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Molecular Genetics and Genomics

, Volume 294, Issue 1, pp 95–110 | Cite as

Identification of the copy number variant biomarkers for breast cancer subtypes

  • Xiaoyong Pan
  • XiaoHua Hu
  • Yu-Hang Zhang
  • Lei Chen
  • LiuCun Zhu
  • ShiBao Wan
  • Tao HuangEmail author
  • Yu-Dong CaiEmail author
Original Article
  • 258 Downloads

Abstract

Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression. Thus, it is meaningful to detect CNVs of different cancers. To identify the CNV biomarker for breast cancer subtypes, we integrated the CNV data of more than 2000 samples from two large breast cancer databases, METABRIC and The Cancer Genome Atlas (TCGA). A Monte Carlo feature selection-based and incremental feature selection-based computational method was proposed and tested to identify the distinctive core CNVs in different breast cancer subtypes. We identified the CNV genes that may contribute to breast cancer tumorigenesis as well as built a set of quantitative distinctive rules for recognition of the breast cancer subtypes. The tenfold cross-validation Matthew’s correlation coefficient (MCC) on METABRIC training set and the independent test on TCGA dataset were 0.515 and 0.492, respectively. The CNVs of PGAP3, GRB7, MIR4728, PNMT, STARD3, TCAP and ERBB2 were important for the accurate diagnosis of breast cancer subtypes. The findings reported in this study may further uncover the difference between different breast cancer subtypes and improve the diagnosis accuracy.

Keywords

Breast cancer Copy number variant Monte Carlo feature selection Dagging Quantitative distinctive rule 

Notes

Funding

This study was funded by the National Natural Science Foundation of China [31701151, 31571343, 61462018], Natural Science Foundation of Shanghai [17ZR1412500, 16ZR1403100], Shanghai Sailing Program, the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245], the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703], Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000].

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.

Supplementary material

438_2018_1488_MOESM1_ESM.pdf (647 kb)
Supplementary material 1 (PDF 646 KB)
438_2018_1488_MOESM2_ESM.pdf (57 kb)
Supplementary material 2 (PDF 57 KB)
438_2018_1488_MOESM3_ESM.pdf (48 kb)
Supplementary material 3 (PDF 47 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Life ScienceShanghai UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Medical InformaticsErasmus MCRotterdamThe Netherlands
  3. 3.Department of Biostatistics and Computational Biology, School of Life SciencesFudan UniversityShanghaiPeople’s Republic of China
  4. 4.Institute of Health Sciences, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiPeople’s Republic of China
  5. 5.College of Information EngineeringShanghai Maritime UniversityShanghaiPeople’s Republic of China
  6. 6.Shanghai Key Laboratory of PMMPEast China Normal UniversityShanghaiPeople’s Republic of China

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