胶质母细胞瘤(GBM)代表了一种与预后暗淡有关的侵袭性和异质性脑肿瘤。缺氧,GBM中的一个共同特征,与肿瘤进展和治疗抵抗有关。在这项研究中,我们旨在鉴定低氧相关差异表达基因(DEGs),并利用多组学分析构建GBM患者的预后特征.患者队列是从公开可用的数据库中收集的,包括基因表达综合(GEO),中国胶质瘤基因组图谱(CGGA),和癌症基因组图谱-多形性胶质母细胞瘤(TCGA-GBM),便于全面分析。缺氧相关基因(HRG)从分子特征数据库(MSigDB)获得。差异表达分析显示GBM患者中有41例缺氧相关的DEGs。一种共识聚类方法,利用这些DEG的表达模式,确定了四个不同的簇,第1组显示明显更好的总生存率。机器学习技术,包括单变量Cox回归和LASSO回归,描绘了一个包含六个基因(ANXA1,CALD1,CP,IGFBP2、IGFBP5和LOX)。多变量Cox回归分析证实了一组三个最佳特征基因(CP,IGFBP2和LOX)。使用缺氧相关的预后特征,患者分为高危和低危.生存分析表明,与低风险组相比,高风险组的总体生存率较低。预后特征显示出良好的预测性能,如曲线下面积(AUC)值所示-,三-,和5年总生存率。此外,DEGs的功能富集分析确定了与缺氧相关的生物过程和途径,提供对GBM潜在机制的见解。深入研究肿瘤免疫微环境,我们的分析显示,GBM中缺氧相关的预后特征与免疫细胞浸润相关.总的来说,我们的研究强调了缺氧相关预后标志作为预测GBM患者生存结局的宝贵资源的潜力.整合批量测序的多组学方法,单细胞分析,免疫微环境评估增强了我们对GBM复杂生物学特征的理解,从而潜在地告知治疗干预措施的定制设计。
Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas-Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs\' expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP,
IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP,
IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.