Breen Michael S, Stein Dan J, Baldwin David S
Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Hum Psychopharmacol. 2016 Sep;31(5):373-81. doi: 10.1002/hup.2546.
The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.
We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.
A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.
Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards. Copyright © 2016 John Wiley & Sons, Ltd.
血液用于全基因组基因表达谱分析和生物标志物发现，在被诊断患有主要神经精神疾病的患者中受到了广泛关注。虽然已经进行了大量研究，但在基于血液的生物标志物发现、验证和测试方面，仍需要统计严谨性和清晰度。
我们对文献进行了系统综述，以研究方法学途径，并评估血液转录组谱分析在精神障碍研究中的价值。我们特别关注与机器学习、基因网络分析以及不同疾病间趋同相关的统计考量。
共调查了15种疾病的108项外周血转录组研究：25项研究使用了多种机器学习技术来评估候选生物标志物的假定临床可行性；11项研究利用高阶系统水平视角来识别基于基因模块的生物标志物；9项研究对两种或更多神经精神表型进行了分析。值得注意的是，约50%的被调查研究样本量（病例和对照）少于50例，而约75%的研究样本量少于100例。
在实验规划的早期阶段详细考虑统计分析，对于确保基于血液的生物标志物发现和验证至关重要。本文提出了统计指南，以提高机器学习和基因网络分析在独立研究中的实施和可重复性。利用更大样本量和新兴的下一代技术的未来研究，为推动该领域向前发展奠定了基础。版权所有© 2016约翰威立父子有限公司。
