Che Yannan, Sun Xuxin, Liu Linhua, Peng Ling, Weng Yanshan, Du Shaohui
The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.
Shenzhen Institute for Drug Control, Shenzhen, China.
Medicine (Baltimore). 2025 Jul 18;104(29):e43402. doi: 10.1097/MD.0000000000043402.
The incidence of ischemic stroke (IS) is escalating rapidly, and glycolysis significantly influences the pathogenesis and prognosis of these patients. However, current methods for assessing this are insufficient. This study aimed to identify molecular biomarkers of glycolysis in patients with IS. We retrieved relevant data from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs). Gene set enrichment analysis was conducted on all genes within the integrated Gene Expression Omnibus dataset. Glycolysis-related differentially expressed genes (GRDEGs) were subjected to gene ontology and pathway analysis (Kyoto Encyclopedia of Genes and Genomes) to determine the functions of DEGs. The protein-protein interaction network of GRDEGs was established using the STRING database. The miRNAs of glycolysis-associated hub genes were acquired from the StarBase and miRDB databases, followed by an analysis of the relationship between glycolysis-related core genes and miRNAs. The mRNA-miRNA regulatory network was visualized. Lastly, a cross-comparison of immune-related genes and GRDEGs in IS was conducted to compare immune cell infiltration between the 2 groups. In the IS group, there were 42 up-regulated genes, 73 down-regulated genes, and 27 GRDEGs compared with the control group. These genes are involved in regulating various biological processes and signaling pathways. The protein-protein interaction network identified 7 hub genes related to glycolysis, including C-C motif chemokine receptor 7, ribosomal protein S3, and ribosomal protein SA, which also have immune correlations. Ribosomal protein SA, ribosomal protein S3, eukaryotic translation elongation factor 1 gamma, CD163, arginase 1, C-C motif chemokine receptor 7, and matrix metallopeptidase 9 are the hub genes related to glycolysis in IS. Our research will contribute to the discovery of potential biomarkers and fresh approaches to the clinical management of IS.
缺血性中风（IS）的发病率正在迅速上升，糖酵解对这些患者的发病机制和预后有显著影响。然而，目前评估这一情况的方法并不充分。本研究旨在识别IS患者糖酵解的分子生物标志物。我们从基因表达综合数据库中检索相关数据，并识别差异表达基因（DEGs）。对综合基因表达综合数据集中的所有基因进行基因集富集分析。对糖酵解相关差异表达基因（GRDEGs）进行基因本体论和通路分析（京都基因与基因组百科全书），以确定DEGs的功能。使用STRING数据库建立GRDEGs的蛋白质-蛋白质相互作用网络。从StarBase和miRDB数据库中获取糖酵解相关枢纽基因的miRNAs，然后分析糖酵解相关核心基因与miRNAs之间的关系。将mRNA-miRNA调控网络可视化。最后，对IS中免疫相关基因和GRDEGs进行交叉比较，以比较两组之间的免疫细胞浸润情况。与对照组相比，IS组有42个上调基因、73个下调基因和27个GRDEGs。这些基因参与调节各种生物过程和信号通路。蛋白质-蛋白质相互作用网络确定了7个与糖酵解相关的枢纽基因，包括C-C基序趋化因子受体7、核糖体蛋白S3和核糖体蛋白SA，它们也具有免疫相关性。核糖体蛋白SA、核糖体蛋白S3、真核翻译延伸因子1γ、CD163、精氨酸酶1、C-C基序趋化因子受体7和基质金属肽酶9是IS中与糖酵解相关的枢纽基因。我们的研究将有助于发现潜在的生物标志物以及为IS的临床管理提供新的方法。
