Relationship Between Obesity and Hypertension From Bioinformatics Analysis

Abstract Background To explore the relationship between obesity and hypertension using bioinformatics analyses. Methods Disease databases (GeneCards, OMIM, CTD, TTD, DisGeNET, Drugbank) were used to obtain obesity-related targets. The intersection targets of constructed Venn diagrams. STRING online platform was protein–protein interaction networks common targets, Cytohubba plug-in screen core Gene ontology (GO) analysis enrichment Kyoto encyclopedia genes genomes (KEGG) carried out DAVID database. Results A total 459 551 obtained for hypertension, respectively. Among them, 135 both in which tumor necrosis factor (TNF), cell antigen p53 (TP53), chemokine 2 (CCL2), Toll-like receptor 4 (TLR4), interleukin (IL) 1B, nitric oxide synthase 3 (NOS3), IL-6, serine/threonine protein kinase 1 (AKT1) key regulating hypertension. Enrichment yielded 306 GO entries [P < 0.05, false discovery rate (FDR) <0.05], involved positive regulation mitogen-activated (MAPK) cascade, biosynthetic process, cilium basal body, hormone activity, RNA polymerase II repeating transcription binding. 86 enriched KEGG (P FDR <0.05) included adipocytokine signaling pathway, lipolysis adipocytes, PI3K–Akt pathway forkhead O (FoxO) pathway. Conclusions TNF, TP53, CCL2, TLR4, IL-1B, NOS3, AKT1, pathways including adipocyte lipolysis, FoxO play a regulatory role interactions These findings provide new insights into complex