Shen Ru, Wang Xiaosheng, Guda Chittibabu
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA ; Bioinformatics and Systems Biology Core, University of Nebraska Medical Center, Omaha, NE 68198, USA ; Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA ; Fred and Pamela Buffet Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Biomed Res Int. 2015;2015:146365. doi: 10.1155/2015/146365. Epub 2015 Sep 30.
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.
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.
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.
不同癌症类型中呈现的分子特征差异很大；因此，发现与特定癌症类型相关的独特功能模块对于理解与之相关的独特功能非常重要。蛋白质-蛋白质相互作用网络携带了细胞系统中分子相互作用的重要信息，而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。
在本研究中，我们开发了一种基于图论的新方法，用于从九个不同的癌症蛋白质-蛋白质相互作用网络中识别独特的功能模块。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）从派生模块中识别独特的子图；（iii）从独特子图中识别独特子图模式。使用来自英睿达知识库的实验确定的癌症特异性蛋白质-蛋白质相互作用数据对子图模式进行评估，以识别每种癌症类型特有的独特功能模块。
我们识别出了癌症类型特异性子图模式，这些模式可能代表了参与不同癌症类型分子发病机制的功能模块。我们的方法可作为从大型蛋白质-蛋白质相互作用网络中发现癌症类型特异性功能模块的有效工具。
