Abstract
This study aims to elucidate the underlying mechanisms of pyroptosis in hypertension through bioinformatics and machine learning approaches. R language was utilized to integrate differentially expressed genes (DEGs) between hypertension samples and healthy control samples in GSE24752 and GSE75360 datasets, followed by GO analysis, KEGG enrichment analysis, and GSEA. Key genes were screened based on the expression levels of DEGs using logistic regression, LASSO regression, and support vector machine (SVM). A visualized protein-protein interaction regulatory network was constructed, and immune cell infiltration analysis was performed on integrated GEO datasets of hypertensive samples. Collect serum samples from hypertensive subjects and healthy control subjects for RT-qPCR detection of key gene expression. A total of 1005 DEGs were obtained from peripheral blood samples of 13 hypertension cases and 14 control samples. GO analysis, KEGG enrichment analysis, and GSEA revealed that the DEGs function synergistically in various biological pathways. LASSO regression and SVM identified six key genes related to pyroptosis (CASP7 (caspase-7), CYBB, NEK7, NLRP2, RAB5A, VDR (vitamin D receptor)). Immune infiltration analysis showed that activated B cell, effector memory CD8 T cell, immature B cell, MDSC, and T follicular helper cell accounted for the largest proportion of immune cells. RT-qPCR results indicated significantly higher relative expression levels of caspase-7 and vitamin D receptor in hypertensive samples compared to controls. These findings suggest that CASP7 and the vitamin D receptor gene may offer new research targets for the diagnosis and treatment of hypertension, and they also provide fresh evidence for the involvement of pyroptosis in hypertension.