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Predicating Candidate Cancer-Associated Genes in the Human Signaling Network Using Centrality

[ Vol. 11 , Issue. 1 ]


Xueming Liu and Linqiang Pan   Pages 87 - 92 ( 6 )


The development of cancer evolves gene mutations according to the somatic mutation theory. The identification and prediction of the cancer-associated genes is one of the most important aims in cancer research. We apply four centrality metrics (degree, betweenness, closeness and PageRank) to prioritize and predict the candidate cancer-associated genes in the human signaling network. We find that the genes with higher centrality scores are more likely to be cancer-associated. Taking the top 47 genes for each centrality measure, we get 89 central genes. Among these 89 central genes, 58 genes are known to be cancer-associated, 4 genes encode non-protein and 27 genes are inferred genes. For the 27 inferred genes, by literature mining we find that 21 genes have been confirmed to be cancerassociated and the other 6 genes (CAMP, GSK3A, MTG1, GNGT1, ISGF3G and DYT10) are strong candidates for cancer research. These results show that the four centrality metrics are effective in predicting candidate cancer-associated genes for further experimental analysis.


Human signaling network, cancer-associated gene, systems biology, complex network, centrality, PageRank, betweeness.


Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

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