基因芯片技术在肝细胞癌候选诊断指标筛选中的应用

Identification of Candidate Diagnostic Tumor Markers for Human Hepatocellular Carcinoma Using Genechip Technology

目的在全基因组范围内筛选肝细胞癌候选诊断指标。方法采用基因芯片技术比较40例肝细胞癌肿瘤标本及其邻近无瘤肝组织之间基因表达谱的差异,寻找与肝细胞癌发生、发展相关的基因。基因芯片由美国国立卫生院癌症研究所制定,每张芯片有9984个点,含9180个基因。基因芯片实验采用双色荧光直接标记法,每例患者的肝细胞癌组织总RNA200μg用绿色荧光素Cy5-dUTP标记,其相应的无瘤肝组织总RNA100μg用红色荧光素Cy3-dUTP标记。混合后与芯片上的基因进行杂交。对基因芯片资料进行统计分析以寻找肝细胞癌组织与无瘤肝组织之间表达有差异的基因。结果用非先导分层聚类分析法进行筛选,发现有10个基因在80%以上的肝细胞癌组织中的表达水平明显高于(2倍或2倍以上)其相应的无瘤肝组织,包括ESTs、Homosapi-enscDNAFLJ、原钙粘连素-α9(protocadherinalpha9)、KPNA2、RPS20、SNRPE、CDKN2A、UBD、MDK和ANXA2基因。这些基因与多种肿瘤相关,他们可能在肝细胞癌发生、发展过程中也发挥非常重要的作用。结论进一步研究这些基因的功能可能筛选出新的肝细胞癌诊断指标。

Objective To identify genes associated with hepatocellular carcinoma （HCC） as candidate diagnostic markers in a genome-wide scale. Methods The gene expression profiles of 40 pairs of HCC tumor tissue and peripheral non-tumorous liver tissue were analyzed by using gene chip technology. The gene chips were fabricated at the National Cancer Institute （NCI）. Each gene chip contained 9 180 genes. The fluorescent targets were prepared by a direct labeling approach using two kinds of fluorescences as following： 100 μg of total RNA from non-cancerous liver tissue was labeled with Cy3-dUTP and 200 μg of total RNA from HCC was labeled with CyS-dUTP. The targets were mixed together and hybridized with genes on the gene chips. Unsupervised hierarchical clustering analysis was done by CLUSTER and TREEVIEW software using median centered correlation and complete linkage. Results A total of 10 genes were found up-regulated in over 80 % of primary tumors comparing with that of their corresponding non-tumorous liver tissues at a two-fold filter with an unsupervised hierarchical clustering algorithm, including protocadherin-alpha 9, ESTs, Homo sapiens cDNA FLJ, KPNA2, RPS20, SNRPE, CDKN2A, UBD, MDK and ANXA2. Conclusion These genes are supposed to be candidates for the diagnosis of HCC. Further investigation of these genes in a large scale of patients with HCC and patients with non-malignant hepatic diseases will be needed to disclose whether they could be used clinically as novel diagnostic tumor markers for HCC.