摘要
目的:应用cDNA表达谱芯片研究不同分化程度人子宫内膜癌组织的基因表达谱,分析子宫内膜癌发生、发展过程中基因表达的变化。方法:用一步法抽提5例高分化和4例低分化人子宫内膜癌组织和36例正常子宫内膜组织的总RNA,在反转录合成cDNA探针的同时以Cy3和Cy5标记肿瘤组和正常对照组,将Cy3和Cy5标记探针混合后与含有13 824个基因的人表达谱芯片杂交。经洗片、扫描、软件处理后,分析子宫内膜癌和正常子宫内膜对照之间基因表达谱的差异。对高分化和低分化子宫内膜癌基因表达谱进行聚类分析。结果:子宫内膜癌组织和正常对照基因表达谱比较,差异2倍以上基因共有26条,表达上调和下调的基因分别为2条和24条。聚类分析显示,通过基因表达谱基本可将高分化和低分化子宫内膜癌分开。结论:通过基因表达谱差异研究可以发现与子宫内膜癌发生、发展相关的基因;癌基因表达谱聚类分析可能会帮助判断高危和低危子宫内膜癌,有利于子宫内膜癌患者个体化治疗方案的制定。
Objective：To study the gene expression profiling in endometrial cancer of different grade by using cDNA microarray, explore the cancer related genes and their function in the development and progression of endometrial cancer. Methods：The total RNA of 9 cases of endometriod endometrial cancer including 5 well differentiated and 4 poorly differentiated and 36 normal endometrium was isolated and labeled by reverse transcription reaction with Cy3 and Cy5 for cDNA probe. Equal quantity of labeled cDNA was mixed and hybridized to the cDNA microarray,which contained 13 824 genes. After washing, scanning and image processing, the different gene expression profiling of endometrial cancer and normal control was analysed. The hierachical cluster analysis was applied to the gene expression profiling of well differentiated and poorly differentiated endometriat cancers. Results ：Twenty-six genes had more than two fold different expression, the upregulated and down regulated genes were 2 and 24, respectively. The well differentiated and poorly differentiated endometrial cancer could be separated by hierachi- cal cluster analysis. Conclusion：The cancer related genes in the development and progression of endometrial cancer can be identified by different gene expression profiling and high risk endometrial cancer may be distinguished by hierachical cluster analysis, which may be benefit to the individualized therapy of endometrial cancer.
出处
《现代妇产科进展》
CSCD
北大核心
2006年第3期177-180,i0001,共5页
Progress in Obstetrics and Gynecology
基金
国家自然科学基金资助项目(30371481)
关键词
CDNA微阵列
子宫内膜肿瘤
基因表达谱
层次聚类分析
cDNA mieroarray
Endometrial neoplasm
Gene expression profiling
Hierachical cluster analysis
