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乳腺癌预后基因网络模块具有广阔前景。

Prognostic gene network modules in breast cancer hold promise.

机构信息

Medical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK.

出版信息

Breast Cancer Res. 2010;12(6):317. doi: 10.1186/bcr2774. Epub 2010 Dec 8.

DOI:10.1186/bcr2774
PMID:21143771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3046436/
Abstract

A substantial proportion of lymph node-negative patients who receive adjuvant chemotherapy do not derive any benefit from this aggressive and potentially toxic treatment. However, standard histopathological indices cannot reliably detect patients at low risk of relapse or distant metastasis. In the past few years several prognostic gene expression signatures have been developed and shown to potentially outperform histopathological factors in identifying low-risk patients in specific breast cancer subgroups with predictive values of around 90%, and therefore hold promise for clinical application. We envisage that further improvements and insights may come from integrative expression pathway analyses that dissect prognostic signatures into modules related to cancer hallmarks.

摘要

相当一部分接受辅助化疗的淋巴结阴性患者并未从中获益,尽管这种治疗方法具有侵袭性且潜在毒性。然而,标准的组织病理学指标并不能可靠地检测出复发或远处转移风险低的患者。在过去的几年中,已经开发出了几种预后基因表达特征,并显示出在特定乳腺癌亚组中识别低风险患者方面可能优于组织病理学因素,其预测值约为 90%,因此具有临床应用的潜力。我们预计,进一步的改进和深入研究可能来自于将预后特征分解为与癌症标志相关模块的综合表达途径分析。

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Prognostic gene network modules in breast cancer hold promise.乳腺癌预后基因网络模块具有广阔前景。
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本文引用的文献

1
Identification of high-quality cancer prognostic markers and metastasis network modules.鉴定高质量的癌症预后标志物和转移网络模块。
Nat Commun. 2010 Jul 13;1(4):34. doi: 10.1038/ncomms1033.
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Increased entropy of signal transduction in the cancer metastasis phenotype.癌症转移表型中信号转导的熵增加。
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Dynamic modularity in protein interaction networks predicts breast cancer outcome.蛋白质相互作用网络中的动态模块化可预测乳腺癌预后。
Nat Biotechnol. 2009 Feb;27(2):199-204. doi: 10.1038/nbt.1522. Epub 2009 Feb 1.
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A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer.一种具有高预测价值的强大分类器,用于识别雌激素受体阴性乳腺癌的预后良好患者。
Breast Cancer Res. 2008;10(4):R73. doi: 10.1186/bcr2138. Epub 2008 Aug 28.
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Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes.与乳腺癌临床结果相关的生物学过程取决于分子亚型。
Clin Cancer Res. 2008 Aug 15;14(16):5158-65. doi: 10.1158/1078-0432.CCR-07-4756.
6
The humoral immune system has a key prognostic impact in node-negative breast cancer.体液免疫系统在淋巴结阴性乳腺癌中具有关键的预后影响。
Cancer Res. 2008 Jul 1;68(13):5405-13. doi: 10.1158/0008-5472.CAN-07-5206.
7
Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer.浸润淋巴细胞和雌激素受体对乳腺癌基因表达及预后的影响。
Breast Cancer Res Treat. 2009 Jul;116(1):69-77. doi: 10.1007/s10549-008-0105-3. Epub 2008 Jul 1.
8
Enabling personalized cancer medicine through analysis of gene-expression patterns.通过分析基因表达模式实现个性化癌症医学。
Nature. 2008 Apr 3;452(7187):564-70. doi: 10.1038/nature06915.
9
Network-based classification of breast cancer metastasis.基于网络的乳腺癌转移分类
Mol Syst Biol. 2007;3:140. doi: 10.1038/msb4100180. Epub 2007 Oct 16.
10
An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer.一种免疫反应基因表达模块可识别雌激素受体阴性乳腺癌的良好预后亚型。
Genome Biol. 2007;8(8):R157. doi: 10.1186/gb-2007-8-8-r157.