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BACKGROUND: Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS: A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS: A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS: The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.
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Mass spectrometry (MS) based proteomics have achieved a near-complete proteome coverage in humans and in several other organisms, producing a wealth of information stored in databases and bioinformatics resources. Recent implementation of selected/multiple reaction monitoring (SRM/MRM) technology in targeted proteomics introduced the possibility of quantitatively follow-up specific protein targets in a hypothesis-driven experiment. In contrast to immunoaffinity-based workflows typically used in biological and clinical research for protein quantification, SRM/MRM is characterized by high selectivity, large capacity for multiplexing (approx. 200 proteins per analysis) and rapid, cost-effective transition from assay development to deployment. The concept of SRM/MRM utilizes triple quadrupole (QqQ) mass analyzer to provide inherent reproducibility, unparalleled sensitivity and selectivity to efficiently differentiate isoforms, post-translational modifications and mutated forms of proteins. SRM-like targeted acquisitions such as parallel reaction monitoring (PRM) are pioneered on high resolution/accurate mass (HR/AM) platforms based on the quadrupole-orbitrap (Q-orbitrap) mass spectrometer. The expansion of HR/AM also caused development in data independent acquisition (DIA). This review presents a step-by-step tutorial on development of SRM/MRM protein assay intended for researchers without prior experience in proteomics. We discus practical aspects of SRM-based quantitative proteomics workflow, summarize milestones in basic biological and medical research as well as recent trends and emerging techniques.