Background/Objectives: The recurrence of glioblastoma is an inevitable event in this disease’s course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy. Methods: Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography–time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a samplet-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase. Results: Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery. Conclusions: We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma.
背景/目的：胶质母细胞瘤的复发是该疾病病程中不可避免的事件。本研究旨在识别接受手术和放射治疗的复发性胶质母细胞瘤患者的代谢组学特征。方法：前瞻性收集六例复发性IDH野生型胶质母细胞瘤患者的血液样本，这些患者在确诊时接受首次手术，复发时接受二次手术。采用非靶向气相色谱-飞行时间质谱法分析样本以测定代谢物丰度。数据分析技术包括单变量分析、相关性分析和样本t检验。在预测建模方面，应用多项式逻辑回归、梯度提升和随机森林等机器学习算法，以预测样本在正确治疗阶段的分类。结果：将首次手术后和复发手术后的样本与术前样本进行比较，发现山梨醇和甘露醇显著减少；尿素、氧脯氨酸、葡萄糖和丙氨酸显著增加。放化疗后，赤藓糖醇和6-脱氧葡萄糖醇两种代谢物减少，其中6-脱氧葡萄糖醇的减少具有显著性（截断值为三倍）；而2,4-二氟甲苯和9-肉豆蔻酸酯在放疗后增加，变化倍数截断值为三倍。梯度提升机器学习模型在预测接受复发手术的胶质母细胞瘤患者肿瘤状态方面表现出高性能。结论：我们基于血浆代谢组学特征开发了一种肿瘤阶段的机器学习预测模型。研究表明，将代谢组学与机器学习相结合，有望成为胶质母细胞瘤患者肿瘤进展风险分层的新工具。
肿瘤(癌症)患者之家