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

Ru Shen, Xiaosheng Wang, Chittibabu Guda

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Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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3 authors.

Ru ShenDepartment of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.

Xiaosheng WangDepartment of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.

Chittibabu GudaDepartment 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.

Funding

Cataloging the subcellular and suborganellar proteomes of sequenced genomesR01GM086533 · NIGMS · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI GUDA, CHITTIBABU · 2009 to 2013

$911k

NIGMS NIH HHS 1R01GM086533NIGMS NIH HHS R01 GM086533

6 · The paper itself

Abstract

backgroundThe 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.

resultsIn 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.

conclusionWe 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.

Indexed as

Models, BiologicalAlgorithmsAnimalsBiomarkers, TumorComputer SimulationHumansNeoplasm ProteinsNeoplasmsProtein Interaction MappingProtein Interaction MapsBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID26495282

DOI10.1155/2015/146365

PMCPMC4606133

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