Proteomics-based prognostic signature and nomogram construction of hypoxia microenvironment on deteriorating glioblastoma (GBM) pathogenesis

The present study aimed to construct and evaluate a novel experiment-based hypoxia signature to help evaluations of GBM patient status. First, the 426 proteins, which were previously found to be differentially expressed between normal and hypoxia groups in glioblastoma cells with statistical significance, were converted into the corresponding genes, among which 212 genes were found annotated in TCGA. Second, after evaluated by single-variable Cox analysis, 19 different expressed genes (DEGs) with prognostic value were identified. Based on λ value by LASSO, a gene-based survival risk score model, named RiskScore, was built by 7 genes with LASSO coefficient, which were FKBP2, GLO1, IGFBP5, NSUN5, RBMX, TAGLN2 and UBE2V2. Kaplan–Meier (K–M) survival curve analysis and the area under the curve (AUC) were plotted to further estimate the efficacy of this risk score model. Furthermore, the survival curve analysis was also plotted based on the subtypes of age, IDH, radiotherapy and chemotherapy. Meanwhile, immune infiltration, GSVA, GSEA and chemo drug sensitivity of this risk score model were evaluated. Third, the 7 genes expression were evaluated by AUC, overall survival (OS) and IDH subtype in datasets, importantly, also experimentally verified in GBM cell lines exposed to hypoxic or normal oxygen condition, which showed significant higher expression in hypoxia than in normal group. Last, combing the hypoxia RiskScore with clinical and molecular features, a prognostic composite nomogram was generated, showing the good sensitivity and specificity by AUC and OS. Meanwhile, univariate analysis and multivariate analysis were used for performed to identify variables in nomogram that were significant in independently predicting duration of survival. It is a first time that we successfully established and validated an independent prognostic risk model based on hypoxia microenvironment from glioblastoma cells and public database. The 7 key genes may provide potential directions for future biochemical and pharmaco-therapeutic research.