Huang Qian-Rong, Li Jian-Wen, Yan Ping, Jiang Qian, Guo Fang-Zhou, Zhao Yin-Nong, Mo Li-Gen
Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China.
Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China.
Front Neurol. 2022 Jun 27;13:861438. doi: 10.3389/fneur.2022.861438. eCollection 2022.
The prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG.
We first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG.
A total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups.
In summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.
低级别胶质瘤（LGG）的预后差异很大，仍需要更准确的预测指标。我们研究的目的是探讨铁死亡相关长链非编码RNA（lncRNA）在LGG中的预后价值，并开发一种用于预测LGG患者生存情况的新型风险特征。
我们首先整合多个数据集以筛选LGG中与预后相关的铁死亡相关lncRNA。然后利用最小绝对收缩和选择算子（LASSO）分析来开发用于预后预测的风险特征。基于多变量Cox分析的结果，构建了LGG的预后列线图模型。最后，进行功能富集分析、单样本基因集富集分析（ssGSEA）、免疫和m6A相关性分析，以探讨这些铁死亡相关lncRNA影响LGG患者生存的可能机制。
共鉴定出11个与LGG预后相关的铁死亡相关lncRNA。基于预后lncRNA，构建了一个由8个lncRNA组成的风险特征，并在训练和验证队列中均表现出良好的预测性能。相关性分析表明，该风险特征与临床特征密切相关。我们通过结合风险特征和临床参数构建的列线图模型在预测LGG预后方面被证明更准确。此外，高风险组和低风险组在免疫细胞浸润水平、免疫相关功能、免疫检查点和m6A相关基因表达方面存在差异。
总之，我们的铁死亡相关lncRNA特征在预测LGG预后方面表现良好。本研究可能为LGG的治疗提供有用的见解。
