Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke

Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.