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Updated: Jan 7, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Comprehensive Differential Gene Expression Analysis in Glioblastoma Using PyDESeq2: A Comparison with Normal Brain

Panagiotis Karanikolaos¹, Themis P Exarchos², Panagiotis Vlamos²

¹Bioinformatics and Neuroinformatics MSc Program, Hellenic Open University, Patras, Greece. std529049@ac.eap.gr.

Advances in Experimental Medicine and Biology

|January 1, 2026

Summary

This summary is machine-generated.

This study analyzed gene expression in glioblastoma (GBM) to find age-related differences and identify key genes. Findings reveal age-specific signatures and dysregulated genes in GBM, aiding targeted therapy development.

Keywords:

Brain cancer Enrichment analysis Glioblastoma multiforme PyDESeq2 RNA-seq data

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Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

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Area of Science:

Genomics
Oncology
Bioinformatics

Background:

Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor prognosis.
Current treatments show limited efficacy due to GBM's complexity and therapy resistance.

Purpose of the Study:

To conduct a comprehensive differential gene expression analysis of GBM.
To investigate age-related gene expression patterns in GBM and normal brain tissues.
To identify consistently dysregulated genes in GBM compared to normal brain tissue.

Main Methods:

Utilized RNA-sequencing data from the Gene Expression Omnibus (GEO) database.
Employed PyDESeq2 for differential gene expression analysis.
Performed age stratification (≤65 and ≤76 years) and Principal Component Analysis (PCA).

Main Results:

Identified significant age-specific transcriptional signatures with upregulated and downregulated genes.
PCA confirmed distinct transcriptional clustering between GBM and normal brain tissues.
Discovered key differentially expressed genes (DEGs) consistently dysregulated in GBM.

Conclusions:

The study provides a detailed characterization of age-related and GBM-specific transcriptional changes.
Findings enhance understanding of GBM molecular mechanisms.
Identified DEGs and pathways offer potential for developing age-specific targeted therapies.