Establishment and Validation of a Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Lower-Grade Glioma - Huang, Qian-Rong; Li, Jian-Wen; Yan, Ping; Jiang, Qian; Guo, Fang-Zhou; Zhao, Yin-Nong; Mo, Li-Gen (Frontiers in Neurology, 2022)
Background: 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 LGG survival. Methods: We first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. The Lasso analysis was then utilized to develop a risk signature for prognostic prediction. Based on 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 mechanism of these ferroptosis-related lncRNAs affecting LGG survival. Results: 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 training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed combining the risk signature and clinical parameters proved to be more accurate in predicting LGG prognosis. In addition, there were differences in levels of immune cell infiltration, immune-related functions, immune checkpoints and m6A-related genes expression between high- and low-risk groups. Conclusion: In summary, our ferroptosis-related lncRNA signature exhibits a good performance in predicting LGG prognosis. This study may provide a useful insight into the treatment of LGG patients.