Prognostic Significance of MicroRNAs in Glioma: A Systematic Review and Meta-Analysis

Yanming Zhang1, Jigang Chen2, Qiang Xue2

  • 1Second Sub-Team, Fourth Team, Undergraduate Management Team, Second Military Medical University, Shanghai, China.

Abstract

Insights

This meta-analysis identifies specific microRNAs (miRs) as valuable biomarkers for predicting glioma patient outcomes. Several miRs, including miR-15b and miR-21, are linked to poor prognosis, suggesting potential clinical applications.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • MicroRNAs (miRs) play crucial roles in cellular processes and have emerged as potential regulators of cancer development and progression.
  • The prognostic significance of various miRs in glioma, the most common primary brain tumor, remains incompletely understood due to conflicting study results.

Purpose of the Study:

  • To conduct a comprehensive meta-analysis to clarify the association between specific microRNA expression levels and the prognosis of glioma patients.
  • To identify reliable microRNA biomarkers for predicting overall survival in glioma.

Main Methods:

  • Systematic literature search of PubMed, Embase, and Cochrane Library databases for relevant studies.
  • Meta-analysis of pooled hazard ratios (HRs) and 95% confidence intervals (CIs) using a random-effects model to assess the impact of microRNA expression on glioma survival.

Main Results:

  • Fifteen microRNAs (miRs) and data from 4708 glioma patients were included in the final analysis.
  • Increased expression of miR-15b, miR-21, miR-148a, miR-196, miR-210, and miR-221 was significantly correlated with poor glioma prognosis.
  • Decreased expression of miR-106a and miR-124 was also associated with unfavorable outcomes in glioma patients.

Conclusions:

  • Specific microRNAs, including miR-15b, miR-21, miR-148a, miR-196, miR-210, miR-221, miR-106a, and miR-124, are identified as significant prognostic biomarkers for glioma.
  • These microRNAs hold potential for clinical application in predicting glioma patient outcomes and guiding treatment strategies.

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