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

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.

Triple acquisition mass spectrometry (TRAM) combining targeted and non-targeted metabolomics in a single run

“…Promising preliminary data suggests that meningioma grades can be partially differentiated based on MRM-MS2 12 C/ 13 C area ratios acquired with TRAM (Supporting Information Figure S9). These preliminary findings indicate that LC-MS-based metabolomics can not only be applied to tumor tissue samples, as already described in literature [31][32][33] , but also to CSF collected from patients. Future studies with larger cohorts and different brain tumor subtypes will further explore this potential and could find metabolite patterns for which intraoperative diagnostics by sensors can be envisaged combined with methylation profiles.…”

“…The meningioma grade could be distinguished based on metabolic pathways such as glycine/serine metabolism, choline/tryptophan, purine and pyrimidine metabolism. 31 The same study revealed an additional set of changing metabolites by unsupervised statistical analysis. Taurine, creatine, serine, choline, thiamine, phenylalanine, biotin, glutamine, and arginine were proposed as signature metabolites by hierarchical clustering.…”

“…While several promising metabolomics/lipidomics reports on tumor tissue biopsies exist, proof of principle studies in cerebrospinal fluid are lacking. Amino acids and small organic acids were discovered to be among the potential signature metabolites [31][32][33] in tumor tissue. The meningioma grade could be distinguished based on metabolic pathways such as glycine/serine metabolism, choline/tryptophan, purine and pyrimidine metabolism.…”

“…Taurine, creatine, serine, choline, thiamine, phenylalanine, biotin, glutamine, and arginine were proposed as signature metabolites by hierarchical clustering. 31 The MRM targets were selected accordingly, focusing on amino acids and organic acids found relevant in meningioma research. Finally, the target list also included Phosphocholine, Glycerophosphocholine, and N-Acetyl-aspartate, with the aim of distinguishing meningioma from glial and oligodendrogliallike brain tumors, as IDH mutations are currently debated in meningioma samples.…”