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首页> 外文期刊>BMC Genomics >Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer
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Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

机译：定量或定性的转录诊断签名？大肠癌病例研究

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Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a na?ve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.
机译：由于实验批次效应，定量转录特征在疾病诊断中的应用通常需要样本间数据标准化，这在普通临床环境下几乎不适用。许多癌症可能在基因表达模式上与非癌症状态有质的差异。因此，探索定性诊断特征的能力是合理的，这些特征对实验批次效应和其他随机因素具有鲁棒性。首先，使用来自微阵列质量控制（MAQC）项目的技术重复样品的数据，我们证明了基于低通量PCR的技术对于基因表达也存在较大的测量差异，即使在同一测试位点测量样品也是如此。然后，我们通过使用支持向量机和朴素贝叶斯分类器的案例研究，通过将定量转录特征应用于个体样品，证明了分类器低稳定性的关键局限性，该案例研究将大肠癌组织与正常组织区分开来。为了解决这个问题，我们基于这些基因对的样本内相对表达顺序（REO），确定了由三个基因对组成的特征标记，用于将结直肠癌组织与非癌（正常和炎症性肠病）组织区分开。使用22个独立的数据集（由不同的微阵列和RNA_seq平台测量）很好地验证了签名，从而避免了样品间数据标准化的需要。在当前的技术条件下，基因表达测量的细微定量信息趋于不稳定，这将给定量转录诊断标记的临床应用带来不确定性。对于具有定性转录特征的疾病状态的诊断，可以将基于定性REO的特征可靠地应用于通过不同平台测量的单个样本。

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