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Meningioma, the most prevalent primary intracranial tumor, presents significant clinical challenges due to unclear molecular mechanisms underlying its progression from low-grade (LG) to high-grade (HG) and lack of grade-specific biomarkers. Here, we employed high-resolution mass spectrometry-based integrated tissue metabolomics and lipidomics on ∼45 samples. Our findings highlight dysregulated pathways like nucleotide, choline, sphingolipid, and glycerophospholipid metabolism, with purine metabolism-related metabolites notably upregulated in tumor samples. We further performed targeted verification of a subset of purine metabolism-related metabolites using targeted metabolomics. Further, serum lipidomics profiling was performed on ∼75 samples to identify a set of candidate markers. A set of lipid markers was identified as dysregulated in both tissue and serum samples, showing the effects of tumor-associated metabolic changes. The major dysregulated lipid classes were phosphatidylcholines, phosphatidylethanolamines accounting for around 70%, with variations in saturation and carbon chain length. Additionally, machine-learning-based feature selection was used to identify a panel of lipid markers capable of distinguishing HG from LG samples. This analysis identified 18 top classifier lipids, two of which were also dysregulated in tissue samples. Longitudinal analysis of these lipids further emphasized their role in tumor progression. This exploratory study lays the foundation for further validation of candidate markers in a larger cohort of samples.
Halder et al. (Wed,) studied this question.