title: DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.
abstract: Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Min
authors.initials: M
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Shaojun
authors.initials: S
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Wen
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Yihan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Wei
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
authors.forename: Dongwei
authors.initials: D
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Jianzhong
authors.initials: J
authors.lastname: Su
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000378:metabolism; D003198:Computer Simulation; D018899:CpG Islands; D019175:DNA Methylation; D018450:Disease Progression; D005260:Female; D006801:Humans; D008875:Middle Aged; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism*; D011960:Receptors, Estrogen / Q000378:metabolism*; D011980:Receptors, Progesterone / Q000378:metabolism*; D012307:Risk Factors
chemical_list: D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C508053:ERBB2 protein, human