Identification of Target Genes in Hypertension and Left Ventricular Remodeling

Abstract

INTRODUCTION: Hypertension occurs profoundly in the world, and left ventricular (LV) remodeling containing functional, structural, and mechanical changes induced by uncontrolled blood pressure is a well-known complication, however the underlying mechanism is still obscure. METHODS: To determine differences in gene expression profiles of hypertension and LV remodeling consequence to hypertension, Gene Expression Omnibus 2R online tool was used to identify differently expressed genes. Publicly available databases including GeneMANIA, database for annotation, visualization and integrated discovery, search tool for the retrieva predicting associated transcription factors (TF) from annotated affinities interacting genes, Predicting Associated TF from Annotated Affinities, JASPAR and Comparative Toxicogenomics Database (CTD) were accessed to perform an integrated bioinformatic analysis. RESULTS: Twenty-one genes (SEC14L3, EML7, PSMD7, PSMA1, GLRX, CNOT10, NBR1, DUSP12, STRAP, SMIM14, RBM8A, TMEM59, TMEM87A,PSMC1, CASP4, ITGB8, DNAJA1, PINK1, PRNP, SAP30L, and EIF3M) were found overexpression in both hypertension and hypertensive LV remodeling. Biological process analysis first revealed that enrichment of these target genes correlated with regulation of cellular amino acid metabolic process, antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-dependent and proteasome complex, 3 different expression genes (DEGs) participate significantly enriched in NFκB, WNT, and MAPK pathways, meanwhile, 47% DEGs displayed similar co-expression characteristics. Furthermore, the transcription factors associated with key DEGs were identified. Finally, the TF (HAND1, E4BP4, ESR1, VBP, ELK-1, POU3F2) associated with LV remodeling in hypertension were confirmed to act a crucial role in correlated heart diseases. CONCLUSION: The present study reveals the targeted genes probably associated with LV remodeling in hypertension by bioinformatics-based analyses, which provides clues for prognosis judgement and pharmacological therapies.