鉴定新型免疫相关 lncRNA 特征，预测 LGG 的预后结果和治疗效果。

Identification of a novel immune-related lncRNA signature to predict prognostic outcome and therapeutic efficacy of LGG.

机构信息

Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, 100853 Beijing, China.

Department of General Surgery, The Air Force Hospital of Northern Theater PLA, 110042 Shenyang, Liaoning, China.

出版信息

J Integr Neurosci. 2022 Mar 22;21(2):55. doi: 10.31083/j.jin2102055.

DOI:10.31083/j.jin2102055

PMID:35364643

Abstract

BACKGROUND

Recent studies have shown that the prognosis of low-grade glioma (LGG) patients is closely correlated with the immune infiltration and the expression of long-stranded non-coding RNAs (lncRNAs). It's meaningful to find the immune-related lncRNAs (irlncRNAs).

METHODS

The Cancer Genome Atlas (TCGA) data was employed in the study to identify irlncRNAs and Cox regression model was applied to construct the risk proportional model based on irlncRNAs.

RESULTS

In the study, we retrieved transcriptomic data of LGG from TCGA and identified 10 lncRNA signatures consisting of irlncRNAs by co-expression analysis. Then we plotted 1-year receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC). LGG patients were divided into high-risk and low-risk groups according to the risk model. We found there were differences in survival prognosis, clinical characteristics, degree of immune cell infiltration, expression of immune gene checkpoint genes, and sensitivity to the commonly used chemotherapeutic agents of high-risk and low-risk groups.

CONCLUSIONS

IrlncRNA-based risk assessment model can be used as a prognostic tool to predict the survival outcome and clinical characteristics of LGG and to guide treatment options.

摘要

背景

最近的研究表明，低级别胶质瘤（LGG）患者的预后与免疫浸润和长链非编码 RNA（lncRNA）的表达密切相关。寻找与免疫相关的 lncRNA（irlncRNA）具有重要意义。

方法

本研究利用癌症基因组图谱（TCGA）数据来鉴定 irlncRNA，并应用 Cox 回归模型基于 irlncRNA 构建风险比例模型。

结果

我们从 TCGA 中检索了 LGG 的转录组数据，并通过共表达分析鉴定了由 10 个 lncRNA 特征组成的 irlncRNA signature。然后我们绘制了 1 年的接收器工作特征（ROC）曲线，并计算了曲线下面积（AUC）。根据风险模型，LGG 患者被分为高危组和低危组。我们发现高危组和低危组在生存预后、临床特征、免疫细胞浸润程度、免疫基因检查点基因表达以及对常用化疗药物的敏感性方面存在差异。