Abstract
Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes. To this end, we introduce a general network searching method, MaxLink, and apply it to find and rank cancer gene candidates by their connectivity to known cancer genes. Using a comprehensive protein interaction network, we searched for genes connected to known cancer genes. First, we compiled a new set of 812 genes involved in cancer, more than twice the number in the Cancer Gene Census. Their network neighbors were then extracted. This candidate list was refined by selecting genes with unexpectedly high levels of connectivity to cancer genes and without previous association to cancer. This produced a list of 1891 new cancer candidates with up to 55 connections to known cancer genes. We validated our method by cross-validation, Gene Ontology term bias, and differential expression in cancer versus normal tissue. An example novel cancer gene candidate is presented with detailed analysis of the local network and neighbor annotation. Our study provides a ranked list of high priority targets for further studies in cancer research. Supplemental material is included.
Highlights
Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes
We have developed an analysis pipeline to identify and rank candidate cancer genes based on their connectivity to known cancer genes in the FunCoup network
By “known cancer gene,” we mean any gene with clear evidence for cancer
Summary
Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes To this end, we introduce a general network searching method, MaxLink, and apply it to find and rank cancer gene candidates by their connectivity to known cancer genes. We compiled a new set of 812 genes involved in cancer, more than twice the number in the Cancer Gene Census This candidate list was refined by selecting genes with unexpectedly high levels of connectivity to cancer genes and without previous association to cancer. The network-based methods normally connect gene networks with phenotype networks to infer gene-disease relationships These works, are limited to using only direct interaction data and/or were only applied to rank a short list of candidate genes in a genomic interval
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