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Breast cancer is the most common cancer in the world, and DNA methylation plays a key role in the occurrence and development of breast cancer. However, the effect of DNA methylation in different gene functional regions on gene expression and the effect of gene expression on breast cancer is not completely clear. In our study, we computed and analyzed DNA methylation, gene expression, and clinical data in the TCGA database. Firstly, we calculated the distribution of abnormal DNA methylated probes in 12 regions, found the abnormal DNA methylated probes in down-regulated genes were highly enriched, and the number of hypermethylated probes in the promoter region was 6.5 times than that of hypomethylated probes. Secondly, the correlation coefficients between abnormal DNA methylated values in each functional region of differentially expressed genes and gene expression values were calculated. Then, co-expression analysis of differentially expressed genes was performed, 34 hub genes in cancer-related pathways were obtained, of which 11 genes were regulated by abnormal DNA methylation. Finally, a multivariate Cox regression analysis was performed on 27 probes of 11 genes. Three DNA methylation probes (cg13569051 and cg14399183 of GSN, and cg25274503 of CAV2) related to survival were used to construct a prognostic model, which has a good prognostic ability. Furthermore, we found that the cg25274503 hypermethylation in the promoter region inhibited the expression of the CAV2, and the hypermethylation of cg13569051 and cg14399183 in the 5′UTR region inhibited the expression of GSN. These results may provide possible molecular targets for breast cancer.

Background Breast cancer is the malignant tumor with the highest incidence in women. DNA methylation has an important effect on breast cancer, but the effect of abnormal DNA methylation on gene expression in breast cancer is still unclear. Therefore, it is very important to find therapeutic targets related to DNA methylation. Results In this work, we calculated the DNA methylation distribution and gene expression level in cancer and para-cancerous tissues for breast cancer samples. We found that DNA methylation in key regions is closely related to gene expression by analyzing the relationship between the distribution characteristics of DNA methylation in different regions and the change of gene expression level. Finally, the 18 key genes (17 tumor suppressor genes and 1 oncogene) related to prognosis were confirmed by the survival analysis of clinical data. Some important DNA methylation regions in these genes that result in breast cancer were found. Conclusions We believe that 17 TSGs and 1 oncogene may be breast cancer biomarkers regulated by DNA methylation in key regions. These results will help to explore DNA methylation biomarkers as potential therapeutic targets for breast cancer.

Four genes related to DNA methylation were found to be independent prognostic factors and potential biomarkers for hepatocellular carcinoma.