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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.

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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
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  2. Li Yan PhD
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  7. Song Yao PhD
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  9. Eigo Otsuji MD, PhD
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  10. Kazuaki Takabe MD, PhD, FACS
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Corresponding author

Correspondence to Kazuaki Takabe MD, PhD, FACS.

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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

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

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