Background: 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.
Objective: 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
Methodology: 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.
Results: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological
parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment
of the essentiality of the node in the epigenetic network.
Conclusion: 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.