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DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.

Zhang M, Zhang S, Wen Y, Wang Y, Wei Y, Liu H, Zhang D, Su J, Wang F, Zhang Y - PLoS ONE (2015)

Bottom Line: 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).We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time.The predict power was validated through two independent datasets from the GEO database.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

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.

No MeSH data available.


Related in: MedlinePlus

The Kaplan-Meier survival analysis of overall survival on four independent dataset from GEO database.(A) GSE37754 from 450K arrays. (B) GSE20712 from 27K arrays.
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pone.0142279.g004: The Kaplan-Meier survival analysis of overall survival on four independent dataset from GEO database.(A) GSE37754 from 450K arrays. (B) GSE20712 from 27K arrays.

Mentions: Additionally, we evaluated the performance of 12 featured genes using two independent breast cancer datasets (GSE37754 and GSE20712). NARS and SLC25A21 were not included in the 27K DNA methylation dataset (GSE20712), and an additional 10 featured genes were used. All 12 featured genes were used in the 450K dataset (GSE37754). Using two independent cohorts, we found differential outcomes between the high- and low-MRS groups (Fig 4); p values of the log-rank test were especially significant in GSE20712 (Fig 4B). MRS showed a preferable distinguishing power between the high- and low-MRS groups. This finding suggested that DNA methylation biomarkers might be robust factors for the prediction of breast cancer outcomes.


DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.

Zhang M, Zhang S, Wen Y, Wang Y, Wei Y, Liu H, Zhang D, Su J, Wang F, Zhang Y - PLoS ONE (2015)

The Kaplan-Meier survival analysis of overall survival on four independent dataset from GEO database.(A) GSE37754 from 450K arrays. (B) GSE20712 from 27K arrays.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4638352&req=5

pone.0142279.g004: The Kaplan-Meier survival analysis of overall survival on four independent dataset from GEO database.(A) GSE37754 from 450K arrays. (B) GSE20712 from 27K arrays.
Mentions: Additionally, we evaluated the performance of 12 featured genes using two independent breast cancer datasets (GSE37754 and GSE20712). NARS and SLC25A21 were not included in the 27K DNA methylation dataset (GSE20712), and an additional 10 featured genes were used. All 12 featured genes were used in the 450K dataset (GSE37754). Using two independent cohorts, we found differential outcomes between the high- and low-MRS groups (Fig 4); p values of the log-rank test were especially significant in GSE20712 (Fig 4B). MRS showed a preferable distinguishing power between the high- and low-MRS groups. This finding suggested that DNA methylation biomarkers might be robust factors for the prediction of breast cancer outcomes.

Bottom Line: 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).We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time.The predict power was validated through two independent datasets from the GEO database.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

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.

No MeSH data available.


Related in: MedlinePlus