Open Access Research article

Gene expression profile analysis of human hepatocellular carcinoma using SAGE and LongSAGE

Hui Dong125, Xijin Ge3, Yan Shen4, Linlei Chen6, Yalin Kong7, Hongyi Zhang7, Xiaobo Man2, Liang Tang2, Hong Yuan6, Hongyang Wang2, Guoping Zhao145* and Weirong Jin45*

Author Affiliations

1 Department of Microbiology and Microbial Engineering, School of Life Sciences, Fudan University, Shanghai 200433, PR China

2 International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai 200438, PR China

3 Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA

4 National Engineering Center for Biochip at Shanghai, Shanghai 201203, PR China

5 Chinese National Human Genome Center at Shanghai, 351 Guo Shou-Jing Road, Shanghai 201203, PR China

6 Center for Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha 410013, PR China

7 Department of Hepatobiliary Surgery, General Hospital of Air Force PLA, Beijing 100036, PR China

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BMC Medical Genomics 2009, 2:5  doi:10.1186/1755-8794-2-5

Published: 26 January 2009

Abstract

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and the second cancer killer in China. The initiation and malignant transformation of cancer result from accumulation of genetic changes in the sequences or expression level of cancer-related genes. It is of particular importance to determine gene expression profiles of cancers on a global scale. SAGE and LongSAGE have been developed for this purpose.

Methods

We performed SAGE in normal liver and HCC samples as well as the liver cancer cell line HepG2. Meanwhile, the same HCC sample was simultaneously analyzed using LongSAGE. Computational analysis was carried out to identify differentially expressed genes between normal liver and HCC which were further validated by real-time quantitative RT-PCR.

Results

Approximately 50,000 tags were sequenced for each of the four libraries. Analysis of the technical replicates of HCC indicated that excluding the low abundance tags, the reproducibility of SAGE data is high (R = 0.97). Compared with the gene expression profile of normal liver, 224 genes related to biosynthesis, cell proliferation, signal transduction, cellular metabolism and transport were identified to be differentially expressed in HCC. Overexpression of some transcripts selected from SAGE data was validated by real-time quantitative RT-PCR. Interestingly, sarcoglycan-ε (SGCE) and paternally expressed gene (PEG10) which is a pair of close neighboring genes on chromosome 7q21, showed similar enhanced expression patterns in HCC, implicating that a common mechanism of deregulation may be shared by these two genes.

Conclusion

Our study depicted the expression profile of HCC on a genome-wide scale without the restriction of annotation databases, and provided novel candidate genes that might be related to HCC.