Advertisement

The prognostic value of NRF2 in breast cancer patients: a systematic review with meta-analysis

  • Micaela Almeida
  • Mafalda Soares
  • Ana Cristina RamalhinhoEmail author
  • José Fonseca Moutinho
  • Luiza Breitenfeld
  • Luísa Pereira
Review
  • 72 Downloads

Abstract

Purpose

Nuclear factor E2-related factor 2 (NRF2) is a transcription factor that plays a major role in the regulation of intracellular antioxidant response. The effect of NRF2 overexpression in many malignancies is still unclear and recent meta-analysis correlated NRF2 overexpression with poor prognosis in a variety of human cancers. However, the effect of NRF2 overexpression in breast cancer is still unclear. Thus, the main goal of this work was to clarify the role of NRF2 expression in survival and relapse of breast cancer patients by performing a systematic review according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement, followed by a meta-analysis.

Methods

The electronic search was conducted in PubMed, Scopus, SciELO, Web of Science and Embase between November of 2017 and September of 2018. To be included, studies should evaluate NRF2 expression in breast cancer tissue, through immunohistochemistry and/or mRNA and had to report one or more of the following outcomes: overall survival (OS), disease-free survival (DFS), mean survival and median survival.

Results

For the meta-analysis, seven studies were included and NRF2 expression was correlated with OS and DFS. It was observed that compared to patients with low NRF2 expression, patients with NRF2 overexpression had poorer OS with a hazard ratio of 1.82 (95% CI 1.32–2.50; p value < 0.0001), and poorer DFS, with a hazard ratio of 1.79 (95% CI 1.07–3.01; p value = 0.03).

Conclusions

These results suggest that tumours that overexpress NRF2 have a worse clinical outcome. Thus, NRF2 expression could be a marker for the prognostic of breast cancer patients and, in the future, it would be pertinent to focus on improving treatment efficacy for patients with NRF2 overexpression.

Keywords

NRF2 Breast cancer Systematic review Meta-analysis 

Notes

Acknowledgements

We would like to thank the financial support of our research through the project “Validation of risk assessment model for breast cancer based on genetic polymorphisms of low penetrance to assess breast cancer risk” (Ref. PTDC/DTP-PIC/4743/2014), funded by the Portuguese Foundation for Science and Technology (FCT) through the European Fund for the Regional Development (FEDER) and through the Operational Program of Competitiveness and Internationalization (Ref. POCI-01-0145-FEDER-16620). This project is developed in Health Sciences Research Centre of University of Beira Interior (CICS-UBI) in collaboration with Group of Systematic Reviews of University of Beira Interior (GRUBI), Centre of Mathematics and Applications, University of Beira Interior (CMA-UBI) and with University Hospital Centre of Cova da Beira (CHUCB). We also thank to “Data mining for systematic reviews and Meta-Analyses in Health Sciences” C4—Cloud Computing Competences Centre (Ref. CENTRO-01-0145-FEDER-000019), funded by the Portuguese Foundation for Science and Technology (FCT) through the European Fund for the Regional Development (FEDER). We would also like to knowledge Novartis for giving us access to Embase.

Funding

This study was funded by the Portuguese Foundation for Science and Technology (FCT), Ref. PTDC/DTP-PIC/4743/2014, through the European Fund for the Regional Development (FEDER) and through the Operational Program of Competitiveness and Internationalization, Ref. POCI-01-0145-FEDER-16620.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Jaramillo MC, Zhang DD (2013) The emerging role of the Nrf2–Keap1 signaling pathway in cancer. Genes Dev 27:2179–2191CrossRefGoogle Scholar
  2. 2.
    Cho H-Y, Marzec J, Kleeberger SR (2015) Functional polymorphisms in Nrf2: implications for human disease. Free Radic Biol Med 88:362–372CrossRefGoogle Scholar
  3. 3.
    Jeddi F, Soozangar N, Sadeghi MR, Somi MH, Samadi N (2017) Contradictory roles of Nrf2/Keap1 signaling pathway in cancer prevention/promotion and chemoresistance. DNA Repair 54:13–21CrossRefGoogle Scholar
  4. 4.
    Guo Y, Yu S, Zhang C, Kong A-NT (2015) Epigenetic regulation of Keap1-Nrf2 signaling. Free Radic Biol Med 88:337–349CrossRefGoogle Scholar
  5. 5.
    Lau A, Villeneuve NF, Sun Z, Wong PK, Zhang DD (2008) Dual roles of Nrf2 in cancer. Pharmacol Res 58:262–270CrossRefGoogle Scholar
  6. 6.
    Pandey P, Singh AK, Singh M, Tewari M, Shukla HS, Gambhir IS (2017) The see-saw of Keap1-Nrf2 pathway in cancer. Crit Rev Oncol/Hematol 116:89–98CrossRefGoogle Scholar
  7. 7.
    Menegon S, Columbano A, Giordano S (2016) The dual roles of NRF2 in cancer. Trends Mol Med 22:578–593CrossRefGoogle Scholar
  8. 8.
    Goldstein LD, Lee J, Gnad F, Klijn C, Schaub A, Reeder J, Daemen A, Bakalarski CE, Holcomb T, Shames DS (2016) Recurrent loss of NFE2L2 exon 2 is a mechanism for Nrf2 pathway activation in human cancers. Cell Rep 16:2605–2617CrossRefGoogle Scholar
  9. 9.
    Geismann C, Arlt A, Sebens S, Schäfer H (2014) Cytoprotection “gone astray”: Nrf2 and its role in cancer. Onco Targets Ther 7:1497–1518PubMedPubMedCentralGoogle Scholar
  10. 10.
    Guo Y, Shen L (2017) Overexpression of NRF 2 is correlated with prognoses of patients with malignancies: a meta-analysis. Thorac Cancer 8:558–564CrossRefGoogle Scholar
  11. 11.
    Wang L, Zhang C, Qin L, Xu J, Li X, Wang W, Kong L, Zhou T, Li X (2018) The prognostic value of NRF2 in solid tumor patients: a meta-analysis. Oncotarget 9:1257–1265PubMedGoogle Scholar
  12. 12.
    Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin D, Piñeros M, Znaor A, Bray F (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 144:1941–1953CrossRefGoogle Scholar
  13. 13.
    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151:264–269CrossRefGoogle Scholar
  14. 14.
    Borenstein M, Hedges L, Higgins J, Rothstein H (2009) Introduction to meta-analysis. John Wiley & Sons Ltd, ChichesterCrossRefGoogle Scholar
  15. 15.
    Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560CrossRefGoogle Scholar
  16. 16.
    Light R, Pillemer D (1984) Summing up: the science of reviewing research. Harvard University Press, CambridgeGoogle Scholar
  17. 17.
    Light R, Singer J, Willett J (1994) The visual presentation and interpretation of meta-analyses. In: Cooper H, Hedges LV (eds) The handbook of research synthesis. Russell Sage Foundation, New York, pp 439–445Google Scholar
  18. 18.
    Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634CrossRefGoogle Scholar
  19. 19.
    Duval S, Tweedie R (2000) A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J Am Stat Assoc 95:89–98Google Scholar
  20. 20.
    Duval S, Tweedie R (2000) Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56:455–463CrossRefGoogle Scholar
  21. 21.
    Bekele RT, Venkatraman G, Liu R-Z, Tang X, Mi S, Benesch MG, Mackey JR, Godbout R, Curtis JM, McMullen TP (2016) Oxidative stress contributes to the tamoxifen-induced killing of breast cancer cells: implications for tamoxifen therapy and resistance. Sci Rep 6:21164CrossRefGoogle Scholar
  22. 22.
    Cha YJ, Jung WH, Koo JS (2017) Differential site-based expression of pentose phosphate pathway-related proteins among breast cancer metastases. Dis Markers 2017:7062517PubMedPubMedCentralGoogle Scholar
  23. 23.
    Onodera Y, Motohashi H, Takagi K, Miki Y, Shibahara Y, Watanabe M, Ishida T, Hirakawa H, Sasano H, Yamamoto M (2014) NRF2 immunolocalization in human breast cancer patients as a prognostic factor. Endocr Relat Cancer 21:241–252CrossRefGoogle Scholar
  24. 24.
    Xiao Y, Hu G, Dong D-D, Tian W, Li T-T, Jiang X-H, Wang L (2016) Prognostic value of NRF2 in breast cancer patients and its role as a tumor suppressor by directly inhibiting HER2 expression. Int J Clin Exp Pathol 9:4292–4306Google Scholar
  25. 25.
    Wolf B, Goebel G, Hackl H, Fiegl H (2016) Reduced mRNA expression levels of NFE2L2 are associated with poor outcome in breast cancer patients. BMC Cancer 16:821CrossRefGoogle Scholar
  26. 26.
    Zhang C, Wang H-J, Bao Q-C, Wang L, Guo T-K, Chen W-L, Xu L-L, Zhou H-S, Bian J-L, Yang Y-R (2016) NRF2 promotes breast cancer cell proliferation and metastasis by increasing RhoA/ROCK pathway signal transduction. Oncotarget 7:73593–73606PubMedPubMedCentralGoogle Scholar
  27. 27.
    Bocci F, Tripathi SC, Mercedes SV, George JT, Casabar J, Wong PK, Hanash S, Levine H, Onuchic JN, Jolly MK (2018) NRF2 activates a partial epithelial-mesenchymal transition and is maximally present in a hybrid epithelial/mesenchymal phenotype. biorxiv 11(6):251–263Google Scholar
  28. 28.
    Zhang DD (2006) Mechanistic studies of the Nrf2-Keap1 signaling pathway. Drug Metab Rev 38:769–789CrossRefGoogle Scholar
  29. 29.
    Zhang DD (2010) The Nrf2-Keap1-ARE signaling pathway: the regulation and dual function of Nrf2 in cancer. Antioxid Redox Signal 13:1623–16266CrossRefGoogle Scholar
  30. 30.
    Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y (2012) The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups. Nature 486:346–352CrossRefGoogle Scholar
  31. 31.
    Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, Szallasi Z (2010) An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1809 patients. Breast Cancer Res Treat 123:725–731CrossRefGoogle Scholar
  32. 32.
    Győrffy B, Surowiak P, Budczies J, Lánczky A (2013) Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PLoS ONE 8:e82241CrossRefGoogle Scholar
  33. 33.
    Weigman VJ, Chao H-H, Shabalin AA, He X, Parker JS, Nordgard SH, Grushko T, Huo D, Nwachukwu C, Nobel A (2012) Basal-like Breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival. Breast Cancer Res Treat 133:865–880CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CICS-UBI—Centro de Investigação em Ciências da SaúdeUniversidade da Beira InteriorCovilhãPortugal
  2. 2.GRUBI, Grupo de Revisões Sistemáticas da Universidade da Beira InteriorCovilhãPortugal
  3. 3.Centro Hospitalar Universitário Cova da Beira, E.P.E. Quinta do AlvitoCovilhãPortugal
  4. 4.CMA-UBI, Centro de Matemática e AplicaçõesUniversidade da Beira InteriorCovilhãPortugal

Personalised recommendations