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


Autoria(s): Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

03/12/2014

03/12/2014

25/10/2013

Resumo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Processo FAPESP: 10/20684-3

Processo FAPESP: 12/13450-1

Processo FAPESP: 12/00741-8

Processo FAPESP: 13/02018-4

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.

Formato

12

Identificador

http://dx.doi.org/10.1371/journal.pone.0077521

Plos One. San Francisco: Public Library Science, v. 8, n. 10, 12 p., 2013.

1932-6203

http://hdl.handle.net/11449/112607

10.1371/journal.pone.0077521

WOS:000326155400041

WOS000326155400041.pdf

Idioma(s)

eng

Publicador

Public Library Science

Relação

PLOS ONE

Direitos

openAccess

Tipo

info:eu-repo/semantics/article