胶质母细胞瘤中的代谢重塑：一项纵向多词研究
Metabolic remodeling in glioblastoma: a longitudinal multi-omics study
影响因子:5.70000
分区:医学1区 Top / 神经科学1区
发表日期:2024 Oct 12
作者:
Maxime Fontanilles, Jean-David Heisbourg, Arthur Daban, Frederic Di Fiore, Louis-Ferdinand Pépin, Florent Marguet, Olivier Langlois, Cristina Alexandru, Isabelle Tennevet, Franklin Ducatez, Carine Pilon, Thomas Plichet, Déborah Mokbel, Céline Lesueur, Soumeya Bekri, Abdellah Tebani
摘要
监测肿瘤的演化并使用非侵入性液体活检预测生存是对胶质母细胞瘤患者的需求。蛋白质组学和代谢组学血液分析的时代可能在这种情况下有助于。进行了病例对照研究。患者被包括在Glioplak试验中（ClinicalTrials.gov识别剂：NCT02617745），这是一项前瞻性双分化研究，于2015年11月至2022年12月之间进行。仅接受活检并接受了放射治疗和替莫唑胺。在三个不同的时间点收集血液样本：伴随放射化学疗法之前和之后，在肿瘤进展时。使用代谢组学和蛋白质组学分析了来自患者和对照组的血浆样品，产生了371个OMICS特征。进行了描述性，差异和预测分析，以评估血浆OMICS特征水平与患者结局之间的关系。还分析了诊断性能和纵向变化。该研究包括67名受试者（34例患者和33例对照）。观察到了患者和对照组之间代谢物和蛋白质的显着差异表达。使用OMICS特征的预测模型在区分患者和对照组方面表现出很高的精度。纵向分析揭示了一些OMICS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OMICS特征，而ERBB2和ITGAV在所有时间点都始终链接到OS。途径分析揭示了与胶质母细胞瘤进展有关的动态致癌途径。这项研究提供了有关等离子体磁素特征作为胶质母细胞瘤诊断，进展和整体生存的生物标志物的潜力的见解。现在，应在专门的前瞻性试验中探索临床意义。
Abstract
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.