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