Meta-gene markers predict meningioma recurrence with high accuracy

Background Meningiomas, the most common adult brain tumors, recur in up to half of cases[1–3]. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors, which constitute more than 90% of all cases. Moreover, attempts to find molecular markers, particularly for predicting recurrence of intermediate tumors have been impeded by low[4] or heterogenous[5–8] genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence. Methods We apply gene co-expression networks to a cohort of 252 adult patients from the publicly-available genetic repository Gene Expression Omnibus. Resultant gene clusters (“modules”) were represented by the first principle component of their expression levels, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using an independent merged cohort of 108 patients using the same modules. We used the bioinformatics database DAVID[9] to examine the gene ontology associations of each module. Results Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related functions (AUC of 0.81 ± 0.09 in external validation). Further, the module maintained its accuracy with removal of up to 20 of it’s most individually predictive genes, suggesting robustness against noise at the single gene level. Conclusions With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management.