基因芯片癌与癌旁组织分析中药品混合对结果的影响

journal6 ›› 2006, Vol. 27 ›› Issue (2): 88-91.

基因芯片癌与癌旁组织分析中药品混合对结果的影响

(1.湖南省第一师范学校，湖南长沙 410000；2.湖南师范大学生命科学学院，湖南长沙 410081；3.生物芯心北京国家工程研究中心，北京 100081)

出版日期:2006-03-25 发布日期:2012-09-16
作者简介:庄远红(1971-)，女，湖南师范大学生命科学学院研究生，湖南省第一师范学校讲师，主要从事基因芯片技术研究.
基金资助:
国家“863”计划资助项目（2002AA2Z2011）

Effects of Pooling RNA on the Research in the DNA Microarray Analysis ofCancer and Its Corresponding Adjacent Tissues

(1.Hunan First Normal College,Changsha 410000,China；2.College of Life Science,Hunan Normal University,Changsha 410081,China;3.National Engineering Research Center for Beijing Biochip Technology,Beijing 100081,China)

Online:2006-03-25 Published:2012-09-16

摘要/Abstract

摘要：基因芯片技术已经广泛地应用到生物科学与医学研究的多个领域中.由于实验费用的制约，在使用芯片数目较少的情况下，一些研究者倾向于把经过同一种处理的多个个体的RNA进行混合，希望能消除个体差异.但应用一个研究肿瘤的具体例子，比较单个个体与多个个体的RNA混合后得到的基因芯片的结果表明，多个样品的RNA混合后得到的结果和单个样品得到的结果，无论从正确率、假阳性、假阴性来比较，在统计学上都没有明显差异，因此：结合多个因素考虑，建议进行基因芯片分析的RNA样品不用进行混合.

关键词： 基因芯片技术, 样品混合, 单个个体

Abstract: Microarray technology has been applied in a variety of biological and medical research fields.Due to the relative higher costs,some researchers are apt to pooling RNA samples extracted from different subjects onto a single microarray to defray the cost of microarray experiment.The basic assumption of pooling is that the pooling RNA can lessen the diversity between individual subjects.Employing an example in cancer research,the authors analyzed the differential expressed genes between gastric cancer tissues and its corresponding adjacent normal tissues using ten individual RNA and their pooling RNA.The results indicate that there are no significant variances between pooling and individual RNA concerning of the numbers of correct gene,false positive gene and false negative gene based on a statistical approach.So the authors suggest using RNA from individual subject instead of pooling them even if in the primary studies.

Key words: DNA microarray, pooling RNA, individual subject

庄远红, 张亮, 孟欣, 任永红. 基因芯片癌与癌旁组织分析中药品混合对结果的影响[J]. journal6, 2006, 27(2): 88-91.

ZHUANG Yuan-Hong, ZHANG Liang, MENG Xin, REN Yong-Hong. Effects of Pooling RNA on the Research in the DNA Microarray Analysis ofCancer and Its Corresponding Adjacent Tissues[J]. journal6, 2006, 27(2): 88-91.

参考文献

[1] VEER L J,DAI H,VAN DE VIJVER M J,et al.Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer [J].Nature，2002，415(6 871):530-536.

[2] CHEN C D,WELSBIE D S,TRAN C,et al.Molecular Determinants of Resistance to Antiandrogen Therapy [J].Nat. Med.，2004，10(1):33-39.

[3] CHIANG M K,MELTON D A.Single-Cell Transcript Analysis of Pancreas Development [J].Dev. Cell，2003，4(3):383-393.

[4] IIZUKA N,OKA M,YAMAMOTO K,et al.Identification of Common or Distinct genes Related to Antitumor Activities of a Medicinal Herb and Its Major Component by Oligonucleotide Microarray [J].Int. J. Cancer，2003，107(4):666-672.

[5] KAMME F,SALUNGA R,YU J,et al.Single-Cell Microarray Analysis in Hippocampus CA1:Demonstration and Validation of Cellular Heterogeneity [J].J. Neurosci，2003，23(9):3 607-3 615.

[6] DIXON A K,RICHARDSON P J,PINNOCK D,et al.Gene-Expression Analysis at the Single-Cell Level [J].Trends. Pharmacol Sci.，2000，21(2):65-70.

[7] SHIH J H,MICHALOWSKA A M,DOBBIN K,et al.Effects of Pooling mRNA in Microarray Class Comparisons [J].Bioinformatics，2004，20(18):3 318-3 325.

[8] KENDZIORSKI C M,ZHANG Y,LAN H,et al.The Efficiency of Pooling mRNA in Microarray Experiments [J].Biostatistics，2003，4(3):465-477.

[9] YANG Y H,DUDOIT S,LUU P,et al.Normalization for cDNA Microarray Data:a Robust Composite Method Addressing Single and Multiple Slide Systematic Variation [J].Nucleic Acids. Res.，2002，30(4):15.

[10] WEIGELT B,GLAS A M,WESSELS L F,et al.Gene Expression Profiles of Primary Breast Tumors Maintained in Distant Metastases [J].Proc. Natl. Acad. Sci. USA，2003，100(26):15 901-15 905.
[11] PIPER M D,DARAN-LAPUJADE P,BRO C,et al.Reproducibility of Ol

igonucleotide Microarray Transcriptome Analyses [J].An Interlaboratory Comparison Using Chemostat Cultures of Saccharomyces Cerevisiae.J. Biol. Chem.，2002，277(40):37 001-37 008.