%0 Journal Article %T Identification and Validation of Novel Biomarkers Related to the Calcium Metabolism Pathway in Hypertension Patients Based on Comprehensive Bioinformatics Methods %A Xiangguang Chang %A Lei Guo %A Liying Zou %A Yazhao Ma %A Jilin Feng %J Health %P 173-186 %@ 1949-5005 %D 2024 %I Scientific Research Publishing %R 10.4236/health.2024.163015 %X Background: Hypertension is a universal risk factor for cardiovascular diseases and is thus the leading cause of death worldwide. The identification of novel prognostic and pathogenesis biomarkers plays a key role in disease management. Methods: The GSE145854 and GSE164494 datasets were downloaded from the Gene Expression Omnibus (GEO) database and used for screening and validating hypertension signature genes, respectively. Gene Ontology (GO) enrichment analysis was performed on the differentially expressed genes (DEGs) related to calcium ion metabolism in patients with hypertension. The core genes related to immune infiltration were analyzed and screened, and the activity of the signature genes and related pathways was quantified using gene set enrichment analysis (GSEA). The infiltration of immune cells in the blood samples was analyzed, and the DEGs that were abnormally expressed in the clinical blood samples of patients with hypertension were verified via RT-qPCR. Results: A total of 176 DEGs were screened. GO showed that DEGs was involved in the regulation of calcium ion metabolism in biological processes (BP), actin mediated cell contraction, negative regulation of cell movement, and calcium ion transmembrane transport, and in the regulation of protease activity in molecular functions (MF). KEGG analysis revealed that the DEGs were involved mainly in the cGMP-PKG signaling pathway, ubiquitin-protein transferase, tight junction-associated proteins, and the regulation of myocardial cells. MF analysis revealed the immune infiltration function of the cells. RT-qPCR revealed that the expression of %K Hypertension %K Biomarkers %K Differentially Expressed Genes %K Ca< %K sup> %K 2+< %K /sup> %K Metabo-lism %K Bioinformatics Analysis %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=131783