ArticlePLoS computational biology2009
Human cancer protein-protein interaction network: a structural perspective.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
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Who cites it
103 citing papers in PubMed.
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.NPJ systems biology and applications · 2026Review
- Within and beyond the boundaries of proteasomal assembly: PSMD9 chaperone as a multifunctional protein.Essays in biochemistry · 2025Review
- Dynamic Organization of Cells in Colonic Epithelium is Encoded by Five Biological Rules.Biology of the cell · 2025Article
- Target Mapping in Cancer: Ligandable Protein Pockets on 3D OncoPPI Networks.Pharmaceuticals (Basel, Switzerland) · 2025Article
- The Potential of Peptide-Based Inhibitors in Disrupting Protein-Protein Interactions for Targeted Cancer Therapy.International journal of molecular sciences · 2025Review
- Article
- [Overexpression of CDHR2 inhibits proliferation of breast cancer cells by inhibiting the PI3K/Akt pathway].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions.bioRxiv : the preprint server for biology · 2024Article
- Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer.iScience · 2024Article
- Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network andInternational journal of molecular sciences · 2024Article
- Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis.Interdisciplinary sciences, computational life sciences · 2023Article
- From antimicrobial to anticancer: the pioneering works of Prof. Luiz Rodolpho Travassos on bioactive peptides.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023Review
- Long noncoding RNAs with peptide-encoding potential identified in esophageal squamous cell carcinoma: KDM4A-AS1-encoded peptide weakens cancer cell viability and migratory capacity.Molecular oncology · 2023Article
- Identification of novel interacts partners of ADAR1 enzyme mediating the oncogenic process in aggressive breast cancer.Scientific reports · 2023Article
- Network analysis of liver cancer: a system biology approach.Gastroenterology and hepatology from bed to bench · 2023Article
- A NovelDiagnostics (Basel, Switzerland) · 2022Article
- Severe testing with high-dimensional omics data for enhancing biomedical scientific discovery.NPJ systems biology and applications · 2022Article
- Review
- Are transient protein-protein interactions more dispensable?PLoS computational biology · 2022Article
43 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.
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