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CpG methylation signature predicts prognosis in breast cancer

  • Preclinical study
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

DNA methylation can be used as prognostic biomarkers in various types of cancers. We aimed to identify a CpG methylation pattern for breast cancer.

Methods

In this study, using the microarray data from the cancer genome atlas (TCGA) and gene expression omnibus (GEO), we profiled DNA methylation between 97 healthy control samples and 786 breast cancer samples in a training cohort (from TCGA, n = 883) to build a gene classifier using a penalized regression model. We validated the prognostic accuracy of this gene classifier in an internal validation cohort (from GEO, n = 72).

Results

A total of 1777 differentially methylated CpGs corresponding to 1777 different methylated genes (DMGs) between breast cancer and control were chosen for this study. Subsequently, 16 CpGs were generated to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter overall survival (hazard ratio [HR], 4.674; 95% CI 2.918 to 7.487; P = 1.678e–12) than patients with low-risk scores. The prognostic accuracy was also validated in the validation cohorts. Furthermore, among patients with low-risk scores in the combined training and validation cohorts, the patients with the age > 60 years compared with the patients with the age < 60 years were associated with improved overall survival (HR 2.088, 95% CI 1.348 to 3.235; p = 7.575e–04) in patients with a high-risk score but not in patients with low-risk score (HR 1.246, 95% CI 0.515 to 3.011; p = 0.625). The patients treated with radiotherapy compared with the patients without radiotherapy were associated with improved overall survival (HR 0.418, 95% CI 0.249 to 0.703; p = 6.991e-04) in patients with a high-risk score but not in patients with low-risk score (HR 2.092, 95% CI 0.574 to 7.629; p = 0.253). For the patients with recurrence and the patients without recurrence both groups were all associated with improved overall survival (HR 7.475, 95% CI 4.333 to 12.901; p = 6.991e–04) in patients with a high-risk score and in patients with low-risk score (HR 14.33, 95% CI 4.265 to 48.17; p = 4.883e–13).

Conclusion

The 16 CpG-based signature is useful as a biomarker in predicting prognosis for patients with breast cancer.

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Authors and Affiliations

  1. Department of Breast Surgery, The Second Clinical Hospital Of Jilin University, NO. 218, Ziqiang Street, Nanguan District, Changchun, 130000, Jilin, China

    Tonghua Du, Bin Liu, Zhenyu Wang & Xiaoyu Wan

  2. Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, No.126 Xiantai St, Changchun, Jilin, 130033, China

    Yuanyu Wu

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  1. Tonghua Du
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  2. Bin Liu
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  3. Zhenyu Wang
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  4. Xiaoyu Wan
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Correspondence to Yuanyu Wu.

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10549_2019_5417_MOESM1_ESM.pdf

Supplementary material 1 (PDF 247 kb) Figure S1 WGCNA of DMGs between control group and breast cancer group. (A) Hierarchical cluster tree showing comethylation modules identified by WGCNA. Each leaf in the tree represents one gene. The major tree branches constitute 6 modules, labeled with different colors. (B) Module–trait association. Each row corresponds to a module, labeled with a color as in (A). Each column corresponds to a clinical trait (death, recurrence, age, ER, PR, HER2, type, M, N, T, stage, radiation and group). The color of each cell at the row—column intersection indicates the correlation coefficient between the module and the trait. A p value was also listed in the brackets under the coefficient

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Du, T., Liu, B., Wang, Z. et al. CpG methylation signature predicts prognosis in breast cancer. Breast Cancer Res Treat 178, 565–572 (2019). https://doi.org/10.1007/s10549-019-05417-3

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  • DOI: https://doi.org/10.1007/s10549-019-05417-3

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