A pathway-based network analysis of hypertension-related genes

TL;DR

This study constructs a gene network model based on biological pathways to analyze hypertension-related genes, revealing key hub genes and network properties that could inform multi-gene treatment strategies.

Contribution

It introduces a pathway-based network model of hypertension-related genes, identifying key hub genes and network features relevant to hypertension mechanisms.

Findings

Network has small-world but not scale-free properties.

Seven key hub genes identified: Jun, Rps6kb1, Cycs, Creb3l2, Cdk4, Actg1, RT1-Da.

Network exhibits modular structure.

Abstract

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, Creb3l2, 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.