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癌症中小细胞样本的表达谱分析:少即是多。

Expression profiling of small cellular samples in cancer: less is more.

作者信息

Glanzer J G, Eberwine J H

机构信息

Department of Pharmacology, University of Pennsylvania Medical Center, Philadelphia, PA 19104-6058, USA.

出版信息

Br J Cancer. 2004 Mar 22;90(6):1111-4. doi: 10.1038/sj.bjc.6601668.

Abstract

Expression profiling of tumours from cancer patients has uncovered several genes that are critically important in the progression of a normal cell to an oncogenic phenotype. Leading the way in these discoveries is the use of microarrays, a technology that is currently in transition from basic science applications to use in the clinic. Microarrays can determine the global gene regulation of an individual cancer, which may be useful in formulating an individualised therapy for the patient. Currently, cells used in breast cancer microarray studies often come from either homogenous cultures or heterogeneous biopsy samples. Both cell sources are at a disadvantage in determining the most accurate gene profile of cancer, which often consists of multiple subspecies of cancerous cells within a background of normal cells. Therefore, acquisition of small, but highly specific biopsies for analysis may be required for an accurate expression analysis of the disease. Amplification methods, such as polymerase chain reaction (PCR) and amplified antisense RNA (aRNA) amplification, have been used to amplify the mRNA signal from very small samples, which can then be used for microarray analysis. In this study, we describe the acquisition, amplification, and analysis of very small samples (<10000 cells) for expression analysis and demonstrate that the ultimate resolution of cancer expression analysis, one cell, is both feasible and practical.

摘要

对癌症患者肿瘤的表达谱分析发现了几个在正常细胞向致癌表型转变过程中至关重要的基因。在这些发现中处于领先地位的是微阵列的使用,这项技术目前正从基础科学应用向临床应用转变。微阵列可以确定个体癌症的整体基因调控情况,这可能有助于为患者制定个性化治疗方案。目前,乳腺癌微阵列研究中使用的细胞通常来自同质培养物或异质活检样本。这两种细胞来源在确定癌症最准确的基因谱方面都存在劣势,癌症通常由正常细胞背景下的多种癌细胞亚群组成。因此,为了对该疾病进行准确的表达分析,可能需要获取少量但高度特异的活检样本进行分析。诸如聚合酶链反应(PCR)和扩增反义RNA(aRNA)扩增等扩增方法已被用于从非常小的样本中扩增mRNA信号,然后可将其用于微阵列分析。在本研究中,我们描述了用于表达分析的非常小的样本(<10000个细胞)的获取、扩增和分析,并证明癌症表达分析的最终分辨率,即单个细胞,是可行且实际的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6de5/2409658/7fc96ad397ff/90-6601668f1.jpg

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