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

Autor(es)

Acencio, Marcio Luis ^[1] ; Bovolenta, Luiz Augusto ^[1] ; Camilo, Esther ^[1] ; Lemke, Ney ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Paulista, Dept Phys & Biophys, Botucatu Biosci Inst, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 8, n. 10 OCT 25 2013.
• Citações Web of Science: 5

Resumo

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)

• Processo FAPESP: 13/02018-4 - Aprendizado de máquina em biologia molecular de sistemas (AMBiS) aplicação em letalidade sintética, genes condicionalmente essenciais e transcrição gênica cooperativa
• Beneficiário: Ney Lemke
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/00741-8 - Previsão de fenótipos em Escherichia coli através de redes biológicas e aprendizado de máquina
• Beneficiário: Esther Camilo dos Reis
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 10/20684-3 - Utilização de aprendizado de máquina em redes biológicas para previsão e determinação de regras para emergência de fenótipos de interesse
• Beneficiário: Marcio Luis Acencio
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 12/13450-1 - Análise exploratória em larga escala de miRNAs expressos em tilápia do Nilo utilizando ferramentas de bioinformática.
• Beneficiário: Luiz Augusto Bovolenta
• Modalidade de apoio: Bolsas no Brasil - Doutorado