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血清代谢谱分析有助于脑干胶质瘤的诊断、预后评估及监测。

Serum metabolic profiling enables diagnosis, prognosis, and monitoring for brainstem gliomas.

作者信息

Li Keke, Wang Ruimin, Gu Zhengying, Weng Wenyun, Liu Wanshan, Huang Yida, Wu Jiao, Zhang Ziyue, Yang Shouzhi, Su Jun, Tang Yujie, Qian Kun, Jiang Mawei, Huang Lin, Wan Jingjing

机构信息

Department of Oncology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, PR China.

State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, PR China.

出版信息

Nat Commun. 2025 Jul 3;16(1):6108. doi: 10.1038/s41467-025-61163-9.

Abstract

Brainstem gliomas (BSG) is a highly malignant central nervous system childhood tumors with 5-year survival rate <10%. Metabolism during radiotherapy is a dynamic and precisely programmed process, improving clinical outcomes and guiding therapy decisions of BSG. Here we construct diagnostic and prognostic assays of BSG via circulating metabolites based on both cross-sectional study and longitudinal cohort study with 106 BSG patients. We employ nanoparticle enhanced laser desorption/ionization mass spectrometry to characterize static and dynamic snapshots of metabolites during BSG radiotherapy. We show that this serological tool reaches the area under the curve of 0.933 for BSG diagnosis in an independent blind test and predicts risk of patients with significant differences (p < 0.05) in prognostic outcomes. We further identify eight distinct temporal patterns of metabolite regulation associated with radiotherapy responses and tracked the metabolic trajectory via dynamic metabolic snapshots throughout radiotherapy process. If further validated, this framework could be extended to derive comprehensive metabolic pictures for cancers including but not limited to BSG.

摘要

脑干胶质瘤(BSG)是一种高度恶性的儿童中枢神经系统肿瘤,5年生存率<10%。放疗期间的代谢是一个动态且精确编程的过程,可改善BSG的临床结果并指导治疗决策。在此,我们基于对106例BSG患者的横断面研究和纵向队列研究,通过循环代谢物构建了BSG的诊断和预后分析方法。我们采用纳米颗粒增强激光解吸/电离质谱法来表征BSG放疗期间代谢物的静态和动态情况。我们表明,在独立盲测中,这种血清学工具用于BSG诊断时曲线下面积达到0.933,并能预测预后结果有显著差异(p<0.05)的患者风险。我们进一步确定了与放疗反应相关的八种不同的代谢物调节时间模式,并通过整个放疗过程中的动态代谢情况追踪代谢轨迹。如果得到进一步验证,该框架可扩展用于推导包括但不限于BSG在内的癌症的全面代谢图谱。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bba8/12223127/45d88f464c09/41467_2025_61163_Fig1_HTML.jpg

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