Serum Metabolomics Profiling Coupled with Machine Learning Identifies Potential Diagnostic and Prognostic Candidate Markers in Meningioma Using Raman Spectroscopy, ATR-FTIR, and LC–MS/MS

Meningioma,
the most prevalent brain tumor, poses significant challenges
due to its unclear transition from low-grade to aggressive forms,
with limited knowledge about grade-specific markers. We have utilized
vibrational spectroscopic techniques such as ATR-FTIR and Raman spectroscopy,
alongside LC–MS/MS-based mass spectrometry to understand the
systemic cues and evaluate them for clinical practice. The acquired
Raman and ATR-FTIR spectra of 46 meningioma patients (27 low-grade
and 19 high-grade) and 8 healthy individuals revealed 98.15% and 83.33%
accuracy based on PC-LDA. The grade classification revealed an accuracy
of around 70%, implying the presence of subtypes and transition phases.
The observed alterations corresponded to lipids, nucleic acids, and
proteins. Further, the LC–MS/MS-based study identified different
derivatives of cholines, indoles, lipids, sphingosine, tryptophan,
and their respective metabolic pathways as contributors in tumorigenesis
and progression. Further, PRM-based targeted validation and feature
selection was carried out on 43 meningioma patients and 17 healthy
controls. Glycochenodeoxycholic acid, indole-3-acetic acid, trans-3-indoleacrylic
acid, glycodeoxycholic acid, 5α-dihydrotestosteroneglucornide,
and glycocholic acid segregated meningioma samples with an accuracy
of around 90% while features like indole-3-acetic acid, stercobilin,
sphingosine-1-phosphate, deoxycholic acid, and citric acid could classify
grades with around 70% accuracy. These findings suggest that further
validation across larger cohorts could enhance its usage in clinical
settings.