Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein-protein interaction network.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide and one of the deadliest cancers in Asia. But at present, effective targets for HCC clinical therapy are still limited. The "guilt by association" rule suggests that interacting proteins share the same or similar functions and hence may be involved in the same pathway. This assumption can be used to identify disease related genes from protein association networks constructed from existing PPI data. Given the close association between Hepatitis B virus and Hepatitis B which may lead to HCC, here we develop a computational method to identify hepatocellular carcinoma related genes based on k-th shortest paths in the protein-protein interaction (PPI) network (we set k=1, 2 in this study). Finally, we found 33 genes whose p-values were less than 0.05, and most of them have been reported to be involved in HCC tumorigenesis and development. The results also provide a new reference for research into HCC oncogenesis and for development of new strategies for HCC clinical therapies.