LAUSR

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.