Identification of oxidative stress-related genes associated with immune cells in Aortic Valve Stenosis based on bioinformatics analysis

Aortic valve stenosis (AS) is the most common clinical valvular heart disease. Without effective pharmaceutical therapy at present, identifying effective therapeutic targets is critical. However, the pathological and molecular mechanisms of aortic stenosis are complex, including inflammatory infiltration, oxidative stress and so on. In this study, we investigated how oxidative stress interacts with immune cell infiltration in aortic stenosis using bioinformatics analysis, and provide a better understanding of aortic valve stenosis at the pathophysiologic level. After obtaining the datasets, including GSE153555, GSE51472 and GSE12644, from the Gene Expression Omnibus (GEO) database, the package 'limma' was applied to identify the differentially expressed genes (DEGs) in GSE153555. The GeneCards database searched for oxidative stress-related genes. We evaluated the expression of 22 immune cells using Cibersort. Clustering differentially expressed genes into different modules via Weighted gene correlation network analysis (WGCNA) and exploring the relationship among modules and immune cell types. The genes in modules intersected with oxidative stress-related genes to find oxidative stress genes related to immune infiltration. Finally, the GSE51472 and GSE12644 datasets were used to initially verify oxidative stress-related genes in aortic valve stenosis. A total of 1213 differentially expressed genes were identified in the GSE153555 dataset, and 279 of them were oxidative stress-related genes. Increased infiltration of B cell navie and Macrophages M1 in aortic stenosis was found. Using WGCNA, we clustered 15 modules. The brown module was identified as the most significant template positively correlated with T-cell regulatory Treg, and the magenta module was identified as a critical module associated with M1 macrophages with the highest negative correlation coefficient. The results verified by other datasets showed that in comparison to normal people, the aortic stenosis patients exhibited dramatically high IGFBP2 and SPHK1 expression. Both IGFBP2 and SPHK1 may be significantly involved in the mechanism of aortic stenosis pathophysiologically and can be used for aortic stenosis early detection, therapy, and therapeutic targets.