Qin Rongxing, Liang Xiaojun, Yang Yue, Chen Jiafeng, Huang Lijuan, Xu Wei, Qin Qingchun, Lai Xinyu, Huang Xiaoying, Xie Minshan, Chen Li
Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, China.
National Center for International Research of Biological Targeting Diagnosis and Therapy (Guangxi Key Laboratory of Biological Targeting Diagnosis and Therapy Research), Guangxi Medical University, Guangxi Zhuang Autonomous Region, Nanning, 530021, China.
Heliyon. 2024 Aug 30;10(17):e36559. doi: 10.1016/j.heliyon.2024.e36559. eCollection 2024 Sep 15.
Ischemic stroke (IS) is a significant health concern with high disability and fatality rates despite available treatments. Immune cells and cuproptosis are associated with the onset and progression of IS. Investigating the interaction between cuproptosis-related genes (CURGs) and immune cells in IS can provide a theoretical basis for IS treatment.
We obtained IS datasets from the Gene Expression Omnibus (GEO) and employed machine learning to identify CURGs. The diagnostic efficiency of the CURGs was evaluated using receiver operating characteristic (ROC) curves. KEGG and gene set enrichment analysis (GSEA) were also conducted to identify biologically relevant pathways associated with CURGs in IS patients. Single-cell analysis was used to confirm the expression of 19 CURGs, and pathway activity calculations were performed using the AUCell package. Additionally, a risk prediction model for IS patients was developed, and core modules and hub genes related to IS were identified using weighted gene coexpression network analysis (WGCNA). We classified IS patients using a method of consensus clustering.
We established a precise diagnostic model for IS. Enrichment analysis revealed major pathways, including oxidative phosphorylation, the NF-kappa B signaling pathway, the apoptosis pathway, and the Wnt signaling pathway. At the single-cell level, compared to those in non-IS samples, 19 CURGs were primarily overexpressed in the immune cells of IS samples and exhibited high activity in natural killer cell-mediated cytotoxicity, steroid hormone biosynthesis, and oxidative phosphorylation. Two clusters were obtained through consensus clustering. Notably, immune cell types including B cells, plasma cells, and resting NK cells, varied between the two clusters. Furthermore, the red module and hub genes associated with IS were uncovered. The expression patterns of CURGs varied over time.
This study developed a precise diagnostic model for IS by identifying CURGs and evaluating their interaction with immune cells. Enrichment analyses revealed key pathways involved in IS, and single-cell analysis confirmed CURG overexpression in immune cells. A risk prediction model and core modules associated with IS were also identified.
缺血性中风（IS）是一个重大的健康问题，尽管有可用的治疗方法，但致残率和死亡率仍然很高。免疫细胞和铜死亡与IS的发生和发展有关。研究IS中铜死亡相关基因（CURGs）与免疫细胞之间的相互作用可为IS治疗提供理论依据。
我们从基因表达综合数据库（GEO）获得IS数据集，并采用机器学习来识别CURGs。使用受试者工作特征（ROC）曲线评估CURGs的诊断效率。还进行了KEGG和基因集富集分析（GSEA），以识别与IS患者中CURGs相关的生物学相关途径。单细胞分析用于确认19个CURGs的表达，并使用AUCell软件包进行途径活性计算。此外，还开发了IS患者的风险预测模型，并使用加权基因共表达网络分析（WGCNA）识别与IS相关的核心模块和枢纽基因。我们使用共识聚类方法对IS患者进行分类。
我们建立了一个精确的IS诊断模型。富集分析揭示了主要途径，包括氧化磷酸化、NF-κB信号通路、凋亡途径和Wnt信号通路。在单细胞水平上，与非IS样本相比，19个CURGs主要在IS样本的免疫细胞中过度表达，并在自然杀伤细胞介导的细胞毒性、类固醇激素生物合成和氧化磷酸化中表现出高活性。通过共识聚类获得了两个簇。值得注意的是，包括B细胞、浆细胞和静息NK细胞在内的免疫细胞类型在两个簇之间有所不同。此外，还发现了与IS相关的红色模块和枢纽基因。CURGs的表达模式随时间变化。
本研究通过识别CURGs并评估它们与免疫细胞的相互作用，开发了一个精确的IS诊断模型。富集分析揭示了IS中涉及的关键途径，单细胞分析证实了CURGs在免疫细胞中的过度表达。还确定了与IS相关的风险预测模型和核心模块。
