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 几个临床变量与缺血性中风后的结果相关。但是，临床 人口参数仅占结果差异的一部分，因此临床医生很难 可靠地预测长期是结果。因此，需要新的生物标志物。血液中的分子也是 与IS结果有关，包括促炎性细胞因子，抗炎细胞因子等。 遗传危险因素也与结果有关。不幸的是，血液和遗传结合 与年龄，性别和性别以来的临床参数相比，生物标志物没有改善是结果预测 据说最初的NIHSS可以通过适度的C统计方法预测结果0.7。我们的初步数据显示 血液中血液中的基因表达可以预测90天的结果比年龄，性别和NIHSS更好。我们 假设是测量RNA的全基因组方法，它反映了基因组×环境× 生活方式相互作用，评估炎症，营养和闭合基因将改善结果预测 与仅临床特征相比。这是我们提出以下目标：目标＃1a。执行全基因组 在IS 1天和3天后，在IS的衍生组中，血液的RNA测序（RNASEQ） 与匹配的血管风险因素控制（VRFC）相比。目标＃1B。确定最重要的监管 通过三个结果来衡量，在1D/3D的血液中的基因和途径与结果相关 量表 - MRS（改良的Rankin量表），NIHSS（NIH中风量表）和Barthel指数在IS之后的90天。目的 ＃1C。使用网络分析以识别关键集线器基因与可能是病因的结果相关联。 目标＃1D。使用功能工程和逻辑回归和/或其他机器学习方法，例如 作为支持向量机（SVM）和SVM回归，以确定1和/或3的最少基因 预测三个中风90天的结果度量的天数。目标＃1E。在单独的验证队列中 患者进行RNASEQ以从AIM＃1D获得生物标志物基因的表达。将这些输入 机器学习算法可预测患者90天的结果（MRS，NIHSS，Barthel指数）。目标＃2A。 证明基因表达是一个更好的90天结果预测指标 临床变量，例如中风量，初始NIHS，位置，病因，年龄，性别，葡萄糖水平，血液 压力，心房颤动和中性粒细胞，单核细胞，淋巴细胞和血小板计数。目标＃2B。描述 这些临床结果贡献者的潜在生物学通过鉴定与与之相关的基因和网络 他们并指出了哪些基因/网络也与三个结果中的每一个相关。 意义：这项研究的结果将发展出缺血性中风结果的生物标志物，以帮助临床医生 预测是结果，并在分层中有助于未来的临床试验是患者，因此大大增加了机会 取得审判成功。同样重要的是，调查结果将提供急需的潜在新治疗目标和 关于周围血液转录组如何有助于结果并改善我们的前所未有的知识 在人类中了解修复和恢复的生物学。