Identification and validation of a novel prognostic signature with DNA methylation-driven genes for survival prediction of breast cancer

Abstract

Abstract Background: Research has been accruing to demonstrate that DNA methylation plays a crucial role in the diagnosis of breast cancer mainly through regulating mRNA expression. Our study aims to construct a risk signature based on the methylation-driven genes (MDGs) to predict patients’ prognoses and identify tumors’ underlying molecular mechanisms. Methods: The data included in this study were downloaded from TCGA and GEO databases. Subsequently, univariate Cox regression and LASSO Cox regression analyses were constructed to identify prognostic MDGs and construct a risk signature. We have also used the ROC curve and Kaplan-Meier analysis to assess the predictive performance of the signature. Multivariate Cox regression analysis was used to identify the independent prognostic factor, and a nomogram was built to facilitate the use of the signature in clinical. Finally, GSVA, TISIDB, CIBERSORT, and drug-sensitive analyses were used to explore the potential mechanisms, and an eRNA network was constructed to identify potential regulators of the risk signature. Results: A total of 288 MDGs were identified in breast cancer, and 19 prognosis-related MDGs were included in the risk signature to predict patients’ overall survival with satisfactory performance. We identified that the 19-gene risk signature is an independent prognostic factor and could stratify patients into low- and high-risk groups with different prognoses. Furthermore, patients under different risk situations have diverse proportions of infiltrating immune cells, frequently mutated genes, and sensitive drugs. Nomogram integrating risk signature and clinicopathological factors achieve excellent predictive ability. A ceRNA network consisting of 9 lncRNA, 38 miRNA, and 10 mRNA was constructed based on the MDGs identified in the risk signature. Conclusions: We have successfully constructed an MDG-based prognostic risk signature in breast cancer and established a corresponding nomogram model. Additionally, we uncovered the underlying molecular mechanisms and therapeutic targets in tumors with different risks.