ArticleMolecular systems biology2011
Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.
Article in Molecular systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
52 citing papers in PubMed, 91 citations in OpenAlex.
- Inferring Phenotypes of Copy Number Clones in Cancer Populations Using TreeAlign.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells.Nature communications · 2024Article
- Gα12 signaling regulates transcriptional and phenotypic responses that promote glioblastoma tumor invasion.Scientific reports · 2023Article
- Identifying the driver miRNAs with somatic copy number alterations driving dysregulated ceRNA networks in cancers.Biology direct · 2023Article
- Exploiting allele-specific transcriptional effects of subclonal copy number alterations for genotype-phenotype mapping in cancer cell populations.bioRxiv : the preprint server for biology · 2023Article
- An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.PLOS digital health · 2022Article
- Necdin: A purposive integrator of molecular interaction networks for mammalian neuron vitality.Genes to cells : devoted to molecular & cellular mechanisms · 2021Review
- The Role of Network Science in Glioblastoma.Cancers · 2021Review
- ComHub: Community predictions of hubs in gene regulatory networks.BMC bioinformatics · 2021Article
- Systematic analysis of enhancer regulatory circuit perturbation driven by copy number variations in malignant glioma.Theranostics · 2021Article
- Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers.Molecular therapy. Nucleic acids · 2020Article
- Integrative discovery of treatments for high-risk neuroblastoma.Nature communications · 2020Article
- Engrailed 2 (EN2) acts as a glioma suppressor by inhibiting tumor proliferation/invasion and enhancing sensitivity to temozolomide.Cancer cell international · 2020Article
- Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer.Molecular therapy. Nucleic acids · 2019Article
- Identification of Dysregulated Competitive Endogenous RNA Networks Driven by Copy Number Variations in Malignant Gliomas.Frontiers in genetics · 2019Article
- The biology and mathematical modelling of glioma invasion: a review.Journal of the Royal Society, Interface · 2017Review
- HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology.Genome research · 2017Article
- Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis.Genome biology · 2016Article
- Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma.EBioMedicine · 2016Article
- Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.Cell systems · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.