Suppr超能文献

预测淋巴结阴性原发性乳腺癌转移的基因特征的通路分析。

Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer.

作者信息

Yu Jack X, Sieuwerts Anieta M, Zhang Yi, Martens John W M, Smid Marcel, Klijn Jan G M, Wang Yixin, Foekens John A

机构信息

Veridex LLC, a Johnson & Johnson Company, 3210 Merryfield Row, San Diego, CA 92121, USA.

出版信息

BMC Cancer. 2007 Sep 25;7:182. doi: 10.1186/1471-2407-7-182.

Abstract

BACKGROUND

Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures.

METHODS

Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients.

RESULTS

The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways.

CONCLUSION

We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.

摘要

背景

已发表的乳腺癌预后基因特征中共同的基因很少。在此,我们通过研究不同基因特征的预后能力和潜在生物学途径,为这一观察结果提供一个合理的解释。

方法

使用500个重新采样的训练集,确定预测雌激素受体阳性和雌激素受体阴性肿瘤转移发生的基因特征,并映射到基因本体生物学过程以识别过度富集的途径。全局检验程序证实,常见途径中的基因表达谱与患者的转移相关。

结果

凋亡途径和细胞分裂,或细胞生长调节和G蛋白偶联受体信号转导,分别与雌激素受体阳性或雌激素受体阴性肿瘤的转移能力最显著相关。源自常见途径的基因特征在一个独立队列中预测转移。对不同已发表预后特征所代表的途径进行映射显示,它们共享53%已识别的途径。

结论

我们表明,针对相同临床终点对患者进行分类的不同基因集代表相似的生物学过程,并且途径衍生的特征可用于预测预后。此外,我们的研究揭示,与乳腺癌雌激素受体亚组侵袭性相关的潜在生物学差异很大。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f57/2077336/2673bd47b06a/1471-2407-7-182-1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验