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Updated: Jun 16, 2026
On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
Published on: May 31, 2020
Glioma region-specific metabolites associated with patient seizures
Kevin Tran1, Dylan A Goodin1, Tyler Stephenson2
1Department of Bioengineering, University of Louisville, Louisville, KY, USA.
Purpose:
Seizures are often the first and only clinical symptom of glioma presence, especially in low-grade tumors. Anti-epilepsy drugs have shown mixed success due to tumor-induced biochemical changes that uniquely alter brain tissue functionality and activity. Recently, it was shown that glioma metabolism is differentially dysregulated between core (contrast enhancing) and edge (T2/FLAIR hyperintense) tissue. To provide insight into the biochemical alterations underlying seizures, this study hypothesized that glioma region-specific metabolites could be associated with seizure occurrence.
Methods:
Log-transformed metabolite signal intensities were obtained by 2D liquid chromatography mass spectrometry/mass spectrometry of patient core and edge glioma samples (n = 39 patients). To identify metabolites associated with seizure occurrence, metabolites were used as features for classification of seizure via machine learning, including forward feature selection and rigorous model validation using repeated cross-validation and a test (holdout) set.
Results:
Pre-op seizure occurrence could be accurately classified with five core metabolites (AUROCTEST=0.892, PRAUCTEST=0.835, maximum F1TEST=0.800). Edge metabolites provided a weaker signal, with seizure classification using two metabolites (AUROCTEST=0.750, PRAUCTEST=0.733, maximum F1TEST=0.800). Seizures could also be accurately classified using one edge and four core metabolites (AUROCTEST=0.871, PRAUCTEST=0.889, maximum F1TEST=0.800). Furthermore, seizure occurrence was associated with differential relative abundance of various core and edge metabolites, linked to dysregulation of metabolic pathways identified from KEGG database.
Conclusion:
Tumor region-specific metabolites in glioma patients were associated with seizure occurrence, identifying metabolomic dysregulation potentially reflecting biochemical alterations. This study represents a first step towards identifying metabolites linked to seizure occurrence, with the ultimate goal of improving glioma patient outcomes.
Insights
Glioma seizures are linked to specific metabolites in tumor core and edge tissues. Identifying these metabolites can help predict seizures and improve patient outcomes.
Area of Science:
- Neuro-oncology
- Metabolomics
- Machine Learning
Background:
- Seizures are a common initial symptom of gliomas, particularly low-grade types.
- Standard anti-epilepsy drugs show variable efficacy due to tumor-specific biochemical changes affecting brain function.
- Glioma metabolism differs between core and edge tissues, influencing brain activity.
Purpose of the Study:
- To investigate the association between glioma region-specific metabolites and seizure occurrence.
- To identify specific metabolites that can serve as biomarkers for seizure prediction in glioma patients.
Main Methods:
- Analyzed metabolite signal intensities from core and edge glioma samples using liquid chromatography-mass spectrometry/mass spectrometry.
- Employed machine learning, including feature selection and cross-validation, to classify seizure occurrence based on metabolite data.
- Utilized KEGG database to identify metabolic pathways associated with differential metabolite abundance.
Main Results:
- Accurate classification of pre-operative seizure occurrence was achieved using five core metabolites (AUROC=0.892).
- Edge metabolites showed a weaker but significant association with seizures (AUROC=0.750).
- A combination of core and edge metabolites further improved seizure classification accuracy (AUROC=0.871).
Conclusions:
- Tumor region-specific metabolites are significantly associated with seizure occurrence in glioma patients.
- Metabolomic dysregulation in gliomas reflects underlying biochemical alterations contributing to seizures.
- This research is a foundational step toward developing targeted metabolomic biomarkers for improved glioma patient management and outcomes.
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