Genome-Wide Analysis of Methylation-Driven Genes and Identification of an Eight-Gene Panel for Prognosis Prediction in Breast Cancer

BACKGROUND: Aberrant DNA methylation is a crucial epigenetic regulator that is closely related to the occurrence and development of various cancers, including breast cancer (BC). The present study aimed to identify a novel methylation-based prognosis biomarker panel by integrally analyzing gene expression and methylation patterns in BC patients. METHODS: DNA methylation and gene expression data of breast cancer (BRCA) were downloaded from The Cancer Genome Atlas (TCGA). R packages, including ChAMP, SVA, and MethylMix, were applied to identify the unique methylation-driven genes. Subsequently, these genes were subjected to Metascape for GO analysis. Univariant Cox regression was used to identify survival-related genes among the methylation-driven genes. Robust likelihood-based survival modeling was applied to define the prognosis markers. An independent data set (GSE72308) was used for further validation of our risk score system. RESULTS: A total of 879 DNA methylation-driven genes were identified from 765 BC patients. In the discovery cohort, we identified 50 survival-related methylation-driven genes. Finally, we built an eight-methylation-driven gene panel that serves as prognostic predictors. CONCLUSIONS: Our analysis of transcriptome and methylome variations associated with the survival status of BC patients provides a further understanding of basic biological processes and a basis for the genetic etiology in BC.