Introduction: Meningioma is a primary tumor of the central nervous system (CNS) that accounts for approximately one-third of all CNS neoplasms. Despite the fact that these tumors are mostly benign, meningiomas exhibit complex histologic features and can be located in compromised sites, which has proven to be challenging in terms of management and predicting prognostic outcomes.

Advances in molecular characterization of meningiomas have revealed potential applications for addressing the lack of standardization in diagnostic and therapeutic choices, which could improve staging and prediction of prognosis. However, further studies are required to gain a molecular understanding of this cancer.

Methods: 24 fresh-frozen meningioma tissue samples were digested using the S-Trap protocol and measured via liquid-chromatography mass-spectrometry (LC-MS/MS) based proteomics in data independent acquisition mode (DIA). Matching patient serum was digested via a depletion-based protocol and measured in DIA as well.

Results: More than 7000 unique proteins were identified per sample on average, more than 6900 of these with a consistency of less than 30% missing values.

Principal Component Analysis was unable to differentiate between subtypes or WHO grading based on the data alone. However, Partial Least Squares Discriminant Analysis (PLS-DA) was able to separate the meningothelial and fibrous subtypes. Nevertheless, most subtypes still exhibited overlapping characteristics. Further analysis is required to determine whether the tissue heterogeneity permits the identification of a defined proteomic profile.

The objective of our future research is to compare different meningioma subtypes and correlate tissue samples with matching serum samples, which has yielded over 750 identified proteins, over 650 of which were identified with less than 30% missingness.

Conclusion: In conclusion, proteomic characterization of meningioma tissue samples holds great promise for addressing the diagnostic challenges of this diverse tumor. Despite progress, the lack of reliable biomarkers remains a significant hurdle. Our research aims to identify proteins that could be potential biomarkers using advanced proteomic techniques.