Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
BMC Genomics. 2010 Dec 1;11 Suppl 3(Suppl 3):S5. doi: 10.1186/1471-2164-11-S3-S5.
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.
癌症是一种复杂的疾病。到目前为止，已经有许多基因被报道与癌症的发生有关。与传统的研究单个基因或基因座的方法不同，在人类蛋白质-蛋白质相互作用网络中系统地研究癌症相关蛋白质，可能为揭示癌症的分子机制以及其他复杂疾病的分子机制提供重要的生物学信息。
我们探讨了人类互作组中编码癌症基因（癌症蛋白）的蛋白质的全局和局部网络特征。我们发现，癌症蛋白的网络拓扑结构与必需基因（必需蛋白）或调控基因（调控蛋白）编码的蛋白质有很大的不同。与必需蛋白或调控蛋白相比，癌症蛋白在人类互作组中具有更高的度、更高的介数、更短的最短路径距离和更弱的聚类系数。我们进一步将癌症蛋白分为两组（隐性和显性癌症蛋白），并比较它们的拓扑特征。隐性癌症蛋白的介数比显性癌症蛋白高，而它们的度分布和特征最短路径距离也有显著差异。最后，我们发现癌症蛋白在人类互作组中不是随机分布的，它们之间相互连接紧密。
我们的研究揭示了与整个人类互作组中的必需蛋白或调控蛋白相比，癌症蛋白具有更强的蛋白质-蛋白质相互作用特征。我们还发现隐性癌症蛋白比显性癌症蛋白具有更强的网络特征。这些结果有助于癌症候选基因的优先级排序和验证、生物标志物的发现，最终从系统生物学水平理解癌症的病因。
