Classification of brain tumor extracts by high resolution 1H MRS using partial least squares discriminant analysis

A. V. Faria, F. C. Macedo, A. J. Marsaioli, M. M.C. Ferreira, F. Cendes

Research output: Contribution to journalArticlepeer-review

Abstract

High resolution proton nuclear magnetic resonance spectroscopy (1H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T 1H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out crossvalidation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Original languageEnglish (US)
Pages (from-to)149-164
Number of pages16
JournalBrazilian Journal of Medical and Biological Research
Volume44
Issue number2
DOIs
StatePublished - 2011

Keywords

  • Brain
  • Magnetic resonance spectroscopy
  • Metabolism
  • Spectroscopy
  • Tumor

ASJC Scopus subject areas

  • Biophysics
  • Neuroscience(all)
  • Biochemistry
  • Physiology
  • Immunology
  • Pharmacology, Toxicology and Pharmaceutics(all)
  • Cell Biology

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