Information Technology Engineering Department, School of Systems and Industrial Engineering, Tarbiat Modares University (TMU), Chamran/Al-e-Ahmad Highways Intersection, Tehran, P.O. Box 14115-111, Iran.
Biosystems. 2021 Mar;201:104326. doi: 10.1016/j.biosystems.2020.104326. Epub 2020 Dec 10.
One of the important problems in oncology is finding the genes that perturb the cell functionality and cause cancer. These genes, namely cancer driver genes (CDGs), when mutated, lead to the activation of the abnormal proteins. This abnormality is passed on to other genes by protein-protein interactions, which can cause cells to uncontrollably multiply and become cancerous. So, many methods have been introduced to predict this group of genes. Most of these methods are computational-based, which identify the CDGs based on mutations and genomic data. In this study, we proposed KatzDriver, as a network-based approach, in order to detect CDGs. This method is able to calculate the relative impact of each gene in the spread of abnormality in the gene regulatory network. In this approach, we firstly create the studied networks using gene expression and regulatory interaction data. Then by combining the topological and biological data, the weights of edges (regulatory interactions) and nodes (genes) are calculated. Afterward, based on the KATZ approach, the receiving and broadcasting powers of each gene were calculated to find the relative impact of each gene. At the end, the top genes with the highest relative impact ranks were selected as potential cancer drivers. The result of the proposed approach was compared with 18 existing computational and network-based methods in terms of F-measure, and the number of the predicted cancer driver genes. The result shows that our proposed algorithm is better than most of the other methods. KatzDriver is also able to detect a significant number of unique driver genes compared to other computational and network-based methods.
肿瘤学中的一个重要问题是找到扰乱细胞功能并导致癌症的基因。这些基因，即癌症驱动基因（CDG），在发生突变时会导致异常蛋白的激活。这种异常通过蛋白质-蛋白质相互作用传递给其他基因，从而导致细胞不受控制地增殖并癌变。因此，已经引入了许多方法来预测这组基因。这些方法大多数都是基于计算的，它们基于突变和基因组数据来识别 CDG。在这项研究中，我们提出了一种基于网络的方法 KatzDriver，用于检测 CDG。该方法能够计算每个基因在基因调控网络中传播异常的相对影响。在这种方法中，我们首先使用基因表达和调控相互作用数据创建所研究的网络。然后，通过结合拓扑和生物学数据，计算边缘（调控相互作用）和节点（基因）的权重。之后，基于 KATZ 方法，计算每个基因的接收和广播能力，以找到每个基因的相对影响。最后，选择具有最高相对影响排名的前几个基因作为潜在的癌症驱动基因。我们提出的方法的结果是基于 F 度量和预测的癌症驱动基因数量与 18 种现有的计算和基于网络的方法进行比较的。结果表明，我们提出的算法优于大多数其他方法。与其他计算和基于网络的方法相比，KatzDriver 还能够检测到大量独特的驱动基因。
