Abstract
DNA methylation has been reported as one of the most critical epigenetic aberrations during the tumorigenesis and development of breast cancer (BC). This study explored a novel promoter CpG-based signature for long-term survival prediction of BC patients. We used The Cancer Genome Atlas (TCGA) data as training set, and results were validated in an independent dataset from Gene Expression Omnibus (GEO). First, the differential methylation CpG sites were screened in TCGA dataset, of which the candidate promoter CpG sites were preliminarily identified with the univariate Cox regression analysis and the least absolute shrinkage and selection operator regression analysis. Second, the signature was constructed with stepwise regression analysis and multivariate Cox proportional hazards model, which was validated with the survival analysis of two cohorts each from TCGA and GEO databases. The 10-year receiver operating characteristic curves of risk score presented an area under the curve of over 0.7 for both cohorts. A nomogram was also constructed and released. Moreover, Gene Set Enrichment Analysis was performed to identify the more active pathways in high-risk patients. The CpG sites–target gene correlations and differential methylation regions were further explored. In conclusion, the promoter CpG-based signature exhibited good prognostic prediction efficacy in the long-term overall survival of BC patients.
Highlights
Breast cancer (BC) has become one of the most concerned public health issues in the worldwide, because of the growing incidence, high mortality, and huge economic burden [1, 2]
The differential methylation CpG sites were screened in The Cancer Genome Atlas (TCGA) dataset, of which the candidate promoter CpG sites were preliminarily identified with the univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis
The datasets from TCGA and Gene Expression Omnibus (GEO) databases were preprocessed for further comparison, including filter, normalization, and batch correction
Summary
Breast cancer (BC) has become one of the most concerned public health issues in the worldwide, because of the growing incidence, high mortality, and huge economic burden [1, 2]. More than 1 million new BC cases were diagnosed in 2002 [3]. The treatment costs of BC have been generally escalated with the advance of disease stage at diagnosis [5]. BC patients can greatly benefit from early diagnosis, both in therapeutic efficacy and economic burden. With the advances of molecular diagnosis technology, the heterogeneity and complexity of BC have been revealed [6]. The molecular characterization of BC would provide much information for understanding the pathogenesis of BC and exploring potential markers for early diagnosis and target therapy [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
