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Biosystems. 2018 Oct;172:18-25. doi: 10.1016/j.biosystems.2018.08.002. Epub 2018 Aug 12.

Pathway-based gene-gene interaction network modelling to predict potential biomarkers of essential hypertension.

Author information

1
Key Laboratory of Phytochemistry, College of Chemistry and Chemical Engineering, Baoji University of Arts and Sciences, Baoji, 721013, China.
2
Department of Orthopedics, Weinan Central Hospital, Shaanxi, 714000, China.
3
College of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji, 721013, China; Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University, Kunming, 650091, China.
4
College of Computer Science and Technology, Baoji University of Arts and Sciences, Baoji, 721013, China.
5
Key Laboratory of Phytochemistry, College of Chemistry and Chemical Engineering, Baoji University of Arts and Sciences, Baoji, 721013, China. Electronic address: xlwangwang@163.com.

Abstract

Essential hypertension (EH) is a major risk factor for cardiovascular disease. Despite considerable efforts to elucidate the pathogenesis of EH, there is an imperious need for novel indicators of EH. This study aimed to develop a method to predict potential biomarkers of EH from the point of view of network. A pathway-based gene-gene interaction (GGI) network model was constructed and analyzed, containing 116 nodes and 1272 connections. The nodes represented EH-related genes, and that connections represented their interactions. The network showed a small-world property and uneven degree distribution, suggesting that a few highly interconnected hubs played a vital role in EH. An inherent hierarchy and assortative mixing pattern were also observed in the network. GNAS, GNB3, PF4 and PPBP showed the highest values of degrees and centrality indices, and were chosen as potential biomarkers of EH. A two-mode network model based on the potential biomarkers demonstrated that hemostasis and GPCR ligand binding pathway were key pathways contributing to EH. Results of this study improve our current understanding of the molecular mechanisms driving EH. The selected genes and pathways have the potential to be used in the diagnosis and treatment of EH. Moreover, the combination of pathway analysis and complex network methodology provides a novel strategy for searching new genetic indicators of complex diseases.

KEYWORDS:

Biomarkers; Complex network; Essential hypertension; Gene-gene interaction; Pathway

[Indexed for MEDLINE]

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