Discovering distinct functional modules of specific cancer types using protein-protein interaction networks.
Discovering distinct functional modules of specific cancer types using protein-protein interaction networks.
复制标题
使用蛋白质-蛋白质相互作用网络发现特定癌症类型的独特功能模块。
DOI：
10.1155/2015/146365
复制
发表时间：
2015
影响因子：
--
通讯作者：
Guda,Chittibabu
中科院分区：
文献类型：
--
作者：
Shen,Ru;Wang,Xiaosheng;Guda,Chittibabu
背景在不同的癌症类型中表现出的分子谱是非常不同的;因此，发现与特定癌症类型相关的不同功能模块对于理解与它们相关的不同功能非常重要。蛋白质-蛋白质相互作用网络承载着细胞系统中分子相互作用的重要信息，识别这些网络中的功能模块（子图）是生物网络分析最重要的应用之一。在这项研究中，我们开发了一种新的基于图论的方法来识别来自九种不同癌症蛋白质相互作用网络的不同功能模块。该方法由三个主要步骤组成：（i）使用网络聚类算法从蛋白质-蛋白质相互作用网络中提取模块;（ii）从导出的模块中识别不同的子图;以及（iii）从不同的子图中识别不同的子图模式。使用实验确定的癌症特异性蛋白质-蛋白质相互作用数据评估子图模式，以识别特定于每种癌症类型的不同功能模块。我们确定了癌症类型特异性子图模式，这些模式可能代表参与不同癌症类型分子发病机制的功能模块。我们的方法可以作为一种有效的工具，从大型蛋白质-蛋白质相互作用网络中发现癌症类型特异性功能模块。
Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein‐protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis.Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein‐protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein‐protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer‐specific protein‐protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type.Conclusion. We identified cancer‐type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer‐type specific functional modules from large protein‐protein interaction networks.