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Poster Display session 3

3290 - Identification of meningioma patients in high risk of tumor recurrence using microRNA profiling


30 Sep 2019


Poster Display session 3


Translational Research

Tumour Site


Josef Srovnal


Annals of Oncology (2019) 30 (suppl_5): v760-v796. 10.1093/annonc/mdz268


J. Srovnal1, H. Slavik1, V. Balik1, M. Vaverka2, L. Hrabalek2, K. Staffova1, J. Vrbkova1, M. Hajduch1

Author affiliations

  • 1 Faculty Of Medicine, Palacky University, 77900 - Olomouc/CZ
  • 2 Dpt. Of Neurosurgery, University Hospital Olomouc, Olomouc/CZ


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Abstract 3290


Meningioma growth rates are highly variable, even within benign subgroups, causing some cases to remain stable while others grow rapidly despite radiotherapy. Biomarkers that differentiate meningiomas by aggression and enable prediction of their biological behavior would therefore be clinically beneficial.


Microarrays were used to identify microRNA (miRNA) expression in primary recurrent, non-recurrent and secondary meningiomas of all grades. miRNAs found to be deregulated in the microarray experiments were validated by quantitative real-time PCR using samples from a cohort of 191 patients (median age 56). Statistical analysis of the resulting dataset revealed miRNA predictors of meningioma recurrence.


miRNAs exhibiting differential expression (independently of histological grade) in primary recurrent, non-recurrent and secondary meningiomas were identified. The most effective predictive model included miR-331-3p, extent of tumor resection and its localization as predictive markers. The model with a recurrence probability cut-off of 28% and small number of the input data (7) had a high area under the curve (AUC) (0.829), sensitivity (75%), specificity (75%), and acceptable leave-one-out cross-validation (LOOCV) test error (23.2%). miR-18a-5p, miR-130b-3p, miR-146a-5p, miR-1271-5p, age at diagnosis, gender and histological grade showed to be supportive but not predictive factors in the tested models.


This model is a novel predictor of meningioma recurrence that could facilitate optimal postoperative management. Moreover, combining this model with information on the molecular processes underpinning recurrence could enable the identification of distinct meningioma subtypes and targeted therapies.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.


Ministry of Health of the Czech Republic (15-29021A); Palacky University Olomouc (LF 2019_003); Ministry of Education, Youth and Sports of the Czech Republic (LO1304, LM2015091); European Regional Development Fund (ENOCH CZ.02.1.01/0.0/0.0/16_019/0000868).


All authors have declared no conflicts of interest.

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