摘要
目的 探讨聚类分析方法在自身免疫病基因表达谱研究中的应用价值。方法 从正常对照及自身免疫病患者外周血提取总RNA ,逆转录合成单链、双链cDNA后 ,体外转录合成生物素标记的cRNA与基因芯片进行杂交 ,再经抗原抗体反应和标记荧光染料Cy3标记后 ,用基因芯片扫描仪进行图像扫描。经QuantArray分析软件将扫描的图像信息转化为数据。然后 ,进一步将标准化的数据用GeneSpring分析软件进行试验样本和基因的聚类分析。 结果 10例系统红斑狼疮 (SLE)、1例多发性肌炎 (PM )和 1例类风湿关节炎 (RA)患者与 1名正常对照外周血细胞的基因表达谱是不同的 ,患者之间的表达谱因临床表现的异质性 ,其表达谱也有差别 ,但SLE的聚类可与PM、RA相区分 ,同一SLE患者 ,2次采血所得的表达谱具有极强的相似性。基因聚类分析显示 ,具有相似功能的基因具有相似表达模式 ,据此可发现许多已知基因的免疫功能。结论 聚类分析方法能根据基因表达谱 ,将基因进行功能分类 ,故可将自身免疫病的样本进行疾病分类。因此 。
Objective This paper describes the application of cluster analysis to investigate the gene expression profile of peripheral blood in autoimmunity disease. Methods Total RNA was extracted from peripheral blood cells of healthy and patients groups; synthesis double strand cDNA template from total RNA; transcription of cRNA probe with Biotin labeling; hybridization of probe with Microarray; labeling Cy3 dye by the reaction of antigen and antibody; detection of Cy3 dye with Scan array 5000; converting the scan image information into numeric data by Quant Array analysis software; normalization and clustering analysis with Gene Spring analysis software.Results The clustering resu lts show 10 patients of SLE, 1 patient of PM and 1 patients of RA were clearly s eparated from normal control based on their gene expression profile. The gene ex pression profiles were different among the SLE patients by reason of different c linical manifestation. It can separate from PM and RA based on their gene expres sion profile. The gene expression profiles of same SLE patient were very similar . Gene clustering results show these genes of related function could be grouped together based on their expression profiles. So we can find new gene function of immunity in these known genes. Conclusions These results demonstrate that the use of the cluster analysis was effective in analyzing the oligonucleotide DNA microarray data.
出处
《中华检验医学杂志》
CAS
CSCD
北大核心
2003年第8期473-475,T001,共4页
Chinese Journal of Laboratory Medicine
基金
国家自然科学基金资助项目 ( 39730 4 3030 0 0 0 1 54)
关键词
聚类分析
自身免疫病
基因表达谱
研究
临床应用
Cluster analysis
Oligonucleotide microarray
Gene expression
Autoimmune disease
