Blood transcriptome changes after stroke in an African American population.
Blood transcriptome changes after stroke in an African American population.
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DOI：
10.1002/acn3.272
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发表时间：
2016-02
影响因子：
5.3
通讯作者：
Simon RP
中科院分区：
文献类型：
--
作者：
Meller R;Pearson AN;Hardy JJ;Hall CL;McGuire D;Frankel MR;Simon RP
分子诊断医学有望改变护理点治疗。需要额外诊断工具的一个领域是急性中风护理，以协助诊断和预后。先前使用基于微阵列的外周血基因表达分析的研究表明，这种方法可能是有效的。下一代测序（NGS）方法已经扩展了基因组分析，并且不局限于先前在微阵列芯片上鉴定的基因。在这里，我们报告了一项初步的NGS研究，以确定预测中风诊断和预后的基因表达和外显子表达模式。我们招募了28名中风患者和28名年龄和性别匹配的高血压对照组。从3ml血样中提取RNA，组装RNA - Seq文库并测序。对齐RNA数据的生物信息学分析显示外显子（30%），内含子（36%）和新的RNA成分（目前未注释：33%）。我们的研究集中在大脑中动脉闭塞缺血性脑卒中患者（n = 17）。基于我们对基因转录物差异剪接的观察，我们使用所有外显子RNA表达而不是基因表达（组合外显子）来使用支持向量机算法构建预测模型。基于模型构建，这些模型的预测准确率高达90%（规范为88% ~ 92%）。我们进一步根据出院时NIHss评分的改善对结果进行分层；基于模型构建，我们观察到预测的准确率为100%。基于NGS‐的外显子表达分析方法在患者诊断和预后预测方面具有很高的潜力，在临床患者护理方面具有明确的实用性。
Molecular diagnostic medicine holds much promise to change point of care treatment. An area where additional diagnostic tools are needed is in acute stroke care, to assist in diagnosis and prognosis. Previous studies using microarray‐based gene expression analysis of peripheral blood following stroke suggests this approach may be effective. Next‐generation sequencing (NGS) approaches have expanded genomic analysis and are not limited to previously identified genes on a microarray chip. Here, we report on a pilot NGS study to identify gene expression and exon expression patterns for the prediction of stroke diagnosis and prognosis. We recruited 28 stroke patients and 28 age‐ and sex‐matched hypertensive controls. RNA was extracted from 3 mL blood samples, and RNA‐Seq libraries were assembled and sequenced. Bioinformatical analysis of the aligned RNA data reveal exonic (30%), intronic (36%), and novel RNA components (not currently annotated: 33%). We focused our study on patients with confirmed middle cerebral artery occlusion ischemic stroke (n = 17). On the basis of our observation of differential splicing of gene transcripts, we used all exonic RNA expression rather than gene expression (combined exons) to build prediction models using support vector machine algorithms. Based on model building, these models have a high predicted accuracy rate >90% (spec. 88% sen. 92%). We further stratified outcome based on the improvement in NIHss scores at discharge; based on model building we observe a predicted 100% accuracy rate. NGS‐based exon expression analysis approaches have a high potential for patient diagnosis and outcome prediction, with clear utility to aid in clinical patient care.