Gene Expression Signatures Identify Biologically Homogenous Subgroups of Grade 2 Meningiomas

Abstract

Introduction: Meningiomas are the most common brain tumor, with prevalence of approximately 3%. Histological grading has a major role in determining treatment choice and predicting outcome. While indolent grade 1 and aggressive grade 3 meningiomas exhibit relatively homogeneous clinical behavior, grade 2 meningiomas are far more heterogeneous, making outcome prediction challenging. We hypothesized two subgroups of grade 2 meningiomas which biologically resemble either World Health Organization (WHO) grade 1 or WHO grade 3. Our aim was to establish gene expression signatures that separate grade 2 meningiomas into two homogeneous subgroups: a more indolent subtype genetically resembling grade 1 and a more aggressive subtype resembling grade 3. Methods: We carried out an observational meta-analysis on 212 meningiomas from six distinct studies retrieved from the open-access platform Gene Expression Omnibus. Microarray data was analyzed with systems-level gene co-expression network analysis. Fuzzy C-means clustering was employed to reclassify 34 of the 46 grade 2 meningiomas (74%) into a benign "grade 1-like" (13/46), and malignant "grade 3like" (21/46) subgroup based on transcriptomic profiles. We verified shared biology between matching subgroups based on meta-gene expression and recurrence rates. These results were validated further using an independent RNA-seq dataset with 160 meningiomas, with similar results. Results: Recurrence rates of "grade 1-like" and "grade 3-like" tumors were 0 and 75%, respectively, statistically similar to recurrence rates of grade 1 (17%) and 3 (85%). We also found overlapping biological processes of new subgroups with their adjacent grades 1 and 3. Conclusion: These results underpin molecular signatures as complements to histological grading systems. They may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials.

The integrated multiomic diagnosis of sporadic meningiomas: a review of its clinical implications

“…Gene co-expression analysis is also being used to predict tumor behavior using meta-gene markers. One recent co-expression module identified, E2F4/FOXM1, predicts increased meningioma aggressiveness [55], correlating with previous identification of FOXM1 and E2F2 expression networks activated in atypical meningiomas [20]. Further insight into genetic alterations and gene co-expression networks will improve our ability to predict tumor behavior.…”

m6A Regulator Expression Segregates Meningiomas Into Biologically Distinct Subtypes

“…Zador et al. found that WHO grade II meningiomas could be further segregated into two distinct subgroups (a benign “grade I-like” and a malignant “grade III-like”) with different tumor recurrence rates (0 and 75%, respectively) ( 44 ). Williams et al.…”

MicroRNAs miR-16 and miR-519 control meningioma cell proliferation via overlapping transcriptomic programs shared with the RNA-binding protein HuR

“…However, it is only recently that meningioma genomic and epigenomic landscapes were described with enough accuracy to be helpful in precision medicine ( 2 , 4 , 5 ). By paving the way toward refined and clinically relevant classification systems ( 6 – 8 ), by fueling biomarker and drug target discoveries ( 9 – 12 ), these omic studies opened new areas of exploration to decipher the molecular characteristics of various meningioma subgroups ( 13 – 15 ).…”