Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results.
OBJECTIVE
To analyse the expression profile of hepatocellular carcinoma compared with normal liver by using bioinformatics methods.


METHODS
In this study, we analysed the microarray expression data of HCC and adjacent normal liver samples from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes. Then, functional analyses were performed using GenCLiP analysis, Gene Ontology categories, and aberrant pathway identification. In addition, we used the CMap database to identify small molecules that can induce HCC.


RESULTS
Overall, 2721 differentially expressed genes (DEGs) were identified. We found 180 metastasis-related genes and constructed co-occurrence networks. Several significant pathways, including the transforming growth factor β (TGF-β) signalling pathway, were identified as closely related to these DEGs. Some candidate small molecules (such as betahistine) were identified that might provide a basis for developing HCC treatments in the future.


CONCLUSIONS
Although we functionally analysed the differences in the gene expression profiles of HCC and normal liver tissues, our study is essentially preliminary, and it may be premature to apply our results to clinical trials. Further research and experimental testing are required in future studies.
Department of Pathology, Chinese PLA General Hospital, Beijing, China; Medical College, Nankai University, Tianjing, China.
Jia
J
Li
Institute of Oncology, Nanfang Medical University, Guangzhou, Guangdong, China.
Zhongxi
Z
Huang
Department of Pathology, Chinese PLA General Hospital, Beijing, China.
Lixin
L
Wei
bioinformatics analysis; differentially expressed genes; functional analysis; hepatocellular carcinoma