A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients
A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients
复制标题
预测胶质母细胞瘤患者生存概率的 5 基因预后列线图
DOI：
10.1002/brb3.1258
复制
发表时间：
2019-04-01
影响因子：
3.1
通讯作者：
Lu, Quqin
中科院分区：
文献类型：
--
作者：
Wang, Lingchen;Yan, Zhengwei;Lu, Quqin
背景 胶质母细胞瘤 (GBM) 仍然是胶质瘤中最具生物学侵袭性的亚型，平均生存期为 10 至 12 个月。考虑到每个GBM患者的总生存期（OS）是个体治疗的关键因素，因此在临床实践中预测新诊断的GBM患者的生存概率是有意义的。材料和方法使用 TCGA 数据集和两个独立的 GEO 数据集，我们鉴定了与 OS 相关且在 GBM 组织和邻近正常组织之间差异表达的基因。应用基于可能性的稳健生存建模方法来选择用于建模的最佳基因。生成预后列线图后，使用不同平台上的独立数据集来评估其有效性。结果 我们鉴定了 168 个与 OS 相关的差异表达基因。选择其中五个基因来生成基因预后列线图。外部验证表明5基因预后列线图具有预测GBM患者OS的能力。结论 我们基于五个基因开发了一种新颖且方便的预后工具，在预测新诊断 GBM 患者的生存概率方面具有临床价值，并且所有这五个基因都可以代表 GBM 治疗的潜在靶基因。该模型的发展将为癌症研究人员提供很好的参考。
Background Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice. Material and Methods Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness. Results We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients. Conclusion We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.