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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

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Acencio, Marcio Luis [1] ; Bovolenta, Luiz Augusto [1] ; Camilo, Esther [1] ; Lemke, Ney [1]
Total Authors: 4
[1] Univ Estadual Paulista, Dept Phys & Biophys, Botucatu Biosci Inst, Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: PLoS One; v. 8, n. 10 OCT 25 2013.
Web of Science Citations: 5

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. (AU)

FAPESP's process: 10/20684-3 - Development of machine learning approaches based on biological networks for prediction and determination of rules governing the emergence of phenotypes of interest
Grantee:Marcio Luis Acencio
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 12/13450-1 - Large scale exploratory analysis of Nile Tilapia's miRNA expression using bioinformatics tools
Grantee:Luiz Augusto Bovolenta
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 13/02018-4 - Machine learning for molecular systems biology (MLMSB) application on synthetic lethality, conditionally essential genes and cooperative transcription
Grantee:Ney Lemke
Support type: Regular Research Grants
FAPESP's process: 12/00741-8 - Prediction of Escherichia coli phenotypes through biological networks and machine learning
Grantee:Esther Camilo dos Reis
Support type: Scholarships in Brazil - Doctorate