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BMC Genomics. 2010 Dec 1;11 Suppl 3:S5. doi: 10.1186/1471-2164-11-S3-S5.

A comparative study of cancer proteins in the human protein-protein interaction network.

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  • 1Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.



Cancer is a complex disease. So far, many genes have been reported to involve in the development of cancer. Rather than the traditional approach to studying individual genes or loci, a systematic investigation of cancer proteins in the human protein-protein interaction network may provide important biological information for uncovering the molecular mechanisms of cancer and, potentially, other complex diseases.


We explored global and local network characteristics of the proteins encoded by cancer genes (cancer proteins) in the human interactome. We found that the network topology of the cancer proteins was much different from that of the proteins encoded by essential genes (essential proteins) or control genes (control proteins). Relative to the essential proteins or control proteins, cancer proteins tended to have higher degree, higher betweenness, shorter shortest-path distance, and weaker clustering coefficient in the human interactome. We further separated the cancer proteins into two groups (recessive and dominant cancer proteins) and compared their topological features. Recessive cancer proteins had higher betweenness than dominant cancer proteins, while their degree distribution and characteristic shortest path distance were also significantly different. Finally, we found that cancer proteins were not randomly distributed in the human interactome and they connected strongly with each other.


Our study revealed much stronger protein-protein interaction characteristics of cancer proteins relative to the essential proteins or control proteins in the whole human interactome. We also found stronger network characteristics of recessive than dominant cancer proteins. The results are helpful for cancer candidate gene prioritization and verification, biomarker discovery, and, ultimately, understanding the etiology of cancer at the systems biological level.

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