2022 / 06

Next Paper
DOI: 10.24075/brsmu.2022.056

ORIGINAL RESEARCH

Identification of prognostically significant DNA methylation signatures in patients with various breast cancer types

Kalinkin AI1, Sigin VO1, Nemtsova MV1,2, Strelnikov VV1,3
About authors

1 Research Centre for Medical Genetics, Moscow, Russia

2 Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

3 Pirogov Russian National Research Medical University, Moscow, Russia

Correspondence should be addressed: Alexey I. Kalinkin
Moskvorechye, 1, Moscow, 115522; ur.xednay@2akiexela

About paper

Funding: the study was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of the Federal Scientific and Technical Program for the Development of Genetic Technologies in 2019–2027 (agreement № 075-15-2021-1073).

Author contribution: Kalinkin AI — study design, data acquisition, analysis and interpretation, manuscript writing; Sigin VO — manuscript writing; Nemtsova MV — study concept and design; Strelnikov VV — study concept and design, scientific editing.

Received: 2022-10-18 Accepted: 2022-11-11 Published online: 2022-11-25
|
  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71 (3): 209–49. DOI: 10.3322/caac.21660
  2. Bernhardt SM, Dasari P, Walsh D, Townsend AR, Price TJ, Ingman WV. Hormonal Modulation of Breast Cancer Gene Expression: Implications for Intrinsic Subtyping in Premenopausal Women. Front Oncol. 2016; 6: 241. DOI: 10.3389/fonc.2016.00241.
  3. Zaha DC. Significance of immunohistochemistry in breast cancer. World J Clin Oncol. 2014; 5 (3): 382–92. DOI: 10.5306/wjco. v5.i3.382. PMID: 25114853; PMCID: PMC4127609.
  4. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000; 406 (6797): 747–52. DOI: 10.1038/35021093.
  5. Echeverria GV, Ge Z, Seth S, Zhang X, Jeter-Jones S, Zhou X, et al. Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state. Sci Transl Med. 2019; 11 (488): eaav0936. DOI: 10.1126/scitranslmed. aav0936.
  6. Blanchette P, Sivajohanathan D, Bartlett J, Eisen A, Feilotter H, Pezo R, et al. Clinical Utility of Multigene Profiling Assays in EarlyStage Invasive Breast Cancer: An Ontario Health (Cancer Care Ontario) Clinical Practice Guideline. Curr Oncol. 2022; 29 (4): 2599–615. DOI: 10.3390/curroncol29040213.
  7. Edwards JR, Yarychkivska O, Boulard M, Bestor TH. DNA methylation and DNA methyltransferases. Epigenetics Chromatin. 2017; 10: 23. DOI: 10.1186/s13072-017-0130-8.
  8. Vietri MT, D'Elia G, Benincasa G, Ferraro G, Caliendo G, Nicoletti GF, et al. DNA methylation and breast cancer: A way forward (Review). Int J Oncol. 2021; 59 (5): 98. DOI: 10.3892/ ijo.2021.5278.
  9. Esteller M. Cancer epigenomics: DNA methylomes and histonemodification maps. Nat Rev Genet. 2007; 8 (4): 286–98. DOI: 10.1038/nrg2005.
  10. Karami Fath M, Azargoonjahromi A, Kiani A, Jalalifar F, Osati P, Akbari Oryani M, et al. The role of epigenetic modifications in drug resistance and treatment of breast cancer. Cell Mol Biol Lett. 2022; 27 (1): 52. DOI: 10.1186/s11658-022-00344-6.
  11. Lee G, Bang L, Kim SY, Kim D, Sohn KA. Identifying subtypespecific associations between gene expression and DNA methylation profiles in breast cancer. BMC Med Genomics. 2017; 10 (Suppl 1): 28. DOI: 10.1186/s12920-017-0268-z.
  12. Hu WL, Zhou XH. Identification of prognostic signature in cancer based on DNA methylation interaction network. BMC Med Genomics. 2017; 10 (Suppl 4): 63. DOI: 10.1186/s12920-0170307-9.
  13. Zhang M, Wang Y, Wang Y, Jiang L, Li X, Gao H, et al. Integrative Analysis of DNA Methylation and Gene Expression to Determine Specific Diagnostic Biomarkers and Prognostic Biomarkers of Breast Cancer. Front Cell Dev Biol. 2020; 8: 529386. DOI: 10.3389/fcell.2020.529386.
  14. Hao X, Luo H, Krawczyk M, Wei W, Wang W, Wang J, et al. DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci U S A. 2017; 114 (28): 7414–9. DOI: 10.1073/pnas.1703577114.
  15. de Almeida BP, Apolónio JD, Binnie A, Castelo-Branco P. Roadmap of DNA methylation in breast cancer identifies novel prognostic biomarkers. BMC Cancer. 2019; 19 (1): 219. DOI: 10.1186/s12885-019-5403-0.
  16. Gao Y, Wang X, Li S, Zhang Z, Li X, Lin F. Identification of a DNA Methylation-Based Prognostic Signature for Patients with TripleNegative Breast Cancer. Med Sci Monit. 2021; 27: e930025. DOI: 10.12659/MSM.930025.
  17. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016; 44 (8): e71. DOI: 10.1093/nar/gkv1507.
  18. Abd ElHafeez S, D'Arrigo G, Leonardis D, Fusaro M, Tripepi G, Roumeliotis S. Methods to Analyze Time-to-Event Data: The Cox Regression Analysis. Oxid Med Cell Longev. 2021; 2021: 1302811. DOI: 10.1155/2021/1302811.
  19. Utazirubanda JC, Leon T, Ngom P. Variable selection with Group LASSO approach: Application to Cox regression with frailty model. Commun Stat Simul Comput. 2021; 50 (3): 881–901. DOI: 10.1080/03610918.2019.1571605.
  20. Bhattacharjee A, Pawar A. SurvHiDim: high dimensional survival data analysis. R package version 0.1.1. 2021. Available from: https://CRAN.R-project.org/package=SurvHiDim.
  21. Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis — an introduction to concepts and methods. Br J Cancer. 2003; 89 (3): 431–6. DOI: 10.1038/ sj.bjc.6601119.
  22. LeDell E, Petersen M, van der Laan M. cvAUC: Cross-Validated Area Under the ROC Curve Confidence Intervals. R package version 1.1-4. 2022 Available from: http://CRAN.R-project.org/ package=cvAUC.
  23. Kassambara A, Kosinski M, Biecek P. survminer: Drawing Survival Curves using 'ggplot2'. R package version 0.4.6. Available from: https://CRAN.R-project.org/package=survminer.
  24. Team RCR Foundation for Statistical Computing; Vienna, Austria: 2015.
  25. Guo W, Zhu L, Zhu R, Chen Q, Wang Q, Chen JQ. A four-DNA methylation biomarker is a superior predictor of survival of patients with cutaneous melanoma. Elife. 2019; 8: e44310. DOI: 10.7554/ eLife.44310.
  26. de Ruijter TC, van der Heide F, Smits KM, Aarts MJ, van Engeland M, Heijnen VCG. Prognostic DNA methylation markers for hormone receptor breast cancer: a systematic review. Breast Cancer Res. 2020; 22 (1): 13. DOI: 10.1186/s13058-020-1250-9.
  27. Dai JB, Zhu B, Lin WJ, Gao HY, Dai H, Zheng L, et al. Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis. Biosci Rep. 2020; 40 (2): BSR20193678. DOI: 10.1042/BSR20193678.
  28. Jiang S, Bu X, Tang D, Yan C, Huang Y, Fang K. A tumor suppressor gene-based prognostic classifier predicts prognosis, tumor immune infiltration, and small molecule compounds in breast cancer. Front Genet. 2022; 12: 783026. DOI: 10.3389/fgene.2021.783026.
  29. Mohamadalizadeh-Hanjani Z, Shahbazi S, Geranpayeh L. Investigation of the SPAG5 gene expression and amplification related to the NuMA mRNA levels in breast ductal carcinoma. World J Surg Oncol. 2020; 18 (1): 225. DOI: 10.1186/s12957020-02001-8.
  30. Linares A, Assou S, Lapierre M, Thouennon E, Duraffourd C, Fromaget C, et al. Increased expression of the HDAC9 gene is associated with antiestrogen resistance of breast cancers. Mol Oncol. 2019; 13 (7): 1534–47. DOI: 10.1002/1878-0261.
  31. Li Y, Huang J, Sun J, Xiang S, Yang D, Ying X, et al. The transcription levels and prognostic values of seven proteasome alpha subunits in human cancers. Oncotarget. 2017 Jan 17; 8 (3): 4501–19. DOI: 10.18632/oncotarget.13885.