使用非侵入性液体活检监测肿瘤演变和预测生存是胶质母细胞瘤患者未满足的需求。蛋白质组学和代谢组学血液分析时代可能会在这方面有所帮助。进行了病例对照研究。患者被纳入 GLIOPLAK 试验（ClinicalTrials.gov 标识符：NCT02617745），这是一项于 2015 年 11 月至 2022 年 12 月期间进行的前瞻性双中心研究。患者仅接受活检，并接受放疗和替莫唑胺治疗。在三个不同的时间点收集血样：同步放化疗之前和之后，以及肿瘤进展时。使用代谢组学和蛋白质组学分析患者和对照的血浆样本，生成 371 个组学特征。进行描述性、差异性和预测性分析，以评估血浆组学特征水平与患者结果之间的关系。还分析了诊断性能和纵向变化。该研究包括 67 名受试者（34 名患者和 33 名对照者）。观察到患者和对照之间代谢物和蛋白质的表达存在显着差异。使用组学特征的预测模型在区分患者和对照方面表现出很高的准确性。纵向分析揭示了一些组学特征的时间变化，包括 CD22、CXCL13、EGF、IL6、GZMH、KLK4 和 TNFRSP6B。生存分析确定了 77 个与 OS 显着相关的组学特征，其中 ERBB2 和 ITGAV 在所有时间点都与 OS 一致相关。通路分析揭示了参与胶质母细胞瘤进展的动态致癌通路。这项研究深入探讨了血浆组学特征作为胶质母细胞瘤诊断、进展和总体生存的生物标志物的潜力。现在应该在专门的前瞻性试验中探讨临床意义。© 2024。作者。

Monitoring tumor evolution and predicting survival using non-invasive liquid biopsy is an unmet need for glioblastoma patients. The era of proteomics and metabolomics blood analyzes, may help in this context. A case-control study was conducted. Patients were included in the GLIOPLAK trial (ClinicalTrials.gov Identifier: NCT02617745), a prospective bicentric study conducted between November 2015 and December 2022. Patients underwent biopsy alone and received radiotherapy and temozolomide. Blood samples were collected at three different time points: before and after concomitant radiochemotherapy, and at the time of tumor progression. Plasma samples from patients and controls were analyzed using metabolomics and proteomics, generating 371 omics features. Descriptive, differential, and predictive analyses were performed to assess the relationship between plasma omics feature levels and patient outcome. Diagnostic performance and longitudinal variations were also analyzed. The study included 67 subjects (34 patients and 33 controls). A significant differential expression of metabolites and proteins between patients and controls was observed. Predictive models using omics features showed high accuracy in distinguishing patients from controls. Longitudinal analysis revealed temporal variations in a few omics features including CD22, CXCL13, EGF, IL6, GZMH, KLK4, and TNFRSP6B. Survival analysis identified 77 omics features significantly associated with OS, with ERBB2 and ITGAV consistently linked to OS at all timepoints. Pathway analysis revealed dynamic oncogenic pathways involved in glioblastoma progression. This study provides insights into the potential of plasma omics features as biomarkers for glioblastoma diagnosis, progression and overall survival. Clinical implication should now be explored in dedicated prospective trials.© 2024. The Author(s).