Whole Transcriptome Studies of Blood to Predict Stroke Outcome

预测中风结果的血液全转录组研究

• 批准号: 10655229
• 负责人: Bradley Pearce Ander
• 金额: $ 64.19万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655229
• 关键词: 3-Dimensional; AMPA Receptors; Age; Anti-Inflammatory Agents; Atrial Fibrillation; Biological Markers; Biology; Blood; Blood Platelets; Blood Pressure; Brain; Brain-Derived Neurotrophic Factor; C-reactive protein; Clinical; Clinical Trials; Coagulation Process; Cytochrome P450; Data; Derivation procedure; Diabetes Mellitus; Engineering; Environment; Etiology; Future; Gene Expression; Genes; Genetic Markers; Genome; Glucose; Growth Factor; HMGB1 gene; Heat shock proteins; Hemoglobin concentration result; Human; Hyperlipidemia; IL6 gene; ITGB3 gene; Immune; Infection; Inflammatory; Interleukin-1; Interleukin-10; Ischemic Stroke; Knowledge; Leukocytes; Life Style; Light; Location; Logistic Regressions; Lymphocyte Count; Machine Learning; Measures; Molecular; Neurologic; Outcome; Outcome Measure; PARK2 gene; PTGS2 gene; Pathway Analysis; Pathway interactions; Patients; Pattern; Phosphoric Monoester Hydrolases; Platelet Count measurement; Prognosis; Proteins; RNA; Recovery; Regulator Genes; Sensitivity and Specificity; Severities; Stroke; Stroke Volume; System; TNF gene; United States National Institutes of Health; Validation; cohort; cytokine; functional outcomes; gene network; genetic risk factor; improved; indexing; kidney dysfunction; machine learning algorithm; machine learning prediction; molecular marker; monocyte; neurofilament; neuronal excitability; neutrophil; outcome prediction; patient stratification; peripheral blood; post stroke; presynaptic; repaired; response; sex; statistics; stroke outcome; stroke patient; success; support vector machine; transcriptome; transcriptome sequencing; vascular risk factor; whole genome

Abstract Several clinical variables are associated with outcomes following ischemic stroke (IS). However, clinical and demographic parameters account only for a portion of the outcome variance, thus it is difficult for clinicians to reliably predict long-term IS outcome. Hence, new biomarkers are needed. Molecules in blood are also associated with IS outcome including pro-inflammatory cytokines, anti-inflammatory cytokines and others. Genetic risk factors have also been associated with IS outcome. Unfortunately, combined blood and genetic biomarkers have not improved IS outcome predictions compared to clinical parameters since age, sex and initial NIHSS is said to predict outcome with a modest c-statistic approaching 0.7. Our preliminary data show gene expression in blood after IS can predict 90-day outcome better than age, sex and NIHSS. We hypothesize that a whole-genome approach of measuring RNA, which reflects the genome × environment × lifestyle interaction, to assess inflammatory, trophic and clotting genes will improve IS outcome prediction compared to clinical features alone. Thus, we propose the following aims: Aim #1a. Perform whole-genome RNA sequencing (RNAseq) of blood on a derivation cohort of IS patients at 1 day and 3 days after IS compared to matched vascular risk factor controls (VRFC). Aim #1b. Identify the most significantly regulated genes and pathways in blood at 1d/3d after IS that correlate with outcome, as measured by three outcome scales – mRS (modified Rankin Scale), NIHSS (NIH Stroke Scale) and Barthel Index at 90 days after IS. Aim #1c. Use Network Analysis to identify key hub genes after IS associated with outcome that might be causative. Aim #1d. Use Feature Engineering and Logistic Regression and/or other Machine Learning approaches, such as Support Vector Machines (SVM) and SVM Regression, to identify the least number of genes at 1 and/or 3 days that predict the three stroke 90-day outcome measures. Aim #1e. In a separate validation cohort of IS patients perform RNAseq to obtain the expression of the biomarker genes from Aim #1d. Input these into Machine Learning algorithms to predict patient 90-day outcomes (mRS, NIHSS, Barthel Index). Aim #2a. Demonstrate that gene expression is a better 90-day outcome predictor compared to each or a combination of clinical variables, such as stroke volume, initial NIHSS, location, etiology, age, sex, glucose levels, blood pressure, atrial fibrillation, and neutrophil, monocyte, lymphocyte, and platelet counts. Aim #2b. Delineate the underlying biology of these clinical outcome contributors by identifying genes and networks that correlate with them and pinpoint which of the genes/networks also correlate with each of the three outcomes. Significance: The findings of this study will develop biomarkers of ischemic stroke outcome to help clinicians predict IS outcome and aid future clinical trials in stratifying IS patients, thus significantly increasing chances for trial success. Equally important, the findings will provide much needed potential new treatment targets and unprecedented knowledge of how the peripheral blood transcriptome contributes to outcome and improve our understanding of the biology of repair and recovery after IS in humans.

抽象的 一些临床变量与缺血性中风（IS）后的结果相关。然而，临床 而人口统计参数仅占结果方差的一部分，因此临床医生很难 可靠地预测长期 IS 结果。因此，需要新的生物标志物。血液中的分子也 与 IS 结果相关的因素包括促炎细胞因子、抗炎细胞因子等。 遗传风险因素也与 IS 结局相关。不幸的是，结合了血液和遗传 与年龄、性别和临床参数相比，生物标志物并没有改善 IS 结果预测。 据说最初的 NIHSS 可以用接近 0.7 的适度 c 统计量来预测结果。我们的初步数据显示 IS 后血液中的基因表达可以比年龄、性别和 NIHSS 更好地预测 90 天的结果。我们 假设测量 RNA 的全基因组方法，反映了基因组 × 环境 × 生活方式的相互作用，评估炎症、营养和凝血基因将改善 IS 结果预测 与单独的临床特征相比。因此，我们提出以下目标：目标#1a。执行全基因组 IS 患者衍生队列在 IS 后 1 天和 3 天进行血液 RNA 测序 (RNAseq) 与匹配的血管危险因素对照（VRFC）相比。目标#1b。确定最受监管的 IS 后 1 天/3 天血液中与结果相关的基因和通路（通过三个结果测量） 量表 – IS 后 90 天的 mRS（改良 Rankin 量表）、NIHSS（NIH 中风量表）和 Barthel 指数。目的 #1c。使用网络分析来识别 IS 后与可能致病结果相关的关键中心基因。 目标#1d。使用特征工程和逻辑回归和/或其他机器学习方法，例如 作为支持向量机 (SVM) 和 SVM 回归，识别 1 和/或 3 处的最少基因数 预测三种中风 90 天结果指标的天数。目标#1e。在 IS 的单独验证队列中 患者执行 RNAseq 以获得 Aim #1d 中生物标志物基因的表达。将这些输入到 用于预测患者 90 天结果的机器学习算法（mRS、NIHSS、Barthel 指数）。目标#2a。 证明与单个或组合相比，基因表达是更好的 90 天结果预测因子 临床变量，例如每搏输出量、初始 NIHSS、位置、病因、年龄、性别、血糖水平、血液 压力、心房颤动以及中性粒细胞、单核细胞、淋巴细胞和血小板计数。目标#2b。划定 通过识别与相关的基因和网络来了解这些临床结果贡献者的基础生物学 并查明哪些基因/网络也与这三个结果中的每一个相关。 意义：这项研究的结果将开发缺血性中风结果的生物标志物，以帮助临床医生 预测 IS 结果并帮助未来的临床试验对 IS 患者进行分层，从而显着增加机会 为了试验成功。同样重要的是，这些发现将提供急需的潜在新治疗目标和 关于外周血转录组如何影响结果并改善我们的健康状况有了前所未有的了解 了解人类 IS 后修复和恢复的生物学。