Bladder cancer specific pathway interaction networks

Abstract

With the development of high-Throughput technologies, monitoring biological systems comprehensively has became feasible and affordable. However, the transition from highthroughput data to the underlying biology of various phenotypes remains challenging. Pathway analysis identifies biological processes that are associated with a particular phenotype, which provides insights into the underlying biological mechanisms. Therefore, pathway analysis has became a popular tool for analyzing high-Throughput data. Most existing pathway analysis methods are based on a simple assumption that pathways act in isolation whereas they cooperate with each other in a complex manner. In this study, we focus on pathway interactions that are associated with bladder cancer risk. We identify disease-specific pathway-pathway interactions based on SNP-SNP interactions and gene-gene coexpression relationships. By analyzing the structure of pathway interaction networks, we highlight the "central" pathways that should be further studied.