Appunni Sandeep, Rubens Muni, Ramamoorthy Venkataraghavan, Sharma Hina, Singh Anjani Kumar, Swarup Vishnu, Singh Himanshu Narayan
Government Medical College, Kozhikode, Kerala, India.
Miami Cancer Institute, Florida, USA.
Malays J Med Sci. 2020 Dec;27(6):53-67. doi: 10.21315/mjms2020.27.6.6. Epub 2020 Dec 29.
Ischaemic stroke (IS), a multifactorial neurological disorder, is mediated by interplay between genes and the environment and, thus, blood-based IS biomarkers are of significant clinical value. Therefore, this study aimed to find global differentially expressed genes (DEGs) in-silico, to identify key enriched genes via gene set enrichment analysis (GSEA) and to determine the clinical significance of these genes in IS.
Microarray expression dataset GSE22255 was retrieved from the Gene Expression Omnibus (GEO) database. It includes messenger ribonucleic acid (mRNA) expression data for the peripheral blood mononuclear cells of 20 controls and 20 IS patients. The bioconductor-package 'affy' was used to calculate expression and a pairwise test was applied to screen DEGs ( 0.01). Further, GSEA was used to determine the enrichment of DEGs specific to gene ontology (GO) annotations.
GSEA analysis revealed 21 genes to be significantly plausible gene markers, enriched in multiple pathways among all the DEGs ( 881). Ten gene sets were found to be core enriched in specific GO annotations. JunD, NCX3 and fibroblast growth factor receptor 4 (FGFR4) were under-represented and glycoprotein M6-B (GPM6B was persistently over-represented.
The identified genes are either associated with the pathophysiology of IS or they affect post-IS neuronal regeneration, thereby influencing clinical outcome. These genes should, therefore, be evaluated for their utility as suitable markers for predicting IS in clinical scenarios.
缺血性中风(IS)是一种多因素神经疾病,由基因与环境之间的相互作用介导,因此,基于血液的IS生物标志物具有重要的临床价值。因此,本研究旨在通过计算机分析找到全局差异表达基因(DEG),通过基因集富集分析(GSEA)识别关键富集基因,并确定这些基因在IS中的临床意义。
从基因表达综合数据库(GEO)中检索微阵列表达数据集GSE22255。它包括20名对照和20名IS患者外周血单个核细胞的信使核糖核酸(mRNA)表达数据。使用生物导体包“affy”计算表达,并应用成对检验筛选DEG(P<0.01)。此外,GSEA用于确定特定于基因本体论(GO)注释的DEG的富集情况。
GSEA分析显示21个基因是显著合理的基因标志物,在所有DEG(共881个)中的多个途径中富集。发现10个基因集在特定的GO注释中核心富集。JunD、NCX3和成纤维细胞生长因子受体4(FGFR4)表达不足,而糖蛋白M6-B(GPM6B)持续表达过度。
所鉴定的基因要么与IS的病理生理学相关,要么影响IS后的神经元再生,从而影响临床结果。因此,应评估这些基因作为临床情况下预测IS的合适标志物的效用。