Roy Nimisha, Raj Utkarsh, Rai Sneha, Varadwaj Pritish K
1Department of Bioinformatics and Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India; 2Department of Biotechnology and Bioinformatics, NIIT University, Neemrana, Rajasthan, India; 3Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India.
Curr Genomics. 2019 Dec;20(8):545-555. doi: 10.2174/1389202921666191227100441.
Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.
In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks.
In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy.
The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, . have been evaluated for the assessment of the essentiality of the node in the epigenetic network.
The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.
即便经过数十年研究，总体而言，癌症仍是一项挑战，且是全球主要死因之一。长期以来，人们认为癌症仅仅是基因层面变化的结果，但如今，遗传学和表观遗传学共同作用导致正常细胞转变为癌细胞已成为既定事实。
在当前情况下，研究人员正专注于靶向表观遗传机制。靶向表观遗传机制的主要优势在于其可逆性。因此，细胞可被重新编程恢复到正常状态。图论是数学的一项强大成果，它使我们能够理解复杂网络。
在本研究中，图论被用于对肝细胞癌（HCC）的表观遗传网络进行定量分析，并随后找出由此获得的网络中的重要顶点。其次，该网络被用于定位肝细胞癌表观遗传治疗的新靶点。
顶点代表参与HCC表观遗传机制的基因。已评估了聚类系数、离心率、度等拓扑参数，以评估表观遗传网络中节点的重要性。
本研究报道的参与HCC的十大新型表观遗传靶基因是细胞周期蛋白依赖性激酶6（cdk6）、细胞周期蛋白依赖性激酶4（cdk4）、细胞周期蛋白依赖性激酶抑制剂2A（cdkn2a）、Smad7、Smad3、细胞周期蛋白D1（ccnd1）、E2F转录因子1（e2f1）、剪接因子3b亚基1（sf3b1）、β - 连环蛋白（ctnnb1）和转化生长因子β1（tgfb1）。
