Martha Sarah R, Cheng Qiang, Fraser Justin F, Gong Liyu, Collier Lisa A, Davis Stephanie M, Lukins Doug, Alhajeri Abdulnasser, Grupke Stephen, Pennypacker Keith R
School of Nursing, University of Washington, Seattle, WA, United States.
Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States.
Front Neurol. 2020 Jan 15;10:1391. doi: 10.3389/fneur.2019.01391. eCollection 2019.
Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcomes within the acute period of stroke would be essential for care planning and rehabilitation. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) study collects arterial blood immediately distal and proximal to the intracranial thrombus at the time of mechanical thrombectomy. These blood samples are an innovative resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and important immune factors during mechanical thrombectomy for emergent large vessel occlusion (ELVO) and which patient demographics were predictors for stroke outcomes (infarct and/or edema volume) in acute ischemic stroke patients. The BACTRAC study is a non-probability sampling of male and female subjects (≥18 year old) treated with mechanical thrombectomy for ELVO. We evaluated 28 subjects (66 ± 15.48 years) relative concentrations of mRNA for gene expression in 84 inflammatory molecules in arterial blood distal and proximal to the intracranial thrombus who underwent thrombectomy. We used the machine learning method, Random Forest to predict which inflammatory genes and patient demographics were important features for infarct and edema volumes. To validate the overlapping genes with outcomes, we perform ordinary least squares regression analysis. Machine learning analyses demonstrated that the genes and subject factors CCR4, IFNA2, IL-9, CXCL3, Age, T2DM, IL-7, CCL4, BMI, IL-5, CCR3, TNFα, and IL-27 predicted infarct volume. The genes and subject factor IFNA2, IL-5, CCL11, IL-17C, CCR4, IL-9, IL-7, CCR3, IL-27, T2DM, and CSF2 predicted edema volume. The overlap of genes CCR4, IFNA2, IL-9, IL-7, IL-5, CCR3, and IL-27 with T2DM predicted both infarct and edema volumes. These genes relate to a microenvironment for chemoattraction and proliferation of autoimmune cells, particularly Th2 cells and neutrophils. Machine learning algorithms can be employed to develop prognostic predictive biomarkers for stroke outcomes in ischemic stroke patients, particularly in regard to identifying acute gene expression changes that occur during stroke.
缺血性中风仍然是最使人衰弱的疾病之一，是美国第五大死因。在中风急性期预测中风结果的能力对于护理规划和康复至关重要。血液与血栓取栓登记与协作研究（BACTRAC；clinicaltrials.gov NCT03153683）在机械取栓时收集颅内血栓远侧和近侧的动脉血。这些血样是评估缺血性中风时急性基因表达变化的创新资源。本研究的目的是确定在紧急大血管闭塞（ELVO）的机械取栓过程中的炎症基因和重要免疫因子，以及哪些患者人口统计学特征是急性缺血性中风患者中风结果（梗死和/或水肿体积）的预测指标。BACTRAC研究是对接受ELVO机械取栓治疗的男性和女性受试者（≥18岁）进行的非概率抽样。我们评估了28名受试者（66±15.48岁），这些受试者在接受取栓治疗时，颅内血栓远侧和近侧动脉血中84种炎症分子的基因表达的mRNA相对浓度。我们使用机器学习方法随机森林来预测哪些炎症基因和患者人口统计学特征是梗死和水肿体积的重要特征。为了验证与结果重叠的基因，我们进行普通最小二乘回归分析。机器学习分析表明，基因和受试者因素CCR4、IFNA2、IL-9、CXCL3、年龄、2型糖尿病、IL-7、CCL4、BMI、IL-5、CCR3、TNFα和IL-27可预测梗死体积。基因和受试者因素IFNA2、IL-5、CCL11、IL-17C、CCR4、IL-9、IL-7、CCR3、IL-27、2型糖尿病和CSF2可预测水肿体积。基因CCR4、IFNA2、IL-9、IL-7、IL-5、CCR3和IL-27与2型糖尿病的重叠可预测梗死和水肿体积。这些基因与自身免疫细胞，特别是Th2细胞和中性粒细胞的化学吸引和增殖的微环境有关。机器学习算法可用于开发缺血性中风患者中风结果的预后预测生物标志物，特别是在识别中风期间发生的急性基因表达变化方面。
