Discovering distinct functional modules of specific cancer types using protein-protein interaction networks.

Ru Shen,Chittibabu Guda,Xiaosheng Wang

doi:10.1155/2015/146365

Ru Shen, Chittibabu Guda + Show 1 more

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https://doi.org/10.1155/2015/146365

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Abstract

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.

Highlights

The molecular profiles exhibited in different cancer types are very different; discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them
Cancer PPI networks were constructed from a comprehensive, nonredundant dataset of experimentally derived PPIs that were collected from five major databases including IntAct [10], MINT [11], HPRD [12], DIP [13], and BIND [14]
From the network modules generated by RNSC, we identified hundreds of distinct subgraphs for each of the nine cancer PPI networks (Table 1) by filtering out those that appear in other networks, including those that are subgraphs of other modules based on the edge set enclosure

Summary

Introduction

The molecular profiles exhibited in different cancer types are very different; discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. We developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks. We studied the commonality among the nine PPI networks and identified the common modules that frequently occur in these networks These common modules could be functionally important as they were frequently identified in multiple cancer types. In contrast to our previous study, this study is focused on discovering distinct cancer-specific functional modules that could offer direct targets for effective drug discovery. Distinct modules are those that exist exclusively in one network and can be discovered by finding distinct patterns in PPI networks. From the graph theory perspective, identification of distinct patterns is differential from identification of common patterns, in that the latter converges as the size of modules increase, while the former diverges

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