Pan-cancer mapping of differential protein-protein interactions.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
24 02 2020
Historique:
received: 17 06 2019
accepted: 04 02 2020
entrez: 26 2 2020
pubmed: 26 2 2020
medline: 15 12 2020
Statut: epublish

Résumé

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.

Identifiants

pubmed: 32094374
doi: 10.1038/s41598-020-60127-x
pii: 10.1038/s41598-020-60127-x
pmc: PMC7039988
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3272

Références

Sevimoglu, T. & Arga, K. Y. The role of protein interaction networks in systems biomedicine. CSBJ 11, 22–27 (2014).
doi: 10.1016/j.csbj.2014.08.008
Uhlén, M. et al. Tissue-based map of the human proteome. Science (80-.). 347 (2015).
Yeger-Lotem, E. & Sharan, R. Human protein interaction networks across tissues and diseases. Front. Genet 6, 1–5 (2015).
doi: 10.3389/fgene.2015.00257
Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 45, D369–D379 (2017).
doi: 10.1093/nar/gkw1102
Rolland, T. et al. A proteome-scale map of the human interactome network. Cell. 159, 1212–1226 (2014).
doi: 10.1016/j.cell.2014.10.050
Karagoz, K., Sevimoglu, T. & Arga, K. Y. Integration of multiple biological features yields high confidence human protein interactome. J. Theor. Biol. 403, 85–96 (2016).
doi: 10.1016/j.jtbi.2016.05.020
Basha, O., Shpringer, R., Argov, C. M. & Yeger-Lotem, E. The DifferentialNet database of differential protein-protein interactions in human tissues. Nucleic Acids Res. 46, D522–D526 (2018).
doi: 10.1093/nar/gkx981
Uhlen, M. et al. A pathology atlas of the human cancer transcriptome. Science (80-.). 357 (2017).
Vogelstein, B. et al. Cancer genome landscapes. Science 340, 1546–1558 (2013).
doi: 10.1126/science.1235122
Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–20 (2013).
doi: 10.1038/ng.2764
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
doi: 10.1038/ng.2653
Edfors, F. et al. Gene‐specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. 12, 883 (2016).
doi: 10.15252/msb.20167144
Ayyildiz, D., Gov, E., Sinha, R. & Arga, K. Y. Ovarian cancer differential interactome and network entropy analysis reveal new candidate biomarkers. OMICS 21, 285–294 (2017).
doi: 10.1089/omi.2017.0010
Li, Z. et al. The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies. Nat. Commun. 8 (2017).
Ivanov, A. A. et al. The OncoPPi Portal: an integrative resource to explore and prioritize protein–protein interactions for cancer target discovery. Bioinformatics 34, 1183–1191 (2017).
doi: 10.1093/bioinformatics/btx743
Turanli, B. et al. Multi-omic data interpretation to repurpose subtype specific drug candidates for breast cancer. Front. Genet. 10 (2019).
Barabási, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: A network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
doi: 10.1038/nrg2918
Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
doi: 10.1038/s41568-018-0060-1
Turanli, B. et al. A network-based cancer drug discovery: From integrated multi-omics approaches to precision medicine. Curr. Pharm. Des 24, 3778–3790 (2018).
doi: 10.2174/1381612824666181106095959
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–74 (2011).
doi: 10.1016/j.cell.2011.02.013
Fouad, Y. A. & Aanei, C. Revisiting the hallmarks of cancer. Am. J. Cancer Res. 7, 1016–1036 (2017).
pubmed: 28560055 pmcid: 5446472
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
doi: 10.1038/nature12912
Ivanov, A. A., Khuri, F. R. & Fu, H. Targeting protein-protein interactions as an anticancer strategy. Trends Pharmacol. Sci. 34, 393–400 (2013).
doi: 10.1016/j.tips.2013.04.007
Avendaño, C. & Menéndez, J. C. Chapter 11 - Other Nonbiological Approaches to Targeted Cancer Chemotherapy. In (eds. Avendaño, C. & Menéndez, J. C. B. T.-M. C. of A. D. (Second E.) 493–560 (Elsevier, 2015).
Caldera, M., Buphamalai, P., Müller, F. & Menche, J. Interactome-based approaches to human disease. Curr. Opin. Syst. Biol. 3, 88–94 (2017).
doi: 10.1016/j.coisb.2017.04.015
Cava, C. et al. Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis. BMC Genomics 19, 1–16 (2018).
doi: 10.1186/s12864-017-4423-x
Leiserson, M. D. M. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114, https://doi.org/10.1038/ng.3168 (2015).
doi: 10.1038/ng.3168 pubmed: 25501392 pmcid: 25501392
Pinheiro, F. L. & Hartmann, D. Intermediate Levels of Network Heterogeneity Provide the Best Evolutionary Outcomes. Sci. Rep. 7, 1–9 (2017).
doi: 10.1038/s41598-017-15555-7
Wang, X. Role of clinical bioinformatics in the development of network-based Biomarkers. J. Clin. Bioinforma. 1, 28 (2011).
doi: 10.1186/2043-9113-1-28
Gov, E. & Arga, K. Y. Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer. Sci. Rep. 7 (2017).
Li, K., Wang, F. & Hu, Z.-W. Targeting TRIB3 and PML-RARα interaction against APL. Oncotarget 8, 52012–52013 (2017).
pubmed: 28881708 pmcid: 5581007
Turanli, B. & Arga, K. Y. Systems biomedicine acts as a driver for the evolution of pharmacology. Ann Pharmacol Pharm. 2, 1087 (2017).
Turanli, B. et al. Systems biology based drug repositioning for development of cancer therapy. Semin. Cancer Biol. https://doi.org/10.1016/j.semcancer.2019.09.020 (2019).
Turanli, B. et al. Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning. EBioMedicine 42, 386–396 (2019).
doi: 10.1016/j.ebiom.2019.03.009
Tomczak, K., Czerwińska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 19, A68–A77 (2015).
pubmed: 25691825 pmcid: 4322527
Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. The ConsensusPathDB interaction database: 2013 Update. Nucleic Acids Res. 41 (2013).
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
doi: 10.1093/nar/gkw1092
Ashburner, M. et al. Gene ontology: Tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
doi: 10.1038/75556
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504, http://www.genome.org/cgi/doi/10.1101/gr.1239303 (2003).
doi: 10.1101/gr.1239303
Aguirre-Gamboa, R. et al. SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis. PLoS One 8, e74250 (2013).
doi: 10.1371/journal.pone.0074250
Xue, M. et al. Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients. J. Cancer 10, 4921 (2019).
doi: 10.7150/jca.30618

Auteurs

Gizem Gulfidan (G)

Department of Bioengineering, Marmara University, 34722, Istanbul, Turkey.

Beste Turanli (B)

Department of Bioengineering, Marmara University, 34722, Istanbul, Turkey.
Department of Bioengineering, Istanbul Medeniyet University, 34720, Istanbul, Turkey.

Hande Beklen (H)

Department of Bioengineering, Marmara University, 34722, Istanbul, Turkey.

Raghu Sinha (R)

Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, 17033, Pennsylvania, United States.

Kazim Yalcin Arga (KY)

Department of Bioengineering, Marmara University, 34722, Istanbul, Turkey. kazim.arga@marmara.edu.tr.

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