Roles of Immune and Oxidative Stress-Related Factors in the Diagnosis of Coronary Artery Disease: A Retrospective Study.

Abstract

Coronary artery disease (CAD) is one of the main causes of sudden death, but its exact pathogenesis requires further study. Thus, this study aimed to explore the immune and oxidative stress-related factors in CAD progression and their roles in CAD diagnosis. Bioinformatics analysis was used in this study, and the GSE23561 dataset (training set) we used contained the transcriptome sequencing results of six CAD peripheral blood samples and nine control samples. The data were obtained and analysed by querying the Gene Expression Omnibus database. First, the differentially expressed immune and oxidative stress-related genes (DEIOGs) between the groups were identified. DEIOGs were then analysed based on Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. A protein-protein interaction (PPI) network for DEIOGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and hub genes were identified through the PPI network. Moreover, transcription factors and microRNAs (miRNAs) targeting hub genes were identified to explore the potential regulatory mechanisms of hub genes. The receiver operating characteristic (ROC) curve analysis was constructed to examine the role of hub genes in CAD diagnosis. Finally, the data of GSE23561 (validated set) were used to validate the diagnostic potential of these hub genes. Primarily, 66 DEIOGs were identified, which are involved in many important pathways related to CAD, such as the "mitogen-activated protein kinase (MAPK) signalling pathway" and "lipid and atherosclerosis". A PPI network of DEIOGs was then constructed, and 10 hub genes were identified sequentially. A total of 37 transcription factors and 481 miRNAs that played important roles in hub genes regulation were identified. The ROC curves indicated that five special hub genes (Fos, Il6, Jun, Mapk3, and Mmp9) could serve as potential diagnostic biomarkers for CAD. Bioinformatics analysis technology was used to identify 10 hub DEIOGs that might play a crucial role in CAD progression, and five special hub genes (Fos, Il6, Jun, Mapk3, and Mmp9) could be regarded as potential biomarkers for CAD diagnosis. However, further studies are required to verify the functions of these hub genes.