Rahimi Majid, Teimourpour Babak, Akhavan-Safar Mostafa
Department of information technology, School of Systems and Industrial Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
School of Systems and Industrial Engineering, Tarbiat Modares University (TMU) Chamran/Al-e-Ahmad Highways Intersection, Tehran, Iran.
Iran J Biotechnol. 2022 Apr 1;20(2):e3066. doi: 10.30498/ijb.2022.289013.3066. eCollection 2022 Apr.
Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using networking techniques and the concept of influence maximization, some models have been proposed to identify these genes.
We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a network science approach without the use of mutation and genomic data.
In this study, we will employ the social influence network theory to identify CDGs in the human gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs.
The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method can identify many CDGs that are overlooked by all previously published methods.
Our study demonstrated that the Google's PageRank algorithm can be utilized and modified as a network-based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.
癌症是一组在生物学研究中备受关注的疾病，因其高死亡率以及缺乏对其根本原因的准确识别。在这类研究中，研究人员通常试图识别引发细胞癌变的癌症驱动基因（CDGs）。以往提出的大多数识别CDGs的方法都是基于基因表达数据和基因组数据中的突变概念。最近，利用网络技术和影响最大化的概念，已经提出了一些模型来识别这些基因。
我们旨在构建癌症转录调控网络，并使用一种不依赖突变和基因组数据的网络科学方法来识别癌症驱动基因。
在本研究中，我们将采用社会影响网络理论，基于网页的影响力和权力概念，在人类基因调控网络（GRN）中识别CDGs。首先，我们将使用基因表达数据以及现有的节点和边来创建GRN网络。接下来，我们将通过使用影响传播概念对调控相互作用边进行加权，在正在研究的GRN网络上实施改进算法。具有最高评分的节点将被选为CDGs。
结果表明，我们提出的方法优于大多数其他基于计算和网络的方法，并且在识别CDGs方面比许多其他方法更具优势。此外，该方法能够识别许多被之前所有已发表方法忽略的CDGs。
我们的研究表明，谷歌的PageRank算法可以被利用和修改，作为一种在转录调控网络中识别癌症驱动基因的基于网络的方法。此外，所提出的方法可以被视为基于计算的癌症驱动基因识别工具的一种补充方法。
