In Silico Identification of Potent Natural Compounds for Hepatocellular Carcinoma Therapy

Hepatocellular carcinoma (HCC) is the most common type of liver cancer. This type of liver cancer is highly aggressive and lethal. Genetic and environmental factors combine to cause the development of HCC. Important risk factors for the development of HCC include liver cirrhosis, infection with the hepatitis B and hepatitis C viruses, excessive alcohol use, aflatoxin B1 intake, and nonalcoholic steatohepatitis (NASH). In spite of the advancements in medicine, HCC is still one of the most difficult cancers to treat. The pathophysiologic complexity of HCC makes medical treatment challenging. Therapy options for HCC include surgical procedures such as resection and liver transplantation (LT), stereoscopic body radiation therapy (SBRT), and other non-catheter-based treatments, such as chemoembolization and radioembolization. More potent and less toxic therapies are still required, even though immunotherapy and chemotherapy have demonstrated potential in reducing the growth of HCC. The potential use of herbal medicine in the diagnosis, treatment, and prevention of HCC has drawn more attention in recent years. A natural substance called bufalin, which is derived from the traditional Chinese medication Chan Su, has demonstrated encouraging anticancer potential in a number of cancer cell lines by causing apoptosis and preventing cell division. The objective of this work was to cover a range of computational methods involving machine learning and statistics for determining the important candidate genes for HCC and examine their binding relationships with bufalin. To achieve this, a sequential order of computational techniques was employed like virtual screening, molecular docking interactions, ADMET profiling to establish a potential lead compound.