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Updated: Nov 26, 2025

Characterize Disease-related Mutants of RAF Family Kinases by Using a Set of Practical and Feasible Methods
Published on: July 17, 2019
Prognostic Role of BRAF Mutation in Low-Grade Gliomas: Meta-analysis
Zhang Kai1, Liu Dingyang1, Yang Zhuanyi1
1Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Objective:
Newly emerged molecular markers in gliomas provide prognostic values beyond the capabilities of histologic classification. BRAF mutation, especially BRAF V600E, is common in a subset of gliomas and may represent a potential prognostic marker. The aim of our study is to investigate the potential use of BRAF mutations on the prognosis of low-grade glioma patients.
Methods:
Four electronic databases were searched for potential articles including PubMed, Web of Science, Embase, and Cochrane. Data of hazard ratio (HR) for overall survival and progression-free survival were directly obtained from original papers or indirectly estimated from the Kaplan-Meier curve. A random effect model weighted by inverse variance method was used to calculate the pooled HR. From 483 articles, we finally included 8 articles with 698 glioma patients for the final analysis. The overall estimates showed that BRAF V600E was associated with an improved overall survival in glioma patients (HR = 0.64; 95% confidence interval = 0.45-0.92).
Results:
Results for progression-free survival, however, were not statistically significant (HR = 0.97; 95% confidence interval = 0.7-1.36). In subgroup analyses, BRAF V600E showed its effect in improving survival in pediatric patients but did not have prognostic value in adult. Our meta-analysis provides evidence that BRAF mutation has a favorable prognostic impact in low-grade gliomas, and its prognostic value might be dependent on patient age.
Conclusions:
This mutation can be used as a prognostic factor in low-grade glioma, but additional studies are required to clarify its prognostic value taking into account other confounding factors.
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