Department of Head and Neck Oncology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, Guizhou Province, People's Republic of China.
Clin Transl Oncol. 2019 Oct;21(10):1327-1335. doi: 10.1007/s12094-019-02057-3. Epub 2019 Feb 14.
Glioma is a common malignant tumor of the central nervous system, which is characterized by a low cure rate, high morbidity, and high recurrence rate. Consequently, it is imperative to explore some indicators for prognostic prediction in glioma.
We obtained glioma data from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were obtained by R software from TCGA data sets. Through Cox regression analysis, risk scores were obtained to assess the weighted gene-expression levels, which could predict the prognosis of patients with glioma. The validity and the prognostic value of this model in glioma were confirmed by the manifestation of receiver-operating characteristic (ROC) curves, area under the curve (AUC), and 5-year overall survival (OS).
In total, 920 DEGs of transcriptome genes in glioma were extracted from the TCGA database. We identified a novel seven-gene signature associated with glioma. Among them, AL118505.1 and SMOC1 were positively related to the 5-year OS of patients with glioma, showing a better prognosis for glioma; however, RAB42, SHOX2, IGFBP2, HIST1H3G, and IGF2BP3 were negatively related to 5-year OS, displaying a worse prognosis. In addition, according to risk scores, AL118505.1 was also a protective factor, while others were risk factors. Furthermore, the expression levels of SHOX2, IGFBP2, and IGF2BP3 were significantly positively correlated with glioma grades. Receiver-operating characteristic (ROC) curve assessed the accuracy and sensitivity of the gene signature. Each of the seven genes for patients with the distribution of the risk score was presented in the heat map.
We identified a novel seven-gene signature in patients with glioma, which could be used as a predictor for the prognosis of patients with glioma in the future.
脑胶质瘤是一种常见的中枢神经系统恶性肿瘤，其治愈率低、发病率高、复发率高。因此，探索一些用于脑胶质瘤预后预测的指标迫在眉睫。
我们从癌症基因组图谱（TCGA）中获得脑胶质瘤数据。通过 R 软件从 TCGA 数据集中获取差异表达基因（DEGs）。通过 Cox 回归分析，获得风险评分以评估加权基因表达水平，从而预测脑胶质瘤患者的预后。通过绘制受试者工作特征（ROC）曲线、曲线下面积（AUC）和 5 年总生存率（OS）来验证该模型在脑胶质瘤中的有效性和预后价值。
从 TCGA 数据库中提取了 920 个脑胶质瘤转录组基因的差异表达基因。我们确定了一个与脑胶质瘤相关的新的七基因标志物。其中，AL118505.1 和 SMOC1 与脑胶质瘤患者的 5 年 OS 呈正相关，提示预后较好；而 RAB42、SHOX2、IGFBP2、HIST1H3G 和 IGF2BP3 与 5 年 OS 呈负相关，提示预后较差。此外，根据风险评分，AL118505.1 也是一个保护因素，而其他则是危险因素。此外，SHOX2、IGFBP2 和 IGF2BP3 的表达水平与脑胶质瘤分级呈显著正相关。ROC 曲线评估了基因标志物的准确性和敏感性。每个患者的风险评分分布都在热图中呈现。
我们在脑胶质瘤患者中确定了一个新的七基因标志物，它可以作为未来预测脑胶质瘤患者预后的指标。
