Zhao Junsheng, Liu Zhengtao, Zheng Xiaoping, Gao Hainv, Li Lanjuan
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Genet. 2021 Nov 8;12:753680. doi: 10.3389/fgene.2021.753680. eCollection 2021.
Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years. For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals' one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell's concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature. A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan-Meier (K-M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG. In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.
低级别胶质瘤(LGG)被认为是年轻成年人的致命疾病,其总生存期因组织病理学和分子亚型的不同而在1至15年之间广泛波动。作为一种新型的程序性细胞死亡方式,铁死亡被报道参与肿瘤的发生和发展,近年来受到了广泛研究。对于发现队列,使用来自癌症基因组图谱(TCGA)和基因型-组织表达(GTEx)的数据来识别差异表达和具有预后意义的铁死亡相关基因(FRG)。使用最小绝对收缩和选择算子(LASSO)和多变量Cox回归,用上述选择的FRG建立一个预后特征。然后,在TCGA和中国胶质瘤基因组图谱(CGGA)数据库中对该特征进行开发和验证。通过结合临床病理特征和FRG特征,建立一个列线图来预测个体的1年、3年和5年生存概率,并通过Harrell一致性指数(C指数)和校准曲线评估其预测性能。进行富集分析以探索该特征所调控的信号通路。构建了一个包含7个FRG的新型风险特征,并用于将患者分为两组。Kaplan-Meier(K-M)生存曲线和受试者工作特征(ROC)曲线分析证实了风险模型的预后性能,随后基于CGGA的数据进行外部验证。基于风险特征和临床特征的列线图在预测LGG生存率方面表现良好。最后,功能分析显示两个风险组之间的免疫状态不同,这可能有助于解释LGG中铁死亡的潜在机制。总之,本研究构建了一个新颖且稳健的7-FRG特征,并建立了一个用于预测LGG生存的预后列线图。