Li Chaoxing, Liu Li, Dinu Valentin
School of Life Sciences, Arizona State University, Tempe, AZ, United States of America.
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States of America.
PeerJ. 2018 Apr 9;6:e4571. doi: 10.7717/peerj.4571. eCollection 2018.
Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.
诸如癌症之类的复杂疾病通常是环境因素与由一组基因组成的一个或多个生物途径共同作用的结果。每个生物途径通过基因网络传递信号来发挥其功能。从理论上讲，在正常生理条件下，一个途径应该具有稳健的拓扑结构。然而，在某些病理条件下，该途径的拓扑结构可能会发生改变。众所周知，正常的生物网络包括少量连接良好的枢纽节点和大量非枢纽节点。此外，据报道，连接性丧失是癌症网络的一个常见拓扑特征，这也是我们方法的一个假设。因此，从正常状态到癌症状态，网络失去连接性的过程可能就是网络结构被破坏的过程，也就是说，与正常状态相比，癌症中枢纽基因的数量可能会发生改变，或者基因拓扑秩的分布可能会发生改变。基于此，我们提出了一种新的基于PageRank的方法，称为拓扑秩分析途径（PoTRA），用于检测与癌症相关的途径。我们使用PageRank来衡量每个生物途径中基因的相对拓扑秩，然后为每个途径选择枢纽基因，并使用Fisher精确检验来测试每个途径中枢纽基因的数量从正常状态到癌症状态是否发生了改变。或者，如果一个途径中基因拓扑秩的分布在正常状态和癌症状态之间发生了改变，那么这个途径也可能与癌症有关。因此，我们使用Kolmogorov-Smirnov检验来检测在两种表型之间基因拓扑秩分布发生改变的途径。我们将PoTRA应用于研究肝细胞癌（HCC）及其几种亚型。非常有趣的是，我们发现HCC中所有显著的途径通常都与癌症相关，而HCC亚型中的几个显著途径则特别与HCC亚型相关。总之，PoTRA是一种探索和发现与癌症相关途径的新方法。PoTRA可以作为其他现有方法的补充，以在系统层面拓宽我们对癌症背后生物学机制的理解。
