The prevalence of
hypertension reported around the world is increasing
and is an important public health challenge. This study was designed
to explore the disease’s genetic variations and to identify
new hypertension-related genes and target proteins. We analyzed 22
publicly available Affymetrix cDNA datasets of hypertension using
an integrated system-level framework involving differential expression
genetic (DEG) analysis, data mining, gene enrichment, protein–protein
interaction, microRNA analysis, toxicogenomics, gene regulation, molecular
docking, and simulation studies. We found potential DEGs after screening
out the extracellular proteins. We studied the functional role of
seven shortlisted DEGs (ADM, EDN1, ANGPTL4, NFIL3, MSR1, CEBPD, and
USP8) in hypertension after disease gene curation analysis. The expression
profiling and cluster analysis showed significant variations and enriched
GO terms. hsa-miR-365a-3p, hsa-miR-2052, hsa-miR-3065-3p, hsa-miR-603,
hsa-miR-7113-3p, hsa-miR-3923, and hsa-miR-524-5p were identified
as hypertension-associated miRNA targets for each gene using computational
algorithms. We found functional interactions of source DEGs with target
and important gene signatures including EGFR, AGT, AVP, APOE, RHOA,
SRC, APOB, STAT3, UBC, LPL, APOA1, and AKT1 associated with the disease.
These DEGs are mainly involved in fatty acid metabolism, myometrial
pathways, MAPK, and G-alpha signaling pathways linked with hypertension
pathogenesis. We predicted significantly disordered regions of 71.2,
48.8, and 45.4% representing the mutation in the sequence of NFIL3,
USP8, and ADM, respectively. Regulation of gene expression was performed
to find upregulated genes. Molecular docking analysis was used to
evaluate Food and Drug Administration-approved medicines against the
four DEGs that were overexpressed. For each elevated target protein,
the three best drug candidates were chosen. Furthermore, molecular
dynamics (MD) simulation using the target’s active sites for
100 ns was used to validate these 12 complexes after docking. This
investigation establishes the worth of systems genetics for finding
four possible genes as potential drug targets for hypertension. These
network-based approaches are significant for finding genetic variant
data, which will advance the understanding of how to hasten the identification
of drug targets and improve the understanding regarding the treatment
of hypertension.