Cai Yaning, Guo Hao, Zhou JinPeng, Zhu Gang, Qu Hongwen, Liu Lingyu, Shi Tao, Ge Shunnan, Qu Yan
Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, No. 569 Xinsi Road, Xi'an 710038, China.
J Cancer Res Clin Oncol. 2023 Nov;149(15):13575-13589. doi: 10.1007/s00432-023-05155-6. Epub 2023 Jul 29.
The alternative extension of the telomeres (ALT) mechanism is activated in lower grade glioma (LGG), but the role of the ALT mechanism has not been well discussed. The primary purpose was to demonstrate the significance of the ALT mechanism in prognosis estimation for LGG patients.
Gene expression and clinical data of LGG patients were collected from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohort, respectively. ALT-related genes obtained from the TelNet database and potential prognostic genes related to ALT were selected by LASSO regression to calculate an ALT-related risk score. Multivariate Cox regression analysis was performed to construct a prognosis signature, and a nomogram was used to represent this signature. Possible pathways of the ALT-related risk score are explored by enrichment analysis.
The ALT-related risk score was calculated based on the LASSO regression coefficients of 22 genes and then divided into high-risk and low-risk groups according to the median. The ALT-related risk score is an independent predictor of LGG (HR and 95% CI in CGGA cohort: 5.70 (3.79, 8.58); in TCGA cohort: 1.96 (1.09, 3.54)). ROC analysis indicated that the model contained ALT-related risk score was superior to conventional clinical features (AUC: 0.818 vs 0.729) in CGGA cohorts. The results in the TCGA cohort also shown a powerful ability of ALT-related risk score (AUC: 0.766 vs 0.691). The predicted probability and actual probability of the nomogram are consistent. Enrichment analysis demonstrated that the ALT mechanism was involved in the cell cycle, DNA repair, immune processes, and others.
ALT-related risk score based on the 22-gene is an important factor in predicting the prognosis of LGG patients.
端粒替代延长（ALT）机制在低级别胶质瘤（LGG）中被激活，但ALT机制的作用尚未得到充分讨论。主要目的是证明ALT机制在LGG患者预后评估中的意义。
分别从中国胶质瘤基因组图谱（CGGA）和癌症基因组图谱（TCGA）队列中收集LGG患者的基因表达和临床数据。从TelNet数据库中获取ALT相关基因，并通过LASSO回归选择与ALT相关的潜在预后基因，以计算ALT相关风险评分。进行多变量Cox回归分析以构建预后特征，并使用列线图来表示该特征。通过富集分析探索ALT相关风险评分的可能途径。
基于22个基因的LASSO回归系数计算出ALT相关风险评分，然后根据中位数分为高风险和低风险组。ALT相关风险评分是LGG的独立预测因子（CGGA队列中的HR和95%CI：5.70（3.79，8.58）；TCGA队列中的HR和95%CI：1.96（1.09，3.54））。ROC分析表明，在CGGA队列中，包含ALT相关风险评分的模型优于传统临床特征（AUC：0.818对0.729）。TCGA队列中的结果也显示出ALT相关风险评分的强大预测能力（AUC：0.766对0.691）。列线图的预测概率与实际概率一致。富集分析表明，ALT机制参与细胞周期、DNA修复、免疫过程等。
基于22个基因的ALT相关风险评分是预测LGG患者预后的重要因素。
