Department of Neurology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Neurology, Shanghai Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Folia Neuropathol. 2024;62(2):171-186. doi: 10.5114/fn.2024.135846.
This study aimed to screen immune-related marker genes of ischemic stroke (IS).
Two IS-related gene expression datasets were downloaded. The significantly differentially expressed genes (DEGs) and miRNAs (DEMs) between IS and control groups were selected. The differential immune cells were analysed. Weighted gene co-expression network analysis (WGCNA) was applied to analyse immune-related genes, followed by function analysis and interaction network construction. Then, key genes were further screened using optimization algorithm to construct a diagnostic model. Finally, miRNA regulatory network of several key genes was established.
In total 321 DEGs and 140 DEMs were obtained. 11 immune cell types were significantly different between IS and control groups. WGCNA identified two key modules, involving 202 differential immune genes. The greenyellow module was enriched in biological processes and pathways associated with T cells, while the midnightblue module was mainly associated with apoptosis, and inflammatory response-related functions and pathways. Protein interaction network identified 10 hub nodes, such as CD8A, ITGAM and TLR4. LASSO regression selected 8 key feature genes, and a risk score model was established. Key model genes were enriched in 63 GO biological processes, such as microglial cell activation, and B cell apoptotic process, and 3 KEGG pathways, such as negative regulation of nuclear cell cycle DNA replication, and hematopoietic cell lineage. Finally, a total of 25 miRNA-target relationship pairs were obtained.
This study identified some immune-related marker genes and constructed a diagnostic model based on 8 immune-related genes in IS.
本研究旨在筛选缺血性脑卒中（IS）的免疫相关标志物基因。
下载了两个与 IS 相关的基因表达数据集。选择 IS 组和对照组之间差异表达的基因（DEGs）和 microRNAs（DEMs）。分析差异免疫细胞。应用加权基因共表达网络分析（WGCNA）分析免疫相关基因，然后进行功能分析和互作网络构建。接着，使用优化算法筛选关键基因，构建诊断模型。最后，建立几个关键基因的 miRNA 调控网络。
共获得 321 个 DEGs 和 140 个 DEMs。IS 组和对照组之间有 11 种免疫细胞类型存在显著差异。WGCNA 鉴定出两个关键模块，包含 202 个差异免疫基因。绿黄色模块富集与 T 细胞相关的生物学过程和途径，而午夜蓝色模块主要与凋亡和炎症反应相关的功能和途径相关。蛋白质互作网络确定了 10 个枢纽节点，如 CD8A、ITGAM 和 TLR4。LASSO 回归选择了 8 个关键特征基因，并建立了风险评分模型。关键模型基因富集于 63 个 GO 生物学过程，如小胶质细胞激活和 B 细胞凋亡过程，以及 3 个 KEGG 途径，如负向调控核细胞周期的 DNA 复制和造血细胞谱系。最后，共获得 25 个 miRNA-靶关系对。
本研究鉴定了一些免疫相关标志物基因，并基于 8 个免疫相关基因构建了 IS 的诊断模型。
