Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.
Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.
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DOI：
10.1038/npjgenmed.2016.38
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发表时间：
2016
影响因子：
5.3
通讯作者：
Barr TL
中科院分区：
文献类型：
--
作者：
O'Connell GC;Petrone AB;Treadway MB;Tennant CS;Lucke-Wold N;Chantler PD;Barr TL
中风的早期和准确诊断提高了积极结果的可能性。本研究的目的是确定外周血中的基因表达模式，该模式可能被优化以加速急性缺血性卒中（AIS）的诊断。招募了一个由39名AIS患者和24名神经学上无症状的对照组成的发现队列。在急诊科入院时采集外周血，并通过微阵列进行全基因组表达谱分析。一种被称为遗传算法k-近邻（GA/kNN）的机器学习技术被用来识别基因表达模式，这种模式可以最佳地区分不同的群体。然后在一个独立的验证队列中通过qRT-PCR评估这种表达模式，在该队列中评估其区分另外39名AIS患者和30名神经学上无症状的对照以及20名急性卒中模拟者的能力。GA/kNN鉴定了10个基因（ANTXR 2、STK 3、PDK 4、CD 163、MAL、GRAP、ID 3、CTSZ、KIF 1B和PLXDC 2），其表达的协调模式能够正确鉴定98.4%的发现队列受试者（97.4%敏感性，100%特异性）。在验证队列中，当将AIS患者与无症状对照进行比较时，相同10个基因的表达水平能够正确识别95.6%的受试者（92.3%敏感性，100%特异性），当将AIS患者与卒中模拟患者进行比较时，能够正确识别94.9%的受试者（97.4%敏感性，90.0%特异性）。在这项研究中确定的转录模式显示出强大的诊断潜力，并保证进一步评估，以确定其真正的临床疗效。
Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy.
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