胶质母细胞瘤
签名(拓扑)
医学
肿瘤科
计算生物学
基因
信使核糖核酸
内科学
生物
癌症研究
遗传学
几何学
数学
作者
Guohong Liu,Yunbao Pan,Yueying Li,Haibo Xu
出处
期刊:Future Oncology
[Future Medicine]
日期:2020-04-06
卷期号:16 (13): 837-848
被引量:8
标识
DOI:10.2217/fon-2019-0538
摘要
Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using 'edgeR' package, survival analysis taking count of single or multiple gene expression level using 'survival' package, univariate and multivariate Cox regression analysis using Cox function plugged in 'survival' package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. 'GPCR ligand binding' and 'Class A/1' are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.
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