Table1_Identification of circulating immune landscape in ischemic stroke based on bioinformatics methods.XLSX

Ischemic stroke (IS) is a high-incidence disease that seriously threatens human life and health. Neuroinflammation and immune responses are key players in the pathophysiological processes of IS. However, the underlying immune mechanisms are not fully understood. In this study, we attempted to identify several immune biomarkers associated with IS. We first retrospectively collected validated human IS immune-related genes (IS-IRGs) as seed genes. Afterward, potential IS-IRGs were discovered by applying random walk with restart on the PPI network and the permutation test as a screening strategy. Doing so, the validated and potential sets of IS-IRGs were merged together as an IS-IRG catalog. Two microarray profiles were subsequently used to explore the expression patterns of the IS-IRG catalog, and only IS-IRGs that were differentially expressed between IS patients and controls in both profiles were retained for biomarker selection by the Random Forest rankings. CLEC4D and CD163 were finally identified as immune biomarkers of IS, and a classification model was constructed and verified based on the weights of two biomarkers obtained from the Neural Network algorithm. Furthermore, the CIBERSORT algorithm helped us determine the proportions of circulating immune cells. Correlation analyses between IS immune biomarkers and immune cell proportions demonstrated that CLEC4D was strongly correlated with the proportion of neutrophils (r = 0.72). These results may provide potential targets for further studies on immuno-neuroprotection therapies against reperfusion injury.

缺血性脑卒中（Ischemic stroke, IS）是一种高发病率疾病，严重威胁人类生命健康。神经炎症与免疫应答是缺血性脑卒中病理生理过程中的核心环节，但其潜在的免疫机制尚未完全阐明。本研究旨在筛选与缺血性脑卒中相关的免疫生物标志物。我们首先回顾性收集已验证的人缺血性脑卒中免疫相关基因（IS-IRGs）作为种子基因；随后通过在蛋白质相互作用（Protein-Protein Interaction, PPI）网络上应用重启随机游走算法，并以置换检验作为筛选策略，挖掘潜在的IS免疫相关基因。据此，将已验证与潜在的IS免疫相关基因集合并，构建IS免疫相关基因目录。随后利用两个基因芯片表达数据集探究该IS免疫相关基因目录的表达模式，仅保留在两个数据集中均在缺血性脑卒中患者与健康对照中存在差异表达的基因，通过随机森林排序进行生物标志物筛选。最终确定CLEC4D与CD163作为缺血性脑卒中的免疫生物标志物，并基于神经网络（Neural Network）算法得到的两种生物标志物权重构建并验证了分类模型。此外，通过CIBERSORT算法分析循环免疫细胞的浸润比例；对缺血性脑卒中免疫生物标志物与免疫细胞比例的相关性分析显示，CLEC4D与中性粒细胞比例呈显著正相关（r=0.72）。本研究结果可为后续针对再灌注损伤的免疫神经保护治疗研究提供潜在靶点。