胶质母细胞瘤（GBM）的进展受到肿瘤内异质性的影响。新兴证据强调了细胞核外循环DNA（eccDNA）在加速肿瘤异质性，尤其是GBM中的关键作用。然而，GBM的eccDNA景观尚未阐明。在本研究中，我们首先使用来自相同样本的圆形和RNA测序数据，并鉴定了GBM和相邻组织中的eccDNA概况。基于eccDNA承载的基因，在mRNA水平上展示一致的上调和下调趋势，我们建立了一个三阶段模型。机器学习算法的组合和堆叠集成模型被用来提高三阶段模型的性能和鲁棒性。在第一阶段，总共构建了113种机器学习算法的组合，并在多个外部队列中进行验证，以准确区分低级别胶质瘤（LGG）和GBM的患者。在所有队列中具有最高曲线下面积（AUC）的模型被选为可解释性分析。在第二阶段，总共建立了101种机器学习算法的组合，用于预测胶质瘤患者的预后。该预测模型在多个胶质瘤队列中表现良好。复发性GBM通常与侵袭性和难治性疾病有关。因此，准确预测复发风险对于制定个体化治疗策略、监测患者状况和改善临床管理至关重要。在第三阶段中，使用包括原发和复发GBM样本的大规模GBM队列来拟合GBM复发预测模型。多种机器学习和堆叠集成模型被拟合，以选择性能最佳的模型。最后，我们开发了一个网络工具，以促进三阶段模型的临床应用。版权所有 © 2023. Elsevier B.V. 发布。

Glioblastoma (GBM) progression is influenced by intratumoral heterogeneity. Emerging evidence has emphasized the pivotal role of extrachromosomal circular DNA (eccDNA) in accelerating tumor heterogeneity, particularly in GBM. However, the eccDNA landscape of GBM has not yet been elucidated. In this study, we first identified the eccDNA profiles in GBM and adjacent tissues using circle- and RNA-sequencing data from the same samples. A three-stage model was established based on eccDNA-carried genes that exhibited consistent upregulation and downregulation trends at the mRNA level. Combinations of machine learning algorithms and stacked ensemble models were used to improve the performance and robustness of the three-stage model. In stage 1, a total of 113 combinations of machine learning algorithms were constructed and validated in multiple external cohorts to accurately distinguish between low-grade glioma (LGG) and GBM in patients with glioma. The model with the highest area under the curve (AUC) across all cohorts was selected for interpretability analysis. In stage 2, a total of 101 combinations of machine learning algorithms were established and validated for prognostic prediction in patients with glioma. This prognostic model performed well in multiple glioma cohorts. Recurrent GBM is invariably associated with aggressive and refractory disease. Therefore, accurate prediction of recurrence risk is crucial for developing individualized treatment strategies, monitoring patient status, and improving clinical management. In stage 3, a large-scale GBM cohort (including primary and recurrent GBM samples) was used to fit the GBM recurrence prediction model. Multiple machine learning and stacked ensemble models were fitted to select the model with the best performance. Finally, a web tool was developed to facilitate the clinical application of the three-stage model.Copyright © 2023. Published by Elsevier B.V.