Table1_ecGBMsub: an integrative stacking ensemble model framework based on eccDNA molecular profiling for improving IDH wild-type glioblastoma molecular subtype classification.XLSX

IDH wild-type glioblastoma (GBM) intrinsic subtypes have been linked to different molecular landscapes and outcomes. Accurate prediction of molecular subtypes of GBM is very important to guide clinical diagnosis and treatment. Leveraging machine learning technology to improve the subtype classification was considered a robust strategy. Several single machine learning models have been developed to predict survival or stratify patients. An ensemble learning strategy combines several basic learners to boost model performance. However, it still lacked a robust stacking ensemble learning model with high accuracy in clinical practice. Here, we developed a novel integrative stacking ensemble model framework (ecGBMsub) for improving IDH wild-type GBM molecular subtype classification. In the framework, nine single models with the best hyperparameters were fitted based on extrachromosomal circular DNA (eccDNA) molecular profiling. Then, the top five optimal single models were selected as base models. By randomly combining the five optimal base models, 26 different combinations were finally generated. Nine different meta-models with the best hyperparameters were fitted based on the prediction results of 26 different combinations, resulting in 234 different stacked ensemble models. All models in ecGBMsub were comprehensively evaluated and compared. Finally, the stacking ensemble model named “XGBoost.Enet-stacking-Enet” was chosen as the optimal model in the ecGBMsub framework. A user-friendly web tool was developed to facilitate accessibility to the XGBoost.Enet-stacking-Enet models (https://lizesheng20190820.shinyapps.io/ecGBMsub/).

IDH野生型胶质母细胞瘤（IDH wild-type glioblastoma, GBM）的内在亚型与不同的分子特征和预后密切相关。准确预测GBM分子亚型对于指导临床诊疗具有重要意义。利用机器学习技术优化亚型分类被认为是一种稳健的策略。此前已有多项单一机器学习模型被开发用于预测患者生存或进行患者分层。集成学习策略通过整合多个基础学习器以提升模型性能，但目前临床实践中仍缺乏高精度且稳健的堆叠集成学习模型。本研究构建了一种全新的整合式堆叠集成模型框架（ecGBMsub），用于优化IDH野生型GBM的分子亚型分类。该框架首先基于染色体外环状DNA（extrachromosomal circular DNA, eccDNA）分子谱拟合了九组最优超参数的单一模型；随后从中选取表现最优的前五组作为基础模型。通过随机组合这五组最优基础模型，最终生成26种不同的模型组合。基于这26种组合的预测结果，进一步拟合了九组最优超参数的元模型，由此得到234种不同的堆叠集成模型。对ecGBMsub框架内的所有模型进行了全面评估与对比，最终选定名为“XGBoost.Enet-stacking-Enet”的堆叠集成模型作为ecGBMsub框架中的最优模型。此外，本研究还开发了一款易用的网页工具，以方便用户使用该XGBoost.Enet-stacking-Enet模型（https://lizesheng20190820.shinyapps.io/ecGBMsub/）。