Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Novel MicroRNA-Based Risk Score Identified by Integrated Analyses to Predict Metastasis and Poor Prognosis in Breast Cancer

  • Translational Research and Biomarkers
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

The use of biomarkers that allow early therapeutic intervention or intensive follow-up evaluation is expected to be a powerful means for reducing breast cancer mortality. MicroRNAs (miRNAs) are known to play major roles in cancer biology including metastasis. This study aimed to develop a novel miRNA risk score to predict patient survival and metastasis in breast cancer.

Methods

An integrated unbiased approach was applied to derive a composite risk score for prognosis based on miRNA expression in primary breast tumors in 1051 breast cancer patients from The Cancer Genome Atlas (TCGA). Further analysis of the risk score with metastasis/recurrence was performed using the TCGA data set and validated in a separate patient population using small RNA sequencing.

Results

The three-miRNAs risk score (miR-19a, miR-93, and miR-106a) was developed using the TCGA cohort, which predicted poor prognosis (p = 0.0005) independently of known clinical risk factors. The prognostic value was validated in another three following independent cohorts: GSE19536 (p = 0.0009), GSE22220 (p = 0.0003), and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (p = 0.0023). The three-miRNAs risk score predicted bone recurrence in TCGA (p = 0.0052), and the findings were validated in another independent population of patients who experienced bone recurrence and age/stage-matched patients without any recurrence. The three-miRNAs risk score enriched multiple metastasis-related gene sets such as angiogenesis and epithelial mesenchymal transition in a gene-set-enrichment analysis.

Conclusions

The authors developed the novel miRNA-based risk score, which is a promising biomarker for prediction of worse survival and bone recurrence potential in breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52.

    Article  CAS  Google Scholar 

  2. Sparano JA, Gray RJ, Makower DF, et al. Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med. 2015;373:2005–14.

    Article  CAS  Google Scholar 

  3. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001;98:10869–74.

    Article  CAS  Google Scholar 

  4. SEER Stat Fact Sheets: Female Breast Cancer, 2016. http://seer.cancer.gov/statfacts/html/breast.html. Accessed 20 Dec 2016.

  5. DeSantis C, Ma J, Bryan L, Jemal A. Breast cancer statistics, 2013. CA Cancer J Clin. 2014;64:52–62.

    Article  Google Scholar 

  6. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75:843–54.

    Article  CAS  Google Scholar 

  7. Lee RC, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001;294:862–4.

    Article  CAS  Google Scholar 

  8. He L, Thomson JM, Hemann MT, et al. A microRNA polycistron as a potential human oncogene. Nature. 2005;435:828–33.

    Article  CAS  Google Scholar 

  9. Lu J, Getz G, Miska EA, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834–8.

    Article  CAS  Google Scholar 

  10. Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer. 2006;6:857–66.

    Article  CAS  Google Scholar 

  11. He L, He X, Lim LP, et al. A microRNA component of the p53 tumour suppressor network. Nature. 2007;447:1130–4.

    Article  CAS  Google Scholar 

  12. Iorio MV, Ferracin M, Liu CG, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65:7065–70.

    Article  CAS  Google Scholar 

  13. Corcoran C, Friel AM, Duffy MJ, Crown J, O’Driscoll L. Intracellular and extracellular microRNAs in breast cancer. Clin Chem. 2011;57:18–32.

    Article  CAS  Google Scholar 

  14. Koboldt DC, Fulton RS, McLellan MD, et al. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61–70.

    Article  CAS  Google Scholar 

  15. Pereira B, Chin SF, Rueda OM, et al. The somatic mutation profiles of 2433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016;7:11479.

    Article  CAS  Google Scholar 

  16. Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups. Nature. 2012;486:346–52.

    Article  CAS  Google Scholar 

  17. Enerly E, Steinfeld I, Kleivi K, et al. miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors. PLoS ONE. 2011;6:e16915.

    Article  CAS  Google Scholar 

  18. Buffa FM, Camps C, Winchester L, et al. MicroRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer Res. 2011;71:5635–45.

    Article  CAS  Google Scholar 

  19. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ. Strategies for subtypes: dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol. 2011;22:1736–47.

    Article  CAS  Google Scholar 

  20. Sobin LH, Gospodarowicz MK, Wittekind C. TNM classification of malignant tumours. 7th ed. New York: John Wiley & Sons; 2009.

    Google Scholar 

  21. Gendoo DM, Ratanasirigulchai N, Schroder MS, et al. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics. 2016;32:1097–9.

    Article  CAS  Google Scholar 

  22. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  Google Scholar 

  23. Crowley JLM, Jacobson J, Salmon S. Proceedings of the First Seattle Symposium in Biostatistics Survival Analysis, vol 123. New York: Springer; 1997.

    Chapter  Google Scholar 

  24. Kim SY, Kawaguchi T, Yan L, Young J, Qi Q, Takabe K. Clinical relevance of microRNA expressions in breast cancer validated using the Cancer Genome Atlas (TCGA). Ann Surg Oncol. 2017;24:2943–49.

    Article  Google Scholar 

  25. Ramanathan R, Olex AL, Dozmorov M, Bear HD, Fernandez LJ, Takabe K. Angiopoietin pathway gene expression associated with poor breast cancer survival. Breast Cancer Res Treat. 2017;162:191–8.

    Article  CAS  Google Scholar 

  26. Young J, Kawaguchi T, Yan L, Qi Q, Liu S, Takabe K. Tamoxifen sensitivity-related microRNA-342 is a useful biomarker for breast cancer survival. Oncotarget. 2017;8:99978–89.

    PubMed  PubMed Central  Google Scholar 

  27. Kawaguchi T, Yan L, Qi Q, et al. Overexpression of suppressive microRNAs, miR-30a, and miR-200c are associated with improved survival of breast cancer patients. Sci Rep. 2017;7:15945.

    Article  Google Scholar 

  28. Hoerl AK. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12:55–67.

    Article  Google Scholar 

  29. Narayanan S, Kawaguchi T, Yan L, Peng X, Qi Q, Takabe K. Cytolytic activity score to assess anticancer immunity in colorectal cancer. Ann Surg Oncol. 2018;25:2323.

    Article  Google Scholar 

  30. Terakawa T, Katsuta E, Yan L, et al. High expression of SLCO2B1 is associated with prostate cancer recurrence after radical prostatectomy. Oncotarget. 2018;9:14207–18.

    Article  Google Scholar 

  31. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50.

    Article  CAS  Google Scholar 

  32. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97:1180–4.

    Article  CAS  Google Scholar 

  33. McBryan J, Fagan A, McCartan D, et al. Transcriptomic profiling of sequential tumors from breast cancer patients provides a global view of metastatic expression changes following endocrine therapy. Clin Cancer Res. 2015;21:5371–9.

    Article  CAS  Google Scholar 

  34. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–25.

    Article  CAS  Google Scholar 

  35. Volinia S, Croce CM. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci U S A. 2013;110:7413–7.

    Article  CAS  Google Scholar 

  36. Peng F, Zhang Y, Wang R, et al. Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine. Oncogenesis. 2016;5:e194.

    Article  CAS  Google Scholar 

  37. Wu X, Zeng R, Wu S, Zhong J, Yang L, Xu J. Comprehensive expression analysis of miRNA in breast cancer at the miRNA and isomiR levels. Gene. 2015;557:195–200.

    Article  CAS  Google Scholar 

  38. Zhou X, Wang X, Huang Z, Xu L, Zhu W, Liu P. An ER-associated miRNA signature predicts prognosis in ER-positive breast cancer. J Exp Clin Cancer Res. 2014;33:94.

    Article  CAS  Google Scholar 

  39. Dews M, Homayouni A, Yu D, et al. Augmentation of tumor angiogenesis by a Myc-activated microRNA cluster. Nat Genet. 2006;38:1060–5.

    Article  CAS  Google Scholar 

  40. Dews M, Fox JL, Hultine S, et al. The myc-miR-17 ~ 92 axis blunts TGF{beta} signaling and production of multiple TGF{beta}-dependent antiangiogenic factors. Cancer Res. 2010;70:8233–46.

    Article  CAS  Google Scholar 

  41. Li Z, Yang CS, Nakashima K, Rana TM. Small RNA-mediated regulation of iPS cell generation. EMBO J 2011;30:823–34.

    Article  Google Scholar 

  42. Stefani G, Slack FJ. Small non-coding RNAs in animal development. Nat Rev Mol Cell Biol. 2008;9:219–30.

    Article  CAS  Google Scholar 

  43. Mendell JT. miRiad roles for the miR-17-92 cluster in development and disease. Cell. 2008;133:217–22.

    Article  CAS  Google Scholar 

  44. Petrocca F, Vecchione A, Croce CM. Emerging role of miR-106b-25/miR-17-92 clusters in the control of transforming growth factor beta signaling. Cancer Res. 2008;68:8191–4.

    Article  CAS  Google Scholar 

  45. O’Donnell KA, Wentzel EA, Zeller KI, Dang CV, Mendell JT. c-Myc-regulated microRNAs modulate E2F1 expression. Nature. 2005;435:839–43.

    Article  Google Scholar 

  46. Dal Bo M, Bomben R, Hernandez L, Gattei V. The MYC/miR-17-92 axis in lymphoproliferative disorders: a common pathway with therapeutic potential. Oncotarget. 2015;6:19381–92.

    Article  Google Scholar 

  47. Kim K, Chadalapaka G, Lee SO, et al. Identification of oncogenic microRNA-17-92/ZBTB4/specificity protein axis in breast cancer. Oncogene. 2012;31:1034–44.

    Article  CAS  Google Scholar 

  48. Yang J, Zhang Z, Chen C, et al. MicroRNA-19a-3p inhibits breast cancer progression and metastasis by inducing macrophage polarization through downregulated expression of Fra-1 proto-oncogene. Oncogene. 2014;33:3014–23.

    Article  CAS  Google Scholar 

  49. Conley RB, Dickson D, Zenklusen JC, et al. Core clinical data elements for cancer genomic repositories: a multi-stakeholder consensus. Cell. 2017;171:982–6.

    Article  CAS  Google Scholar 

  50. Manolio TA, Fowler DM, Starita LM, et al. Bedside back to bench: building bridges between basic and clinical genomic research. Cell. 2017;169:6–12.

    Article  CAS  Google Scholar 

  51. Rodriguez H, Pennington SR. Revolutionizing precision oncology through collaborative proteogenomics and data sharing. Cell. 2018;173:535–9.

    Article  CAS  Google Scholar 

Download references

Acknowledgment

Kazuaki Takabe is supported by NIH/NCI Grant R01CA160688, Susan G. Komen Investigator Initiated Research Grant IIR12222224, and Institutional Grant 71-4085-01. The authors acknowledge Bioinformatics and Biostatistics Shared Resources supported by National Cancer Institute (NCI) Grant P30CA016056, as well as Pathology Resource Network and Clinical Data Delivery and Honest Broker services provided by the Clinical Data Network, which is funded by the National Cancer Institute and Roswell Park Cancer Institute Cancer Center Support Grant shared resource.

Author information

Author notes

  1. Tstutomu Kawaguchi and Li Yan have contributed equally to this work.

Authors and Affiliations

  1. Division of Breast Surgery, Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

    Tstutomu Kawaguchi MD, PhD, Stephen B. Edge MD, Jessica Young MD & Kazuaki Takabe MD, PhD, FACS

  2. Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan

    Tstutomu Kawaguchi MD, PhD & Eigo Otsuji MD, PhD

  3. Department of Biostatistics and Bioinformatics, University at Buffalo, The State University of New York Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA

    Li Yan PhD, Qianya Qi MS, Xuan Peng MS & Song Liu PhD

  4. Department of Surgery, University at Buffalo, The State University of New York Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA

    Stephen B. Edge MD, Jessica Young MD & Kazuaki Takabe MD, PhD, FACS

  5. Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA

    Song Yao PhD

  6. Division of Digestive and General Surgery, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan

    Kazuaki Takabe MD, PhD, FACS

  7. Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo, Japan

    Kazuaki Takabe MD, PhD, FACS

  8. Department of Surgery, Yokohama City University, Yokohama, Japan

    Kazuaki Takabe MD, PhD, FACS

  9. Breast Surgery, Fukushima Medical University, Fukushima, Japan

    Kazuaki Takabe MD, PhD, FACS

Authors
  1. Tstutomu Kawaguchi MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Yan PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qianya Qi MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuan Peng MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stephen B. Edge MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jessica Young MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Song Yao PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Song Liu PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eigo Otsuji MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kazuaki Takabe MD, PhD, FACS
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kazuaki Takabe MD, PhD, FACS.

Ethics declarations

Conflict of interest

There are no conflicts of interest.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawaguchi, T., Yan, L., Qi, Q. et al. Novel MicroRNA-Based Risk Score Identified by Integrated Analyses to Predict Metastasis and Poor Prognosis in Breast Cancer. Ann Surg Oncol 25, 4037–4046 (2018). https://doi.org/10.1245/s10434-018-6859-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1245/s10434-018-6859-x

Keywords

Search

Navigation