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代谢组学和转录组学数据的综合分析揭示了胶质瘤患者的代谢变化。

Integrative Analysis of Metabolomic and Transcriptomic Data Reveals Metabolic Alterations in Glioma Patients.

机构信息

Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, P. R. China.

Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, Henan Province 450052, P. R. China.

出版信息

J Proteome Res. 2021 May 7;20(5):2206-2215. doi: 10.1021/acs.jproteome.0c00697. Epub 2021 Mar 25.

DOI:10.1021/acs.jproteome.0c00697
PMID:33764076
Abstract

Glioma is a malignant brain tumor. There is growing evidence that its progression involves altered metabolism. This study's objective was to understand how those metabolic perturbations were manifested in plasma and urine. Metabolic signatures in blood and urine were characterized by liquid chromatography-tandem mass spectrometry. The results were linked to gene expression using data from the Gene Expression Omnibus database. Genes and pathways associated with the disease were thus identified. Forty metabolites were identified, which were differentially expressed in the plasma of glioma patients, and 61 were identified in their urine. Twenty-two metabolites and five disturbed pathways were found both in plasma and urine. Twelve metabolites in plasma and three in urine exhibited good diagnostic potential for glioma. Transcriptomic analyses revealed specific changes in the expression of 1437 genes associated with glioma. Seventeen differentially expressed genes were found to be correlated with four of the metabolites. Enrichment analysis indicated that dysregulation of glutamatergic synapse pathway might affect the pathology of glioma. Integration of metabolomics with transcriptomics can provide both a broad picture of novel cancer signatures and preliminary information about the molecular perturbations underlying glioma. These results may suggest promising targets for developing effective therapies.

摘要

脑胶质瘤是一种恶性脑肿瘤。越来越多的证据表明,其进展涉及代谢改变。本研究的目的是了解这些代谢改变在血浆和尿液中的表现形式。采用液相色谱-串联质谱法对血液和尿液中的代谢特征进行了分析。利用基因表达综合数据库中的数据将结果与基因表达联系起来。从而确定了与疾病相关的基因和途径。鉴定出 40 种在脑胶质瘤患者血浆中差异表达的代谢物,在尿液中鉴定出 61 种。在血浆和尿液中均发现 22 种代谢物和 5 种失调途径。在血浆中有 12 种代谢物和在尿液中有 3 种代谢物对脑胶质瘤具有良好的诊断潜力。转录组分析显示与脑胶质瘤相关的 1437 个基因表达有特定变化。发现 17 个差异表达基因与四种代谢物相关。富集分析表明,谷氨酸能突触通路的失调可能影响脑胶质瘤的病理。代谢组学与转录组学的整合可以提供新颖的癌症特征的广泛图景,以及脑胶质瘤潜在分子改变的初步信息。这些结果可能提示有希望的靶点,以开发有效的治疗方法。

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引用本文的文献

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Towards non-invasive diagnosis of glioblastoma: identifying metabolic biomarkers in liquid biopsies using a ROC-based approach.迈向胶质母细胞瘤的非侵入性诊断:使用基于ROC的方法在液体活检中识别代谢生物标志物。
Discov Oncol. 2025 Aug 2;16(1):1456. doi: 10.1007/s12672-025-03310-8.
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Integrated Metabolomics and Transcriptomics Analyses Reveal Metabolic Changes in Primary Angiitis of the Central Nervous System.
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J Inflamm Res. 2025 Feb 25;18:2767-2780. doi: 10.2147/JIR.S503058. eCollection 2025.
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A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors.比较不同的机器学习技术,以选择一组允许早期检测脑肿瘤的代谢物。
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