Research Institute, Osaka Medical Center for Cancer and Cardiovascular Diseases, 1-3-3 Nakamichi, Higashinari-ku, Osaka, 537-8511, Japan.
BMC Med Genomics. 2010 Nov 10;3:52. doi: 10.1186/1755-8794-3-52.
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.
基因表达谱分析的出现有望显著改善癌症诊断。然而，尽管进行了大量的努力和有几个成功的例子，基于谱的诊断系统的发展仍然是一个困难的任务。在本工作中，我们建立了一种将基于衔接子标记竞争 PCR（ATAC-PCR）的分子分类器（数据格式类似于微阵列）转换为基于实时 PCR 的分类器的方法。
以前，我们使用 ATAC-PCR 获得的基因表达数据构建了一个用于胶质母细胞瘤的预后预测器，ATAC-PCR 是一种高通量逆转录 PCR 技术。ATAC-PCR 获得的基因表达数据的分析类似于双色微阵列数据的分析。预后预测器是一个基于第一主成分（PC1）得分的线性分类器，是 58 个基因表达值的加权和。在本研究中，我们采用实时 PCR 的 delta-delta Ct 方法进行测量。使用线性回归将预测器转换为基于 Ct 值的预测器。
我们从与以前的分析研究中中位数表达模式最相似的基因组中选择 UBL5 作为参考基因。在不影响预后预测器性能的情况下，诊断基因的数量减少到 27 个。从实时 PCR 获得的数据计算的 PC1 得分与 ATAC-PCR 获得的 PC1 得分高度线性相关（r=0.94）。单个基因表达模式的相关性（r=0.43 至 0.91）小于 PC1 得分，表明在表达值的加权和中，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（对数秩 p 值分别为 0.023 和 0.137），用于预测预后。
我们成功地将 ATAC-PCR 获得的分子分类器转换为基于 Ct 值的预测器。我们的转换过程也应适用于从微阵列数据获得的线性分类器。由于在计算过程中可能会消除测量误差，因此单个基因表达的转换不是一个合适的过程。胶质母细胞瘤的预测器仍处于初步开发阶段，需要进行分析性临床验证和临床实用性研究。
