Assessing network characteristics of cancer associated genes in metabolic and signaling networks

In the post-genome era, high-throughput experimental methods have elucidated many of the complex interactions in metabolic, regulatory, and signal transduction pathways. Graph theoretic methods have been broadly applied to study properties of these interactions. Here we explore the relationship between network properties of genes and their implication in cancer etiology. We extract pathway interactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) to create global signaling and metabolic networks. Using a generalized linear model, we evaluate the predictive power of centrality measures and clustering coefficient. We then apply a random-walk algorithm to discover communities enriched with cancer-associated genes. Our findings show cancer genes in metabolic and signaling networks exhibit significant topological differences considering degree, clustering coefficient, and community cohesiveness; and these features demonstrate greater predictive power in signaling networks. These results support an empirical basis for algorithms using similar network-based measures to prioritize disease genes or predict disease states.