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Updated: Jun 2, 2026
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
Rebecka Jörnsten1, Tobias Abenius, Teresia Kling
1Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden.
Abstract:
DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
Insights
We developed EPoC, a new method to analyze how DNA copy number aberrations (CNAs) impact gene expression in cancer. EPoC identifies disease-driving CNAs and predicts patient survival, offering insights into cancer biology.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- DNA copy number aberrations (CNAs) are common in cancer genomes but their impact on gene expression is not well understood.
- Understanding the link between CNAs and gene expression is crucial for cancer research and patient stratification.
Purpose of the Study:
- To develop a computational framework, EPoC (Endogenous Perturbation analysis of Cancer), to model the effects of CNAs on gene expression.
- To identify disease-driving CNAs and their association with patient survival outcomes.
- To stratify cancer patients based on predicted survival.
Main Methods:
- Constructed causal network models integrating genome-wide DNA and RNA data.
- Utilized singular value decomposition on network models to derive prognostic scores.
- Applied the EPoC framework to glioblastoma data from The Cancer Genome Atlas (TCGA).
Main Results:
- The EPoC models identified known cancer-relevant genes and potential novel hub genes.
- The analysis revealed predictors of patient survival in glioblastoma.
- Experimental validation in glioblastoma cell lines supported predictions and implicated Necdin in tumor suppression.
Conclusions:
- Large-scale network modeling of CNA effects on gene expression provides valuable insights into cancer biology.
- EPoC is a powerful tool for identifying disease mechanisms and predicting patient outcomes.
- The study highlights the potential of Necdin as a therapeutic target in glioblastoma.
