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, 17 (1), 380

A Novel Epigenetic Signature for Overall Survival Prediction in Patients With Breast Cancer

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A Novel Epigenetic Signature for Overall Survival Prediction in Patients With Breast Cancer

Xuanwen Bao et al. J Transl Med.

Abstract

Background: Breast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer.

Method: DNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases.

Results: The combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts.

Conclusion: An epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

Keywords: Breast cancer; Epigenetics; Individualized therapy; Mammary carcinoma; Molecular marker; Molecular signature; Prognosis; Response.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Construction of a prognostic epigenetic model in patients with breast cancer. a Volcano plot for DEGs in the tumour and normal tissues. b Volcano plot for DMGs in the tumour and normal tissues. c The expression of the DNA methylation-regulated genes shown by heatmap. d LASSO Cox regression model. e Coefficients distribution of the gene signature. DEGs differentially expressed genes, DMGs differentially methylated genes
Fig. 2
Fig. 2
Epigenetic signature-based risk score in the training and inner validation cohort. a Risk score per patient. b Survival status. c Heatmap for the 13 genes
Fig. 3
Fig. 3
The prognostic model in breast tumour. a The Kaplan–Meier curve for OS in patients with breast tumour. b The Kaplan–Meier curve for RFS in patients with breast tumour. c Time-dependent ROC analysis for the epigenetic signature, TNM stage, age and molecular subtypes. d ssGSEA showed the correlation between the hallmarks and the epigenetic signature
Fig. 4
Fig. 4
WGCNA on breast cancer RNA-seq datasets. a Clustering dendrogram of genes in breast cancer tissues. b Heatmap depicting TOM among all genes. Light colours represent low adjacency and dark colours represent high adjacency. c Correlation between modules and traits. d A scatter plot of GS for risk score versus MM in blue module, with correlation coefficient = 0.4 and p = 2e−16. e Visualisation of genes in the blue module with weights higher than the threshold (weight > 0.15). f GO analysis on the hub-genes. g KEGG analysis on the hub-genes
Fig. 5
Fig. 5
The correlation between gene expression and DNA methylation level in breast tissues
Fig. 6
Fig. 6
Validation of the signature in three external cohorts. Patients with a low risk score showed better OS in the validation cohorts GSE20685 (a), GSE86948 (b) and GSE17907 (c). Patients with a low risk score showed better RFS in the validation cohort GSE12093 (d)
Fig. 7
Fig. 7
Construction of a nomogram for survival prediction. a Nomogram including the epigenetic signature and clinicopathological traits. b Calibration plot

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