Inflammation Related circRNAs Polymorphism and Ischemic Stroke Prognosis

Inflammation Related circRNAs Polymorphism and Ischemic Stroke Prognosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Original Research Inflammation Related circRNAs Polymorphism and Ischemic Stroke Prognosis xu liu, qianwen wang, jingjing zhao, hongtao chang, Rui-xia Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-201390/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jul, 2021 Read the published version in Journal of Molecular Neuroscience → Version 1 posted 4 You are reading this latest preprint version Abstract Background CircRNAs belong to a novel class of noncoding RNAs that are generated by exons of genes by alternative mRNA splicing and involved in pathophysiological processes of ischemic stroke by regulating neuroinflammation. Methods A total of 982 patients were enrolled in our study for stroke recovery analysis. The aim of our study was to first explore the association between the inflammation related circRNA polymorphism and functional outcome 3 months after ischemic stroke by using multivariate logistic regression model. Next, we further investigated the role of circRNA polymorphism in predicting stroke recurrence by using cox proportional hazards regression model. Five circRNA polymorphisms were genotyped by using polymerase chain reaction and ligation detection reaction method. Results We identified circ-STAT3(signal transducer and activator of transcription) rs2293152 GG genotype to be associated with poorer recovery 90 days after stroke (OR=1.452; 95%CI:1.165-4.362, p=0.016). After adjusting for confound factors, the association for rs2293152 with 3 months outcome after IS was stronger, suggesting a mechanism that rs2293152 is an independent risk factor for stroke recovery (OR=2.255; 95%CI:1.034-2.038, p=0.031). However, no other circRNA polymorphisms (circ-DLGAP4 rs41274714,circ-TRAF2 rs10870141, circ-ITCH rs10485505,rs4911154 ) was associated with functional outcome 3 months after stroke in any genetic models. Subgroup analysis revealed that the negative effect of rs2293152 GG genotype was greater in female and older patients, subjects with history of hypertension. Additionally, all the circRNA polymorphisms were not correlated with recurrent risk of ischemic stroke. Conclusions Our results indicated that circ-STAT3 might be a novel biomarker for predicting functional outcome after stroke and an important contributor to the ischemic stroke recovery. Cellular & Molecular Neuroscience ischemic stroke prognosis circRNA polymorphism inflammation Introduction Ischemic stroke ranks the major cause for mortality and disability in China [ 1 ]. Although thrombolysis is the most effective method to improve the functional outcome for the IS patients, only a small part of patients can access to this treatment due to limited window [ 1 ]. In order to develop more effective and feasible strategies for ameliorating brain injury and disability after stroke, we need to understand the pathophysiology of cerebral ischemia from the molecular perspective. Genetic factors could influence functional recovery and recurrent stroke risk, accounting for unexplained factor in stroke recovery [ 2 ]. Previous studies have reported some candidate SNPs such as apolipoprotein E and BDNF (brain derived neurotrophic factor) gene variants was associated with post-stroke recovery functional outcome after ischemic stroke [ 3 ]. However, the studies on the functional roles of circRNA in brain injury and repair after IS are just beginning. Identifying non-coding features would provide a comprehensive map and uncover potential processes of IS. Circular RNAs (circRNAs) are new class of noncoding RNA generated by the back-splicing of introns or exons [ 4 ]. CircRNAs may act as miRNA sponges by binding to microRNA response elements and regulate the expression of miRNA [ 5 ]. Besides, circRNAs could also exert transcriptional regulation and post-transcriptional regulation on gene expression by binding to RNA-associated proteins [ 6 ]. The generation procedure of circRNAs competed with linear spicing and influenced production of linear mRNAs. Recent study indicated that several circRNAs were aberrantly expressed in the ischemic cerebral tissue in MACO animal model as well as blood of IS patients [ 7 ]. It is suggested that circRNAs could be biomarkers for IS diagnosis and prediction of stroke outcomes. Furthermore, circRNAs was reported to contribute to pathophysiology process of stroke by mediating neuroinflammation, apoptosis, atherosclerosis and neurogenesis [ 8 ]. Recent studies have provided evidence on single nucleotide polymorphisms (SNPs) associated with circRNA expression. Ahmed et al. [ 9 ] integrated circular RNA expression from RNA-seq data of lymphoblastoid cell lines with genome sequence variation from the 1000 Genomes Project and identified thousands of cis-acting genetic variants at the circRNA influencing its expression, referred to as circRNA quantitative trait loci (circQTLs). Additionally, circQTLs existed independently of eQTLs and exerted no effect on mRNA expression. Furthermore, recent studies have also identified 196,255 circQTLs, which might influence circRNA expression by altering the canonical back-splicing sites [ 10 ]. Holdt’s work revealed that the presence of specific intronic binding sites may contribute to circRNA biogenesis [ 11 ]. These results revealed that genetic factors could influence circRNA expression variation and enrich for the GWAS SNP associated with complex diseases. However, the study about genetic variants within circRNAs and ischemic stroke is in the early stage. CircRNA DLGAP4 was located on chrome 20 and generated from the exons 8, 9 10 of DLGAP4 gene. Bai et al ., and his colleagues [ 12 ] first found that circDLGAP4 controlled the endothelial–mesenchymal transition and be involved in blood-brain barrier (BBB) integrity. Circ-DLGAP4 promoted the maintenance of BBB integrity and improved functional outcome after stroke by sponging miR-143. Subsequently, Zhu’s study indicated that circ-DLGAP4 was negatively related with the inflammation cytokines level (TNF-α, IL-6, IL-8, IL-22) in IS patients [ 13 ]. Additionally, Wang et al. identified that circ-ITCH suppressed the active of Wnt/β-catenin signaling activation by sponging miR-214 and miR-17 through increasing expression of its ITCH linear isoform [ 14 ]. The Wnt/β-catenin signaling not only played important roles in microglia activity and neuro-inflammation but also be crucial for regulating synaptic plasticity and BBB integrity and function [ 15 ]. Circ-STAT3 were derived from exons 12, 13, and 14 of STAT3 (Signal Transducer and Activator of Transcription 3) and involved in pro-inflammatory cytokines signaling [ 16 ]. Another study by Zhang et al. [ 17 ], has found that circ-TRAF2 was associated with colorectal cancer risk through regulating neuro-inflammation. Neuro-inflammation is a vital pathogenesis after stroke, which can cause secondary brain damage and unfavorable functional recovery [ 18 ]. Furthermore, microglia activation and BBB integrity are two important factors in the regulation of ischemia-induced neuroinflammatory process. Under this background, we speculated that circ-DLGAP4, circ-ITCH, circ-TRAF2 and circ-STAT3 were involved in the progression of IS recovery. The prognosis on neurological deficit can be divided into functional outcome and stroke recurrence. The aim of our study was to first explore the association between the neuro-inflammation related circRNA SNPs and functional outcome after stroke. Next, we further investigated the role of circRNA polymorphisms in predicting stroke recurrence. Methods Study subjects Our study included 982 first-ever suffered from ischemic stroke and hospitalized in Department of Neurology, the First Affiliated Hospital of China Medical University between November 2016 and December 2019. Eligible cases were diagnosed ischemic stroke for the first time according to clinical manifestation and neurological examination (computed tomography and magnetic resonance imaging). The National Institute of Health stroke scale (NIHSS) score and modified Rankin Scale (mRS) score were used to assess stroke severity and functional outcome of the disease, the former was carried out on admission and the later was implemented after 3 months of the disease onset respectively. Patients with mRS score less than or equal 2 were defined as good outcome, while others were classified into poor outcome group. Patients who emerged new neurological impairments or pre-existing symptoms exacerbated after 21 days from the first-ever attacked were considered as recurrent cases. Moreover, the definition and boundary of hypertension, diabetes mellitus, dyslipidemia, smoking and drinking were same as our previous study [ 19 ]. All the patients were followed up by clinical visit or telephone interview until stroke recurrence or the latest follow up. Our study was approved by Ethics Committee of the First Hospital of China Medical University and in accordance with the principles of the Helsinki Declaration, all participants have signed the informed consents. SNP Selection and Genotyping. The dbSNP database ( http://www.ncbi.nlm.nih.gov/SNP ) and circBase ( http://www.circbase.org ) was used for selecting snp for circRNAs. The rule for tagSNPs selection is as following: minor allele frequency (MAF) larger than 0.05 and linkage disequilibrium (LD) patterns r 2 less than 0.8. circ-DLGAP4 rs41274714; circ-STAT3 rs2293152; circ-TRAF2 rs10870141; circ-ITCH rs10485505, rs4911154 were selected for our study. All polymorphisms were genotyped by PCR-LDR method (polymerase chain reaction and ligation detection reaction), which have been described in our previous research [ 19 ]. Statistical Analysis We used Chi-square and t-test to calculate and compare the discrepancy of categorical and continuous variables respectively. The association between circRNA SNPs and functional outcome after stroke were accessed using multivariate logistic regression model, 95% confidence interval (95% CI) and odds ratio (OR) were calculated. The associations between circRNA SNP polymorphisms and stroke recurrence risk were accessed by cox proportional hazards regression model and by calculating hazard ratio (HR) and 95% CI. Data analysis was performed by using the SPSS 17. P < 0.05 was considered as statistical significance. The potential functional effect of circRNA polymorphisms on functional outcome of IS was performed by using Haploreg v4.1 webserver ( http://www.broad institute.org/mammals/haploreg/haploreg/Php). Results Clinical Characteristic of patients group by short-term outcome The clinical characteristics of participants were listed in Table 1 . Among them, 263 cases (29.9%) were recognized as poor outcome, 615 patients (70.1%) had a favorable outcome. The age, NIHSS score, diabetes and stroke subtype were associated with functional outcome of IS. Patients with mRS of 3–6 are prone to older, diabetes status, large artery atherosclerosis-IS and higher NIHSS score. Table 1 ,Clinical characteristics of patients grouped by short-term prognosis Variable MRS(0–2) N = 615 MRS(3–5) N = 263 p Age ≥ 60 Male Hypertension Diabetes Hyperlipidemia Smoking Drinking NIHSS TOAST 350 424 474 221 247 253 134 3.86 ± 1.784 293 186 170 208 126 93 115 68 10.34 ± 3.284 190 0.000 0.212 0.511 0.001 0.181 0.477 0.190 0.000 0.000 CircRNA polymorphisms and short-term outcome of IS Short-term prognosis of IS was assessed at 3 months after stroke by mRS and the results were shown in Table 2 . Patients with GG genotype had a trend to be unfavorable outcome compared to CC genotype (p = 0.071). Furthermore, we identified circ-STAT3 rs2293152 GG genotype to be associated with poorer outcome and greater disability under recessive model 90 days post-stroke (OR = 1.452; 95% CI :1.034–2.038; p = 0.031). In other words, compared with GG, patients with the CG + CC genotype had a higher probability of good recovery. After adjusting confound factors, the GG genotype of rs2293152 is associated with a 2.255-fold higher risk of having a poorer outcome as compared to CG + CC genotype, which is statistically significant (OR = 2.255; 95% CI :1.165–4.362; p = 0.016), as shown in Table 3 . However, no other circRNA polymorphisms (circ-DLGAP4 rs41274714, circ-TRAF2 rs10870141, circ-ITCH rs10485505, rs4911154) were associated with functional outcome 3 months after stroke in any genetic models. Table 2 , circRNA polymorphism and their association with IS short-term outcome after 3 months SNP MRS(0–2) N = 615 MRS(3–5) N = 263 p OR 95%CI rs10485505 CC CT TT Dominant model CT + TT VS CC Recessive model TT VS CT + CC rs10870141 AA AG GG Dominant model GG + AG VS AA Recessive model GG VS AG + AA Rs2293152 CC GC GG Dominant model GG + CG VS CC Recessive model GG VS CG + CC Rs41274714 GG AG AA Dominant model AA + AG VSGG Recessive model AA VS AG + GG Rs49111154 GG GA AA Dominant model AA + GA VS GG Recessive model AA VS GA + GG 447 152 16 168/449 16/599 454 144 17 161/454 17/598 178 316 121 437/178 121/494 531 81 3 84/531 3/612 411 179 25 204/411 25/590 193 67 3 70/193 3/260 200 56 7 63/200 7/256 70 124 69 193/70 69/194 217 46 0 46/217 0/263 187 66 10 76/187 10/253 Reference 0.903 0.177 0.83 0.173 0.486 0.883 0.489 0.932 0.990 0.071 0.483 0.031 0.101 0.269 0.143 0.257 0.213 0.737 0.213 0.855 1.021 0.434 0.965 0.432 0.883 0.935 0.888 0.962 0.998 1.45 1.123 1.452 1.39 0.71 0.143 0.257 0.81 0.879 0.819 0.933 0.732–1.425 0.125–1.508 0.697–1.337 0.125–1.495 0.622–1.254 0.382–2.289 0.635–1.242 0.394–2.384 0.706–1.410 0.967–2.174 0.812–1.553 1.034–2.038 0.936–2.062 0.678–0.743 0.905–1.984 —— 0.582–1.128 0.414–1.868 0.598–1.122 0.442–1.971 Table 3 , Stratification analysis for rs2293152 with short-term outcome according to the common factors Variables GG mRS (0–2)/(3–5) CC + CG mRS (0–2)/(3–5) P OR (95% CI) Age (years) < 60 56/17 209/60 0.858 1.057(0.572–1.954) ≥ 60 65/52 285/134 0.012 1.701(1.12–2.586) Gender Male 85/37 339/133 0.64 1.11 (0.718–1.714) Female 36/32 155/61 0.004 2.256(1.289–3.957) Hypertension No 33/6 108/49 0.049 0.401 (0.158–1.019) Yes 88/63 386/145 0.001 1.906 (1.309–2.775) Diabetes No 74/35 320/102 0.091 1.484(0.937–2.350) Yes 47/34 174/92 0.226 1.32 (0.823–2.275) Hyperlipidemia No 66/43 302/127 0.048 1.549 (1.001–2.397) Yes 55/26 192/67 0.272 1.355 (0.787–2.332) Stratification analysis To further access the effect of circRNA polymorphisms on functional outcome of post-stroke, stratified analysis was performed by subgroups of common factors using recessive model (GG vs CG + CC) (Table 4 ). The increased risk for rs2293152 GG genotype was more evident in sub-group of older subjects (OR = 1.701, 95% CI = 1.12–2.586, p = 0.012), females (OR = 2.256,95%CI = 1.289–3.957, p = 0.004), indicating the effect was enhanced by the potential interactions between rs2293152 and age, gender. Additionally, individuals with the GG genotype had a 1.906-fold increased risk of unfavorable outcome in hypertension group, which indicated that the negative effect was more pronounced in subjects who had history of hypertension (OR = 1,906, 95%CI:1.309–2.775, p = 0.001). Table 4 , Short-term outcome prognosis factors of ischemic stroke in the logistic regression analysis p OR 95%CI Sex Age Hypertension Diabetes Hypercholesterolemia Smoking Drinking NIHSS Genotype GG of rs2293152 0.448 0.779 0.560 0.227 0.117 0.114 0.707 0.000 0.016 0.762 1.087 1.225 1.407 0.634 1.727 0.873 2.936 2.255 0.378–1.536 0.608–1.944 0.619–2.423 0.809–2.445 0.359–1.120 0.877-3.400 0.429–1.776 2.494–3.456 1.165–4.362 CircRNA polymorphisms and IS recurrence A total of 982 the patients were enrolled in our study for stroke recurrence analysis, among them 42 patients (1.5%) were lost to follow up. The median follow-up time was 14 months. Basic characteristics of patients classified by stroke recurrence were summarized in Table 5 . We found that age and stroke subtype were related to IS recurrence. In the further analysis, none of the five polymorphisms was significantly associated with stroke recurrence in any genetic models from cox regression analysis (Table 6 ). Table 5 ,Clinical characteristics of patients grouped by long-term prognosis Variables Patients N = 940 (%) Recurrence N = 139 Log-rank p Age ≤ 55 > 55 Sex Male Female Diabetes No Yes Smoking No Yes TOAST LAA SVD Hypertension No Yes Hyperlipidemia No Yes Drinking No YES 358 582 634 306 560 380 551 589 527 413 211 729 574 366 724 216 42 97 94 45 76 63 84 55 91 48 26 113 83 56 108 31 0.019 0.78 0.096 0.286 0.024 0.132 0.918 0.536 Table 6 , Association between circRNA polymorphism and IS recurrence Genotype of SNP Patients Recurrence Log-rank P HR 95 % CI rs10485505 CC CT TT Dominant model CT + TT VS CC Recessive model TT VS CT + CC rs10870141 AA AG GG Dominant model GG + AG VS AA Recessive model GG VS AG + AA Rs2293152 CC GC GG Dominant model GG + CG VS CC Recessive model GG VS CG + CC Rs41274714 GG AG AA Dominant model AA + AG VSGG Recessive model AA VS AG + GG Rs49111154 GG GA AA Dominant model AA + GA VS GG Recessive model AA VS GA + GG 687 233 20 97/687 136/920 698 217 25 105/698 137/915 267 467 204 37/267 107/736 797 140 3 116/797 139/937 634 266 40 89/634 134/900 97 39 3 42/253 3/20 105 32 2 34/242 2/25 37 70 32 102/673 32/204 116 23 0 23/143 0/3 89 45 5 50/306 5/40 0.25 0.701 0.686 0.227 0.535 0.394 0.534 0.462 0.726 0.679 0.716 0.881 0.616 0.473 0.76 0.93 0.349 0.781 0.33 0.691 1.267 1.252 0.884 0.592 1.073 1.031 1.072 - 1.19 0.834 0.402–3.999 0.87–1.802 0.600-1.303 0.146–2.395 0.736–1.564 0.693–1.534 0.684–1.681 - 0.839–1.686 0.341–2.039 Discussion In our study, we accessed the possibility of circRNA polymorphisms as prognostic biomarkers for IS. To the best of our knowledge, our study is the first study to examine the role of circ-RNA in the recurrence and recovery of IS. Our findings identified that circ-STAT3 rs2293152 GG genotype were significantly associated with unfavorable functional outcome of IS, and rs2293152 could be served as prognostic biomarkers for IS patients. However, the other four SNPs were not associated with functional outcome of IS after 3 months. We also failed to find the association between all the circRNA polymorphisms and IS recurrence risk. Our study will provide novel perspectives for prognosis prediction and target gene-therapy for IS. Recent studies have suggested that circRNAs might be novel diagnostic and prognostic biomarkers for the disease. Zuo et al. [ 20 ] found that three differentially expressed circRNAs (circFUNDC1, circPDS5B, and circCDC14A)in blood of IS patients through two stage studies. Subsequently, the study of Dong et al. [ 7 ] indicated that 521 differentially expressed circRNAs (373 increased and 148 circRNAs decreased) in the IS group compared with controls. CircRNA expression profiles were altered significantly in the PBMCs of IS patient and may be participate in the pathogenesis of IS. Meantime, recent association studies revealed that circRNAs polymorphisms conferred prognostic and susceptibility biomarkers for the disease. Burd et al. [ 21 ] suggested that the rs7341786 within 9p21 contribute to atherosclerotic vascular disease susceptibility through regulation of ANRIL splicing and circular ANRIL expression. In addition, Paraboschi et al. reported that hsa-circ_0043813 from the STAT3 gene was associated with multiple sclerosis risk and the genotype CC of rs2293152 could increase circ-STAT3 expression [ 16 ]. Moreover, Zhang and his colleges [ 17 ]. have found that rs25497 in circ-TUBB was associated with colorectal cancer risk in both Chinese and European populations by influencing the expression of ELF5 targeted to miR-4664-3p. Additionally, another study indicated that circ-FOXO3 rs12196996 at the gene flanking intron was associated with coronary artery disease risk in the Chinese Han population, which affected circ-FOXO3 expression, but not linear FOXO3 levels [ 22 ]. Furthermore, Guo et al. [ 23 ] found that circ-ITCH rs10485505 and rs4911154 were significantly associated with increased hepatocellular carcinoma risk. Until now, study about the circRNA polymorphisms and stroke prognosis is fewer. Our study demonstrated that circ-STAT3 rs2293152 predicted functional outcome after stroke, carrying GG genotype exhibited worse outcomes 3 months post-stroke. We identified that the GG genotype was associated with increased risk of unfavorable outcome of stroke and that the CC + CG genotype was associated with a better outcome at 3 months. After adjustment for NIHSS score and other factors, the association for rs2293152 after 3 months outcome after IS was stronger, suggesting that rs2293152 is an independent risk factor for stroke recovery. However, no significant association between circ-DLGAP4 rs41274714, circ-TRAF2 rs10870141, circ-ITCH rs10485505, rs4911154 and short-term outcome of stroke was detected. Sub-group analysis revealed that the negative effect of rs2293152 GG genotype was greater in female, older patients and subjects with hypertension status. The results indicated the interaction of age, sex, blood pressure and rs2293152 enhanced the poor recovery of IS. There is a fact that risk factors for stroke recovery including hypertension, depression, atrial fibrillation were significantly more common in women [ 24 ], which may contribute the different risk among the gender. Moreover, individuals with older age are prone to have other chronic disease compared with youngers, which may influence stroke recovery. Additionally, all the circRNA polymorphisms were not correlated with a recurrent ischemic stroke risk. The possible explanation of circ-STAT3 role in short-term prognosis might be as follows. First, the genetic variation at circ-STAT3 might influence the expression of STAT3 by functioning as miRNA sponge [ 25 ]. Second, we speculated that circ-STAT3 rs2293152 located at flanking intron may act as circ-eQTL and affect the circRNA biogenesis, which eventually influenced the expression level of circ-STAT3 [ 11 ]. This is consistent with Liu et al. study that genetic variants of circRNA within flanking intron region would influence circRNA expression [ 10 ]. Third, functional prediction revealed that rs2293152 was located at potential functional regions and might alter the binding affinity of regulatory motifs, which would influence circ-STAT3 expression. From the above evidence, we can speculate that the effects of circ-STAT3 rs2293152 on short-term prognosis of stroke seem to be mediated by regulating of circ-STAT3 expression. The increased expression of circ-STAT3 might promote the release of inflammatory cytokines and activate the inflammatory response. In addition, STAT3 may also regulated astrocyte activation through targeting of the JAK2/STAT3 pathway [ 26 ]. Astrocyte activation can aggravate inflammatory reactions and brain injury. Thus, circ-STAT3 may influence IS recovery by influence the neuro-inflammation processes after neural injury. The highlights of this study were as follows. First, previous studies focused mainly on protein-coding genes, but our studies emphasized non-coding RNAs such as circRNAs. Our study is first and comprehensive study to demonstrate the potential role of circRNAs polymorphisms for functional outcome after stroke. Besides, NIHSS score was an important factor for the short-term outcome after stroke, which was took into account in our study. Thus, logistic regression was applied in our study to adjust the confound factors. Finally, we also did stratification analysis to find the interactions between rs2293152 and sex, age. However, there are limitations in our study. First, the sample size of our study was not large enough, further studies in different populations with larger sample are needed to validate the association between the circRNAs polymorphisms and functional outcome after stroke. Moreover, functional mechanisms of rs2293152 on the short-term prognosis after stroke are still not clear, which is needed to be clarified in the further study. Our study demonstrated that circ-STAT3 rs2293152 GG genotype was associated with unfavorable outcomes 3 months after stroke. Moreover, rs2293152 of circ-STAT3 can also be used as the biomarker for predicting functional outcome after stroke. Further research is needed to explore the exact biological mechanism of these genetic variations on stroke recovery. A comprehensive understanding of genetic variants effect on stroke recovery is needed for setting up personalized therapeutic interventions after stroke. Declarations Acknowledgments We are deeply grateful to all participants of this study. Conflict of interest The authors have no conflict of interests. Availability of data and material The data used in our study are available from the authors on reasonable request. Funding This study was supported by Natural Science Foundation of Liaoning province of China（2019-MS-364) and the National Natural Science Foundation of China (81501006). Authors' contributions Designed the experiments: Ruixia Zhu,Xu Liu；Performed the experiments: Ruixia Zhu, Xu Liu ,Jingjing zhao；Analyzed the data: Xu Liu ,Qianwen Wang,Hongtao Chang. Wrote the paper: Ruixia Zhu ,Qianwen Wang Compliance with ethical standards and consent to participate This study was approved by the ethics committee of the First Affiliated Hospital of China Medical University approval, in accordance with the principles of the Helsinki Declaration（AF-SOP-07-1.0-01). Written informed consents were obtained from all the participants. Consent for publication Not applicable. References 1.Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, et al.Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults. 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PLoS Genet. 2010; 6:e1001233. 22.Zhou YL, Wu WP, Cheng J, Liang LL, Cen JM, Chen C, et al. CircFOXO3 rs12196996, a polymorphism at the gene flanking intron, is associated with circFOXO3 levels and the risk of coronary artery disease. Aging (Albany NY). 2020; 2;12(13):13076-13089. 23.Guo W, Zhang J, Zhang D, et al. Polymorphisms and expression pattern of circular RNA circ-ITCH contributes to the carcinogenesis of hepatocellular carcinoma. Oncotarget. 2017;8(29):48169-48177. 24.Arboix A, Milian M, Oliveres M, García-Eroles L, Massons J. Impact of female gender on prognosis in type 2 diabetic patients with ischemic stroke. Eur Neurol. 2006;56(1):6-12. 25.Chen LL. The biogenesis and emerging roles of circular RNAs. Nat Rev Mol Cell Biol 2016; 17(4):205–11 26.Chen S, Dong Z, Cheng M, Zhao Y, Wang M, Sai N, et al. Homocysteine exaggerates microglia activation and neuroinflammation through microglia localized STAT3 overactivation following ischemic stroke. J Neuroinflammation. 2017 ;18;14(1):187. Cite Share Download PDF Status: Published Journal Publication published 17 Jul, 2021 Read the published version in Journal of Molecular Neuroscience → Version 1 posted Reviews received at journal 21 Apr, 2021 Reviewers invited by journal 03 Feb, 2021 First submitted to journal 03 Feb, 2021 Editor assigned by journal 02 Feb, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-201390","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Original Research","associatedPublications":[],"authors":[{"id":10359408,"identity":"772ca4a3-0b09-483c-8de6-4e12a75ec6d7","order_by":0,"name":"xu liu","email":"","orcid":"","institution":"China Medical College Hospital: China Medical University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"xu","middleName":"","lastName":"liu","suffix":""},{"id":10359409,"identity":"6a0eee58-75c6-4168-93ce-246463b57b34","order_by":1,"name":"qianwen wang","email":"","orcid":"","institution":"China Medical College Hospital: China Medical University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"qianwen","middleName":"","lastName":"wang","suffix":""},{"id":10359410,"identity":"577e98bd-93a6-4418-88c6-57d7e0cedcac","order_by":2,"name":"jingjing zhao","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"jingjing","middleName":"","lastName":"zhao","suffix":""},{"id":10359411,"identity":"70cbf87a-a7ab-4b3b-acf1-898dc1401d33","order_by":3,"name":"hongtao chang","email":"","orcid":"","institution":"The First Hospital of China Medical University: The First Affiliated Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"hongtao","middleName":"","lastName":"chang","suffix":""},{"id":10359412,"identity":"79ad7c81-1e55-4283-a5c9-47d7e6eb7fd8","order_by":4,"name":"Rui-xia Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie2RsUrEQBCGZ1lImg22E5TkFRK2sTjQR9ltrpIDuSaFyMrBXhOsD058jKtHFrTZB0h7+gKxs7BwtbPZpLxiv25gPmb+GYBE4lQZG6zO8i29jB1W9RyD7bqFLHuvHfpL2ZoZChd+qZvhWlJhOw000V7vfQOFdQoGUFQ+o2KGH9+H2FKPSwWldSu2fyC6PeAqh0zKm+hSnKC1bs0vKEw54JoZkZ3HlEwwA9o6bVE1VDyhNjShiN8pFOL3f4qZoaDIQuBwZBReOXxF2W4mstS9aPh3eOXV29Z9jnf3VZ1vjh8xJZB//a95vD2RSCQSM/gB5kpMNIfGoGsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2683-4674","institution":"China Medical College Hospital: China Medical University Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rui-xia","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2021-02-03 21:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-201390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-201390/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12031-021-01889-5","type":"published","date":"2021-07-17T15:05:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":13660597,"identity":"f8534a5a-3f13-4792-9891-595f61f1ed36","added_by":"auto","created_at":"2021-09-17 10:25:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":377328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-201390/v1/20b46324-288e-4829-9e3a-bdc7b66d146e.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eInflammation Related circRNAs Polymorphism and Ischemic Stroke Prognosis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke ranks the major cause for mortality and disability in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although thrombolysis is the most effective method to improve the functional outcome for the IS patients, only a small part of patients can access to this treatment due to limited window [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In order to develop more effective and feasible strategies for ameliorating brain injury and disability after stroke, we need to understand the pathophysiology of cerebral ischemia from the molecular perspective. Genetic factors could influence functional recovery and recurrent stroke risk, accounting for unexplained factor in stroke recovery [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previous studies have reported some candidate SNPs such as apolipoprotein E and BDNF (brain derived neurotrophic factor) gene variants was associated with post-stroke recovery functional outcome after ischemic stroke [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the studies on the functional roles of circRNA in brain injury and repair after IS are just beginning. Identifying non-coding features would provide a comprehensive map and uncover potential processes of IS.\u003c/p\u003e\u003cp\u003eCircular RNAs (circRNAs) are new class of noncoding RNA generated by the back-splicing of introns or exons [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. CircRNAs may act as miRNA sponges by binding to microRNA response elements and regulate the expression of miRNA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Besides, circRNAs could also exert transcriptional regulation and post-transcriptional regulation on gene expression by binding to RNA-associated proteins [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The generation procedure of circRNAs competed with linear spicing and influenced production of linear mRNAs. Recent study indicated that several circRNAs were aberrantly expressed in the ischemic cerebral tissue in MACO animal model as well as blood of IS patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is suggested that circRNAs could be biomarkers for IS diagnosis and prediction of stroke outcomes. Furthermore, circRNAs was reported to contribute to pathophysiology process of stroke by mediating neuroinflammation, apoptosis, atherosclerosis and neurogenesis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies have provided evidence on single nucleotide polymorphisms (SNPs) associated with circRNA expression. Ahmed et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] integrated circular RNA expression from RNA-seq data of lymphoblastoid cell lines with genome sequence variation from the 1000 Genomes Project and identified thousands of cis-acting genetic variants at the circRNA influencing its expression, referred to as circRNA quantitative trait loci (circQTLs). Additionally, circQTLs existed independently of eQTLs and exerted no effect on mRNA expression. Furthermore, recent studies have also identified 196,255 circQTLs, which might influence circRNA expression by altering the canonical back-splicing sites [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Holdt\u0026rsquo;s work revealed that the presence of specific intronic binding sites may contribute to circRNA biogenesis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These results revealed that genetic factors could influence circRNA expression variation and enrich for the GWAS SNP associated with complex diseases. However, the study about genetic variants within circRNAs and ischemic stroke is in the early stage.\u003c/p\u003e\u003cp\u003eCircRNA DLGAP4 was located on chrome 20 and generated from the exons 8, 9 10 of DLGAP4 gene. Bai \u003cem\u003eet al\u003c/em\u003e., and his colleagues [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] first found that circDLGAP4 controlled the endothelial\u0026ndash;mesenchymal transition and be involved in blood-brain barrier (BBB) integrity. Circ-DLGAP4 promoted the maintenance of BBB integrity and improved functional outcome after stroke by sponging miR-143. Subsequently, Zhu\u0026rsquo;s study indicated that circ-DLGAP4 was negatively related with the inflammation cytokines level (TNF-α, IL-6, IL-8, IL-22) in IS patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, Wang et al. identified that circ-ITCH suppressed the active of Wnt/β-catenin signaling activation by sponging miR-214 and miR-17 through increasing expression of its ITCH linear isoform [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Wnt/β-catenin signaling not only played important roles in microglia activity and neuro-inflammation but also be crucial for regulating synaptic plasticity and BBB integrity and function [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Circ-STAT3 were derived from exons 12, 13, and 14 of STAT3 (Signal Transducer and Activator of Transcription 3) and involved in pro-inflammatory cytokines signaling [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Another study by Zhang et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], has found that circ-TRAF2 was associated with colorectal cancer risk through regulating neuro-inflammation. Neuro-inflammation is a vital pathogenesis after stroke, which can cause secondary brain damage and unfavorable functional recovery [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, microglia activation and BBB integrity are two important factors in the regulation of ischemia-induced neuroinflammatory process. Under this background, we speculated that circ-DLGAP4, circ-ITCH, circ-TRAF2 and circ-STAT3 were involved in the progression of IS recovery. The prognosis on neurological deficit can be divided into functional outcome and stroke recurrence. The aim of our study was to first explore the association between the neuro-inflammation related circRNA SNPs and functional outcome after stroke. Next, we further investigated the role of circRNA polymorphisms in predicting stroke recurrence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003cdiv id=\"Sec3\" class=\"Section3\"\u003e\u003ch2\u003eStudy subjects\u003c/h2\u003e\u003cp\u003eOur study included 982 first-ever suffered from ischemic stroke and hospitalized in Department of Neurology, the First Affiliated Hospital of China Medical University between November 2016 and December 2019. Eligible cases were diagnosed ischemic stroke for the first time according to clinical manifestation and neurological examination (computed tomography and magnetic resonance imaging). The National Institute of Health stroke scale (NIHSS) score and modified Rankin Scale (mRS) score were used to assess stroke severity and functional outcome of the disease, the former was carried out on admission and the later was implemented after 3 months of the disease onset respectively. Patients with mRS score less than or equal 2 were defined as good outcome, while others were classified into poor outcome group. Patients who emerged new neurological impairments or pre-existing symptoms exacerbated after 21 days from the first-ever attacked were considered as recurrent cases. Moreover, the definition and boundary of hypertension, diabetes mellitus, dyslipidemia, smoking and drinking were same as our previous study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All the patients were followed up by clinical visit or telephone interview until stroke recurrence or the latest follow up. Our study was approved by Ethics Committee of the First Hospital of China Medical University and in accordance with the principles of the Helsinki Declaration, all participants have signed the informed consents.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSNP Selection and Genotyping.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe dbSNP database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/SNP\u003c/span\u003e\u003c/span\u003e) and circBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.circbase.org\u003c/span\u003e\u003c/span\u003e) was used for selecting snp for circRNAs. The rule for tagSNPs selection is as following: minor allele frequency (MAF) larger than 0.05 and linkage disequilibrium (LD) patterns r\u003csup\u003e2\u003c/sup\u003e less than 0.8. circ-DLGAP4 rs41274714; circ-STAT3 rs2293152; circ-TRAF2 rs10870141; circ-ITCH rs10485505, rs4911154 were selected for our study. All polymorphisms were genotyped by PCR-LDR method (polymerase chain reaction and ligation detection reaction), which have been described in our previous research [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe used Chi-square and t-test to calculate and compare the discrepancy of categorical and continuous variables respectively. The association between circRNA SNPs and functional outcome after stroke were accessed using multivariate logistic regression model, 95% confidence interval (95% CI) and odds ratio (OR) were calculated. The associations between circRNA SNP polymorphisms and stroke recurrence risk were accessed by cox proportional hazards regression model and by calculating hazard ratio (HR) and 95% CI. Data analysis was performed by using the SPSS 17. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistical significance. The potential functional effect of circRNA polymorphisms on functional outcome of IS was performed by using Haploreg v4.1 webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broad\u003c/span\u003e\u003c/span\u003e institute.org/mammals/haploreg/haploreg/Php).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eClinical Characteristic of patients group by short-term outcome\u003c/h2\u003e\u003cp\u003eThe clinical characteristics of participants were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among them, 263 cases (29.9%) were recognized as poor outcome, 615 patients (70.1%) had a favorable outcome. The age, NIHSS score, diabetes and stroke subtype were associated with functional outcome of IS. Patients with mRS of 3\u0026ndash;6 are prone to older, diabetes status, large artery atherosclerosis-IS and higher NIHSS score.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e,Clinical characteristics of patients grouped by short-term prognosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMRS(0\u0026ndash;2)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;615\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMRS(3\u0026ndash;5)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;263\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003cp\u003eNIHSS\u003c/p\u003e\u003cp\u003eTOAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003cp\u003e424\u003c/p\u003e\u003cp\u003e474\u003c/p\u003e\u003cp\u003e221\u003c/p\u003e\u003cp\u003e247\u003c/p\u003e\u003cp\u003e253\u003c/p\u003e\u003cp\u003e134\u003c/p\u003e\u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.784\u003c/p\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186\u003c/p\u003e\u003cp\u003e170\u003c/p\u003e\u003cp\u003e208\u003c/p\u003e\u003cp\u003e126\u003c/p\u003e\u003cp\u003e93\u003c/p\u003e\u003cp\u003e115\u003c/p\u003e\u003cp\u003e68\u003c/p\u003e\u003cp\u003e10.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.284\u003c/p\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e0.212\u003c/p\u003e\u003cp\u003e0.511\u003c/p\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e0.181\u003c/p\u003e\u003cp\u003e0.477\u003c/p\u003e\u003cp\u003e0.190\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eCircRNA polymorphisms and short-term outcome of IS\u003c/h2\u003e\u003cp\u003eShort-term prognosis of IS was assessed at 3 months after stroke by mRS and the results were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Patients with GG genotype had a trend to be unfavorable outcome compared to CC genotype (p\u0026thinsp;=\u0026thinsp;0.071). Furthermore, we identified circ-STAT3 rs2293152 GG genotype to be associated with poorer outcome and greater disability under recessive model 90 days post-stroke (OR\u0026thinsp;=\u0026thinsp;1.452; 95% CI :1.034\u0026ndash;2.038; p\u0026thinsp;=\u0026thinsp;0.031). In other words, compared with GG, patients with the CG\u0026thinsp;+\u0026thinsp;CC genotype had a higher probability of good recovery. After adjusting confound factors, the GG genotype of rs2293152 is associated with a 2.255-fold higher risk of having a poorer outcome as compared to CG\u0026thinsp;+\u0026thinsp;CC genotype, which is statistically significant (OR\u0026thinsp;=\u0026thinsp;2.255; 95% CI :1.165\u0026ndash;4.362; p\u0026thinsp;=\u0026thinsp;0.016), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. However, no other circRNA polymorphisms (circ-DLGAP4 rs41274714, circ-TRAF2 rs10870141, circ-ITCH rs10485505, rs4911154) were associated with functional outcome 3 months after stroke in any genetic models.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e, circRNA polymorphism and their association with IS short-term outcome after 3 months\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMRS(0\u0026ndash;2)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;615\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMRS(3\u0026ndash;5)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;263\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers10485505\u003c/p\u003e\u003cp\u003eCC\u003c/p\u003e\u003cp\u003eCT\u003c/p\u003e\u003cp\u003eTT\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT VS CC\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eTT VS CT\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\u003cp\u003ers10870141\u003c/p\u003e\u003cp\u003eAA\u003c/p\u003e\u003cp\u003eAG\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eGG\u0026thinsp;+\u0026thinsp;AG VS AA\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eGG VS AG\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e\u003cp\u003eRs2293152\u003c/p\u003e\u003cp\u003eCC\u003c/p\u003e\u003cp\u003eGC\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eGG\u0026thinsp;+\u0026thinsp;CG VS CC\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eGG VS CG\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\u003cp\u003eRs41274714\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eAG\u003c/p\u003e\u003cp\u003eAA\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eAA\u0026thinsp;+\u0026thinsp;AG VSGG\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eAA VS AG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\u003cp\u003eRs49111154\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eGA\u003c/p\u003e\u003cp\u003eAA\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eAA\u0026thinsp;+\u0026thinsp;GA VS GG\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eAA VS GA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e447\u003c/p\u003e\u003cp\u003e152\u003c/p\u003e\u003cp\u003e16\u003c/p\u003e\u003cp\u003e168/449\u003c/p\u003e\u003cp\u003e16/599\u003c/p\u003e\u003cp\u003e454\u003c/p\u003e\u003cp\u003e144\u003c/p\u003e\u003cp\u003e17\u003c/p\u003e\u003cp\u003e161/454\u003c/p\u003e\u003cp\u003e17/598\u003c/p\u003e\u003cp\u003e178\u003c/p\u003e\u003cp\u003e316\u003c/p\u003e\u003cp\u003e121\u003c/p\u003e\u003cp\u003e437/178\u003c/p\u003e\u003cp\u003e121/494\u003c/p\u003e\u003cp\u003e531\u003c/p\u003e\u003cp\u003e81\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e84/531\u003c/p\u003e\u003cp\u003e3/612\u003c/p\u003e\u003cp\u003e411\u003c/p\u003e\u003cp\u003e179\u003c/p\u003e\u003cp\u003e25\u003c/p\u003e\u003cp\u003e204/411\u003c/p\u003e\u003cp\u003e25/590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e193\u003c/p\u003e\u003cp\u003e67\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e70/193\u003c/p\u003e\u003cp\u003e3/260\u003c/p\u003e\u003cp\u003e200\u003c/p\u003e\u003cp\u003e56\u003c/p\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e63/200\u003c/p\u003e\u003cp\u003e7/256\u003c/p\u003e\u003cp\u003e70\u003c/p\u003e\u003cp\u003e124\u003c/p\u003e\u003cp\u003e69\u003c/p\u003e\u003cp\u003e193/70\u003c/p\u003e\u003cp\u003e69/194\u003c/p\u003e\u003cp\u003e217\u003c/p\u003e\u003cp\u003e46\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e46/217\u003c/p\u003e\u003cp\u003e0/263\u003c/p\u003e\u003cp\u003e187\u003c/p\u003e\u003cp\u003e66\u003c/p\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e76/187\u003c/p\u003e\u003cp\u003e10/253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003cp\u003e0.903\u003c/p\u003e\u003cp\u003e0.177\u003c/p\u003e\u003cp\u003e0.83\u003c/p\u003e\u003cp\u003e0.173\u003c/p\u003e\u003cp\u003e0.486\u003c/p\u003e\u003cp\u003e0.883\u003c/p\u003e\u003cp\u003e0.489\u003c/p\u003e\u003cp\u003e0.932\u003c/p\u003e\u003cp\u003e0.990\u003c/p\u003e\u003cp\u003e0.071\u003c/p\u003e\u003cp\u003e0.483\u003c/p\u003e\u003cp\u003e0.031\u003c/p\u003e\u003cp\u003e0.101\u003c/p\u003e\u003cp\u003e0.269\u003c/p\u003e\u003cp\u003e0.143\u003c/p\u003e\u003cp\u003e0.257\u003c/p\u003e\u003cp\u003e0.213\u003c/p\u003e\u003cp\u003e0.737\u003c/p\u003e\u003cp\u003e0.213\u003c/p\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003cp\u003e0.434\u003c/p\u003e\u003cp\u003e0.965\u003c/p\u003e\u003cp\u003e0.432\u003c/p\u003e\u003cp\u003e0.883\u003c/p\u003e\u003cp\u003e0.935\u003c/p\u003e\u003cp\u003e0.888\u003c/p\u003e\u003cp\u003e0.962\u003c/p\u003e\u003cp\u003e0.998\u003c/p\u003e\u003cp\u003e1.45\u003c/p\u003e\u003cp\u003e1.123\u003c/p\u003e\u003cp\u003e1.452\u003c/p\u003e\u003cp\u003e1.39\u003c/p\u003e\u003cp\u003e0.71\u003c/p\u003e\u003cp\u003e0.143\u003c/p\u003e\u003cp\u003e0.257\u003c/p\u003e\u003cp\u003e0.81\u003c/p\u003e\u003cp\u003e0.879\u003c/p\u003e\u003cp\u003e0.819\u003c/p\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.732\u0026ndash;1.425\u003c/p\u003e\u003cp\u003e0.125\u0026ndash;1.508\u003c/p\u003e\u003cp\u003e0.697\u0026ndash;1.337\u003c/p\u003e\u003cp\u003e0.125\u0026ndash;1.495\u003c/p\u003e\u003cp\u003e0.622\u0026ndash;1.254\u003c/p\u003e\u003cp\u003e0.382\u0026ndash;2.289\u003c/p\u003e\u003cp\u003e0.635\u0026ndash;1.242\u003c/p\u003e\u003cp\u003e0.394\u0026ndash;2.384\u003c/p\u003e\u003cp\u003e0.706\u0026ndash;1.410\u003c/p\u003e\u003cp\u003e0.967\u0026ndash;2.174\u003c/p\u003e\u003cp\u003e0.812\u0026ndash;1.553\u003c/p\u003e\u003cp\u003e1.034\u0026ndash;2.038\u003c/p\u003e\u003cp\u003e0.936\u0026ndash;2.062\u003c/p\u003e\u003cp\u003e0.678\u0026ndash;0.743\u003c/p\u003e\u003cp\u003e0.905\u0026ndash;1.984\u003c/p\u003e\u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\u003cp\u003e0.582\u0026ndash;1.128\u003c/p\u003e\u003cp\u003e0.414\u0026ndash;1.868\u003c/p\u003e\u003cp\u003e0.598\u0026ndash;1.122\u003c/p\u003e\u003cp\u003e0.442\u0026ndash;1.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e, Stratification analysis for rs2293152 with short-term outcome according to the common factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003emRS (0\u0026ndash;2)/(3\u0026ndash;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCC\u0026thinsp;+\u0026thinsp;CG\u003c/p\u003e\u003cp\u003emRS (0\u0026ndash;2)/(3\u0026ndash;5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56/17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e209/60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.057(0.572\u0026ndash;1.954)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65/52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285/134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.701(1.12\u0026ndash;2.586)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85/37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e339/133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11 (0.718\u0026ndash;1.714)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36/32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155/61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.256(1.289\u0026ndash;3.957)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108/49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.401 (0.158\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88/63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e386/145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.906 (1.309\u0026ndash;2.775)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74/35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320/102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.484(0.937\u0026ndash;2.350)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47/34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174/92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32 (0.823\u0026ndash;2.275)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66/43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302/127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.549 (1.001\u0026ndash;2.397)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192/67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.355 (0.787\u0026ndash;2.332)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStratification analysis\u003c/h2\u003e\u003cp\u003eTo further access the effect of circRNA polymorphisms on functional outcome of post-stroke, stratified analysis was performed by subgroups of common factors using recessive model (GG vs CG\u0026thinsp;+\u0026thinsp;CC) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The increased risk for rs2293152 GG genotype was more evident in sub-group of older subjects (OR\u0026thinsp;=\u0026thinsp;1.701, 95% CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;2.586, p\u0026thinsp;=\u0026thinsp;0.012), females (OR\u0026thinsp;=\u0026thinsp;2.256,95%CI\u0026thinsp;=\u0026thinsp;1.289\u0026ndash;3.957, p\u0026thinsp;=\u0026thinsp;0.004), indicating the effect was enhanced by the potential interactions between rs2293152 and age, gender. Additionally, individuals with the GG genotype had a 1.906-fold increased risk of unfavorable outcome in hypertension group, which indicated that the negative effect was more pronounced in subjects who had history of hypertension (OR\u0026thinsp;=\u0026thinsp;1,906, 95%CI:1.309\u0026ndash;2.775, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e, Short-term outcome prognosis factors of ischemic stroke in the logistic regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003cp\u003eHypercholesterolemia\u003c/p\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003cp\u003eNIHSS\u003c/p\u003e\u003cp\u003eGenotype GG of rs2293152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003cp\u003e0.779\u003c/p\u003e\u003cp\u003e0.560\u003c/p\u003e\u003cp\u003e0.227\u003c/p\u003e\u003cp\u003e0.117\u003c/p\u003e\u003cp\u003e0.114\u003c/p\u003e\u003cp\u003e0.707\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003cp\u003e1.087\u003c/p\u003e\u003cp\u003e1.225\u003c/p\u003e\u003cp\u003e1.407\u003c/p\u003e\u003cp\u003e0.634\u003c/p\u003e\u003cp\u003e1.727\u003c/p\u003e\u003cp\u003e0.873\u003c/p\u003e\u003cp\u003e2.936\u003c/p\u003e\u003cp\u003e2.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.378\u0026ndash;1.536\u003c/p\u003e\u003cp\u003e0.608\u0026ndash;1.944\u003c/p\u003e\u003cp\u003e0.619\u0026ndash;2.423\u003c/p\u003e\u003cp\u003e0.809\u0026ndash;2.445\u003c/p\u003e\u003cp\u003e0.359\u0026ndash;1.120\u003c/p\u003e\u003cp\u003e0.877-3.400\u003c/p\u003e\u003cp\u003e0.429\u0026ndash;1.776\u003c/p\u003e\u003cp\u003e2.494\u0026ndash;3.456\u003c/p\u003e\u003cp\u003e1.165\u0026ndash;4.362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eCircRNA polymorphisms and IS recurrence\u003c/h2\u003e\u003cp\u003eA total of 982 the patients were enrolled in our study for stroke recurrence analysis, among them 42 patients (1.5%) were lost to follow up. The median follow-up time was 14 months. Basic characteristics of patients classified by stroke recurrence were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We found that age and stroke subtype were related to IS recurrence. In the further analysis, none of the five polymorphisms was significantly associated with stroke recurrence in any genetic models from cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e,Clinical characteristics of patients grouped by long-term prognosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;940 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecurrence\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;139\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLog-rank p\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003e\u0026le;\u0026thinsp;55\u003c/p\u003e\u003cp\u003e\u0026gt;\u0026thinsp;55\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eTOAST\u003c/p\u003e\u003cp\u003eLAA\u003c/p\u003e\u003cp\u003eSVD\u003c/p\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e358\u003c/p\u003e\u003cp\u003e582\u003c/p\u003e\u003cp\u003e634\u003c/p\u003e\u003cp\u003e306\u003c/p\u003e\u003cp\u003e560\u003c/p\u003e\u003cp\u003e380\u003c/p\u003e\u003cp\u003e551\u003c/p\u003e\u003cp\u003e589\u003c/p\u003e\u003cp\u003e527\u003c/p\u003e\u003cp\u003e413\u003c/p\u003e\u003cp\u003e211\u003c/p\u003e\u003cp\u003e729\u003c/p\u003e\u003cp\u003e574\u003c/p\u003e\u003cp\u003e366\u003c/p\u003e\u003cp\u003e724\u003c/p\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003cp\u003e97\u003c/p\u003e\u003cp\u003e94\u003c/p\u003e\u003cp\u003e45\u003c/p\u003e\u003cp\u003e76\u003c/p\u003e\u003cp\u003e63\u003c/p\u003e\u003cp\u003e84\u003c/p\u003e\u003cp\u003e55\u003c/p\u003e\u003cp\u003e91\u003c/p\u003e\u003cp\u003e48\u003c/p\u003e\u003cp\u003e26\u003c/p\u003e\u003cp\u003e113\u003c/p\u003e\u003cp\u003e83\u003c/p\u003e\u003cp\u003e56\u003c/p\u003e\u003cp\u003e108\u003c/p\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.78\u003c/p\u003e\u003cp\u003e0.096\u003c/p\u003e\u003cp\u003e0.286\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.132\u003c/p\u003e\u003cp\u003e0.918\u003c/p\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e, Association between circRNA polymorphism and IS recurrence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype of SNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecurrence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLog-rank \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95 % CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers10485505\u003c/p\u003e\u003cp\u003eCC\u003c/p\u003e\u003cp\u003eCT\u003c/p\u003e\u003cp\u003eTT\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT VS CC\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eTT VS CT\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\u003cp\u003ers10870141\u003c/p\u003e\u003cp\u003eAA\u003c/p\u003e\u003cp\u003eAG\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eGG\u0026thinsp;+\u0026thinsp;AG VS AA\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eGG VS AG\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e\u003cp\u003eRs2293152\u003c/p\u003e\u003cp\u003eCC\u003c/p\u003e\u003cp\u003eGC\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eGG\u0026thinsp;+\u0026thinsp;CG VS CC\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eGG VS CG\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e\u003cp\u003eRs41274714\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eAG\u003c/p\u003e\u003cp\u003eAA\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eAA\u0026thinsp;+\u0026thinsp;AG VSGG\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eAA VS AG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\u003cp\u003eRs49111154\u003c/p\u003e\u003cp\u003eGG\u003c/p\u003e\u003cp\u003eGA\u003c/p\u003e\u003cp\u003eAA\u003c/p\u003e\u003cp\u003eDominant model\u003c/p\u003e\u003cp\u003eAA\u0026thinsp;+\u0026thinsp;GA VS GG\u003c/p\u003e\u003cp\u003eRecessive model\u003c/p\u003e\u003cp\u003eAA VS GA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e687\u003c/p\u003e\u003cp\u003e233\u003c/p\u003e\u003cp\u003e20\u003c/p\u003e\u003cp\u003e97/687\u003c/p\u003e\u003cp\u003e136/920\u003c/p\u003e\u003cp\u003e698\u003c/p\u003e\u003cp\u003e217\u003c/p\u003e\u003cp\u003e25\u003c/p\u003e\u003cp\u003e105/698\u003c/p\u003e\u003cp\u003e137/915\u003c/p\u003e\u003cp\u003e267\u003c/p\u003e\u003cp\u003e467\u003c/p\u003e\u003cp\u003e204\u003c/p\u003e\u003cp\u003e37/267\u003c/p\u003e\u003cp\u003e107/736\u003c/p\u003e\u003cp\u003e797\u003c/p\u003e\u003cp\u003e140\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e116/797\u003c/p\u003e\u003cp\u003e139/937\u003c/p\u003e\u003cp\u003e634\u003c/p\u003e\u003cp\u003e266\u003c/p\u003e\u003cp\u003e40\u003c/p\u003e\u003cp\u003e89/634\u003c/p\u003e\u003cp\u003e134/900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003e0.402\u0026ndash;3.999\u003c/p\u003e\u003cp\u003e0.87\u0026ndash;1.802\u003c/p\u003e\u003cp\u003e0.600-1.303\u003c/p\u003e\u003cp\u003e0.146\u0026ndash;2.395\u003c/p\u003e\u003cp\u003e0.736\u0026ndash;1.564\u003c/p\u003e\u003cp\u003e0.693\u0026ndash;1.534\u003c/p\u003e\u003cp\u003e0.684\u0026ndash;1.681\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e0.839\u0026ndash;1.686\u003c/p\u003e\u003cp\u003e0.341\u0026ndash;2.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we accessed the possibility of circRNA polymorphisms as prognostic biomarkers for IS. To the best of our knowledge, our study is the first study to examine the role of circ-RNA in the recurrence and recovery of IS. Our findings identified that circ-STAT3 rs2293152 GG genotype were significantly associated with unfavorable functional outcome of IS, and rs2293152 could be served as prognostic biomarkers for IS patients. However, the other four SNPs were not associated with functional outcome of IS after 3 months. We also failed to find the association between all the circRNA polymorphisms and IS recurrence risk. Our study will provide novel perspectives for prognosis prediction and target gene-therapy for IS.\u003c/p\u003e\u003cp\u003eRecent studies have suggested that circRNAs might be novel diagnostic and prognostic biomarkers for the disease. Zuo et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] found that three differentially expressed circRNAs (circFUNDC1, circPDS5B, and circCDC14A)in blood of IS patients through two stage studies. Subsequently, the study of Dong et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] indicated that 521 differentially expressed circRNAs (373 increased and 148 circRNAs decreased) in the IS group compared with controls. CircRNA expression profiles were altered significantly in the PBMCs of IS patient and may be participate in the pathogenesis of IS. Meantime, recent association studies revealed that circRNAs polymorphisms conferred prognostic and susceptibility biomarkers for the disease. Burd et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] suggested that the rs7341786 within 9p21 contribute to atherosclerotic vascular disease susceptibility through regulation of ANRIL splicing and circular ANRIL expression. In addition, Paraboschi et al. reported that hsa-circ_0043813 from the STAT3 gene was associated with multiple sclerosis risk and the genotype CC of rs2293152 could increase circ-STAT3 expression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, Zhang and his colleges [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. have found that rs25497 in circ-TUBB was associated with colorectal cancer risk in both Chinese and European populations by influencing the expression of ELF5 targeted to miR-4664-3p. Additionally, another study indicated that circ-FOXO3 rs12196996 at the gene flanking intron was associated with coronary artery disease risk in the Chinese Han population, which affected circ-FOXO3 expression, but not linear FOXO3 levels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, Guo et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found that circ-ITCH rs10485505 and rs4911154 were significantly associated with increased hepatocellular carcinoma risk. Until now, study about the circRNA polymorphisms and stroke prognosis is fewer.\u003c/p\u003e\u003cp\u003eOur study demonstrated that circ-STAT3 rs2293152 predicted functional outcome after stroke, carrying GG genotype exhibited worse outcomes 3 months post-stroke. We identified that the GG genotype was associated with increased risk of unfavorable outcome of stroke and that the CC\u0026thinsp;+\u0026thinsp;CG genotype was associated with a better outcome at 3 months. After adjustment for NIHSS score and other factors, the association for rs2293152 after 3 months outcome after IS was stronger, suggesting that rs2293152 is an independent risk factor for stroke recovery. However, no significant association between circ-DLGAP4 rs41274714, circ-TRAF2 rs10870141, circ-ITCH rs10485505, rs4911154 and short-term outcome of stroke was detected. Sub-group analysis revealed that the negative effect of rs2293152 GG genotype was greater in female, older patients and subjects with hypertension status. The results indicated the interaction of age, sex, blood pressure and rs2293152 enhanced the poor recovery of IS. There is a fact that risk factors for stroke recovery including hypertension, depression, atrial fibrillation were significantly more common in women [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which may contribute the different risk among the gender. Moreover, individuals with older age are prone to have other chronic disease compared with youngers, which may influence stroke recovery. Additionally, all the circRNA polymorphisms were not correlated with a recurrent ischemic stroke risk.\u003c/p\u003e\u003cp\u003eThe possible explanation of circ-STAT3 role in short-term prognosis might be as follows. First, the genetic variation at circ-STAT3 might influence the expression of STAT3 by functioning as miRNA sponge [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Second, we speculated that circ-STAT3 rs2293152 located at flanking intron may act as circ-eQTL and affect the circRNA biogenesis, which eventually influenced the expression level of circ-STAT3 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This is consistent with Liu et al. study that genetic variants of circRNA within flanking intron region would influence circRNA expression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Third, functional prediction revealed that rs2293152 was located at potential functional regions and might alter the binding affinity of regulatory motifs, which would influence circ-STAT3 expression. From the above evidence, we can speculate that the effects of circ-STAT3 rs2293152 on short-term prognosis of stroke seem to be mediated by regulating of circ-STAT3 expression. The increased expression of circ-STAT3 might promote the release of inflammatory cytokines and activate the inflammatory response. In addition, STAT3 may also regulated astrocyte activation through targeting of the JAK2/STAT3 pathway [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Astrocyte activation can aggravate inflammatory reactions and brain injury. Thus, circ-STAT3 may influence IS recovery by influence the neuro-inflammation processes after neural injury.\u003c/p\u003e\u003cp\u003eThe highlights of this study were as follows. First, previous studies focused mainly on protein-coding genes, but our studies emphasized non-coding RNAs such as circRNAs. Our study is first and comprehensive study to demonstrate the potential role of circRNAs polymorphisms for functional outcome after stroke. Besides, NIHSS score was an important factor for the short-term outcome after stroke, which was took into account in our study. Thus, logistic regression was applied in our study to adjust the confound factors. Finally, we also did stratification analysis to find the interactions between rs2293152 and sex, age. However, there are limitations in our study. First, the sample size of our study was not large enough, further studies in different populations with larger sample are needed to validate the association between the circRNAs polymorphisms and functional outcome after stroke. Moreover, functional mechanisms of rs2293152 on the short-term prognosis after stroke are still not clear, which is needed to be clarified in the further study.\u003c/p\u003e\u003cp\u003eOur study demonstrated that circ-STAT3 rs2293152 GG genotype was associated with unfavorable outcomes 3 months after stroke. Moreover, rs2293152 of circ-STAT3 can also be used as the biomarker for predicting functional outcome after stroke. Further research is needed to explore the exact biological mechanism of these genetic variations on stroke recovery. A comprehensive understanding of genetic variants effect on stroke recovery is needed for setting up personalized therapeutic interventions after stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to all participants of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in our study are available from the authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Natural Science Foundation of Liaoning province of China（2019-MS-364) and the National Natural Science Foundation of China (81501006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesigned the experiments: Ruixia Zhu,Xu Liu；Performed the experiments: Ruixia Zhu, Xu Liu ,Jingjing zhao；Analyzed the data: Xu Liu ,Qianwen Wang,Hongtao Chang. Wrote the paper: Ruixia Zhu ,Qianwen Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the First Affiliated Hospital of China Medical University approval, in accordance with the principles of the Helsinki Declaration（AF-SOP-07-1.0-01). Written informed consents were obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1.Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, et al.Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480\u0026thinsp;687 Adults. Circulation. 2017; 135:759\u0026ndash;71.\u003c/p\u003e\n\u003cp\u003e2.Lindgren A, Maguire J. Stroke recovery genetics. Stroke 2016;47:2427\u0026ndash;2434\u003c/p\u003e\n\u003cp\u003e3.Stanne TM, Tj\u0026uml;arnlund-Wolf A, Olsson S, Jood K, Blomstrand C, Jern C. Genetic variation at the BDNF locus: evidence for association with long-term outcome after ischemic stroke. PLoS One. 2014;9:e114156.\u003c/p\u003e\n\u003cp\u003e4.Li X, Yang L, Chen LL. The biogenesis, functions, and challenges of circular RNAs. Mol Cell. 2018;71(3):428\u0026ndash;442.\u003c/p\u003e\n\u003cp\u003e5.Gao J, Xu W, Wang J, Wang K, Li P. The role and molecular mechanism of non-coding RNAs in pathological cardiac remodeling. Int J Mol Sci. 2017; 18: E608.\u003c/p\u003e\n\u003cp\u003e6.Jeck WR, Sharpless NE. Detecting and characterizing circular RNAs. Nat Biotechnol. 2014;32(5):453.\u003c/p\u003e\n\u003cp\u003e7.Dong Z, Deng L, Peng Q, Pan J, Wang Y.CircRNA expression profiles and function prediction in peripheral blood mononuclear cells of patients of acute ischemic stroke. J Cell Physiol. 2020;235(3):2609-2618\u003c/p\u003e\n\u003cp\u003e8.Wang Q, Liu X, Zhao J, Zhu R. Circular RNAs: novel diagnostic and therapeutic targets for ischemic stroke. Expert Rev Mol Diagn. 2020;20(10):1039-1049\u003c/p\u003e\n\u003cp\u003e9.Ahmed I, Karedath T, Al-Dasim FM, Malek JA. Identification of human genetic variants controlling circular RNA expression. RNA. 2019;25(12):1765-1778.\u003c/p\u003e\n\u003cp\u003e10.Liu Z, Ran Y, Tao C, Li S, Chen J, Yang E. Detection of circular RNA expression and related quantitative trait loci in the human dorsolateral prefrontal cortex. Genome Biol. 2019; 20:99.\u003c/p\u003e\n\u003cp\u003e11.Holdt LM, Stahringer A, Sass K, Pichler G, Kulak NA, Wilfert W, et al. Circular non-coding RNA ANRIL modulates ribosomal RNA maturation and atherosclerosis in humans. Nat Commun. 2016; 7:12429\u003c/p\u003e\n\u003cp\u003e12.Bai Y, Zhang Y, Han B, Yang L, Chen X, Huang R, et al.Circular RNA DLGAP4 Ameliorates Ischemic Stroke Outcomes by Targeting miR-143 to Regulate Endothelial-Mesenchymal Transition Associated with Blood-Brain Barrier Integrity.J Neurosci. 2018; 3;38(1):32-50\u003c/p\u003e\n\u003cp\u003e13.Zhu X, Ding J, Wang B, Wang J, Xu M.Circular RNA DLGAP4 is down-regulated and negatively correlates with severity, inflammatory cytokine expression and pro-inflammatory gene miR-143 expression in acute ischemic stroke patients.Int J Clin Exp Pathol. 2019;1;12(3):941-948\u003c/p\u003e\n\u003cp\u003e14.Wang ST, Liu LB, Li XM, et al. Circ-ITCH regulates triple-negative breast cancer progression through the Wnt/\u0026beta;-catenin pathway. Neoplasma. 2019;66(2):232-239.\u003c/p\u003e\n\u003cp\u003e15.Jia L, Pi\u0026ntilde;a-Crespo J, Li Y. Restoring Wnt/\u0026beta;-catenin signaling is a promising therapeutic strategy for Alzheimer's disease.\u0026nbsp;Mol Brain. 2019;12(1):104\u003c/p\u003e\n\u003cp\u003e16.Paraboschi EM, Cardamone G, Sold\u0026agrave; G, Duga S, Asselta R. Interpreting non-coding genetic variation in multiple sclerosis genome-wide associated regions. Front Genet. 2018; 9:647\u003c/p\u003e\n\u003cp\u003e17.Zhang K, Li S, Gu D, et al. Genetic variants in circTUBB interacting with smoking can enhance colorectal cancer risk.\u0026nbsp;Arch Toxicol. 2020;94(1):325-333\u003c/p\u003e\n\u003cp\u003e18.Shen L, Bai Y, Han B, Yao H. Non-coding RNA and neuroinflammation: implications for the therapy of stroke.Stroke Vasc Neurol, 2019;2;4(2):96-98.\u003c/p\u003e\n\u003cp\u003e19.Liu X, Wang Q, Zhu R. Association of GWAS-susceptibility loci with ischemic stroke recurrence in a Han Chinese population. J Gene Med. 2020;25:e3264.\u003c/p\u003e\n\u003cp\u003e20.Zuo L, Zhang L, Zu J, et al. Circulating circular RNAs as biomarkers for the diagnosis and prediction of outcomes in acute ischemic stroke. Stroke. 2020;51(1): 319\u0026ndash;323.\u003c/p\u003e\n\u003cp\u003e21.Burd CE, Jeck WR, Liu Y, Sanoff HK, Wang Z, Sharpless NE. Expression of linear and novel circular forms of an INK4/ARF-associated non-coding RNA correlates with atherosclerosis risk. PLoS Genet. 2010; 6:e1001233.\u003c/p\u003e\n\u003cp\u003e22.Zhou YL, Wu WP, Cheng J, Liang LL, Cen JM, Chen C, et al.\u0026nbsp;CircFOXO3\u0026nbsp;rs12196996, a polymorphism at the gene flanking intron, is associated with circFOXO3 levels and the risk of coronary artery disease. Aging (Albany NY). 2020; 2;12(13):13076-13089.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e23.Guo W, Zhang J, Zhang D, et al. Polymorphisms and expression pattern of circular RNA circ-ITCH contributes to the carcinogenesis of hepatocellular carcinoma. Oncotarget. 2017;8(29):48169-48177.\u003c/p\u003e\n\u003cp\u003e24.Arboix A, Milian M, Oliveres M, Garc\u0026iacute;a-Eroles L, Massons J. Impact of female gender on prognosis in type 2 diabetic patients with ischemic stroke. Eur Neurol. 2006;56(1):6-12.\u003c/p\u003e\n\u003cp\u003e25.Chen LL. The biogenesis and emerging roles of circular RNAs. Nat Rev Mol Cell Biol 2016; 17(4):205\u0026ndash;11\u003c/p\u003e\n\u003cp\u003e26.Chen S, Dong Z, Cheng M, Zhao Y, Wang M, Sai N, et al. Homocysteine exaggerates microglia activation and neuroinflammation through microglia localized STAT3 overactivation following ischemic stroke. J Neuroinflammation. 2017 ;18;14(1):187.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jomn","sideBox":"Learn more about [Journal of Molecular Neuroscience](https://www.springer.com/journal/12031)","snPcode":"12031","submissionUrl":"https://submission.nature.com/new-submission/12031/3","title":"Journal of Molecular Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ischemic stroke, prognosis, circRNA, polymorphism, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-201390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-201390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eCircRNAs belong to a novel class of noncoding RNAs that are generated by exons of genes by alternative mRNA splicing and involved in pathophysiological processes of ischemic stroke by regulating neuroinflammation. \u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eA total of 982 patients were enrolled in our study for stroke recovery analysis. The aim of our study was to first explore the association between the inflammation related circRNA polymorphism and functional outcome 3 months after ischemic stroke by using multivariate logistic regression model. Next, we further investigated the role of circRNA polymorphism in predicting stroke recurrence by using cox proportional hazards regression model.\u0026nbsp;Five circRNA polymorphisms were genotyped by using polymerase chain reaction and ligation detection reaction method.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eWe identified circ-STAT3(signal transducer and activator of transcription) rs2293152 GG genotype to be associated with poorer recovery 90 days after stroke (OR=1.452; 95%CI:1.165-4.362, p=0.016). After adjusting for confound factors, the association for rs2293152 with 3 months outcome after IS was stronger, suggesting a mechanism that rs2293152 is an independent risk factor for stroke recovery (OR=2.255; 95%CI:1.034-2.038, p=0.031). However, no other circRNA polymorphisms (circ-DLGAP4 rs41274714,circ-TRAF2 rs10870141, circ-ITCH\u0026nbsp;rs10485505,rs4911154 ) was associated with functional outcome 3 months after stroke in any genetic models. Subgroup analysis revealed that the negative effect of rs2293152 GG genotype was greater in female and older patients, subjects with history of hypertension. Additionally, all the circRNA polymorphisms were not correlated with recurrent risk of ischemic stroke. \u003c/p\u003e\u003cp\u003eConclusions\u003c/p\u003e\u003cp\u003eOur results indicated that circ-STAT3 might be a novel biomarker for predicting functional outcome after stroke and an important contributor to the ischemic stroke recovery.\u003c/p\u003e","manuscriptTitle":"Inflammation Related circRNAs Polymorphism and Ischemic Stroke Prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-16 22:22:25","doi":"10.21203/rs.3.rs-201390/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-04-22T00:00:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-02-04T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Neuroscience","date":"2021-02-03T05:18:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-02-03T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jomn","sideBox":"Learn more about [Journal of Molecular Neuroscience](https://www.springer.com/journal/12031)","snPcode":"12031","submissionUrl":"https://submission.nature.com/new-submission/12031/3","title":"Journal of Molecular Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fea5194a-52b1-404c-8e41-750edeb4e221","owner":[],"postedDate":"February 16th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":2451920,"name":"Cellular \u0026 Molecular Neuroscience"}],"tags":[],"updatedAt":"2021-08-22T15:21:52+00:00","versionOfRecord":{"articleIdentity":"rs-201390","link":"https://doi.org/10.1007/s12031-021-01889-5","journal":{"identity":"journal-of-molecular-neuroscience","isVorOnly":false,"title":"Journal of Molecular Neuroscience"},"publishedOn":"2021-07-17 15:05:31","publishedOnDateReadable":"July 17th, 2021"},"versionCreatedAt":"2021-02-16 22:22:25","video":"","vorDoi":"10.1007/s12031-021-01889-5","vorDoiUrl":"https://doi.org/10.1007/s12031-021-01889-5","workflowStages":[]},"version":"v1","identity":"rs-201390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-201390","identity":"rs-201390","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}