Hinow Peter, Rietman Edward A, Omar Sara Ibrahim, Tuszyński Jack A
Department of Mathematical Sciences, University of Wisconsin - Milwaukee, P.O. Box 413, Milwaukee, WI 53201-0413, United States email:
Math Biosci Eng. 2015 Dec;12(6):1289-302. doi: 10.3934/mbe.2015.12.1289.
Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).
与疾病相关的蛋白质-蛋白质相互作用网络已成为一个突出的研究领域。我们研究了源自京都基因与基因组百科全书（KEGG）数据库的11种人类癌症的蛋白质-蛋白质相互作用网络的代数和拓扑指标。我们发现，一方面相对自同构群大小与拓扑网络复杂性之间存在强相关性，另一方面与五年生存概率之间也存在强相关性。此外，我们识别出几个蛋白质家族（例如PIK、ITG、AKT家族），它们是许多癌症通路中的重复基序。有趣的是，这些对称源通常位于中心而非边缘。我们的结果有助于确定有前景的抗癌药物靶点。除此之外，我们提供了一个统一的框架来研究相关疾病家族（例如神经退行性疾病、病毒性疾病、药物滥用障碍）的蛋白质-蛋白质相互作用网络。
