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.
胶质母细胞瘤（GBM）是一种具有高度侵袭性和异质性的脑部肿瘤，预后极差。缺氧作为GBM的常见特征，与肿瘤进展和治疗抵抗密切相关。本研究旨在通过多组学分析，识别与缺氧相关的差异表达基因（DEGs），并构建GBM患者的预后特征模型。我们从公共数据库中收集了患者队列数据，包括基因表达综合数据库（GEO）、中国胶质瘤基因组图谱（CGGA）以及癌症基因组图谱-多形性胶质母细胞瘤（TCGA-GBM），以进行全面分析。缺氧相关基因（HRGs）来源于分子特征数据库（MSigDB）。差异表达分析在GBM患者中鉴定出41个缺氧相关DEGs。基于这些DEGs的表达模式，采用共识聚类方法识别出四个不同的亚群，其中亚群1显示出显著更优的总生存期。通过单变量Cox回归和LASSO回归等机器学习技术，我们构建了一个包含六个基因（ANXA1、CALD1、CP、IGFBP2、IGFBP5和LOX）的预后特征模型。多变量Cox回归分析进一步证实了其中三个最优特征基因（CP、IGFBP2和LOX）的预后意义。利用该缺氧相关预后特征，患者被分为高风险和低风险组。生存分析表明，高风险组的总生存率显著低于低风险组。该预后特征模型显示出良好的预测性能，其一、三、五年总生存期的曲线下面积（AUC）值均证明了这一点。此外，对DEGs进行的功能富集分析揭示了与缺氧相关的生物学过程和通路，为理解GBM的潜在机制提供了见解。通过对肿瘤免疫微环境的深入探究，我们的分析揭示了缺氧相关预后特征与GBM中免疫细胞浸润的相关性。总之，本研究强调了缺氧相关预后特征作为预测GBM患者生存结局的宝贵资源的潜力。整合了批量测序、单细胞分析和免疫微环境评估的多组学方法，加深了我们对GBM复杂生物学特性的理解，从而可能为个体化治疗策略的设计提供信息。
肿瘤(癌症)患者之家