Breast Cancer Research and Treatment

, Volume 160, Issue 2, pp 371–383 | Cite as

Integrated analysis of gene expression and methylation profiles of 48 candidate genes in breast cancer patients

  • Zibo Li
  • Jianfu Heng
  • Jinhua Yan
  • Xinwu Guo
  • Lili Tang
  • Ming Chen
  • Limin Peng
  • Yepeng Wu
  • Shouman Wang
  • Zhi Xiao
  • Zhongping Deng
  • Lizhong Dai
  • Jun WangEmail author



Gene-specific methylation and expression have shown biological and clinical importance for breast cancer diagnosis and prognosis. Integrated analysis of gene methylation and gene expression may identify genes associated with biology mechanism and clinical outcome of breast cancer and aid in clinical management.


Using high-throughput microfluidic quantitative PCR, we analyzed the expression profiles of 48 candidate genes in 96 Chinese breast cancer patients and investigated their correlation with gene methylation and associations with breast cancer clinical parameters.


Breast cancer-specific gene expression alternation was found in 25 genes with significant expression difference between paired tumor and normal tissues. A total of 9 genes (CCND2, EGFR, GSTP1, PGR, PTGS2, RECK, SOX17, TNFRSF10D, and WIF1) showed significant negative correlation between methylation and gene expression, which were validated in the TCGA database. Total 23 genes (ACADL, APC, BRCA2, CADM1, CAV1, CCND2, CST6, EGFR, ESR2, GSTP1, ICAM5, NPY, PGR, PTGS2, RECK, RUNX3, SFRP1, SOX17, SYK, TGFBR2, TNFRSF10D, WIF1, and WRN) annotated with potential TFBSs in the promoter regions showed negative correlation between methylation and expression. In logistics regression analysis, 31 of the 48 genes showed improved performance in disease prediction with combination of methylation and expression coefficient.


Our results demonstrated the complex correlation and the possible regulatory mechanisms between DNA methylation and gene expression. Integration analysis of methylation and expression of candidate genes could improve performance in breast cancer prediction. These findings would contribute to molecular characterization and identification of biomarkers for potential clinical applications.


Breast cancer Methylation Gene expression Microfludic PCR Next-generation sequencing 



This work was supported in part by grants from the Natural Science Foundation of China (No. 81272296 and 81372228), the Scientific Project of China Hunan Provincial Science and Technology Department (No. 2012SK2013), and the Major Special Projects of the Science and Technology Bureau of Changsha, China (No. K1204017-31 and K1306011-31). Zibo Li was supported by the Hunan Province Postgraduate Student Scientific Innovation Project, China (No. CX2013B087).

Compliance with ethical standards

Conflict of interest

Xingwu Guo, Limin Peng, Ming Chen, Zhongping Deng, and Lizhong Dai are employees of Sanway Gene Technology Inc.

Ethical standards

We declare that the experiments performed in this study comply with the current laws of the People’s Republic of China.

Supplementary material

10549_2016_4004_MOESM1_ESM.doc (64 kb)
Online Resource Methods (DOC 63 kb)
10549_2016_4004_MOESM2_ESM.doc (96 kb)
Online Resource Table 1 (DOC 96 kb)
10549_2016_4004_MOESM3_ESM.xls (178 kb)
Online Resource Table 2 (XLS 177 kb)
10549_2016_4004_MOESM4_ESM.xls (30 kb)
Online Resource Table 3 (XLS 30 kb)


  1. 1.
    Ferlay J SI, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC cancer base no. 11 [Internet]. Lyon: international agency for research on cancer; 2013. Available from: Accessed 17 July 2014
  2. 2.
    Feinberg AP, Ohlsson R, Henikoff S (2006) The epigenetic progenitor origin of human cancer. Nat Rev Genet 7(1):21–33. doi: 10.1038/nrg1748 CrossRefPubMedGoogle Scholar
  3. 3.
    Jones PA, Baylin SB (2007) The epigenomics of cancer. Cell 128(4):683–692. doi: 10.1016/j.cell.2007.01.029 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Deaton AM, Bird A (2011) CpG islands and the regulation of transcription. Genes Dev 25(10):1010–1022. doi: 10.1101/gad.2037511 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Jones PA (2012) Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet 13(7):484–492. doi: 10.1038/nrg3230 CrossRefPubMedGoogle Scholar
  6. 6.
    Chan KC, Jiang P, Chan CW, Sun K, Wong J, Hui EP, Chan SL, Chan WC, Hui DS, Ng SS, Chan HL, Wong CS, Ma BB, Chan AT, Lai PB, Sun H, Chiu RW, Lo YM (2013) Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc Natl Acad Sci U S A 110(47):18761–18768. doi: 10.1073/pnas.1313995110 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Fackler MJ, Umbricht CB, Williams D, Argani P, Cruz LA, Merino VF, Teo WW, Zhang Z, Huang P, Visvananthan K, Marks J, Ethier S, Gray JW, Wolff AC, Cope LM, Sukumar S (2011) Genome-wide methylation analysis identifies genes specific to breast cancer hormone receptor status and risk of recurrence. Cancer Res 71(19):6195–6207. doi: 10.1158/0008-5472.CAN-11-1630 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Laird PW (2003) The power and the promise of DNA methylation markers. Nat Rev Cancer 3(4):253–266. doi: 10.1038/nrc1045 CrossRefPubMedGoogle Scholar
  9. 9.
    Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9(6):465–476. doi: 10.1038/nrg2341 CrossRefPubMedGoogle Scholar
  10. 10.
    Weber M, Hellmann I, Stadler MB, Ramos L, Paabo S, Rebhan M, Schubeler D (2007) Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet 39(4):457–466. doi: 10.1038/ng1990 CrossRefPubMedGoogle Scholar
  11. 11.
    Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, Gilad Y, Pritchard JK (2011) DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol 12(1):R10. doi: 10.1186/gb-2011-12-1-r10 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Pai AA, Bell JT, Marioni JC, Pritchard JK, Gilad Y (2011) A genome-wide study of DNA methylation patterns and gene expression levels in multiple human and chimpanzee tissues. PLoS Genet 7(2):e1001316. doi: 10.1371/journal.pgen.1001316 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Li Z, Guo X, Wu Y, Li S, Yan J, Peng L, Xiao Z, Wang S, Deng Z, Dai L, Yi W, Xia K, Tang L, Wang J (2015) Methylation profiling of 48 candidate genes in tumor and matched normal tissues from breast cancer patients. Breast Cancer Res Treat 149(3):767–779. doi: 10.1007/s10549-015-3276-8 CrossRefPubMedGoogle Scholar
  14. 14.
    Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, Senn HJ, Panel Members (2013) Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 24(9):2206–2223. doi: 10.1093/annonc/mdt303 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Rhee JK, Kim K, Chae H, Evans J, Yan P, Zhang BT, Gray J, Spellman P, Huang TH, Nephew KP, Kim S (2013) Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer. Nucleic Acids Res 41(18):8464–8474. doi: 10.1093/nar/gkt643 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Sakai T, Toguchida J, Ohtani N, Yandell DW, Rapaport JM, Dryja TP (1991) Allele-specific hypermethylation of the retinoblastoma tumor-suppressor gene. Am J Hum Genet 48(5):880–888PubMedPubMedCentralGoogle Scholar
  17. 17.
    Bachman KE, Herman JG, Corn PG, Merlo A, Costello JF, Cavenee WK, Baylin SB, Graff JR (1999) Methylation-associated silencing of the tissue inhibitor of metalloproteinase-3 gene suggest a suppressor role in kidney, brain, and other human cancers. Cancer Res 59(4):798–802PubMedGoogle Scholar
  18. 18.
    Merlo A, Herman JG, Mao L, Lee DJ, Gabrielson E, Burger PC, Baylin SB, Sidransky D (1995) 5′ CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers. Nat Med 1(7):686–692CrossRefPubMedGoogle Scholar
  19. 19.
    Hon GC, Hawkins RD, Caballero OL, Lo C, Lister R, Pelizzola M, Valsesia A, Ye Z, Kuan S, Edsall LE, Camargo AA, Stevenson BJ, Ecker JR, Bafna V, Strausberg RL, Simpson AJ, Ren B (2012) Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res 22(2):246–258. doi: 10.1101/gr.125872.111 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Huang WY, Hsu SD, Huang HY, Sun YM, Chou CH, Weng SL, Huang HD (2015) MethHC: a database of DNA methylation and gene expression in human cancer. Nucleic Acids Res 43(Database issue):D856–D861CrossRefPubMedGoogle Scholar
  21. 21.
    Schubeler D (2015) Function and information content of DNA methylation. Nature 517(7534):321–326. doi: 10.1038/nature14192 CrossRefPubMedGoogle Scholar
  22. 22.
    Kordi Tamandani DM, Hemati S, Davani SK, Arbabi F (2015) Association between promoter methylation and expression of thyroid hormone receptor beta (THRbeta) gene in patients with gastric cancer in an Iranian population. J Gastroenterol Hepatol 30(3):485–489. doi: 10.1111/jgh.12808 CrossRefPubMedGoogle Scholar
  23. 23.
    Malhotra P, Anwar M, Kochhar R, Ahmad S, Vaiphei K, Mahmood S (2014) Promoter methylation and immunohistochemical expression of hMLH1 and hMSH2 in sporadic colorectal cancer: a study from India. Tumour Biol 35(4):3679–3687. doi: 10.1007/s13277-013-1487-3 CrossRefPubMedGoogle Scholar
  24. 24.
    Feng H, Zhang Z, Qing X, Wang X, Liang C, Liu D (2016) Promoter methylation of APC and RAR-beta genes as prognostic markers in non-small cell lung cancer (NSCLC). Exp Mol Pathol 100(1):109–113. doi: 10.1016/j.yexmp.2015.12.005 CrossRefPubMedGoogle Scholar
  25. 25.
    Aitchison AA, Veerakumarasivam A, Vias M, Kumar R, Hamdy FC, Neal DE, Mills IG (2008) Promoter methylation correlates with reduced Smad4 expression in advanced prostate cancer. Prostate 68(6):661–674. doi: 10.1002/pros.20730 CrossRefPubMedGoogle Scholar
  26. 26.
    Balgkouranidou I, Chimonidou M, Milaki G, Tsaroucha E, Kakolyris S, Georgoulias V, Lianidou E (2016) SOX17 promoter methylation in plasma circulating tumor DNA of patients with non-small cell lung cancer. Clin Chem Lab Med. doi: 10.1515/cclm-2015-0776 PubMedGoogle Scholar
  27. 27.
    Fleischer T, Edvardsen H, Solvang HK, Daviaud C, Naume B, Borresen-Dale AL, Kristensen VN, Tost J (2014) Integrated analysis of high-resolution DNA methylation profiles, gene expression, germline genotypes and clinical end points in breast cancer patients. Int J Cancer 134(11):2615–2625. doi: 10.1002/ijc.28606 CrossRefPubMedGoogle Scholar
  28. 28.
    Tate PH, Bird AP (1993) Effects of DNA methylation on DNA-binding proteins and gene expression. Curr Opin Genet Dev 3(2):226–231CrossRefPubMedGoogle Scholar
  29. 29.
    Choy MK, Movassagh M, Goh HG, Bennett MR, Down TA, Foo RS (2010) Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated. BMC Genom 11:519. doi: 10.1186/1471-2164-11-519 CrossRefGoogle Scholar
  30. 30.
    Nan X, Ng HH, Johnson CA, Laherty CD, Turner BM, Eisenman RN, Bird A (1998) Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature 393(6683):386–389. doi: 10.1038/30764 CrossRefPubMedGoogle Scholar
  31. 31.
    Yao L, Shen H, Laird PW, Farnham PJ, Berman BP (2015) Inferring regulatory element landscapes and transcription factor networks from cancer methylomes. Genome Biol 16:105. doi: 10.1186/s13059-015-0668-3 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Ziller MJ, Gu H, Muller F, Donaghey J, Tsai LT, Kohlbacher O, De Jager PL, Rosen ED, Bennett DA, Bernstein BE, Gnirke A, Meissner A (2013) Charting a dynamic DNA methylation landscape of the human genome. Nature 500(7463):477–481. doi: 10.1038/nature12433 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Wu H, Zhang Y (2014) Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell 156(1–2):45–68. doi: 10.1016/j.cell.2013.12.019 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Orlando FA, Brown KD (2009) Unraveling breast cancer heterogeneity through transcriptomic and epigenomic analysis. Ann Surg Oncol 16(8):2270–2279. doi: 10.1245/s10434-009-0500-y CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zibo Li
    • 1
  • Jianfu Heng
    • 1
  • Jinhua Yan
    • 1
  • Xinwu Guo
    • 2
  • Lili Tang
    • 3
  • Ming Chen
    • 2
  • Limin Peng
    • 2
  • Yepeng Wu
    • 1
  • Shouman Wang
    • 3
  • Zhi Xiao
    • 3
  • Zhongping Deng
    • 2
    • 4
    • 5
  • Lizhong Dai
    • 2
    • 4
    • 5
  • Jun Wang
    • 1
    Email author
  1. 1.The State Key Laboratory of Medical Genetics & School of Life SciencesCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Sanway Gene Technology Inc.ChangshaPeople’s Republic of China
  3. 3.Department of Breast Surgery, Xiangya HospitalCentral South UniversityChangshaPeople’s Republic of China
  4. 4.Research Center for Technologies in Nucleic Acid-Based DiagnosticsChangshaPeople’s Republic of China
  5. 5.Research Center for Technologies in Nucleic Acid-Based Diagnostics and TherapeuticsChangshaPeople’s Republic of China

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