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Front Genet. 2014 Feb 3;5:15. doi: 10.3389/fgene.2014.00015. eCollection 2014.

The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks.

Author information

1
Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK.
2
Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK.
3
Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Centre, University Health Network Toronto, Ontario, Canada.
4
Institute for Bioinformatics and Translational Research, UMIT, Eduard Wallnoefer Zentrum 1 Hall in Tyrol, Austria.

Abstract

In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

KEYWORDS:

BC3Net; GPEA; breast cancer; computational genomics; gene regulatory network; statistical inference

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