Klin Onkol 2022; 35(Suppl 2): 107-109. DOI: 10.48095/ccko20221S107.
Background: Meningiomas are the most common primary intracranial tumors divided into three pathological grades. Most of these tumors are characterized by a slow progression and a relatively favorable prognosis. Meningiomas classified by the World Health Organization as grade 2 (atypical) and grade 3 (anaplastic) show signs of invasiveness and are often associated with a higher probability of relapse. The management of more aggressive, higher-grade tumors remains a challenge. Atypical meningiomas (AM) recur in as many as 40% of patients within 5 years following surgery, despite total resection. Adjuvant radiotherapy (ART) is the standard treatment for anaplastic meningiomas, but in AM, there is currently no consensus, and it is challenging to identify radioresistant patients in clinical practice. It is, therefore, necessary to find prognostic and predictive biomarkers that would be able to differentiate patients. MicroRNAs (miRNAs) represent a very promising group of biomarkers. These short non-coding RNAs regulate most biological processes, including cell proliferation, differentiation, and apoptosis. Previous research has described a significant dysregulation of miRNAs in meningioma tumor tissues and demonstrated their involvement in tumor radioresistance. The study aims to identify tissue miRNAs capable of predicting AM patients who might benefit from ART. Furthermore, we hypothesize that there are miRNAs with the ability to predict recurrence risk independently of meningioma grade and Simpson’s grade of resection. Material and methods: The study includes 80 meningioma patients in the exploratory phase and 400 patients in the validation phase. A global miRNA expression profile was generated using TaqMan Array Human MicroRNA Cards. Results and conclusion: The study identified significantly dysregulated miRNAs in AM patients who did and did not relapse (P < 0.01). The data also demonstrated a dysregulated miRNA expression profile in ART-indicated AM patients with and without recurrence (P < 0.01). The results may help to predict the prognosis of surgically intervened patients more accurately and determine which patients may benefit from ART.
