Skip to main content
Top
Published in: Breast Cancer Research and Treatment 3/2019

01-12-2019 | Breast Cancer | Preclinical study

CpG methylation signature predicts prognosis in breast cancer

Authors: Tonghua Du, Bin Liu, Zhenyu Wang, Xiaoyu Wan, Yuanyu Wu

Published in: Breast Cancer Research and Treatment | Issue 3/2019

Login to get access

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.
Appendix
Available only for authorised users
Literature
3.
go back to reference Tang Q, Holland-Letz T, Slynko A, Cuk K, Marme F, Schott S, Heil J, Qu B, Golatta M, Bewerunge-Hudler M, Sutter C, Surowy H, Wappenschmidt B, Schmutzler R, Hoth M, Bugert P, Bartram CR, Sohn C, Schneeweiss A, Yang R, Burwinkel B (2016) DNA methylation array analysis identifies breast cancer associated RPTOR, MGRN1 and RAPSN hypomethylation in peripheral blood DNA. Oncotarget 7(39):64191–64202. https://doi.org/10.18632/oncotarget.1164011640 CrossRefPubMedPubMedCentral Tang Q, Holland-Letz T, Slynko A, Cuk K, Marme F, Schott S, Heil J, Qu B, Golatta M, Bewerunge-Hudler M, Sutter C, Surowy H, Wappenschmidt B, Schmutzler R, Hoth M, Bugert P, Bartram CR, Sohn C, Schneeweiss A, Yang R, Burwinkel B (2016) DNA methylation array analysis identifies breast cancer associated RPTOR, MGRN1 and RAPSN hypomethylation in peripheral blood DNA. Oncotarget 7(39):64191–64202. https://​doi.​org/​10.​18632/​oncotarget.​1164011640 CrossRefPubMedPubMedCentral
4.
go back to reference Cady B (2007) Local therapy and survival in breast cancer. N Engl J Med 357(10):1051–1052 author reply 1052 CrossRef Cady B (2007) Local therapy and survival in breast cancer. N Engl J Med 357(10):1051–1052 author reply 1052 CrossRef
5.
go back to reference Hudis CA (2007) Trastuzumab–mechanism of action and use in clinical practice. N Engl J Med 357(1):39–51CrossRef Hudis CA (2007) Trastuzumab–mechanism of action and use in clinical practice. N Engl J Med 357(1):39–51CrossRef
6.
go back to reference Jones PA, Baylin SB (2007) The epigenomics of cancer. Cell 128(4):683–692CrossRef Jones PA, Baylin SB (2007) The epigenomics of cancer. Cell 128(4):683–692CrossRef
7.
go back to reference Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, Fan JB, Shen R (2011) High density DNA methylation array with single CpG site resolution. Genomics 98(4):288–295CrossRef Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, Fan JB, Shen R (2011) High density DNA methylation array with single CpG site resolution. Genomics 98(4):288–295CrossRef
8.
go back to reference Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S (2005) Bioinformatics and computational biology solutions using R and Bioconductor. Springer, New YorkCrossRef Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S (2005) Bioinformatics and computational biology solutions using R and Bioconductor. Springer, New YorkCrossRef
9.
go back to reference Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological) 57(1):289–300 Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological) 57(1):289–300
10.
go back to reference Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559CrossRef Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559CrossRef
11.
go back to reference Wang P, Wang Y, Hang B, Zou X, Mao JH (2016) A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. Oncotarget 7(34):55343–55351PubMedPubMedCentral Wang P, Wang Y, Hang B, Zou X, Mao JH (2016) A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. Oncotarget 7(34):55343–55351PubMedPubMedCentral
13.
go back to reference Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395CrossRef Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395CrossRef
16.
go back to reference Yang Y, Wu L, Shu XO, Cai Q, Shu X, Li B, Guo X, Ye F, Michailidou K, Bolla MK, Wang Q, Dennis J, Andrulis IL, Brenner H, Chenevix-Trench G, Campa D, Castelao JE, Gago-Dominguez M, Dork T, Hollestelle A, Lophatananon A, Muir K, Neuhausen SL, Olsson H, Sandler DP, Simard J, Kraft P, Pharoah PDP, Easton DF, Zheng W, Long J (2019) Genetically predicted levels of DNA methylation biomarkers and breast cancer risk: data from 228,951 women of European descent. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djz109 CrossRefPubMedPubMedCentral Yang Y, Wu L, Shu XO, Cai Q, Shu X, Li B, Guo X, Ye F, Michailidou K, Bolla MK, Wang Q, Dennis J, Andrulis IL, Brenner H, Chenevix-Trench G, Campa D, Castelao JE, Gago-Dominguez M, Dork T, Hollestelle A, Lophatananon A, Muir K, Neuhausen SL, Olsson H, Sandler DP, Simard J, Kraft P, Pharoah PDP, Easton DF, Zheng W, Long J (2019) Genetically predicted levels of DNA methylation biomarkers and breast cancer risk: data from 228,951 women of European descent. J Natl Cancer Inst. https://​doi.​org/​10.​1093/​jnci/​djz109 CrossRefPubMedPubMedCentral
17.
go back to reference He LH, Ma Q, Shi YH, Ge J, Zhao HM, Li SF, Tong ZS (2013) CHL1 is involved in human breast tumorigenesis and progression. Biochem Biophys Res Commun 438(2):433–438CrossRef He LH, Ma Q, Shi YH, Ge J, Zhao HM, Li SF, Tong ZS (2013) CHL1 is involved in human breast tumorigenesis and progression. Biochem Biophys Res Commun 438(2):433–438CrossRef
18.
go back to reference Martin-Sanchez E, Mendaza S, Ulazia-Garmendia A, Monreal-Santesteban I, Blanco-Luquin I, Cordoba A, Vicente-Garcia F, Perez-Janices N, Escors D, Megias D, Lopez-Serra P, Esteller M, Illarramendi JJ, Guerrero-Setas D (2017) CHL1 hypermethylation as a potential biomarker of poor prognosis in breast cancer. Oncotarget 8(9):15789–15801CrossRef Martin-Sanchez E, Mendaza S, Ulazia-Garmendia A, Monreal-Santesteban I, Blanco-Luquin I, Cordoba A, Vicente-Garcia F, Perez-Janices N, Escors D, Megias D, Lopez-Serra P, Esteller M, Illarramendi JJ, Guerrero-Setas D (2017) CHL1 hypermethylation as a potential biomarker of poor prognosis in breast cancer. Oncotarget 8(9):15789–15801CrossRef
19.
go back to reference Cao WH, Liu XP, Meng SL, Gao YW, Wang Y, Ma ZL, Wang XG, Wang HB (2016) USP4 promotes invasion of breast cancer cells via Relaxin/TGF-beta1/Smad2/MMP-9 signal. Eur Rev Med Pharmacol Sci 20(6):1115–1122PubMed Cao WH, Liu XP, Meng SL, Gao YW, Wang Y, Ma ZL, Wang XG, Wang HB (2016) USP4 promotes invasion of breast cancer cells via Relaxin/TGF-beta1/Smad2/MMP-9 signal. Eur Rev Med Pharmacol Sci 20(6):1115–1122PubMed
21.
go back to reference Turner AW, Nikpay M, Silva A, Lau P, Martinuk A, Linseman TA, Soubeyrand S, McPherson R (2015) Functional interaction between COL4A1/COL4A2 and SMAD3 risk loci for coronary artery disease. Atherosclerosis 242(2):543–552CrossRef Turner AW, Nikpay M, Silva A, Lau P, Martinuk A, Linseman TA, Soubeyrand S, McPherson R (2015) Functional interaction between COL4A1/COL4A2 and SMAD3 risk loci for coronary artery disease. Atherosclerosis 242(2):543–552CrossRef
22.
go back to reference JingSong H, Hong G, Yang J, Duo Z, Li F, WeiCai C, XueYing L, YouSheng M, YiWen O, Yue P, Zou C (2017) siRNA-mediated suppression of collagen type IV alpha 2 (COL4A2) mRNA inhibits triple-negative breast cancer cell proliferation and migration. Oncotarget 8(2):2585–2593PubMed JingSong H, Hong G, Yang J, Duo Z, Li F, WeiCai C, XueYing L, YouSheng M, YiWen O, Yue P, Zou C (2017) siRNA-mediated suppression of collagen type IV alpha 2 (COL4A2) mRNA inhibits triple-negative breast cancer cell proliferation and migration. Oncotarget 8(2):2585–2593PubMed
23.
go back to reference Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE Jr, Wickerham DL, Wolmark N (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24(23):3726–3734CrossRef Paik S, Tang G, Shak S, Kim C, Baker J, Kim W, Cronin M, Baehner FL, Watson D, Bryant J, Costantino JP, Geyer CE Jr, Wickerham DL, Wolmark N (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24(23):3726–3734CrossRef
24.
go back to reference Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9(6):465–476CrossRef Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9(6):465–476CrossRef
Metadata
Title
CpG methylation signature predicts prognosis in breast cancer
Authors
Tonghua Du
Bin Liu
Zhenyu Wang
Xiaoyu Wan
Yuanyu Wu
Publication date
01-12-2019
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 3/2019
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-019-05417-3

Other articles of this Issue 3/2019

Breast Cancer Research and Treatment 3/2019 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine