Abstract
BACKGROUND: With the rapid development of genomics and molecular biology, not only have biochemical indicators been used as tumour markers, but many new molecular markers have emerged. Epigenetic abnormalities are a new type of molecular marker, and DNA methylation is an important part of epigenetics. OBJECTIVE: This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer. METHODS: The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed. RESULTS: The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed. CONCLUSIONS: The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.