Mechanisms of stroke therapy targeting inflammatory cytokines: a big data analysis based on the IEU Open GWAS

doi:10.12307/2026.701

Abstract

Abstract: BACKGROUND: Inflammation is a crucial component of the pathophysiological process in stroke; however, the causal relationship between stroke and inflammation remains unclear.
OBJECTIVE: To explore the mechanisms of stroke treatments targeting 91 inflammatory cytokines using Mendelian randomization and molecular docking techniques.
METHODS: Data on inflammatory cytokines and stroke were obtained from the IEU Open GWAS database (https://gwas.mrcieu.ac.uk/) hosted by the Medical Research Council Comprehensive Epidemiology Unit at the University of Bristol in the United Kingdom. Two-sample Mendelian randomization analysis was performed using the inverse variance weighting method to assess the causal relationship between 91 inflammatory cytokines and stroke. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were then conducted based on the results of Mendelian randomization, and protein-protein interaction networks were constructed. Stroke drug prediction was performed using the Enrichr database (http://amp.pharm.mssm.edu/Enrichr) established by the Icahn School of Medicine at Mount Sinai and the Drug Repurposing Hub database (http://tanlab.ucdenver.edu/dsigdb) established by the University of Colorado Denver. Molecular docking was performed with AutoDock software, and the results were visualized using Discovery Studio 2019.
RESULTS AND CONCLUSION: (1) We identified 11 inflammatory cytokines with significant causal associations with the overall stroke risk; 9 cytokines were strongly associated with ischemic stroke risk; 6 cytokines were significantly related to large artery stroke risk; 7 cytokines exhibited a significant causal relationship with cardioembolic stroke risk; 12 cytokines were significantly related to small vessel stroke risk; and 3 cytokines were significantly associated with the risk of intracerebral hemorrhage. (2) Gene Ontology and Kyoto Gene and Genome Pathway analyses revealed that inflammatory cytokines play significant roles in metabolism, inflammation, and immune responses in stroke. (3) Protein-protein interaction network analysis identified 10 inflammatory cytokines closely linked to stroke. (4) Drug prediction and molecular docking results indicated that atorvastatin and fludrocortisone had high binding affinity to key core targets interleukin-18 and CCL3. (5) The data for this study were sourced from the European population in international databases, and the findings provide valuable insights for genetic epidemiological research on stroke in China. (6) This study clarifies the causal relationship between inflammatory cytokines and stroke, unveiling the mechanisms of inflammatory cytokine therapy in stroke treatment and offering novel therapeutic strategies for stroke.

Key words: stroke, inflammatory cytokines, Mendelian randomization, causality, drug prediction, molecular docking, inverse variance weighting method

CLC Number:

Cheng Le, Zhu Caifeng, Zhou Bingyuan, Gao Dahong, Cui Xiaoya, Li Jing, Wang Xuewei, Yang Gaoshang, Chen Xiyang. Mechanisms of stroke therapy targeting inflammatory cytokines: a big data analysis based on the IEU Open GWAS[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(12): 3198-3216.

Figures/Tables 21

2.1 基因预测炎症细胞因子与脑卒中的关联当使用P < 5×10-5作为全基因组显著性阈值时，所有炎症细胞因子在识别的SNP数量上均大于3。通过计算，所选SNP的F值均大于10(21.028-99.990)，表明此次研究没有出现弱工具偏倚。采用逆方差加权方法进行初步的孟德尔随机化分析，包括91种炎症细胞因子与脑卒中、缺血性脑卒中、缺血性脑卒中亚型以及脑出血之间的关系。随后，进行Bonferroni校正生成森林图。 2.2 炎症细胞因子对全因脑卒中的因果效应结果发现，13种炎症细胞因子与全因脑卒脑卒中险存在显著的因果关联。C-C介导的CCL11、CD6、CXCL11、IL-15RA、IL-18、LIFR、CCL2、TNFSF11这8种炎症细胞因子与脑卒中呈正相关，表明这8种炎症细胞因子可能增加脑卒中的风险。CD40、神经胶质细胞来源的GDNF、IL-12B、OSM和KITLG这5种炎症细胞因子与脑卒中呈负相关，表明它们对脑卒中症状有保护作用，见图2。通过对相关SNPs进行leave-one-out分析，结果发现纳入的工具变量的效应值和总效应值大小较为接近，SNP对称分布，表明因果关系的潜在偏倚较小，见图3。为验证结果的可靠性，进行了敏感性分析，基于Cochran’s Q检验显示CCL2与KITLG与全因脑卒中的孟德尔随机化分析结果存在异质性(P < 0.05)。在MR-Egger回归分析中观察到截距P > 0.05，显示SNPs之间不存在水平多效性。 2.3 炎症细胞因子对缺血性脑卒中的因果效应对于缺血性脑卒中，12种炎症细胞因子与缺血性脑卒中风险有显著的因果相关性。CD6、IL-15RA、LIFR、SULT1A1这4种炎症细胞因子可能增加了缺血性脑卒中的风险，ARTN、CCL23、CCL4、CD40、CD5、IL-12B、OSM、KITLG这8种炎症细胞因子可能降低了缺血性脑卒中的风险，如图4所示。通过留一法分析进一步验证了主要结果的稳健性，见图5。敏感性分析显示，通过Cochran’s Q检验揭示了CCL4、CD40、KITLG与缺血性脑卒中之间的孟德尔随机化分析结果存在异质性(P < 0.05)。在MR-Egger回归分析中观察到截距P > 0.05，未发现水平多效性。 2.4 炎症细胞因子对缺血性脑卒中亚型的因果效应在91种炎症细胞因子中，CD6、IL-18R1、CCL3、TNFRSF9可能与大动脉粥样硬化性脑卒中风险增加有关，NGF、CD40 和MMP10可能与大动脉粥样硬化性脑卒中风险降低有关，见图6。CD6、CXCL10、 IL-15RA、CCL2、TNFSF11与大动脉栓塞性脑卒中发生呈正相关，CCL4、GDNF与大动脉栓塞性脑卒中发生呈负相关，见图7。CD6、CXCL6、CXCL9、IL-15RA、IL-18R1、TNFSF10、TNFSF12这7种炎症细胞因子与小血管疾病发生呈正相关，CD40、CD5、FGF19、OSM、KITLG、LTA、TNFSF14这7种炎症细胞因子与小血管疾病发生呈负相关，见图8。通过Cochran’s Q检验，发现CD6与大动脉粥样硬化性脑卒中之间的孟德尔随机化分析结果有异质性(P < 0.05)，CD6与大动脉栓塞性脑卒中之间的孟德尔随机化分析结果发现异质性，KITLG与小血管疾病之间的孟德尔随机化分析结果发现异质性。在炎症细胞因子与小血管疾病的孟德尔随机化分析中，孟德尔随机化多效性试验检测到CD5相关工具变量的水平多效性(intercept=0.047，P=0.007)，当剔除存在显著交叉的混杂工具变量后，逆方差加权结果表明CD5与小血管疾病无显著的因果关系，通过留一法分析也揭示了主要结果的稳健性，见图9-11。此次研究中KITLG与小血管疾病的孟德尔随机化分析存在显著异质性，可能源于多效性遗传变异与人群特异性效应。同时，尽管CD5的关联存在水平多效性，但加权中位数法与逆方差加权结果方向一致，显示敏感性分析一致性。此次研究通过严格工具变量筛选及多方法验证降低异质性与多效性的干扰，应用严格的LD阈值及F统计量> 10排除弱工具变量。结合MR-Egger、加权中位数法等互补方法，确保结果对方法假设的鲁棒性。 2.5 炎症细胞因子对脑出血的因果效应在评估炎症细胞因子对脑出血的影响时，发现FGF5可能增加了脑出血的风险，CXCL9、MMP10可能降低了脑出血的风险，见图12。基于留一法分析揭示了主要结果的稳健性，见图13。未发现异质性及水平多效性。 2.6 基因本体、京都基因与基因组百科全书富集分析和蛋白质-蛋白质相互作用网络基因本体功能注释结果显示，涉及19个生物过程功能、3种细胞成分功能和158种分子功能。前3个 GO_BPs是信号转导、免疫反应和炎症反应。前3个GO_MF是细胞因子活性、趋化因子活性和信号受体结合。在细胞成分方面，核心基因主要富集在质膜外侧、细胞外区域和细胞外空间，见图14A。京都基因与基因组百科全书通路分析显示，炎症细胞因子通过调节TLR、PI3K/Akt、TNF、NF-κB、MAPK、JAK-STAT和IL-17信号通路在脑卒中中发挥作用，见图14B。这些通路与代谢、炎症和免疫反应有关。通过蛋白质-蛋白质相互作用网络模块分析，确定脑卒中和炎症细胞因子的参与及其功能，筛选出核心子模块，见图14C，D。 2.7 药物预测和分子对接基于DSigDB数据库的转录组特征，利用enrichment识别与炎症细胞因子相互作用的靶向候选药物。根据P值选择10种可用于脑卒中治疗和后续分析的潜在化合物，10种可能的药物分子包括和厚朴酚、阿托伐他汀、维生素D3、氟氢可的松、罗氟司特、辛伐他汀、米非司酮、巴马汀、倍他米松、吡格列酮，这些潜在的药物为推荐治疗脑卒中的常见化合物。在蛋白质数据银行下载炎症细胞因子独立的三维结构，选取CD6、FGF19、IL18、MMP10、CCL3、TNFSF14这6个靶蛋白进行分子对接分析，预测其对脑卒中患者的潜在治疗效果。此文分子对接以原配体的结合位点作为参考结合位点，配体与受体结合的稳定能越低，作用的可能性越大。用均方根偏差评价对接准确性，分子对接结果的均方根偏差值始终保持在2以下，这表明药物分子与炎症细胞因子之间的相互作用很强，见表3，图15显示了与这7种炎症细胞因子靶点结合最好的药物分子。"

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