ArticleNature biotechnology2009
Dynamic modularity in protein interaction networks predicts breast cancer outcome.
Article in Nature biotechnology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 359 papers, 1 of them a synthesis that pooled it.
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Who cites it
359 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Dual Role of Cancer Epithelial-Specific TRAF3 in Regulating Breast Cancer Cell Survival and Lymphocyte Activity.International journal of molecular sciences · 2026Article
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- Modulation of Network Plasticity Opens Novel Therapeutic Possibilities in Cancer, Diabetes, and Neurodegeneration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- A PDCD4-Based Gene Expression Signature Predicts Overall Survival in Renal Cell Carcinoma: A TCGA-Based Discovery and External Validation Study.Current issues in molecular biology · 2025Article
- Integrated network propagation identifies prognostic metabolic signatures in acute myeloid leukemia.Journal of translational medicine · 2025Article
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- Community cohesion looseness in gene networks reveals individualized drug targets and resistance.Briefings in bioinformatics · 2024Article
- Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.BMC bioinformatics · 2024Article
- Identification of a central network hub of key prognostic genes based on correlation between transcriptomics and survival in patients with metastatic solid tumors.Therapeutic advances in medical oncology · 2024Article
- Protein-protein interaction network module changes associated with the vertebrate fin-to-limb transition.Scientific reports · 2023Article
- Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis.Interdisciplinary sciences, computational life sciences · 2023Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.Bioinformatics (Oxford, England) · 2023Article
- Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.Cancer research · 2023Article
- Recent advances in the field of single-cell proteomics.Translational oncology · 2023Article
- Review
299 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may be useful as an indicator of breast cancer prognosis.
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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.