Data_Sheet_5_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.PDF

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.

背景转化生长因子-β（TGF-β）是一种具有多种功能的细胞因子，在组织发育和肿瘤发生中扮演着至关重要的角色。TGF-β能够抑制众多免疫细胞的功能，阻止T细胞进入肿瘤中心，从而使肿瘤细胞逃避免疫监视，导致对免疫治疗的敏感性降低。然而，其在预测胶质母细胞瘤（GBM）的临床预后和肿瘤微环境（TME）免疫特征中的潜在作用，尚需进行深入的研究。方法：从癌症基因组图谱（TCGA-GBM）数据集中获取了数据，并通过基因表达综合数据库（Gene Expression Omnibus）下载了验证数据集。首先，在GBM与正常样本之间筛选出差异表达TGF-β基因（DEGs）。接着，采用单因素和多因素Cox分析识别预后基因并构建TGF-β风险模型。随后，探讨了TGF-β风险评分在预测临床预后和免疫特征中的作用。结果：成功构建了一个具有独立预后价值的TGF-β风险评分特征。TGF-β风险评分与肿瘤浸润免疫细胞的浸润水平以及抗癌免疫步骤的活性呈正相关。此外，TGF-β风险评分与免疫检查点的表达也呈正相关。此外，高评分表明对免疫检查点抑制剂的敏感性更高。结论：我们首次开发并验证了一个TGF-β风险特征，该特征可以预测GBM的临床预后和TME免疫特征。此外，TGF-β特征可指导GBM的更加个性化的治疗方案。