Identification of Lung Cancer Related Function Modules Based on Co-Expression Network

Abstract

Objective: Identifying lung cancer disease-related functional modules is important to understand the mechanism of lung cancer. Methods: In this paper, we propose an integration method of mining disease-related functional module. Using microarray data of normal and lung cancer samples, firstly, rank-based method was applied to construct gene co-expression network. Secondly, gene co-expression modules were mined through Qcut, then disease-related functional modules were screened based on the joint measure of lung cancer differentially expressed genes and the functional consistency. Results: 7 significant disease-related functional modules were screened, which were closely linked with the development of lung cancer by literature confirmation. Further it found that our method could not only return the functional consistency modules, but also find two modules were associated with specific functional annotations named "virus response" that could not be identified by other methods. Conclusions: The method provided additional insights for finding new functional module, which will be helpful for the studies on the pathogenesis of human complex diseases.