A Risk Score System Based on the Methylation Levels of 15 RNAs in Breast Cancer.

Background: Breast cancer (BC) occurs in the epithelial tissues of the breast gland, which is the most common cancer in women. This study is implemented to construct a risk score system for BC. Materials and Methods: The methylation data of BC from The Cancer Genome Atlas database (the training set) and GSE37754 from Gene Expression Omnibus database (the validation set) were downloaded. The differentially methylated RNAs (DMRs) between BC and normal samples were screened by limma package, and the correlations between the expression levels and methylation levels of the DMRs were analyzed to calculate their Pearson correlation coefficients (PCCs) using the cor.test function. To build the risk score system, the optimal RNAs were identified by penalized package. Subsequently, the nomogram survival model was established using the rms package. The lncRNA-mRNA comethylation network was constructed by Cytoscape software, and then enrichment analysis was performed using DAVID tool. Results: From the 1170 DMRs between BC and normal samples, 800 DMRs with significant negative PCCs were screened. For building the risk score system, the 15 optimal RNAs were selected. Afterward, the nomogram survival model based on four independent clinical prognostic factors (including age, radiation therapy, tumor recurrence, and RS model status) was constructed. In the comethylation network, the long noncoding RNA (lncRNA) PRNT was comethylated with FAM19A5 and RBM24. For the mRNAs in the comethylation network, angiogenesis and pathways in cancer were enriched. Conclusion: The risk score system and the nomogram survival model might be of great importance for the prognosis prediction of BC patients.