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NCI60癌细胞系中核受体的表达谱揭示了受体-药物及受体-基因相互作用。

Expression profiling of nuclear receptors in the NCI60 cancer cell panel reveals receptor-drug and receptor-gene interactions.

作者信息

Holbeck Susan, Chang Jianjun, Best Anne M, Bookout Angie L, Mangelsdorf David J, Martinez Elisabeth D

机构信息

National Cancer Institute, National Institutes of Health, Rockville, Maryland 20852, USA.

出版信息

Mol Endocrinol. 2010 Jun;24(6):1287-96. doi: 10.1210/me.2010-0040. Epub 2010 Apr 7.

Abstract

We profiled the expression of the 48 human nuclear receptors (NRs) by quantitative RT-PCR in 51 human cancer cell lines of the NCI60 collection derived from nine different tissues. NR mRNA expression accurately classified melanoma, colon, and renal cancers, whereas lung, breast, prostate, central nervous system, and leukemia cell lines exhibited heterogeneous receptor expression. Importantly, receptor mRNA levels faithfully predicted the growth-inhibitory qualities of receptor ligands in nonendocrine tumors. Correlation analysis using NR expression profiles and drug response information across the cell line panel uncovered a number of new potential receptor-drug interactions, suggesting that in these cases, individual receptor levels may predict response to chemotherapeutic interventions. Similarly, by cross-comparing receptor levels within our expression dataset and relating these profiles to existing microarray gene expression data, we defined interactions among receptors and between receptors and other genes that can now be mechanistically queried. This work supports the strategy of using NR expression profiling to classify various types of cancer, define NR-drug interactions and receptor-gene networks, predict cancer-drug sensitivity, and identify druggable targets that may be pharmacologically manipulated for potential therapeutic intervention.

摘要

我们通过定量逆转录聚合酶链反应(qRT-PCR)分析了来自9种不同组织的NCI60人癌细胞系中48种人类核受体(NRs)的表达情况。NR mRNA表达准确地对黑色素瘤、结肠癌和肾癌进行了分类,而肺癌、乳腺癌、前列腺癌、中枢神经系统癌和白血病细胞系则表现出异质性受体表达。重要的是,受体mRNA水平忠实地预测了非内分泌肿瘤中受体配体的生长抑制特性。使用整个细胞系面板的NR表达谱和药物反应信息进行的相关性分析发现了许多新的潜在受体-药物相互作用,表明在这些情况下,个体受体水平可能预测对化疗干预的反应。同样,通过在我们的表达数据集中交叉比较受体水平,并将这些谱与现有的微阵列基因表达数据相关联,我们定义了受体之间以及受体与其他基因之间的相互作用,现在可以对其进行机制性探究。这项工作支持使用NR表达谱对各种癌症进行分类、定义NR-药物相互作用和受体-基因网络、预测癌症药物敏感性以及识别可能通过药理学操作进行潜在治疗干预的可药物化靶点的策略。

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