ArticleJournal of the American Medical Informatics Association : JAMIA
Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Transcriptomic evidence of remodeling in postpartum levator ani muscle associated with maternal age and recovery time after first vaginal delivery.American journal of obstetrics and gynecology · 2026Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- Protein kinase CK2 - diverse roles in cancer cell biology and therapeutic promise.Molecular and cellular biochemistry · 2023Review
- Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.Briefings in bioinformatics · 2019Review
- Computational approaches for the identification of cancer genes and pathways.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- Computational Reconstruction of NFκB Pathway Interaction Mechanisms during Prostate Cancer.PLoS computational biology · 2016Article
- Multi-Leu PACE4 Inhibitor Retention within Cells Is PACE4 Dependent and a Prerequisite for Antiproliferative Activity.BioMed research international · 2015Article
- Gene expression in rat models for inter-generational transmission of islet dysfunction and obesity.Genomics data · 2014Article
- COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Defining a comprehensive verotype using electronic health records for personalized medicine.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.BMC bioinformatics · 2013Article
- Curation-free biomodules mechanisms in prostate cancer predict recurrent disease.BMC medical genomics · 2013Article
- Article
- Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2013Article
- Deregulation of a Hox protein regulatory network spanning prostate cancer initiation and progression.Clinical cancer research : an official journal of the American Association for Cancer Research · 2012Article
- A systems biology approach to the global analysis of transcription factors in colorectal cancer.BMC cancer · 2012Article
- A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.PloS one · 2012Article
- Integration of breast cancer gene signatures based on graph centrality.BMC systems biology · 2011Article
- 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.Journal of the American Medical Informatics Association : JAMIAArticle
- Computationally translating molecular discoveries into tools for medicine: translational bioinformatics articles now featured in JAMIA.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
4 authors.
Funding
Abstract
objectiveUncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with quantitative models of protein-protein interactions, key pathways will be elucidated and shown to be shared.
designThe authors statistically prioritized common interactors between established cancer genes and genes from each prostate cancer signature of poor prognosis independently via a previously validated single protein analysis of network (SPAN) methodology. Additionally, they computationally identified pathways among the aggregated interactors across signatures and validated them using a similarity metric and patient survival. MEASUREMENT: Using an information-theoretic metric, the authors assessed the mechanistic similarity of the interactor signature. Its prognostic ability was assessed in an independent cohort of 198 patients with high-Gleason prostate cancer using Kaplan-Meier analysis.
resultsOf the 13 prostate cancer signatures that were evaluated, eight interacted significantly with established cancer genes (false discovery rate <5%) and generated a 42-gene interactor signature that showed the highest mechanistic similarity (p<0.0001). Via parameter-free unsupervised classification, the interactor signature dichotomized the independent prostate cancer cohort with a significant survival difference (p=0.009). Interpretation of the network not only recapitulated phosphatidylinositol-3 kinase/NF-κB signaling, but also highlighted less well established relevant pathways such as the Janus kinase 2 cascade.
conclusionsSPAN methodology provides a robust means of abstracting disparate prostate cancer gene expression signatures into clinically useful, prioritized pathways as well as useful mechanistic pathways.
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