Extending pathways and processes using molecular interaction networks to analyse cancer genome data

Glaab, Enrico; Baudot, Ana�s; Krasnogor, Natalio; Valencia, Alfonso

Abstract

BACKGROUND: Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.

RESULTS: We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes.

CONCLUSIONS: The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.

Citation

Glaab, E., Baudot, A., Krasnogor, N., & Valencia, A. (2010). Extending pathways and processes using molecular interaction networks to analyse cancer genome data. BMC Bioinformatics, 11(597),

• Journal Article Type: Article
• Publication Date: Dec 13, 2010
• Deposit Date: Jan 11, 2011
• Publicly Available Date: Jan 11, 2011
• Journal: BMC Bioinformatics
• Electronic ISSN: 1471-2105
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 597
• Public URL: https://nottingham-repository.worktribe.com/output/707036
• Publisher URL: http://www.biomedcentral.com/1471-2105/11/597
• Additional Information: This is an Open Access article distributed under the terms of the Creative Commons; Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in; any medium, provided the original work is properly cited.