Meningiomas represent one of the most common types of primary intracranial tumours. However, the specific molecular mechanisms underlying their pathogenesis remain uncertain. Loss of chromosomes 22q, 1p, and 14q have been implicated in most meningiomas. Inactivation of the NF2 gene at 22q12 has been identified as an early event in their pathogenesis, whereas abnormalities of chromosome 14 have been reported in higher-grade as well as recurrent tumours. It has long been supposed that chromosome 14q32 contains a tumour suppressor gene. However, the identity of the potential 14q32 tumour suppressor remained elusive until the Maternally Expressed Gene 3 (MEG3) was recently suggested as an ideal candidate. MEG3 is an imprinted gene located at 14q32 that encodes a non-coding RNA (ncRNA). In meningiomas, loss of MEG3 expression, its genomic DNA deletion and degree of promoter methylation have been found to be associated with aggressive tumour growth. These findings indicate that MEG3 may have a significant role as a novel long noncoding RNA tumour suppressor in meningiomas.

Glioblastoma multiforme (GBM) represents an extremely chemoresistant tumour type. Here, authors analysed the immunophenotype of GBM tumours by flow cytometry and correlated the immunophenotypic characteristics with sensitivity to chemotherapy. The expression of selected neural and non-neural differentiation markers including A2B5, CD34, CD45, CD56, CD117, CD133, EGFR, GFAP, Her-2/neu, LIFR, nestin, NGFR, Pgp and vimentin was analysed by flow cytometry in eleven GBM (WHO gr.IV) patients. The sensitivity of tumour cells to a panel of chemotherapeutic agents was tested by the MTT assay. All tumours were positive for A2B5, CD56, nestin and vimentin. CD133, EGFR, LIFR, NGFR and Pgp were expressed only by minor tumour cell subpopulations. CD34, CD45, CD117, GFAP and Her-2/neu were constantly negative. Direct correlations were found between the immunophenotypic markers and chemosensitivity: A2B5 vs lomustine (r(2) = 0.642, P = 0.033), CD56 vs cisplatin (r(2) = 0.745, P = 0.013), %Pgp(+) vs vincristine (r(2) = 0.846, P = 0.008), and %NGFR(+) vs daunorubicine (r(2) = 0.672, P = 0.047) and topotecan (r(2) = 0.792, P = 0.011). In contrast, inverse correlations were observed between: EGFR vs paclitaxel (r(2) = -0.676, P = 0.046), CD133 vs dacarbazine (r(2) = -0.636, P = 0.048) and LIFR vs daunorubicine (r(2) = -0.878, P = 0.004). Finally, significant associations were also found among sensitivities to different chemotherapeutic agents and among different immunophenotypic markers. In conclusion, histopathologically identical GBM tumours displayed a marked immunophenotypic heterogeneity. The expression of A2B5, CD56, NGFR and Pgp appeared to be associated with chemoresistance whereas CD133, EGFR and LIFR expression was characteristic of chemosensitive tumours. We suggest that flow cytometric imunophenotypic analysis of GBM may predict chemoresponsiveness and help to identify patients who could potentially benefit from chemotherapy.

The most significant factors influencing outcome in our patients were GCS on admission, age, and associated intradural lesions.

BACKGROUND Meningioma growth rates are highly variable, even within benign subgroups, with some remaining stable, whereas others grow rapidly. OBJECTIVE To identify molecular-genetic markers for more accurate prediction of meningioma recurrence and better-targeted therapy. METHODS Microarrays identified microRNA (miRNA) expression in primary and recurrent meningiomas of all World Health Organization (WHO) grades. Those found to be deregulated were further validated by quantitative real-time polymerase chain reaction in a cohort of 172 patients. Statistical analysis of the resulting dataset revealed predictors of meningioma recurrence. RESULTS Adjusted and nonadjusted models of time to relapse identified the most significant prognosticators to be miR-15a-5p, miR-146a-5p, and miR-331-3p. The final validation phase proved the crucial significance of miR-146a-5p and miR-331-3p, and clinical factors such as type of resection (total or partial) and WHO grade in some selected models. Following stepwise selection in a multivariate model on an expanded cohort, the most predictive model was identified to be that which included lower miR-331-3p expression (hazard ratio [HR] 1.44; P < .001) and partial tumor resection (HR 3.90; P < .001). Moreover, in the subgroup of total resections, both miRNAs remained prognosticators in univariate models adjusted to the clinical factors. CONCLUSION The proposed models might enable more accurate prediction of time to meningioma recurrence and thus determine optimal postoperative management. Moreover, combining this model with current knowledge of molecular processes underpinning recurrence could permit the identification of distinct meningioma subtypes and enable better-targeted therapies.