115574 was designated as the training set, while GSE79768 served as the validation set. Differentially expressed genes (DEGs) associated with AF were identified specifically from the GSE115574 dataset. DEOSGs resulted from the intersection of OSGs and DEGs, followed by bioinformatics analysis to determine hub genes. Potential diagnostic genes were identified through analyses of gene expression, ROC curves, and nomograms. The miRNA-diagnosis gene regulatory network was established. Finally, targeted drug predictions were conducted. Results: A total of 339 DEGs were identified from GSE115574, and 452 OSGs were obtained from the AmiGO 2 database. The intersection of DEGs and OSGs comprised 18 DEOSGs, including 12 oxidative stress-suppressor genes and 6 oxidative stress-inducible genes. Ten hub genes, namely JUN, ADIPOQ, AREG, COL1A1, FOS, IL6, KLF4, NR4A2, SOD2, and UCP2, were chosen. Additionally, five diagnostic genes—JUN, AREG, KLF4, SOD2, and UCP2—were identified. ROC analysis revealed the area under the curves (AUCs) of KLF4, JUN, UCP2, AREG, and SOD2 to be 0.733, 0.800, 0.760, 0.684, and 0.640 in the GSE115574 and 0.833, 0.786, 0.667, 0.952, and 0.786 in the GSE79768 dataset. Lastly, leveraging these five diagnostic genes, we identified potential drugs, such as 1,2-Dimethylhydrazine, for targeting oxidative stress-related AF treatment. Conclusion: The study findings suggest a significant involvement of OSGs in AF. JUN, AREG, KLF4, SOD2, and UCP2 emerge as potential specific biomarkers for early AF diagnosis and therapeutic targeting.