肿瘤科
胶质瘤
免疫系统
比例危险模型
免疫疗法
内科学
癌症研究
单变量分析
生存分析
基因
单变量
生物
多元分析
医学
免疫学
多元统计
遗传学
统计
数学
作者
Chengcheng Wang,Huan Han,Fang Cheng,Sheng Wang,Junlong Wang,Chong Lv,Shi‐Bin Jiang,Peng Yan,Xiaoling Zhao
摘要
Abstract Glioblastomas (GBM), the most common primary brain tumor, lack accurate prognostic markers and have a poor prognosis. Our study was designed to identify effective biomarkers for GBM prognosis analysis and development of precise treatments. Differentially expressed genes (DEGs) between GBM patients and controls were analyzed from the Xena database and GEPIA. Based on the screened DEGs, univariate COX and LASSO regression analysis were performed to identify the most relevant genes associated with GBM prognosis. Genes highly expressed in GBM patients were selected to construct receiver operating characteristic analysis and enrichment analysis was constructed on groups of high and low expression of adipocyte enhancer-binding protein 1 (AEBP1). CIBERSORT, ssGSEA and ESTIMATE were used to perform immune infiltration analysis. About 3297 DEGs were identified using data from Xena database; 8 prognostic genes were identified. AEBP1, which plays a role in neuronal differentiation and development, was positively correlated in GBMs with immune infiltration; its high expression in cancer patients is associated with short overall survival and advanced tumor staging. This study suggests that AEBP1 could serve as a prognostic marker for GBMs and that patients with high expression may have a better response to immunotherapy.
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