Wen Jing, Wang Youjun, Luo Lili, Peng Lu, Chen Caixia, Guo Jian, Ge Yunlong, Li Wenjun, Jin Xin
Xiamen Key Laboratory of Chiral Drugs, School of Medicine, Xiamen University, Xiamen, China.
Department of Neurosurgery, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, China.
Front Oncol. 2020 Nov 23;10:578809. doi: 10.3389/fonc.2020.578809. eCollection 2020.
Previous studies have shown that the prognosis of patients with lower-grade glioma (LGG) is closely related to the infiltration of immune cells and the expression of long non-coding RNAs (lncRNAs). In this paper, we applied single-sample gene set enrichment analysis (ssGSEA) algorithm to evaluate the expression level of immune genes from tumor tissues in The Cancer Genome Atlas (TCGA) database, and divided patients into the high immune group and the low immune group, which were separately analyzed for differential expression. Venn analysis was taken to select 36 immune-related lncRNAs. To construct a prognostic model of LGG based on immune-related lncRNAs, we divided patients into a training set and a verification set at a ratio of 2:1. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to select 11 immune-related lncRNAs associated with the prognosis of LGG, and based on these selected lncRNAs, the risk scoring model was constructed. Through Kaplan-Meier analysis, the overall survival (OS) of patients in the high-risk group was significantly lower than that of the low-risk group. Then, established a nomogram including age, gender, neoplasm histologic grade, and risk score. Meanwhile, the predictive performance of the model was evaluated by calculating the C-index, drawing the calibration chart, the clinical decision curve as well as the Receiver Operating Characteristic (ROC) curve. Similar results were obtained by utilizing the validation data to verify the above consequences. Based on the TIMER database, the correlation analysis showed that the 11 immune-related lncRNAs risk score of LGG were in connection with the infiltration of the subtypes of immune cells. Subsequently, we performed enrichment analysis, whose results showed that these immune-related lncRNAs played important roles in the progress of LGG. In conclusion, these 11 immune-related lncRNAs have the potential to predict the prognosis of patients with LGG, which may play a key role in the development of LGG.
先前的研究表明，低级别胶质瘤（LGG）患者的预后与免疫细胞浸润和长链非编码RNA（lncRNA）的表达密切相关。在本文中，我们应用单样本基因集富集分析（ssGSEA）算法评估癌症基因组图谱（TCGA）数据库中肿瘤组织免疫基因的表达水平，并将患者分为高免疫组和低免疫组，分别进行差异表达分析。采用韦恩分析筛选出36个免疫相关lncRNA。为构建基于免疫相关lncRNA的LGG预后模型，我们以2:1的比例将患者分为训练集和验证集。进行单因素Cox回归和最小绝对收缩和选择算子（LASSO）回归，以筛选出11个与LGG预后相关的免疫相关lncRNA，并基于这些筛选出的lncRNA构建风险评分模型。通过Kaplan-Meier分析，高风险组患者的总生存期（OS）显著低于低风险组。然后，建立了一个包括年龄、性别、肿瘤组织学分级和风险评分的列线图。同时，通过计算C指数、绘制校准图、临床决策曲线以及受试者工作特征（ROC）曲线来评估模型的预测性能。利用验证数据验证上述结果，得到了相似的结果。基于TIMER数据库的相关性分析表明，LGG的11个免疫相关lncRNA风险评分与免疫细胞亚型的浸润有关。随后，我们进行了富集分析，结果表明这些免疫相关lncRNA在LGG的进展中发挥着重要作用。总之，这11个免疫相关lncRNA具有预测LGG患者预后的潜力，可能在LGG的发生发展中起关键作用。
