Smart detectives for early detection of hepatocellular carcinoma related to drug-resistant HBV-mutant infection.

• Main Author: Lee, Kar Kei.
• Other Authors: Chen Wei Ning, William
• Format: Final Year Project
• Language: English
• Published: 2010
• Subjects: DRNTU::Science::Medicine::Biosensors
• Online Access: http://hdl.handle.net/10356/40195
• Institution: Nanyang Technological University

Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Antiviral drug therapy used to treat Chronic Hepatitis B patients at high risk of developing HCC has resulted in drug-resistant HBV mutants, which may lead to a different onset mechanism to HCC. Early detection of HBV-HCC is crucial to the patient’s recovery. However, to date, much of the researches only focused on developing biomarkers for wild-type HBV induced HCC. This research addresses the need to also develop biomarkers for early diagnosis of mutant HBV induced HCC. As a small digression, the research also looked into the possibility of interaction between Hepatitis B x protein (HBx) with Siah1 protein (which plays a role in inducing apoptosis) to establish evidence for HBx as a potential drug target. The research focuses on designing experiment approaches for biomarker discovery and binding mechanism identification. For biomarker discovery, iTRAQ coupled 2D-liquid chromatography-mass spectrometry was chosen as the method to study the protein profiles of different HBV strains induced HCC. Meanwhile, for the HBx-Siah1 interaction, a Siah1 binding motif-like structure was found in HBx from previous studies. To validate the actual binding, fusion protein plasmid constructs of HBx and Siah1 would be used to transfect HepG2 cells. In-vivo binding of the proteins is then confirmed using an anti-HA-Sepharose pull-down assay. Overall, the proposed experiments were anticipated to give potentially useful results for early diagnosis of HBV induced HCC and also reinforcing the prospect of HBx as drug target. Future implementation of the experiments should consider large scale population studies to verify the results.