4.7 Article

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

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 20, Issue 5, Pages 2206-2215

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.0c00697

Keywords

glioma; metabolomic; transcriptomic; tumor metabolism

Funding

  1. Kang Meng Medical Research Foundation [TB204019]
  2. Bethune Medical Science Foundation [B19246AT]
  3. Tong Yi Medical Research Foundation [TYJJ181101]
  4. joint project of Health Commission of Henan Province [LHGJ20190282, LHGJ20200353]
  5. Key Scientific Research Project of Henan Institution of Higher Education [21A320057]
  6. Joint Construction Project of Henan Medical Science Project [SBGJ202002078]

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This study identified metabolic signatures in blood and urine associated with glioma, linking them to gene expression and pathways related to the disease. Specific changes in gene expression and correlation with metabolites were revealed, indicating a potential target for developing effective therapies. Integration of metabolomics with transcriptomics provided valuable insights into the molecular perturbations underlying glioma.
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.

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