基于共表达网络挖掘肺癌相关模块

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

摘要: 目的:识别肺癌疾病相关功能模块，对了解肺癌疾病的发病机制至关重要。方法：本文提出一个挖掘疾病相关功能模块的整合方法。采用包括正常和肺癌样本的微阵列数据，首先，应用rank-based方法构建基因共表达网络；其次，通过Qcut挖掘基因共表达模块；然后基于肺癌差异表达基因及基因模块功能一致性的联合测度，最终筛选出疾病相关功能模块。结果：研究发现，我们的方法获得7个显著疾病相关功能模块，经文献证实都与肺癌的发生发展有着密切的联系。进一步分析发现不仅能获得与传统方法功能一致的模块，而且还发现了传统方法没有获得的病毒层面的两个模块(模块351和352)。结论：我们的方法能够有效地发现新的功能模块，为探索癌症致病机理提供新的视角及依据。

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 mod-ule. 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 con-sistency. 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 func-tional 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 find-ing new functional module, which will be helpful for the studies on the pathogenesis of human complex diseases.