Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning

Hoda Safari Yazd, Sina Feizbakhsh Bazargani, Garrett Fitzpatrick, Richard A. Yost, Jesse Kresak, Timothy J. Garrett

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

Original languageEnglish (US)
Pages (from-to)2187-2198
Number of pages12
JournalJournal of the American Society for Mass Spectrometry
Volume34
Issue number10
DOIs
StatePublished - Oct 4 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 American Society for Mass Spectrometry. Published by American Chemical Society. All rights reserved.

Keywords

  • Brain Tumor
  • Lipidomics
  • Machine Learning
  • Mass Spectrometry
  • Meningioma
  • Metabolomics

PubMed: MeSH publication types

  • Journal Article

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