TY - JOUR
T1 - Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures
AU - Li, Yongsheng
AU - Sahni, Nidhi
AU - Yi, Song
N1 - Funding Information:
This work was supported by the Cancer Prevention and Research Institute of Texas (CPRIT) New Investigator Grant Award RR160021, the University of Texas System Rising STARs award, the NIH/NCI grants P30CA016672, and the University Center Foundation via the Institutional Research Grant program at the University of Texas MD Anderson Cancer Center (to N.S.).
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Comparative network analysis
KW - Network centrality
KW - Prioritization of cancer genes
KW - Protein interaction networks
KW - Systems biology
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U2 - 10.18632/oncotarget.12879
DO - 10.18632/oncotarget.12879
M3 - Article
C2 - 27791983
AN - SCOPUS:84999635257
SN - 1949-2553
VL - 7
SP - 78841
EP - 78849
JO - Oncotarget
JF - Oncotarget
IS - 48
ER -