Meningiomas and Proteomics: Focus on New Potential Biomarkers and Molecular Pathways

Abstract

Meningiomas are one of the most common tumors affecting the central nervous system, exhibiting a great heterogeneity in grading, treatment and molecular background. This article provides an overview of the current literature regarding the molecular aspect of meningiomas. Analysis of potential biomarkers in serum, cerebrospinal fluid (CSF) and pathological tissues was reported. Applying bioinformatic methods and matching the common proteic profile, arising from different biological samples, we highlighted the role of nine proteins, particularly related to tumorigenesis and grading of meningiomas: serpin peptidase inhibitor alpha 1, ceruloplasmin, hemopexin, albumin, C3, apolipoprotein, haptoglobin, amyloid-P-component serum and alpha-1-beta-glycoprotein. These proteins and their associated pathways, including complement and coagulation cascades, plasma lipoprotein particle remodeling and lipid metabolism could be considered possible diagnostic, prognostic biomarkers, and eventually therapeutic targets. Further investigations are needed to better characterize the role of these proteins and pathways in meningiomas. The role of new therapeutic strategies are also discussed.

Keywords: Meningioma; bioinformatic analysis; protein pathways; proteomic.

Figure 1. Associated proteins in meningiomas, depicted as circular nodes, extracted after merging networks derived from in-gel proteomics with those from gel-free proteomics. Edges: Co-expression (violet), colocalization (light blue) and physical interactions (pink).

Figure 2. Network arising after gene enrichment on proteins common to both in-gel proteomics and gel-free proteomics of meningiomas. Query genes/proteins are represented by black nodes, newly found interacting partners are depicted as grey nodes.

Figure 3. Cluster arising from the enriched network reported in Figure 2. The original dataset is reduced to nine proteins highlighted in yellow.

Figure 4. ClueGO pie chart of principal Gene-Ontology (GO) functions associated to the 92 node cluster. In order to avoid redundancy, functions reported in the pie chart are those with the highest numbers of related genes. **Indicates significant association between the 92 node cluster and represented GO terms (ρV≤0.05).

Figure 5. Number of cluster genes associated with each Gene-Ontology function. Please refer to Figure 4 for color designations.