ArticlePLoS computational biology2008
Modeling cancer progression via pathway dependencies.
Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma.PloS one · 2015Pooled it
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.Journal for immunotherapy of cancer · 2021Article
- Data-Driven Methods for Advancing Precision Oncology.Current pharmacology reports · 2018Article
- Bayesian nonlinear model selection for gene regulatory networks.Biometrics · 2015Article
- Between pathways and networks lies context: implications for precision medicine.Science progress · 2015Review
- A method for developing regulatory gene set networks to characterize complex biological systems.BMC genomics · 2015Article
- Pathway and network analysis in proteomics.Journal of theoretical biology · 2014Review
- Epigenetic suppression of EGFR signaling in G-CIMP+ glioblastomas.Oncotarget · 2014Article
- Core modular blood and brain biomarkers in social defeat mouse model for post traumatic stress disorder.BMC systems biology · 2013Article
- Modelling gene expression profiles related to prostate tumor progression using binary states.Theoretical biology & medical modelling · 2013Article
- Spreaders and sponges define metastasis in lung cancer: a Markov chain Monte Carlo mathematical model.Cancer research · 2013Article
- Molecular signaling network complexity is correlated with cancer patient survivability.Proceedings of the National Academy of Sciences of the United States of America · 2012Article
- Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models.Journal of biometrics & biostatistics · 2012Article
- SEAS: a system for SEED-based pathway enrichment analysis.PloS one · 2011Article
- Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer's disease brains.BMC systems biology · 2010Article
- Comprehensive evaluation of the role of EZH2 in the growth, invasion, and aggression of a panel of prostate cancer cell lines.The Prostate · 2010Article
- Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network.BMC bioinformatics · 2010Article
- Comparative study of gene set enrichment methods.BMC bioinformatics · 2009Article
- Identification of differentially expressed subnetworks based on multivariate ANOVA.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a normal cell to a malignancy and ultimately to a metastatic state. Certain genetic perturbations have been implicated for initiation and progression. However, to a great extent, underlying mechanisms often remain elusive. These genetic perturbations are most likely reflected by the altered expression of sets of genes or pathways, rather than individual genes, thus creating a need for models of deregulation of pathways to help provide an understanding of the mechanisms of tumorigenesis. We introduce an integrative hierarchical analysis of tumor progression that discovers which a priori defined pathways are relevant either throughout or in particular steps of progression. Pathway interaction networks are inferred for these relevant pathways over the steps in progression. This is followed by the refinement of the relevant pathways to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined pathways. We apply this approach to model progression in prostate cancer and melanoma, resulting in a deeper understanding of the mechanisms of tumorigenesis. Our analysis supports previous findings for the deregulation of several pathways involved in cell cycle control and proliferation in both cancer types. A novel finding of our analysis is a connection between ErbB4 and primary prostate cancer.
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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.