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原发肿瘤基因特征可识别转移性精原细胞瘤。

A gene signature of primary tumor identifies metastasized seminoma.

机构信息

Department of Hematology, Hemostasis, Oncology, and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany.

出版信息

Urol Oncol. 2011 Nov-Dec;29(6):764-73. doi: 10.1016/j.urolonc.2009.08.008. Epub 2009 Nov 27.

Abstract

BACKGROUND

The aim of this study was the prediction of metastatic status in seminoma based on examination of the primary tumor.

METHODS

Total RNA was isolated from metastasized seminoma (n = 10, T1N1-2M0), non-metastasized seminoma (n = 21, T1-3N0M0), and corresponding normal tissues. Pooled RNA from 10 biopsies of each tissue type was hybridized on whole genome microarrays for screening purposes. Ninety-two selected gene candidates were quantitatively examined using real-time quantitative polymerase chain reaction (RTQ-PCR).

RESULTS

Agreement in gene expression was 88% between the whole genome microarrays and RTQ-PCR. Metastasized seminoma showed 1,912 up-regulated and 2,179 down-regulated genes with ≥ 2-fold differences in gene expression compared non-metastasized seminoma. RTQ-PCR of selected genes showed that mean gene expression values were significantly reduced in metastasized compared with non-metastasized seminoma. The presence of metastases could be predicted based on an 85-gene expression signature by using logistic regression. Sensitivity and accuracy of the 10-fold cross-validation model were 77.8% and 84.2%, respectively.

CONCLUSION

A logistic regression model using an 85 gene expression signature allowed identification of metastasized seminoma from the primary tumor with a sensitivity of 77.8%.

摘要

背景

本研究旨在通过对原发性肿瘤的检查来预测精原细胞瘤的转移状态。

方法

从转移性精原细胞瘤(n=10,T1N1-2M0)、非转移性精原细胞瘤(n=21,T1-3N0M0)和相应的正常组织中分离总 RNA。为了筛选目的,将每种组织类型的 10 个活检的混合 RNA 进行全基因组微阵列杂交。使用实时定量聚合酶链反应(RTQ-PCR)对 92 个选定的基因候选物进行定量检测。

结果

全基因组微阵列和 RTQ-PCR 的基因表达一致性为 88%。转移性精原细胞瘤与非转移性精原细胞瘤相比,上调基因有 1912 个,下调基因有 2179 个,基因表达差异≥2 倍。选定基因的 RTQ-PCR 显示,转移性与非转移性精原细胞瘤相比,平均基因表达值显著降低。基于 logistic 回归,使用 85 个基因表达特征可以预测转移的存在。10 倍交叉验证模型的敏感性和准确性分别为 77.8%和 84.2%。

结论

使用 85 个基因表达特征的 logistic 回归模型可以从原发性肿瘤中识别转移性精原细胞瘤,其敏感性为 77.8%。

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