Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene-expression microarray data

Abstract

Using comparative genomic microarray analysis (CGMA), 104 hepatocellular carcinoma (HCC) gene-expression microarray profiles were analyzed. CGMA identified 13 regions of frequent cytogenetic change in the HCC samples (+lq, -4q, +6p, -8p, +8q, -13q, -16q, -17p, +17q, +20q) three three of which have not been previously identified

Abstract

Background

Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Frequent cytogenetic abnormalities that occur in HCC suggest that tumor-modifying genes (oncogenes or tumor suppressors) may be driving selection for amplification or deletion of these particular genetic regions. In many cases, however, the gene(s) that drive the selection are unknown. Although techniques such as comparative genomic hybridization (CGH) have traditionally been used to identify cytogenetic aberrations, it might also be possible to identify them indirectly from gene-expression studies. A technique we have called comparative genomic microarray analysis (CGMA) predicts regions of cytogenetic change by searching for regional gene-expression biases. CGMA was applied to HCC gene-expression profiles to identify regions of frequent cytogenetic change and to identify genes whose expression is misregulated within these regions.

Results

Using CGMA, 104 HCC gene-expression microarray profiles were analyzed. CGMA identified 13 regions of frequent cytogenetic change in the HCC samples. Ten of these regions have been detected in previous CGH studies (+lq, -4q, +6p, -8p, +8q, -13q, -16q, -17p, +17q, +20q). CGMA identified three additional regions that have not been previously identified by CGH (+5q, +12q, +19p). Genes located in regions of frequent cytogenetic change were examined for changed expression in the HCC samples.

Conclusions

Our results suggest that CGMA predictions using gene-expression microarray datasets are a practical alternative to CGH profiling. In addition, CGMA might be useful for identifying candidate genes within cytogenetically abnormal regions.