Abstract 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.

中文翻译：

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使用 MicroRNA 分析识别具有高肿瘤复发风险的脑膜瘤患者

摘要背景脑膜瘤的生长速度变化很大，即使在良性亚组内，一些保持稳定，而另一些生长迅速。目的 确定分子遗传标记，以更准确地预测脑膜瘤复发和更好的靶向治疗。方法 微阵列确定了所有世界卫生组织 (WHO) 级别的原发性和复发性脑膜瘤中的 microRNA (miRNA) 表达。通过对 172 名患者进行的定量实时聚合酶链反应进一步验证了那些发现被解除管制的药物。结果数据集的统计分析揭示了脑膜瘤复发的预测因素。结果 调整和未调整的复发时间模型确定了最重要的预后因素是 miR-15a-5p、miR-146a-5p 和 miR-331-3p。最后的验证阶段证明了 miR-146a-5p 和 miR-331-3p 的关键意义，以及某些选定模型中切除类型（全部或部分）和 WHO 分级等临床因素。在扩展队列的多变量模型中逐步选择后，确定最具预测性的模型包括较低的 miR-331-3p 表达（风险比 [HR] 1.44；P < .001）和部分肿瘤切除（HR 3.90 ; P < .001)。此外，在全切除亚组中，两种 miRNA 在根据临床因素调整的单变量模型中仍然是预后因素。结论 所提出的模型可能能够更准确地预测脑膜瘤复发的时间，从而确定最佳的术后管理。而且，