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

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.