Mining the Dynamic Genome: A Method for Identifying Multiple Disease Signatures Using Quantitative RNA Expression Analysis of a Single Blood Sample.

Microarrays Pub Date : 2015-12-10 DOI:10.3390/microarrays4040671

Samuel Chao, Changming Cheng, Choong-Chin Liew

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引用次数: 7

Abstract

Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological signatures within the "noisy" variability of whole blood.

Methods: We demonstrate collection tube bias compensation during the process of identifying a liver cancer-specific gene signature. The candidate probe set list of liver cancer was filtered, based on previous repeatability performance obtained from technical replicates. We built a prediction model using differential pairs to reduce the impact of confounding factors. We compared prediction performance on an independent test set against prediction on an alternative model derived by Weka. The method was applied to an independent set of 157 blood samples collected in PAXgene tubes.

Results: The model discriminated liver cancer equally well in both EDTA and PAXgene collected samples, whereas the Weka-derived model (using default settings) was not able to compensate for collection tube bias. Cross-validation results show our procedure predicted membership of each sample within the disease groups and healthy controls.

Conclusion: Our versatile method for blood transcriptomic investigation overcomes several limitations hampering research in blood-based gene tests.

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挖掘动态基因组:一种利用单个血液样本定量RNA表达分析识别多种疾病特征的方法。

背景:血液作为诊断工具比组织样本有优势，血液mRNA转录组学是一个令人兴奋的研究领域。为了充分发挥血液转录组学研究的潜力，需要改进基因表达测量和数据解释方法，以便能够在全血的“嘈杂”变异性中检测生物特征。方法:在鉴定肝癌特异性基因标记的过程中，我们展示了收集管的偏置补偿。根据先前从技术重复中获得的重复性性能，筛选肝癌候选探针集列表。为了减少混杂因素的影响，我们建立了一个差分对的预测模型。我们将独立测试集上的预测性能与Weka派生的替代模型上的预测性能进行了比较。该方法应用于PAXgene管采集的157份独立血液样本。结果:该模型在EDTA和PAXgene收集的样本中同样能很好地识别肝癌，而weka衍生的模型(使用默认设置)无法补偿收集管的偏差。交叉验证结果表明，我们的程序预测了每个样本在疾病组和健康对照组中的成员关系。结论:我们的多功能血液转录组学研究方法克服了阻碍血液基因检测研究的几个限制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microarrays

自引率

0.00%

发文量

审稿时长

11 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.