Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

PPISURV: a novel bioinformatics tool for uncovering the hidden role of specific genes in cancer survival outcome

Oncogene volume 33, pages 1621–1628 (2014)Cite this article

Subjects

Abstract

Multiple clinical studies have correlated gene expression with survival outcome in cancer on a genome-wide scale. However, in many cases, no obvious correlation between expression of well-known tumour-related genes (that is, p53, p73 and p21) and survival rates of patients has been observed. This can be mainly explained by the complex molecular mechanisms involved in cancer, which mask the clinical relevance of a gene with multiple functions if only gene expression status is considered. As we demonstrate here, in many such cases, the expression of the gene interaction partners (gene ‘interactome’) correlates significantly with cancer survival and is indicative of the role of that gene in cancer. On the basis of this principle, we have implemented a free online datamining tool (http://www.bioprofiling.de/PPISURV). PPISURV automatically correlates expression of an input gene interactome with survival rates on >40 publicly available clinical expression data sets covering various tumours involving about 8000 patients in total. To derive the query gene interactome, PPISURV employs several public databases including protein–protein interactions, regulatory and signalling pathways and protein post-translational modifications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1

Similar content being viewed by others

References

  1. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 2009; 37: D885–D890.

    Article  CAS  Google Scholar 

  2. Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 2005; 102: 13550–13555.

    Article  CAS  Google Scholar 

  3. Gyorffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat 2010; 123: 725–731.

    Article  Google Scholar 

  4. Gyorffy B, Lanczky A, Szallasi Z . Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. Endocr Relat Cancer 2012; 19: 197–208.

    Article  CAS  Google Scholar 

  5. Antonov AV, Knight RA, Melino G, Barlev NA, Tsvetkov PO . MIRUMIR: an online tool to test microRNAs as biomarkers to predict survival in cancer using multiple clinical data sets. Cell Death Differ 2013; 20: 367.

    Article  CAS  Google Scholar 

  6. Na II, Rho JK, Choi YJ, Kim CH, Koh JS, Ryoo BY et al. Clinical features reflect exon sites of EGFR mutations in patients with resected non-small-cell lung cancer. J Korean Med Sci 2007; 22: 393–399.

    Article  CAS  Google Scholar 

  7. Tandon S, Tudur-Smith C, Riley RD, Boyd MT, Jones TM . A systematic review of p53 as a prognostic factor of survival in squamous cell carcinoma of the four main anatomical subsites of the head and neck. Cancer Epidemiol Biomarkers Prev 2010; 19: 574–587.

    Article  CAS  Google Scholar 

  8. Xia W, Chen JS, Zhou X, Sun PR, Lee DF, Liao Y et al. Phosphorylation/cytoplasmic localization of p21Cip1/WAF1 is associated with HER2/neu overexpression and provides a novel combination predictor for poor prognosis in breast cancer patients. Clin Cancer Res 2004; 10: 3815–3824.

    Article  CAS  Google Scholar 

  9. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res 2012; 40: D841–D846.

    Article  CAS  Google Scholar 

  10. Croft D, O'Kelly G, Wu G, Haw R, Gillespie M, Matthews L et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 2011; 39: D691–D697.

    Article  CAS  Google Scholar 

  11. Hornbeck PV, Kornhauser JM, Tkachev S, Zhang B, Skrzypek E, Murray B et al. PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res 2012; 40: D261–D270.

    Article  CAS  Google Scholar 

  12. Dietmann S, Lee W, Wong P, Rodchenkov I, Antonov AV . CCancer: a bird’s eye view on gene lists reported in cancer-related studies. Nucleic Acids Res 2010; 38: W118–W123.

    Article  CAS  Google Scholar 

  13. Antonov AV, Schmidt EE, Dietmann S, Krestyaninova M, Hermjakob H . R Spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from reactome and KEGG databases. Nucleic Acids Res 2010; 38: W78–W83.

    Article  CAS  Google Scholar 

  14. Antonov AV . BioProfiling.de: analytical web portal for high-throughput cell biology. Nucleic Acids Res 2011; 39: W323–W327.

    Article  CAS  Google Scholar 

  15. Lee JT, Gu W . The multiple levels of regulation by p53 ubiquitination. Cell Death Differ 2010; 17: 86–92.

    Article  CAS  Google Scholar 

  16. Tomasini R, Mak TW, Melino G . The impact of p53 and p73 on aneuploidy and cancer. Trends Cell Biol 2008; 18: 244–252.

    Article  CAS  Google Scholar 

  17. Rufini A, Agostini M, Grespi F, Tomasini R, Sayan BS, Niklison-Chirou MV et al. p73 in Cancer. Genes Cancer 2011; 2: 491–502.

    Article  CAS  Google Scholar 

  18. Levine AJ, Tomasini R, McKeon FD, Mak TW, Melino G . The p53 family: guardians of maternal reproduction. Nat Rev Mol Cell Biol 2011; 12: 259–265.

    Article  CAS  Google Scholar 

  19. Suzuki A, Tsutomi Y, Akahane K, Araki T, Miura M . Resistance to Fas-mediated apoptosis: activation of caspase 3 is regulated by cell cycle regulator p21WAF1 and IAP gene family ILP. Oncogene 1998; 17: 931–939.

    Article  CAS  Google Scholar 

  20. Gartel AL, Radhakrishnan SK . Lost in transcription: p21 repression, mechanisms, and consequences. Cancer Res 2005; 65: 3980–3985.

    Article  CAS  Google Scholar 

  21. Callagy GM, Pharoah PD, Pinder SE, Hsu FD, Nielsen TO, Ragaz J et al. Bcl-2 is a prognostic marker in breast cancer independently of the Nottingham Prognostic Index. Clin Cancer Res 2006; 12: 2468–2475.

    Article  CAS  Google Scholar 

  22. Castiglione F, Sarotto I, Fontana V, Destefanis M, Venturino A, Ferro S et al. Bcl2, p53 and clinical outcome in a series of 138 operable breast cancer patients. Anticancer Res 1999; 19: 4555–4563.

    CAS  PubMed  Google Scholar 

  23. Dawson SJ, Makretsov N, Blows FM, Driver KE, Provenzano E, Le QJ et al. BCL2 in breast cancer: a favourable prognostic marker across molecular subtypes and independent of adjuvant therapy received. Br J Cancer 2010; 103: 668–675.

    Article  CAS  Google Scholar 

  24. Ciardiello F, Tortora G . Inhibition of bcl-2 as cancer therapy. Ann Oncol 2002; 13: 501–502.

    Article  CAS  Google Scholar 

  25. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 2004; 32: D258–D261.

    Article  CAS  Google Scholar 

  26. Wu H, Zeinab RA, Flores ER, Leng RP . Pirh2, a ubiquitin E3 ligase, inhibits p73 transcriptional activity by promoting its ubiquitination. Mol Cancer Res 2011; 9: 1780–1790.

    Article  Google Scholar 

  27. Malatesta M, Peschiaroli A, Memmi EM, Zhang J, Antonov A, Green DR et al. The Cul4A–DDB1 E3 ubiquitin ligase complex represses p73 transcriptional activity. Oncogene 2013; 32: 4721–4726.

    Article  CAS  Google Scholar 

  28. Hochberg Y, Benjamini Y . More powerful procedures for multiple significance testing. Stat Med 1990; 9: 811–818.

    Article  CAS  Google Scholar 

  29. Harrington F . A class of rank test procedures for censored survival data. Biometrika 1982; 69: 553–566.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the UK Medical Research Council (MRC) and funding from Russian Federal grants 14.B37.21.1967 (to AA) 16.740.11.036 (to NB) and 11.G34.31.0069 (to GM).

Author information

Authors and Affiliations

  1. Toxicology Unit, Hodgkin Building, Medical Research Council, Leicester University, Leicester, UK

    A V Antonov, R A Knight & G Melino

  2. Molecular Pharmacology Laboratory, Technological University, St-Petersburg, Russia

    A V Antonov, G Melino & N A Barlev

  3. Institute for Molecular Medicine Finland FIMM, University of Helsinki,

    M Krestyaninova

  4. Helsinki, Finland

    M Krestyaninova

  5. Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada

    I Rodchenkov

  6. Department of Experimental Medicine and Surgery, University of Rome ‘Tor Vergata’, Via Montpellier 1, Rome, Italy

    G Melino

  7. Department of Biochemistry, University of Leicester, Leicester, UK

    N A Barlev

  8. Gene Expression Programme, Institute of Cytology, St-Petersburg, Russia

    N A Barlev

Authors
  1. A V Antonov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M Krestyaninova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R A Knight
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. I Rodchenkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. G Melino
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. N A Barlev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A V Antonov.

Ethics declarations

Competing interests

The authors declare no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Antonov, A., Krestyaninova, M., Knight, R. et al. PPISURV: a novel bioinformatics tool for uncovering the hidden role of specific genes in cancer survival outcome. Oncogene 33, 1621–1628 (2014). https://doi.org/10.1038/onc.2013.119

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/onc.2013.119

Keywords

This article is cited by

Search

Advanced search

Quick links