Abstract
Complex network is an effective approach for analyzing the complex interactions in diseases. Hypertension is a complex multifactorial disease involving multiple biological pathways and interactions between genetic and environmental factors. By combining network approach with biological knowledge, this study constructs a pathway-based weighted network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationships between genes; in this network model a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power-law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and the edges with the larger weights tend to connect with the higher degree nodes. By introducing an integrated ranking index that comprehensively reflect the contribution of the three indices of nodes (strength, degree, and number of pathways), eight key hub genes are identified by the threshold of integrated ranking index larger than 0.60: Jun, Cdk4, RT1-Da, Pdgfra, Fn1, Actg1, Cycs, and Creb3l2. These genes can be regarded as candidate genes or drug targets for further biological and medical research on their functions. This study provides a new strategy for exploring the underlying mechanisms of hypertension, and further evidences again that complex network is an excellent tool for the study of complex diseases.
Original language | English |
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Article number | 122069 |
Journal | Physica A: Statistical Mechanics and its Applications |
Volume | 533 |
DOIs | |
Publication status | Published - 1 Nov 2019 |
Bibliographical note
Funding Information:This work was supported in part by the National Natural Science Foundation of China (NSFC) (Grant No. 11365023), the Basic Natural Science Research Program of Shaanxi Province, China (Grant Nos. 2017JQ6030 and 2018JQ2064), the Key Scientific Research Program of Department of Education of Shaanxi Province, China (Grant No. 16JS008), the Scientific Research Fund of Department of Education of Yunnan Province, China (Grant No. 2017ZZX233), and the Key Projects of Baoji University of Arts and Sciences, China (Grant Nos. ZK2017037 and ZK16053). We are grateful to the authors of Ref. [4] for providing the gene information of the SS rat, which is the basis of construction of our weighted network model. The authors would like to thank the anonymous reviewer for detailed comments and valuable suggestions, which led us to add Section 4.2.
Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) (Grant No. 11365023 ), the Basic Natural Science Research Program of Shaanxi Province, China (Grant Nos. 2017JQ6030 and 2018JQ2064 ), the Key Scientific Research Program of Department of Education of Shaanxi Province, China (Grant No. 16JS008 ), the Scientific Research Fund of Department of Education of Yunnan Province, China (Grant No. 2017ZZX233 ), and the Key Projects of Baoji University of Arts and Sciences, China (Grant Nos. ZK2017037 and ZK16053 ). We are grateful to the authors of Ref. [4] for providing the gene information of the SS rat, which is the basis of construction of our weighted network model. The authors would like to thank the anonymous reviewer for detailed comments and valuable suggestions, which led us to add Section 4.2 .
Keywords
- Edge weight
- Hub gene
- Hypertension
- Pathway
- Weighted complex network