Whole Transcriptome Studies of Patients with Transient Ischemic Attacks (TIAs)

 描述(由申请人提供)：短暂性脑缺血发作(TIA)是识别的关键，因为预防治疗可以将未来血管事件的风险降低50%。诊断检测和治疗干预必须尽快开始，因为10%-25%的TIA患者在90天内发生中风。由于有如此多的患者紧急出现短暂性神经事件，其中绝大多数人不会继续中风，因此人们一直在寻找识别中风高风险TIA的方法，以便针对那些最需要的人进行工作和治疗，以节省时间、金钱和有限的资源。虽然ABCD2评分和脑弥散加权成像-磁共振成像(DWI-MRI)改善了对TIA卒中的预测，但它们对个别病例预测的敏感性和特异性较差。在这项建议中，我们建议外周血白细胞和血小板在TIA继续发生卒中的过程中起关键作用，通过评估全血中的RNA，我们可以评估继续发生卒中的TIA患者与未发生卒中的TIA患者的白细胞和血小板功能。我们假设，与那些90天后没有中风的TIA患者相比，TIA患者中特定的凝血和免疫基因被激活，这使他们容易在90天后发生中风。这些白细胞和血小板mRNA基因的一个子集将预测90天后中风的TIA。这一假设通过以下具体目标得到解决。目的#1(派生队列)：证明使用RNAseq从全血中测量的mRNA表达在发生中风90天的TIA的派生队列中与那些90天未发生中风的TIA的派生队列中的不同。证明了大多数被发现使用RNAseq调控的mRNA在用qRT-PCR测量时也受到显著调控。目标#2(派生队列)：将机器学习算法应用于来自目标#1的信使核糖核酸，以获得受RNAseq和qRT-PCR共同调控的最佳信使核糖核酸子集，该子集预测哪些TIA在90天内发生中风，而那些在交叉验证中没有95%敏感度的TIA。目标#3(验证队列)：使用机器/预测学习算法证明，当在独立验证队列上使用qRT-PCR测量目标#2的基因时，可以预测哪些TIA在90天内发生中风，敏感度为85%。这些研究的目标是发现血液中的mRNA图谱，以预测哪些TIA患者在90天后会继续中风。当在未来的研究中得到证实时，这将指导对那些最需要预防中风的高危TIA进行深入测试，并减少那些中风风险较低的人进行不必要的测试。同样重要的是，被发现与TIA患者卒中高风险相关的基因将代表潜在的新的卒中预防目标。

 DESCRIPTION (provided by applicant): Transient ischemic attacks (TIA) are critical to identify because prevention therapy can reduce the risk of future vascular events by > 50%. Diagnostic testing and therapeutic intervention must start as soon as possible because 10-25% of TIAs have a stroke within 90 days. Because so many patients present emergently with transient neurological events the large majority of whom do not go on to have a stroke, methods for identifying TIAs at high risk for stroke have been sought so that work up and treatment can be targeted to those who need it most to save time, money and limited resources. Though the ABCD2 score and brain Diffusion Weighted Imaging-MRI (DWI-MRI) have improved prediction of which TIAs have a stroke, their sensitivity and specificity for prediction of individual cases i poor. In this proposal we propose that peripheral blood leukocytes and platelets play pivotal roles in which TIAs go on to have a stroke and by assessing RNA in whole blood we can evaluate leukocyte and platelet function in TIA patients who go on to have stroke versus those that do not have a stroke. We hypothesize that specific coagulation and immune genes are activated in TIA patients that predispose them to have a stroke by 90 days compared to those TIA patients who do NOT have a stroke by 90 days. A subset of these leukocyte and platelet mRNA genes will predict TIAs who have a stroke by 90 days. This hypothesis is addressed by the following specific aims. Aim #1 (Derivation Cohort): Demonstrate that mRNA expression measured using RNAseq from whole blood differs in a derivation cohort of TIAs that go on to have a stroke by 90 days compared to those TIAs who do not have a stroke by 90 days. Demonstrate that most mRNA found to be regulated using RNAseq are also significantly regulated when measured using qRT-PCR. Aim #2 (Derivation Cohort): Apply machine learning algorithms to the mRNA from Aim #1 to derive an optimal subset of mRNA regulated by both RNAseq and qRT-PCR that predict which TIAs have strokes by 90 days compared to those who do not with >95% sensitivity on cross-validation. Aim #3 (Validation Cohort): Use machine/prediction learning algorithms to demonstrate that the genes from Aim #2 when measured using qRT-PCR on an independent validation cohort predict which TIAs have a stroke by 90 days with >85% sensitivity. The goal of these studies is to discover mRNA profiles in blood that predict which TIA patients go on to have strokes by 90 days. When confirmed in future studies, this will direct in depth testing to those high risk TIAs most in need in order to prevent strokes, and decrease unnecessary testing in those with low risk of stroke. Equally as important, the genes discovered to be associated with high risk of stroke in TIA patients will represent potential novel stroke prevention targets.