Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas.
Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas.
复制标题
DOI：
10.1186/1755-8794-3-52
复制
发表时间：
2010-11-10
影响因子：
2.7
通讯作者：
Kato K
中科院分区：
文献类型：
--
作者：
Kawarazaki S;Taniguchi K;Shirahata M;Kukita Y;Kanemoto M;Mikuni N;Hashimoto N;Miyamoto S;Takahashi JA;Kato K
基因表达谱的出现有望显著改善癌症诊断。然而，尽管付出了巨大的努力并取得了一些成功的例子，但基于剖面图的诊断系统的开发仍然是一项艰巨的任务。在本工作中，我们建立了一种方法，将基于适配器标记竞争性PCR （ATAC-PCR）的分子分类器（数据格式与微阵列相似）转换为基于实时PCR的分类器。此前，我们利用高通量反转录PCR技术ATAC-PCR获得的基因表达数据构建了胶质瘤的预后预测因子。ATAC-PCR获得的基因表达数据分析与双色微阵列数据分析类似。预后预测因子是基于第一主成分（PC1）评分的线性分类器，该评分是58个基因表达值的加权总和。在本研究中，我们采用delta-delta Ct法进行实时PCR检测。使用线性回归将预测器转换为基于Ct值的预测器。我们从表达模式与之前的分析研究的中位表达水平最相似的一组基因中选择了UBL5作为内参基因。在不影响预后预测器性能的情况下，诊断基因数量减少到27个。real-time PCR计算的PC1评分与ATAC-PCR计算的PC1评分呈高度线性相关（r = 0.94）。个体基因表达模式的相关性（r = 0.43至0.91）小于PC1得分，这表明在表达值加权求和期间，测量误差可能被抵消。新预测器对测试集（n = 36）的分类比组织病理学诊断更准确（log rank p值分别为0.023和0.137）。我们成功地将ATAC-PCR获得的分子分类器转化为基于Ct值的预测器。我们的转换过程也应该适用于从微阵列数据获得的线性分类器。由于测量误差很可能在计算过程中被抵消，因此个体基因表达的转换不是一个合适的程序。神经胶质瘤的预测仍处于发展的初步阶段，需要分析临床验证和临床应用研究。
The advent of gene expression profiling was expected to dramatically improve cancer diagnosis. However, despite intensive efforts and several successful examples, the development of profile-based diagnostic systems remains a difficult task. In the present work, we established a method to convert molecular classifiers based on adaptor-tagged competitive PCR (ATAC-PCR) (with a data format that is similar to that of microarrays) into classifiers based on real-time PCR. Previously, we constructed a prognosis predictor for glioma using gene expression data obtained by ATAC-PCR, a high-throughput reverse-transcription PCR technique. The analysis of gene expression data obtained by ATAC-PCR is similar to the analysis of data from two-colour microarrays. The prognosis predictor was a linear classifier based on the first principal component (PC1) score, a weighted summation of the expression values of 58 genes. In the present study, we employed the delta-delta Ct method for measurement by real-time PCR. The predictor was converted to a Ct value-based predictor using linear regression. We selected UBL5 as the reference gene from the group of genes with expression patterns that were most similar to the median expression level from the previous profiling study. The number of diagnostic genes was reduced to 27 without affecting the performance of the prognosis predictor. PC1 scores calculated from the data obtained by real-time PCR showed a high linear correlation (r = 0.94) with those obtained by ATAC-PCR. The correlation for individual gene expression patterns (r = 0.43 to 0.91) was smaller than for PC1 scores, suggesting that errors of measurement were likely cancelled out during the weighted summation of the expression values. The classification of a test set (n = 36) by the new predictor was more accurate than histopathological diagnosis (log rank p-values, 0.023 and 0.137, respectively) for predicting prognosis. We successfully converted a molecular classifier obtained by ATAC-PCR into a Ct value-based predictor. Our conversion procedure should also be applicable to linear classifiers obtained from microarray data. Because errors in measurement are likely to be cancelled out during the calculation, the conversion of individual gene expression is not an appropriate procedure. The predictor for gliomas is still in the preliminary stages of development and needs analytical clinical validation and clinical utility studies.
登录
查看更多内容
各种归一化方法对应用生物系统表达阵列系统数据的影响。
影响因子：
3
作者：
Barbacioru, Catalin C.;Wang, Yulei;Canales, Roger D.;Sun, Yongming A.;Keys, David N.;Chan, Frances;Poulter, Karen A.;Samaha, Raymond R.
通讯作者：
Samaha, Raymond R.
影响因子：
168.9
作者：
van den Bent, MJ;Afra, D;Karim, ABMF
通讯作者：
Karim, ABMF
影响因子：
4.4
作者：
Wang, Yulei;Barbacioru, Catalin;Hyland, Fiona;Xiao, Wenming;Hunkapiller, Kathryn L;Blake, Julie;Chan, Frances;Gonzalez, Carolyn;Zhang, Lu;Samaha, Raymond R
通讯作者：
Samaha, Raymond R
影响因子：
5.8
作者：
Paik, Soonmyung
通讯作者：
Paik, Soonmyung
影响因子：
14.9
作者：
Kato, K
通讯作者：
Kato, K