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通过低密度微阵列对乳腺癌细胞系进行分子特征分析。

Molecular characterization of breast cancer cell lines by a low-density microarray.

作者信息

de Longueville Francoise, Lacroix Marc, Barbuto Anna-Maria, Bertholet Vincent, Gallo Dominique, Larsimont Denis, Marcq Laurence, Zammatteo Nathalie, Boffe Sophie, Leclercq Guy, Remacle Jose

机构信息

Eppendorf Array Technologies, 5000 Namur, Belgium.

出版信息

Int J Oncol. 2005 Oct;27(4):881-92.

Abstract

We designed a low-density microarray carrying 132 DNA capture sequences highly specific for genes known to be differentially expressed among breast tumors and BCC lines or associated with specific tumor properties (cell-cycle alteration, proteolysis, adhesion, hormone sensitivity, etc). We analyzed gene expression in 11 BCC lines among which 6 had already been extensively studied (BT-474, Hs578T, MCF-7, MDA-MB-231, MDA-MB-453, T-47D) and 5 were still poorly characterized (Evsa-T, IBEP-1, IBEP-2, IBEP-3, KPL-1). Some data obtained were verified or extended by real-time polymerase chain reaction (real-time PCR), Northern blotting, Western blotting, immunohistochemistry and cell growth studies. Clustering analysis of the low-density microarray data allowed the sorting of BCC lines into two classes and supported a major discriminatory role for ER alpha, confirming data from previous studies. A few genes that are highly and specifically expressed in one cell line were identified, such as MGB1 (mammaglobin 1) in Evsa-T cells, and PIP (prolactin-inducible protein) in MDA-MB-453 BCC, suggesting an apocrine origin for these latter cells. Two BCC lines (IBEP-1 and IBEP-3) that had been previously characterized as ER alpha-negative, were classified by the low-density microarray among ER alpha-positive lines (MCF-7, T-47D, IBEP-2, BT-474, KPL-1) and were indeed confirmed as receptor-positive (at both mRNA and protein levels) and hormone-responsive cells. In conclusion, our results support the utility of a low-density microarray approach in cases where the cost and exhaustiveness of high-density microarrays may constitute a drawback; for instance, in obtaining a rapid phenotype evaluation in cell populations freshly isolated from breast tumors.

摘要

我们设计了一种低密度微阵列,它携带132个DNA捕获序列,这些序列对已知在乳腺肿瘤和基底细胞癌(BCC)细胞系中差异表达或与特定肿瘤特性(细胞周期改变、蛋白水解、黏附、激素敏感性等)相关的基因具有高度特异性。我们分析了11个BCC细胞系中的基因表达情况,其中6个细胞系已被广泛研究(BT-474、Hs578T、MCF-7、MDA-MB-231、MDA-MB-453、T-47D),另外5个细胞系的特征仍不清楚(Evsa-T、IBEP-1、IBEP-2、IBEP-3、KPL-1)。通过实时聚合酶链反应(实时PCR)、Northern印迹、Western印迹、免疫组织化学和细胞生长研究对获得的一些数据进行了验证或扩展。对低密度微阵列数据的聚类分析能够将BCC细胞系分为两类,并支持雌激素受体α(ERα)的主要鉴别作用,这证实了先前研究的数据。鉴定出了一些在一个细胞系中高度特异性表达的基因,如Evsa-T细胞中的MGB1(乳腺珠蛋白1)和MDA-MB-453 BCC中的PIP(催乳素诱导蛋白),这表明后一种细胞具有大汗腺起源。两个先前被鉴定为ERα阴性的BCC细胞系(IBEP-1和IBEP-3),通过低密度微阵列被归类为ERα阳性细胞系(MCF-7、T-47D、IBEP-2、BT-474、KPL-1),并且确实被确认为受体阳性(在mRNA和蛋白水平)以及激素反应性细胞。总之,我们的结果支持低密度微阵列方法在高密度微阵列的成本和详尽性可能成为缺点的情况下的实用性;例如,在对从乳腺肿瘤中新鲜分离的细胞群体进行快速表型评估时。

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