Bai Congxia, Liu Xinran, Wang Fengjuan, Sun Yingying, Wang Jing, Liu Jing, Hao Xiaoyan, Zhou Lei, Yuan Yu, Liu Jiayun
Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Immunol. 2024 Aug 30;15:1421942. doi: 10.3389/fimmu.2024.1421942. eCollection 2024.
Intracerebral hemorrhage (ICH) is a severe stroke subtype with high morbidity, disability, and mortality rates. Currently, no biomarkers for ICH are available for use in clinical practice. We aimed to explore the roles of RNAs in ICH pathogenesis and identify potential diagnostic biomarkers.
We collected 233 individual blood samples from two independent cohorts, including 64 patients with ICH, 59 patients with ischemic stroke (IS), 60 patients with hypertension (HTN) and 50 healthy controls (CTRL) for RNA sequencing. Differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), and weighted correlation network analysis (WGCNA) were performed to identify ICH-specific modules. The immune cell composition was evaluated with ImmuneCellAI. Multiple machine learning algorithms to select potential biomarkers for ICH diagnosis, and further validated by quantitative real-time polymerase chain reaction (RT-PCR). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to evaluate the diagnostic value of the signature for ICH. Finally, we generated M1 and M2 macrophages to investigate the expression of candidate genes.
In both cohorts, 519 mRNAs and 131 lncRNAs were consistently significantly differentially expressed between ICH patients and HTN controls. Gene function analysis suggested that immune system processes may be involved in ICH pathology. ImmuneCellAI analysis revealed that the abundances of 11 immune cell types were altered after ICH in both cohorts. WGCNA and GSEA identified 18 immune-related DEGs. Multiple algorithms identified an RNA panel (CKAP4, BCL6, TLR8) with high diagnostic value for discriminating ICH patients from HTN controls, CTRLs and IS patients (AUCs: 0.93, 0.95 and 0.82; sensitivities: 81.3%, 84.4% and 75%; specificities: 100%, 96% and 79.7%, respectively). Additionally, CKAP4 and TLR8 mRNA and protein levels decreased in RAW264.7 M1 macrophages and increased in RAW264.7 M2 macrophages, while BCL6 expression increased in M1 macrophages but not in M2 macrophages, which may provide potential therapeutic targets for ICH.
This study demonstrated that the expression levels of lncRNAs and mRNAs are associated with ICH, and an RNA panel (CKAP4, BCL6, TLR8) was developed as a potential diagnostic tool for distinguishing ICH from IS and controls, which could provide useful insight into ICH diagnosis and pathogenesis.
脑出血（ICH）是一种严重的中风亚型，具有高发病率、高致残率和高死亡率。目前，尚无用于临床实践的ICH生物标志物。我们旨在探讨RNA在ICH发病机制中的作用，并识别潜在的诊断生物标志物。
我们从两个独立队列中收集了233份个体血液样本，包括64例ICH患者、59例缺血性中风（IS）患者、60例高血压（HTN）患者和50例健康对照（CTRL），用于RNA测序。进行差异表达基因（DEG）分析、基因集富集分析（GSEA）和加权相关网络分析（WGCNA）以识别ICH特异性模块。使用ImmuneCellAI评估免疫细胞组成。采用多种机器学习算法选择用于ICH诊断的潜在生物标志物，并通过定量实时聚合酶链反应（RT-PCR）进一步验证。进行受试者工作特征（ROC）曲线分析和决策曲线分析（DCA）以评估该标志物对ICH的诊断价值。最后，我们生成M1和M2巨噬细胞以研究候选基因的表达。
在两个队列中，ICH患者与HTN对照之间始终有519个mRNA和131个lncRNA存在显著差异表达。基因功能分析表明免疫系统过程可能参与ICH病理。ImmuneCellAI分析显示，两个队列中ICH后11种免疫细胞类型的丰度均发生改变。WGCNA和GSEA识别出18个免疫相关的DEG。多种算法识别出一个对区分ICH患者与HTN对照、CTRL和IS患者具有高诊断价值的RNA组（CKAP4、BCL6、TLR8）（AUC分别为：0.93、0.95和0.82；敏感性分别为：81.3%、84.4%和75%；特异性分别为：100%、96%和79.7%）。此外，CKAP4和TLR8的mRNA和蛋白水平在RAW264.7 M1巨噬细胞中降低，在RAW264.7 M2巨噬细胞中升高，而BCL6的表达在M1巨噬细胞中升高，但在M2巨噬细胞中未升高，这可能为ICH提供潜在的治疗靶点。
本研究表明lncRNA和mRNA的表达水平与ICH相关，并开发了一个RNA组（CKAP4、BCL6、TLR8）作为区分ICH与IS及对照的潜在诊断工具，这可为ICH的诊断和发病机制提供有用的见解。
