Introduction

Hepatocellular carcinoma (HCC) is one of the most common malignancies

worldwide with an annual incidence rate of about 600,000 cases, 55% of which

are in China[1]. The 5-year overall survival rate of individuals with HCC is

relatively low, which has barely been improved over the past two decades. A

better understanding of the molecular pathways that are active in HCC using a

more comprehensive approach would potentially provide new strategies for

clinical prevention and therapy.

Currently, several global approaches including array-based comparative

genomic hybridization (CGH)[2], expression profiling based on DNA

microarrays[3], proteomics based on 2D electrophoresis (2DG) and/or mass

spectrometry (MS)[4] have been used to detect the changes at different

molecular levels (DNA, RNA, or protein) in HCC, such as chromosomal

imbalance and genetic instability, epigenetic alteration, gene expression, and

gene regulation and translation. In particular, microarray-based gene profiling is

the most commonly used method for studies on HCC including comparative

analysis of cancer vs. non-cancerous samples[3], early stage vs. late stage[5],

good prognosis vs. poor prognosis samples[6], and HCC patients with HBV vs.

those with HCV infection[7]. However, array-based expression profiling has

several limitations including incapability of detecting gene fusions and novel

alternative splicing. Array analysis is further complicated by inconsistencies

largely caused by differences in the platforms and compromised by its limited

sensitivity in detecting lowly expressed genes [8, 9].