Departments of Neurosurgery, The Third Hospital of Jilin University, Changchun, Jilin 130033, China.
Ophthalmology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin 130021, China.
Biomed Res Int. 2020 Jul 27;2020:3708231. doi: 10.1155/2020/3708231. eCollection 2020.
Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR = 3.192, 95%CI = 2.182-4.670; CGGA: HR = 1.922, 95%CI = 1.431-2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.
能量代谢过程对肿瘤恶性程度起着重要作用，这表明相关的蛋白编码基因和调控上游基因（如长链非编码 RNA（lncRNA））可能代表预后预测的潜在生物标志物。本研究将为低级别胶质瘤（LGG）患者开发一种新的能量代谢相关 lncRNA-mRNA 预后标志物。从基因表达综合数据库（GEO）中获取的 GSE4290 数据集用于筛选差异表达基因（DEGs）和 lncRNAs（DELs）。癌症基因组图谱（TCGA）数据集被用作预后训练集，而中国脑胶质瘤基因组图谱（CGGA）则被用作验证集。从分子特征数据库（MsigDB）中收集能量代谢相关基因，并建立能量代谢相关 DEGs 和 DELs 之间的共表达网络，以鉴定能量代谢相关 DELs。使用最小绝对收缩和选择算子（LASSO）分析筛选预后标志物，并进行生存分析和列线图构建。在 LGG 和正常脑组织之间共鉴定出 1613 个 DEGs 和 37 个 DELs。有 110 个 DEGs 与能量代谢相关基因重叠。27 个 DELs 可以与 67 个代谢相关的 DEGs 共同表达。LASSO 回归分析显示，共表达网络中的 9 个基因是最优的特征，并用于构建风险评分。Kaplan-Meier 曲线分析显示，高风险评分患者的 OS 明显低于低风险评分患者（TCGA：HR = 3.192，95%CI = 2.182-4.670；CGGA：HR = 1.922，95%CI = 1.431-2.583）。根据 ROC 曲线的 AUC，风险评分的预测准确性也很高（TCGA：0.827；CGGA：0.806）。多变量 Cox 回归分析显示年龄、IDH1 突变和风险评分是独立的预后因素，因此基于这三个变量建立了预后列线图。校准和判别分析证实了该列线图的优异预后性能。总之，本研究结果为低级别胶质瘤患者的预后分层提供了一个新的生物标志物。
