Expression Profile Analysis Identifies a Novel Seven Immune-Related Gene Signature to Improve Prognosis Prediction of Glioblastoma.
Expression Profile Analysis Identifies a Novel Seven Immune-Related Gene Signature to Improve Prognosis Prediction of Glioblastoma.
复制标题
表达谱分析确定了新的七种免疫相关基因特征，以改善胶质母细胞瘤的预后预测
DOI：
10.3389/fgene.2021.638458
复制
发表时间：
2021
影响因子：
3.7
通讯作者：
Lin Z
中科院分区：
文献类型：
--
作者：
Hu L;Han Z;Cheng X;Wang S;Feng Y;Lin Z
多形性胶质母细胞瘤（GBM）是一种恶性中枢神经系统癌症，尽管采用常规治疗，预后仍很差。科学家们对使用免疫疗法治疗GBM非常感兴趣，因为它在许多实体瘤中显示出显着的潜力，包括黑色素瘤，非小细胞肺癌和肾细胞癌。使用基因表达模式、来自癌症基因组图谱数据库（TCGA）的GBM个体的临床数据和来自ImmPort的免疫相关基因（IRG），通过Wilcoxon秩和检验鉴定差异表达的IRG。单因素考克斯回归分析各IRG与患者总生存期（OS）的关系。采用LASSO考克斯回归分析方法，探讨各IRGs对GBM预后的影响，并建立危险度评分公式。创建Kaplan-Meier曲线以估计IRG的预后作用。根据受试者工作特征（ROC）曲线下面积检验模型的有效性。TCGA内部数据集和两个GEO外部数据集用于模型验证。我们评估了IRG在GBM中的表达，并生成了一个风险模型，以估计具有7个最佳预后表达IRG的GBM个体的预后。在胶质母细胞瘤中鉴定了22种类型的肿瘤浸润免疫细胞（TIIC），我们研究了7种IRG与免疫检查点之间的联系。此外，IRGs与GBM的浸润水平之间存在相关性。我们的数据表明，在这项研究中确定的七个IRGs不仅是GBM患者的重要预后预测因子，而且还可以用于研究GBM的发展机制，并为他们设计个性化的治疗方案。
Glioblastoma multiform (GBM) is a malignant central nervous system cancer with dismal prognosis despite conventional therapies. Scientists have great interest in using immunotherapy for treating GBM because it has shown remarkable potential in many solid tumors, including melanoma, non-small cell lung cancer, and renal cell carcinoma. The gene expression patterns, clinical data of GBM individuals from the Cancer Genome Atlas database (TCGA), and immune-related genes (IRGs) from ImmPort were used to identify differentially expressed IRGs through the Wilcoxon rank-sum test. The association between each IRG and overall survival (OS) of patients was investigated by the univariate Cox regression analysis. LASSO Cox regression assessment was conducted to explore the prognostic potential of the IRGs of GBM and construct a risk score formula. A Kaplan–Meier curve was created to estimate the prognostic role of IRGs. The efficiency of the model was examined according to the area under the receiver operating characteristic (ROC) curve. The TCGA internal dataset and two GEO external datasets were used for model verification. We evaluated IRG expression in GBM and generated a risk model to estimate the prognosis of GBM individuals with seven optimal prognostic expressed IRGs. A landscape of 22 types of tumor-infiltrating immune cells (TIICs) in glioblastoma was identified, and we investigated the link between the seven IRGs and the immune checkpoints. Furthermore, there was a correlation between the IRGs and the infiltration level in GBM. Our data suggested that the seven IRGs identified in this study are not only significant prognostic predictors in GBM patients but can also be utilized to investigate the developmental mechanisms of GBM and in the design of personalized treatments for them.