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

被引:40
作者
Martha, Sarah R. [1 ]
Cheng, Qiang [2 ]
Fraser, Justin F. [3 ,4 ,5 ,6 ,7 ]
Gong, Liyu [2 ]
Collier, Lisa A. [3 ]
Davis, Stephanie M. [3 ]
Lukins, Doug [4 ,5 ,6 ,7 ]
Alhajeri, Abdulnasser [3 ,7 ]
Grupke, Stephen [5 ,7 ]
Pennypacker, Keith R. [3 ,6 ]
机构
[1] Univ Washington, Sch Nursing, Seattle, WA 98195 USA
[2] Univ Kentucky, Inst Biomed Informat, Lexington, KY USA
[3] Univ Kentucky, Dept Neurol, Lexington, KY 40536 USA
[4] Univ Kentucky, Coll Med, Lexington, KY USA
[5] Univ Kentucky, Dept Neurosurg, Lexington, KY USA
[6] Univ Kentucky, Neurosci, Lexington, KY 40506 USA
[7] Univ Kentucky, Radiol, Lexington, KY USA
基金
美国国家卫生研究院;
关键词
ischemic stroke; machine learning; gene expression; cytokines; chemokines; TISSUE-PLASMINOGEN ACTIVATOR; CONTROLLED-TRIAL; BRAIN-DAMAGE; T-CELLS; INFLAMMATION; THERAPY; CD4(+); PATHOPHYSIOLOGY; ALPHA; IL-7;
D O I
10.3389/fneur.2019.01391
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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; 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, TNF alpha, 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.
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页数:9
相关论文
共 69 条
[1]   Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging [J].
Albers, G. W. ;
Marks, M. P. ;
Kemp, S. ;
Christensen, S. ;
Tsai, J. P. ;
Ortega-Gutierrez, S. ;
McTaggart, R. A. ;
Torbey, M. T. ;
Kim-Tenser, M. ;
Leslie-Mazwi, T. ;
Sarraj, A. ;
Kasner, S. E. ;
Ansari, S. A. ;
Yeatts, S. D. ;
Hamilton, S. ;
Mlynash, M. ;
Heit, J. J. ;
Zaharchuk, G. ;
Kim, S. ;
Carrozzella, J. ;
Palesch, Y. Y. ;
Demchuk, A. M. ;
Bammer, R. ;
Lavori, P. W. ;
Broderick, J. P. ;
Lansberg, M. G. .
NEW ENGLAND JOURNAL OF MEDICINE, 2018, 378 (08) :708-718
[2]   A multicenter randomized controlled trial of endovascular therapy following imaging evaluation for ischemic stroke (DEFUSE 3) [J].
Albers, Gregory W. ;
Lansberg, Maarten G. ;
Kemp, Stephanie ;
Tsai, Jenny P. ;
Lavori, Phil ;
Christensen, Soren ;
Mlynash, Michael ;
Kim, Sun ;
Hamilton, Scott ;
Yeatts, Sharon D. ;
Palesch, Yuko ;
Bammer, Roland ;
Broderick, Joe ;
Marks, Michael P. .
INTERNATIONAL JOURNAL OF STROKE, 2017, 12 (08) :896-905
[3]   Post-ischemic brain damage: pathophysiology and role of inflammatory mediators [J].
Amantea, Diana ;
Nappi, Giuseppe ;
Bernardi, Giorgio ;
Bagetta, Giacinto ;
Corasaniti, Maria T. .
FEBS JOURNAL, 2009, 276 (01) :13-26
[4]   The biology and therapeutic potential of interleukin 27 [J].
Batten, Marcel ;
Ghilardi, Nico .
JOURNAL OF MOLECULAR MEDICINE-JMM, 2007, 85 (07) :661-672
[5]   Interleukin 27 limits autoimmune encephalomyelitis by suppressing the development of interleukin 17-producing T cells [J].
Batten, Marcel ;
Li, Ji ;
Yi, Sothy ;
Kljavin, Noelyn M. ;
Danilenko, Dimitry M. ;
Lucas, Sophie ;
Lee, James ;
de Sauvage, Frederic J. ;
Ghilardi, Nico .
NATURE IMMUNOLOGY, 2006, 7 (09) :929-936
[6]   UNDERSTANDING SECONDARY INJURY [J].
Ben Borgens, Richard ;
Liu-Snyder, Peishan .
QUARTERLY REVIEW OF BIOLOGY, 2012, 87 (02) :89-127
[7]  
Benjamin EJ, 2019, CIRCULATION, V139, pE56, DOI [10.1161/CIR.0000000000000746, 10.1161/CIR.0000000000000659]
[8]   A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke [J].
Berkhemer, O. A. ;
Fransen, P. S. S. ;
Beumer, D. ;
van den Berg, L. A. ;
Lingsma, H. F. ;
Yoo, A. J. ;
Schonewille, W. J. ;
Vos, J. A. ;
Nederkoorn, P. J. ;
Wermer, M. J. H. ;
van Walderveen, M. A. A. ;
Staals, J. ;
Hofmeijer, J. ;
van Oostayen, J. A. ;
Nijeholt, G. J. Lycklama A. ;
Boiten, J. ;
Brouwer, P. A. ;
Emmer, B. J. ;
de Bruijn, S. F. ;
van Dijk, L. C. ;
Kappelle, L. J. ;
Lo, R. H. ;
Van Dijk, E. J. ;
de Vries, J. ;
de Kort, P. L. M. ;
van Rooij, W. J. J. ;
van den Berg, J. S. P. ;
van Hasselt, B. A. A. M. ;
Aerden, L. A. M. ;
Dallinga, R. J. ;
Visser, M. C. ;
Bot, J. C. J. ;
Vroomen, P. C. ;
Eshghi, O. ;
Schreuder, T. H. C. M. L. ;
Heijboer, R. J. J. ;
Keizer, K. ;
Tielbeek, A. V. ;
den Hertog, H. M. ;
Gerrits, D. G. ;
van den Berg-Vos, R. M. ;
Karas, G. B. ;
Steyerberg, E. W. ;
Flach, H. Z. ;
Marquering, H. A. ;
Sprengers, M. E. S. ;
Jenniskens, S. F. M. ;
Beenen, L. F. M. ;
van den Berg, R. ;
Koudstaal, P. J. .
NEW ENGLAND JOURNAL OF MEDICINE, 2015, 372 (01) :11-20
[9]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[10]  
BUTTERFIELD JH, 1992, BLOOD, V79, P688