A pathway-based network analysis of hypertension-related genes

Huan Wang, Jing Bo Hu, Chuan Yun Xu, De Hai Zhang, Qian Yan, Ming Xu, Ke Fei Cao*, Xu Sheng Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

Original languageEnglish
Pages (from-to)928-939
Number of pages12
JournalPhysica A: Statistical Mechanics and its Applications
Publication statusPublished - 15 Feb 2016

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) (Grant Nos. 11365023 and 61263043 ), the Projects of the Science and Technology Research and Development Program of Baoji City (Grant Nos. 15RKX-1-5-14 and 15RKX-1-5-6 ), the Key Project of Baoji University of Arts and Sciences (Grant No. ZK14035 ), and the Joint Fund of Department of Science and Technology of Guizhou Province, Bureau of Science and Technology of Qiandongnan Prefecture, and Kaili University (Grant No. LH-2014-7231 ). We are grateful to the authors of Ref. [24] for providing the gene information of the SS rat, which is the basis of construction of our network model. The authors would like to thank Professor Huai Cao for his helpful discussions and suggestions.

Publisher Copyright:
© 2015 Elsevier B.V.


  • Complex network
  • Hub gene
  • Hypertension
  • Modular structure
  • Pathway


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