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Analysis of estrogen receptor messenger RNA in breast carcinomas from archival specimens is predictive of tumor biology.

机译:从档案标本中分析乳腺癌中雌激素受体信使RNA可以预测肿瘤生物学。

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摘要

As the size of breast tumors continues to decrease, it has become more difficult to obtain adequate tumor tissue for molecular studies. We have used the estrogen receptor (ER) gene as a model to study the ability to perform a quantitative analysis of ER mRNA extracted from archival breast carcinoma specimens using reverse transcriptase polymerase chain reaction. Based upon ER mRNA abundance, tumors were characterized as having low, medium, or high ER mRNA expression. These data were compared with ER and progesterone receptor (PR) status determined by enzyme immunoassay, tumor histology, and Bloom-Richardson grade. Comparing the low and high ER mRNA groups, there were statistically significant differences in ER-positive status (10% versus 95%; P = 0.0001), PR-positive status (10% versus 90%; P = 0.0001), and tumor grade (2.67 +/- 0.12 versus 2.09 +/- 0.14; P = 0.0025). Of the 28 tumors in the high ER mRNA group, 5 (18%) were invasive lobular carcinomas whereas all 24 tumors with low ER mRNA were invasive ductal carcinomas. These data demonstrate that archival breast tumor specimens can be characterized for ER mRNA abundance. In addition, we conclude that the mechanisms regulating ER gene transcription influence the phenotype of breast carcinomas. These results also suggest that this technique can be designed to provide a quantitative analysis of gene expression for any gene of interest utilizing archival tumor specimens.
机译:随着乳腺肿瘤的大小持续减小,为分子研究获得足够的肿瘤组织变得更加困难。我们已经使用雌激素受体(ER)基因作为模型来研究使用逆转录酶聚合酶链反应从档案乳腺癌样本中提取的ER mRNA进行定量分析的能力。基于ER mRNA的丰度,肿瘤被表征为具有低,中或高的ER mRNA表达。将这些数据与通过酶免疫测定,肿瘤组织学和Bloom-Richardson等级确定的ER和孕激素受体(PR)状态进行比较。比较低和高ER mRNA组,ER阳性状态(10%对95%; P = 0.0001),PR阳性状态(10%对90%; P = 0.0001)和肿瘤等级存在统计学差异(2.67 +/- 0.12对2.09 +/- 0.14; P = 0.0025)。高ER mRNA组的28例肿瘤中,有5例(18%)是浸润性小叶癌,而所有ER mRNA低的24例肿瘤均为浸润性导管癌。这些数据表明,存档乳腺肿瘤标本可以表征ER mRNA的丰度。此外,我们得出结论,调节ER基因转录的机制影响乳腺癌的表型。这些结果还表明,可以利用存档肿瘤标本将该技术设计为定量分析任何目的基因的基因表达。

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