摘要
Accumulation of mutations and alterations in the expression of various genes result in carcinogenesis,and the development of microarray technology has enabled us to identify the comprehensive gene expression alterations in oncogenesis.Many studies have applied this technology for hepatocellular carcinoma(HCC),and identified a number of candidate genes useful as biomarkers in cancer staging,prediction of recurrence and prognosis,and treatment selection.Some of these target molecules have been used to develop new serum diagnostic markers and therapeutic targets against HCC to benefit patients.Previously,we compared gene expression profiling data with classification based on clinicopathological features,such as hepatitis viral infection or liver cancer progression.The next era of gene expression analysis will require systematic integration of expression profiles with other types of biological information,such as genomic locus,gene function,and sequence information.We have reported integration between expression profiles and locus information,which is effective in detecting structural genomic abnormalities,such as chromosomal gains and losses,in which we showed that gene expression profiles are subject to chromosomal bias.Furthermore,array-based comparative genomic hybridization analysis and allelic dosage analysis using genotyping arrays for HCC were also reviewed,with comparison of conventional methods.
Accumulation of mutations and alterations in the expression of various genes .result in carcinogenesis, and the development of microarray technology has enabled us to identify the comprehensive gene expression alterations in oncogenesis. Many studies have applied this technology for hepatocellular carcinoma （HCC）, and identified a number of candidate genes useful as biomarkers in cancer staging, prediction of recurrence and prognosis, and treatment selection. Some of these target molecules have been used to develop new serum diagnostic markers and therapeutic targets against HCC to benefit patients. Previously, we compared gene expression profiling data with classification based on clinicopathological features, such as hepatitis viral infection or liver cancer .progression. The next era of gene expression analysis will require systematic integration of expression profiles with other types of biological information, such as genomic locus, gene function, and sequence information. We have reported integration between expression profiles and locus information, which is effective in detecting structural genomic abnormalities, such as chromosomal gains and losses, in which we showed that gene expression profiles are subject to chromosomal bias. Furthermore, arraybased comparative genomic hybridization analysis and allelic dosage analysis using genotyping arrays for HCC were also reviewed, with comparison of conventional methods.
