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

Breast Cancer Research and Treatment volume 179, pages 523–532 (2020)Cite this article

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.

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References

  1. Jaramillo MC, Zhang DD (2013) The emerging role of the Nrf2–Keap1 signaling pathway in cancer. Genes Dev 27:2179–2191

    Article  CAS  Google Scholar 

  2. Cho H-Y, Marzec J, Kleeberger SR (2015) Functional polymorphisms in Nrf2: implications for human disease. Free Radic Biol Med 88:362–372

    Article  CAS  Google Scholar 

  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 54:13–21

    Article  CAS  Google Scholar 

  4. Guo Y, Yu S, Zhang C, Kong A-NT (2015) Epigenetic regulation of Keap1-Nrf2 signaling. Free Radic Biol Med 88:337–349

    Article  CAS  Google Scholar 

  5. Lau A, Villeneuve NF, Sun Z, Wong PK, Zhang DD (2008) Dual roles of Nrf2 in cancer. Pharmacol Res 58:262–270

    Article  CAS  Google Scholar 

  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 116:89–98

    Article  Google Scholar 

  7. Menegon S, Columbano A, Giordano S (2016) The dual roles of NRF2 in cancer. Trends Mol Med 22:578–593

    Article  CAS  Google Scholar 

  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 16:2605–2617

    Article  CAS  Google Scholar 

  9. Geismann C, Arlt A, Sebens S, Schäfer H (2014) Cytoprotection “gone astray”: Nrf2 and its role in cancer. Onco Targets Ther 7:1497–1518

    PubMed  PubMed Central  Google Scholar 

  10. Guo Y, Shen L (2017) Overexpression of NRF 2 is correlated with prognoses of patients with malignancies: a meta-analysis. Thorac Cancer 8:558–564

    Article  CAS  Google Scholar 

  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 9:1257–1265

    PubMed  Google Scholar 

  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 144:1941–1953

    Article  CAS  Google Scholar 

  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 151:264–269

    Article  Google Scholar 

  14. Borenstein M, Hedges L, Higgins J, Rothstein H (2009) Introduction to meta-analysis. John Wiley & Sons Ltd, Chichester

    Book  Google Scholar 

  15. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560

    Article  Google Scholar 

  16. Light R, Pillemer D (1984) Summing up: the science of reviewing research. Harvard University Press, Cambridge

    Google Scholar 

  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 439–445

    Google Scholar 

  18. Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634

    Article  CAS  Google Scholar 

  19. Duval S, Tweedie R (2000) A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J Am Stat Assoc 95:89–98

    Google Scholar 

  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 56:455–463

    Article  CAS  Google Scholar 

  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 6:21164

    Article  CAS  Google Scholar 

  22. Cha YJ, Jung WH, Koo JS (2017) Differential site-based expression of pentose phosphate pathway-related proteins among breast cancer metastases. Dis Markers 2017:7062517

    PubMed  PubMed Central  Google Scholar 

  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 21:241–252

    Article  CAS  Google Scholar 

  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 9:4292–4306

    CAS  Google Scholar 

  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 16:821

    Article  CAS  Google Scholar 

  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 7:73593–73606

    PubMed  PubMed Central  Google Scholar 

  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):251–263

    Google Scholar 

  28. Zhang DD (2006) Mechanistic studies of the Nrf2-Keap1 signaling pathway. Drug Metab Rev 38:769–789

    Article  CAS  Google Scholar 

  29. Zhang DD (2010) The Nrf2-Keap1-ARE signaling pathway: the regulation and dual function of Nrf2 in cancer. Antioxid Redox Signal 13:1623–16266

    Article  CAS  Google Scholar 

  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 486:346–352

    Article  CAS  Google Scholar 

  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 123:725–731

    Article  CAS  Google Scholar 

  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:e82241

    Article  CAS  Google Scholar 

  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 133:865–880

    Article  CAS  Google Scholar 

Download references

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.

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Affiliations

  1. CICS-UBI—Centro de Investigação em Ciências da Saúde, Universidade da Beira Interior, Av. Infante D. Henrique, 6200-506, Covilhã, Portugal

    Micaela Almeida, Mafalda Soares, Ana Cristina Ramalhinho, José Fonseca Moutinho & Luiza Breitenfeld

  2. GRUBI, Grupo de Revisões Sistemáticas da Universidade da Beira Interior, Covilhã, Portugal

    Micaela Almeida, Ana Cristina Ramalhinho, Luiza Breitenfeld & Luísa Pereira

  3. Centro Hospitalar Universitário Cova da Beira, E.P.E. Quinta do Alvito, 6200-251, Covilhã, Portugal

    Ana Cristina Ramalhinho & José Fonseca Moutinho

  4. CMA-UBI, Centro de Matemática e Aplicações, Universidade da Beira Interior, Covilhã, Portugal

    Luísa Pereira

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  1. Micaela Almeida
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  2. Mafalda Soares
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  3. Ana Cristina Ramalhinho
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  4. José Fonseca Moutinho
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Correspondence to Ana Cristina Ramalhinho.

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Almeida, M., Soares, M., Ramalhinho, A.C. et al. The prognostic value of NRF2 in breast cancer patients: a systematic review with meta-analysis. Breast Cancer Res Treat 179, 523–532 (2020). https://doi.org/10.1007/s10549-019-05494-4

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  • DOI: https://doi.org/10.1007/s10549-019-05494-4

Keywords

  • NRF2
  • Breast cancer
  • Systematic review
  • Meta-analysis