Research Papers:

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Yongsheng Li, Nidhi Sahni and Song Yi _

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Oncotarget. 2016; 7:78841-78849. https://doi.org/10.18632/oncotarget.12879

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Yongsheng Li1,*, Nidhi Sahni1,2, Song Yi1,*

1Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

2Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA

*These authors contributed equally to this work

Correspondence to:

Nidhi Sahni, email: Nsahni@mdanderson.org

Song Yi, email: Syi2@mdanderson.org

Keywords: comparative network analysis, protein interaction networks, prioritization of cancer genes, network centrality, systems biology

Received: August 04, 2016     Accepted: October 14, 2016     Published: October 25, 2016


Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

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