Abstract
BackgroundProtein-protein interaction (PPI) networks carry vital information about proteins' functions. Analysis of PPI networks associated with specific disease systems including cancer helps us in the understanding of the complex biology of diseases. Specifically, identification of similar and frequently occurring patterns (network motifs) across PPI networks will provide useful clues to better understand the biology of the diseases.ResultsIn this study, we developed a novel pattern-mining algorithm that detects cancer associated functional subgraphs occurring in multiple cancer PPI networks. We constructed nine cancer PPI networks using differentially expressed genes from the Oncomine dataset. From these networks we discovered frequent patterns that occur in all networks and at different size levels. Patterns are abstracted subgraphs with their nodes replaced by node cluster IDs. By using effective canonical labeling and adopting weighted adjacency matrices, we are able to perform graph isomorphism test in polynomial running time. We use a bottom-up pattern growth approach to search for patterns, which allows us to effectively reduce the search space as pattern sizes grow. Validation of the frequent common patterns using GO semantic similarity showed that the discovered subgraphs scored consistently higher than the randomly generated subgraphs at each size level. We further investigated the cancer relevance of a select set of subgraphs using literature-based evidences.ConclusionFrequent common patterns exist in cancer PPI networks, which can be found through effective pattern mining algorithms. We believe that this work would allow us to identify functionally relevant and coherent subgraphs in cancer networks, which can be advanced to experimental validation to further our understanding of the complex biology of cancer.
Highlights
Protein-protein interaction (PPI) networks carry vital information about proteins’ functions
Cancer protein interaction networks Our PPI networks are constructed from a comprehensive, non-redundant dataset of experimentally-derived PPIs [22] that are collected from five major databases including IntAct [23], MINT [24], HPRD [25], DIP [26] and BIND [27]
Our goal is to mine for cancer-associated subgraphs from the global interaction networks; PPI data that are specific to a cancer tumor do not exist in the public domain
Summary
Protein-protein interaction (PPI) networks carry vital information about proteins’ functions. Analysis of PPI networks associated with specific disease systems including cancer helps us in the understanding of the complex biology of diseases. Analysis of PPI networks associated with specific disease systems including cancer helps us to better understand the complex biology of diseases. Using graph theory-based algorithms, pairs of networks can be compared to identify common, distinct or frequent sub-networks. These sub-networks containing a set of proteins (nodes) with a distinct set of connections (edges) can represent a functional unit in a pathway or in a biological process. We focus on developing a graph-based algorithm to identify common and frequent network motifs from PPI networks of nine different cancers
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