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首页> 外文期刊>BMC Medical Genomics >Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas
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Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas

机译：通过基因表达谱分析将分子分类器转换为基于实时PCR的分类器：神经胶质瘤的预后指标

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Background 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. Methods 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. Results 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. Conclusion 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.
机译：背景技术预期基因表达谱的出现将显着改善癌症诊断。但是，尽管付出了巨大的努力并取得了一些成功的例子，但基于配置文件的诊断系统的开发仍然是一项艰巨的任务。在目前的工作中，我们建立了一种方法，将基于适配器标记竞争性PCR（ATAC-PCR）（数据格式与微阵列相似）的分子分类器转换为基于实时PCR的分类器。方法以前，我们使用通过高通量逆转录PCR技术ATAC-PCR获得的基因表达数据构建神经胶质瘤的预后预测因子。通过ATAC-PCR获得的基因表达数据的分析类似于来自双色微阵列的数据的分析。预后预测因子是基于第一主成分（PC1）得分的线性分类器，PCI得分是58个基因表达值的加权总和。在本研究中，我们采用了delta-delta Ct方法进行实时PCR测量。使用线性回归将预测变量转换为基于Ct值的预测变量。结果我们从UBL5基因组中选择了UBL5作为参考基因，该基因组的表达模式与之前的分析研究中值的表达水平最为相似。诊断基因的数量减少到27个而不会影响预后预测器的性能。从实时PCR获得的数据计算出的PC1得分与ATAC-PCR获得的PC1得分显示出很高的线性相关性（r = 0.94）。各个基因表达模式的相关性（r = 0.43至0.91）小于PC1分数，这表明在表达值的加权求和期间可能消除了测量误差。新的预测因子对测试集的分类（n = 36）比组织病理学诊断更准确（对数秩p值分别为0.023和0.137），以预测预后。结论我们成功地将通过ATAC-PCR获得的分子分类器转换为基于Ct值的预测器。我们的转换程序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中的错误，因此单个基因表达的转换不是适当的过程。神经胶质瘤的预测器仍处于开发的初期阶段，需要分析性临床验证和临床实用性研究。

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