Liu Jinzhi, Si Zhihua, Liu Ju, Zhang Xu, Xie Cong, Zhao Wei, Wang Aihua, Xia Zhangyong
Department of Gerontology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China.
Department of Neurology, Liaocheng People’s Hospital and Liaocheng Clinical School of Shandong First Medical University, Liaocheng, Shandong Province, China.
Aging (Albany NY). 2024 Apr 3;16(7):6314-6333. doi: 10.18632/aging.205706.
Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune microenvironment of IS.
Aberrant expressed CGs in IS were input into unsupervised consensus clustering to classify IS subtypes. Meanwhile, key CGs involved in IS were further selected by weighted gene co-expression network analysis (WGCNA) and machine learning methods, including random forest (RF), support vector machine (SVM), generalized linear model (GLM) and extreme-gradient boosting (XGB). The diagnostic performance of key CGs were evaluated by receiver operating characteristic (ROC) curves. At last, quantitative PCR (qPCR) was performed to validate the expressions of key CGs in IS.
IS patients were classified into two subtypes with different immune microenvironments by aberrant expressed CGs. Further WGCNA, machine learning methods and ROC curves identified ACTN1, F5, TLN1, JMJD1C and WAS as potential diagnostic biomarkers of IS. In addition, their expressions were significantly correlated with macrophages, neutrophils and/or T cells. GSEA also revealed that those biomarkers may regulate IS via immune and inflammation. Moreover, qPCR verified the expressions of ACTN1, F5 and JMJD1C in IS.
The current study identified ACTN1, F5 and JMJD1C as novel coagulation-related biomarkers associated with IS immune microenvironment, which enriches our knowledge of coagulation-mediated pathogenesis of IS and sheds light on next-step and experiments to elucidate the relevant molecular mechanisms.
目前已知凝血系统与缺血性中风（IS）的发生发展相关。因此，本研究旨在确定凝血基因（CGs）在IS中的诊断价值，并探讨其在IS免疫微环境中的作用。
将IS中异常表达的CGs输入无监督一致性聚类以对IS亚型进行分类。同时，通过加权基因共表达网络分析（WGCNA）和机器学习方法，包括随机森林（RF）、支持向量机（SVM）、广义线性模型（GLM）和极限梯度提升（XGB），进一步筛选参与IS的关键CGs。通过受试者工作特征（ROC）曲线评估关键CGs的诊断性能。最后，进行定量PCR（qPCR）以验证关键CGs在IS中的表达。
通过异常表达的CGs将IS患者分为具有不同免疫微环境的两个亚型。进一步的WGCNA、机器学习方法和ROC曲线确定ACTN1、F5、TLN1、JMJD1C和WAS为IS的潜在诊断生物标志物。此外，它们的表达与巨噬细胞、中性粒细胞和/或T细胞显著相关。基因集富集分析（GSEA）还显示这些生物标志物可能通过免疫和炎症调节IS。此外，qPCR验证了ACTN1、F5和JMJD1C在IS中的表达。
本研究确定ACTN1、F5和JMJD1C为与IS免疫微环境相关的新型凝血相关生物标志物，这丰富了我们对凝血介导的IS发病机制的认识，并为阐明相关分子机制的下一步研究和实验提供了线索。
