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Updated: Jul 6, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
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Microarray gene expression profiling in meningiomas and schwannomas.

V Martinez-Glez1, C Franco-Hernandez, J A Rey

  • 1Unidad de Investigación, Hospital Universitario La Paz, Madrid, Spain. vmartinezg.hulp@salud.madrid.org

Current Medicinal Chemistry
|April 9, 2008
PubMed
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Microarray gene expression profiling offers molecular insights beyond traditional cancer diagnostics. This technology aids in discovering new tumor markers and understanding the molecular pathogenesis of meningiomas and schwannomas for improved clinical practice.

Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Traditional cancer classification relies on morphology, which has limitations in capturing tumor characteristics like proliferation and treatment resistance.
  • Microarray gene expression profiling provides high-throughput molecular data, enabling deeper understanding of biological samples.

Purpose of the Study:

  • To review the contribution of microarray technology to understanding the molecular pathogenesis of meningiomas and schwannomas.
  • To explore the potential of microarrays as a tool for tumor classification and its application in clinical practice.

Main Methods:

  • Gene expression profiling using microarray technology.
  • Functional integration and pattern analysis of large-scale molecular data.
  • Review of existing literature on microarray applications in meningioma and schwannoma research.

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Published on: October 4, 2019

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Main Results:

  • Microarrays identify differentially expressed genes and gene regulation patterns.
  • This technology can uncover novel tumor markers with potential clinical impact.
  • Microarrays offer a molecular perspective that complements conventional diagnostic methods.

Conclusions:

  • Microarray technology enhances the understanding of tumor molecular pathogenesis.
  • It holds significant potential for improving cancer classification and clinical decision-making.
  • Integration of microarrays into diagnostics can lead to more effective cancer therapeutics.