Department of Neurosurgery, The Third Hospital of Jilin University, China.
Department of Neurosurgery, The Third Hospital of Jilin University, China; Department of Thoracic Surgery, The Third Hospital of Jilin University, China.
Int Immunopharmacol. 2020 Aug;85:106636. doi: 10.1016/j.intimp.2020.106636. Epub 2020 Jun 11.
Immune escape is one of the landmark features of glioblastoma (GBM). Immunotherapy is undoubtedly a revolution in the field of tumor treatment, especially the application of immune checkpoint inhibitors and CAR-T cells, which have achieved amazing results in fighting against cancer. This study aimed to establish a TP53-related immune-based score model to improve the prognostic of GBM by investigating the gene mutations and the immune landscape of GBM.
Data were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed genes (DEGs) analysis between the TP53 mutated (TP53) and wild-type (TP53) GBM patients was conducted. The CIBERSORT algorithm was applied to evaluate the proportion of immune cell types and RNA sequencing (RNA-seq) data from the TCGA and CGGA were used as discovery and validation cohorts, respectively, to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression method to eliminate collinearity among DEGs. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors.
PTEN was the top most mutated gene in GBM patients (118/393), followed by TP53 (116/393). 332 immune-related genes were identified and the immune response in the TP53 group was remarkably greater than in the TP53 group. The final IPM model composed three immune-related genes: IPM risk score = (0.392 × S100A8 expression) + (0.174 × CXCL1 expression) + (0.368 × IGLL5 expression), significantly correlated with the overall survival (OS) of GBM in the stratified TP53 status subgroups and was an independent prognostic variate for GBM. By integrating the IPM and clinical characteristics, a nomogram was generated to facilitate clinical utilization, with the results suggesting that it has better predictive performance for GBM prognosis than the IPM.
The IPM model can identify patients at high-risk and can be combined with other clinical factors to estimate the OS of GBM patients, demonstrating that it is a promising biomarker to optimize the prognosis of GBM.
免疫逃逸是胶质母细胞瘤（GBM）的标志性特征之一。免疫疗法无疑是肿瘤治疗领域的一场革命，尤其是免疫检查点抑制剂和 CAR-T 细胞的应用，在抗击癌症方面取得了惊人的效果。本研究旨在通过研究 GBM 的基因突变和免疫图谱，建立一个与 TP53 相关的免疫评分模型，以改善 GBM 的预后。
从癌症基因组图谱（TCGA）和中国脑胶质瘤基因组图谱（CGGA）数据库中获取数据。对 TP53 突变（TP53）和野生型（TP53）GBM 患者的差异表达基因（DEGs）进行分析。应用 CIBERSORT 算法评估免疫细胞类型的比例，使用 TCGA 和 CGGA 的 RNA 测序（RNA-seq）数据分别作为发现和验证队列，构建和验证一个与免疫相关的预后模型（IPM）。首先通过单因素 Cox 分析筛选 IPM 模型中的基因，然后通过最小绝对值收缩和选择算子（LASSO）Cox 回归方法消除 DEGs 之间的共线性。最后通过结合 IPM 和其他临床因素建立并评估列线图。
PTEN 是 GBM 患者中突变最多的基因（118/393），其次是 TP53（116/393）。鉴定出 332 个与免疫相关的基因，TP53 组的免疫反应明显大于 TP53 组。最终的 IPM 模型由三个免疫相关基因组成：IPM 风险评分=（0.392×S100A8 表达）+（0.174×CXCL1 表达）+（0.368×IGLL5 表达），在分层 TP53 状态亚组中与 GBM 的总生存期（OS）显著相关，是 GBM 的独立预后变量。通过整合 IPM 和临床特征，生成了一个列线图，以方便临床应用，结果表明它对 GBM 预后的预测性能优于 IPM。
IPM 模型可以识别高危患者，并与其他临床因素结合，估计 GBM 患者的 OS，表明它是优化 GBM 预后的有前途的生物标志物。
