Zhang Zongyong, Zheng Zongqing, Luo Wenwei, Li Jiebo, Liao Jiushan, Chen Fuxiang, Wang Dengliang, Lin Yuanxiang
Department of Neurosurgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
PLoS One. 2024 Dec 5;19(12):e0310108. doi: 10.1371/journal.pone.0310108. eCollection 2024.
Ischemic stroke (IS) is a leading cause of death and disability worldwide. Screening for marker genes in IS is crucial for its early diagnosis and improvement in clinical outcomes. In the study, the gene expression profiles in the GSE22255 and GSE37587 datasets were extracted from the public database Gene Expression Omnibus. Weighted gene co‑expression network analysis (WGCNA) was used to investigate the gene sets that were related to ubiquitination. A total of 33 ubiquitination-related differentially expressed genes (DEGs) were identified using "limma (version 3.50.0)". Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) analysis enriched multiple pathways that were closely related to IS. The correlations between the HALLMARK signaling pathways and DGEs were analyzed. Receiver operating characteristic analysis was used to validate the diagnostic value of the key genes. Among them, 16 genes were identified as hub genes. Single-sample GSEA was performed to evaluate the infiltration status of immune cells in IS. To understand the potential molecular mechanisms of the hub genes in IS, we constructed RBP-mRNA and mRNA-miRNA-lncRNA interaction networks. Additionally, we used the GeneMANIA database to create a PPI network for the signature genes to investigate their functions. As a result, there was a significant difference in the overall infiltration of immune cells between the IS and control groups. Among the 28 types of immune cells, the degree of infiltration of seven types was significantly different between the two groups (p<0.05). The expression of four types of immune cells, namely type 1 T helper cell, type 17 T helper cell, eosinophil, and mast cell, in the IS group were significantly higher than that in the control group. The expressions of DHFR2 (R = -0.575; p<0.001) and DNAAF2 (R = -0.562; p<0.001) were significantly negatively correlated with eosinophil infiltration. The PPI network demonstrated that the 16 hub genes interacted with each other. In conclusion, we identified DEGs, WGCNA modules, hub genes, enriched pathways, and infiltrating immune cells that may be closely involved in IS. Further studies are required to explore the functions of these genes.
缺血性中风（IS）是全球范围内导致死亡和残疾的主要原因。筛选IS中的标志物基因对其早期诊断和改善临床结局至关重要。在本研究中，从公共数据库基因表达综合数据库（Gene Expression Omnibus）中提取了GSE22255和GSE37587数据集中的基因表达谱。使用加权基因共表达网络分析（WGCNA）来研究与泛素化相关的基因集。使用“limma（版本3.50.0）”共鉴定出33个与泛素化相关的差异表达基因（DEG）。基因集富集分析（GSEA）和基因集变异分析（GSVA）分析富集了多个与IS密切相关的通路。分析了HALLMARK信号通路与DGE之间的相关性。采用受试者工作特征分析来验证关键基因的诊断价值。其中，16个基因被鉴定为枢纽基因。进行单样本GSEA以评估IS中免疫细胞的浸润状态。为了解枢纽基因在IS中的潜在分子机制，我们构建了RBP-mRNA和mRNA-miRNA-lncRNA相互作用网络。此外，我们使用GeneMANIA数据库为特征基因创建了一个蛋白质-蛋白质相互作用（PPI）网络，以研究它们的功能。结果，IS组和对照组之间免疫细胞的总体浸润存在显著差异。在28种免疫细胞中，两组之间七种类型的浸润程度存在显著差异（p<0.05）。IS组中四种免疫细胞，即1型辅助性T细胞、17型辅助性T细胞、嗜酸性粒细胞和肥大细胞的表达显著高于对照组。DHFR2（R = -0.575；p<0.001）和DNAAF2（R = -0.562；p<0.001）的表达与嗜酸性粒细胞浸润显著负相关。PPI网络表明16个枢纽基因相互作用。总之，我们鉴定出了可能与IS密切相关的DEG、WGCNA模块、枢纽基因、富集通路和浸润免疫细胞。需要进一步研究来探索这些基因的功能。
