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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Research

Mining functional subgraphs from cancer protein-protein interaction networks

Ru Shen12, Nalin CW Goonesekere3 and Chittibabu Guda14*

Author Affiliations

1 Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA

2 Department of Computer Science, State University of New York at Albany, 1400 Washington Ave., Albany, NY 12222, USA

3 Department of Chemistry and Biochemistry, University of Northern Iowa, Cedar Falls, IA 50614, USA

4 Bioinformatics and Systems Biology Core Facility, University of Nebraska Medical Center, Omaha, NE, 68198, USA

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BMC Systems Biology 2012, 6(Suppl 3):S2  doi:10.1186/1752-0509-6-S3-S2

Published: 17 December 2012

Abstract

Background

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. 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.

Results

In 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.

Conclusion

Frequent 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.