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衰老影响激素依赖性乳腺癌的转录组,但不影响其基因组。

Aging impacts transcriptomes but not genomes of hormone-dependent breast cancers.

作者信息

Yau Christina, Fedele Vita, Roydasgupta Ritu, Fridlyand Jane, Hubbard Alan, Gray Joe W, Chew Karen, Dairkee Shanaz H, Moore Dan H, Schittulli Francesco, Tommasi Stefania, Paradiso Angelo, Albertson Donna G, Benz Christopher C

机构信息

Buck Institute for Age Research, 8001 Redwood Boulevard, Novato, CA 94945, USA.

出版信息

Breast Cancer Res. 2007;9(5):R59. doi: 10.1186/bcr1765.

DOI:10.1186/bcr1765
PMID:17850661
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2216076/
Abstract

INTRODUCTION

Age is one of the most important risk factors for human malignancies, including breast cancer; in addition, age at diagnosis has been shown to be an independent indicator of breast cancer prognosis. Except for inherited forms of breast cancer, however, there is little genetic or epigenetic understanding of the biological basis linking aging with sporadic breast cancer incidence and its clinical behavior.

METHODS

DNA and RNA samples from matched estrogen receptor (ER)-positive sporadic breast cancers diagnosed in either younger (age <or= 45 years) or older (age >or= 70 years) Caucasian women were analyzed by array comparative genomic hybridization and by expression microarrays. Array comparative genomic hybridization data were analyzed using hierarchical clustering and supervised age cohort comparisons. Expression microarray data were analyzed using hierarchical clustering and gene set enrichment analysis; differential gene expression was also determined by conditional permutation, and an age signature was derived using prediction analysis of microarrays.

RESULTS

Hierarchical clustering of genome-wide copy-number changes in 71 ER-positive DNA samples (27 younger women, 44 older women) demonstrated two age-independent genotypes; one with few genomic changes other than 1q gain/16q loss, and another with amplifications and low-level gains/losses. Age cohort comparisons showed no significant differences in total or site-specific genomic breaks and amplicon frequencies. Hierarchical clustering of 5.1 K genes variably expressed in 101 ER-positive RNA samples (53 younger women, 48 older women) identified six transcriptome subtypes with an apparent age bias (P < 0.05). Samples with higher expression of a poor outcome-associated proliferation signature were predominantly (65%) younger cases. Supervised analysis identified cancer-associated genes differentially expressed between the cohorts; with younger cases expressing more cell cycle genes and more than threefold higher levels of the growth factor amphiregulin (AREG), and with older cases expressing higher levels of four different homeobox (HOX) genes in addition to ER (ESR1). An age signature validated against two other independent breast cancer datasets proved to have >80% accuracy in discerning younger from older ER-positive breast cancer cases with characteristic differences in AREG and ESR1 expression.

CONCLUSION

These findings suggest that epigenetic transcriptome changes, more than genotypic variation, account for age-associated differences in sporadic breast cancer incidence and prognosis.

摘要

引言

年龄是包括乳腺癌在内的人类恶性肿瘤最重要的风险因素之一;此外,诊断时的年龄已被证明是乳腺癌预后的一个独立指标。然而,除了遗传性乳腺癌外,对于将衰老与散发性乳腺癌发病率及其临床行为联系起来的生物学基础,在遗传或表观遗传方面了解甚少。

方法

通过阵列比较基因组杂交和表达微阵列分析来自年龄较小(年龄≤45岁)或年龄较大(年龄≥70岁)的白人女性中配对的雌激素受体(ER)阳性散发性乳腺癌的DNA和RNA样本。使用层次聚类和监督年龄队列比较分析阵列比较基因组杂交数据。使用层次聚类和基因集富集分析来分析表达微阵列数据;还通过条件置换确定差异基因表达,并使用微阵列预测分析得出年龄特征。

结果

对71个ER阳性DNA样本(27名年轻女性,44名老年女性)的全基因组拷贝数变化进行层次聚类,显示出两种与年龄无关的基因型;一种除了1q增益/16q缺失外几乎没有基因组变化,另一种有扩增以及低水平的增益/缺失。年龄队列比较显示,在总的或位点特异性的基因组断裂和扩增子频率方面没有显著差异。对101个ER阳性RNA样本(53名年轻女性,48名老年女性)中可变表达的5.1K基因进行层次聚类,确定了六种具有明显年龄偏差(P<0.05)的转录组亚型。与不良预后相关的增殖特征表达较高的样本主要(65%)是年轻病例。监督分析确定了队列之间差异表达的癌症相关基因;年轻病例表达更多的细胞周期基因以及生长因子双调蛋白(AREG)水平高出三倍以上,而老年病例除了ER(ESR1)外还表达更高水平的四种不同的同源盒(HOX)基因。针对另外两个独立的乳腺癌数据集验证的年龄特征在辨别具有AREG和ESR1表达特征差异的年轻与老年ER阳性乳腺癌病例方面被证明具有>80%的准确性。

结论

这些发现表明,表观遗传转录组变化而非基因型变异是散发性乳腺癌发病率和预后与年龄相关差异的原因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/0b51ee98fdf7/bcr1765-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/291a2f9f7e66/bcr1765-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/011aaeafdcf5/bcr1765-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/dfb08e2be1d4/bcr1765-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/0b51ee98fdf7/bcr1765-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/291a2f9f7e66/bcr1765-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/011aaeafdcf5/bcr1765-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/dfb08e2be1d4/bcr1765-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c38a/2216076/0b51ee98fdf7/bcr1765-4.jpg

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