DNA methylation markers predict recurrence-free interval in triple-negative breast cancer

DNA Methylation and Demethylation in Triple-Negative Breast Cancer: Associations with Clinicopathological Characteristics and the Chemotherapy Response

Objectives: Triple-negative breast cancer (TNBC) is an aggressive cancer subtype with limited treatment options due to the absence of estrogen, progesterone receptors, and HER2 expression. This study examined the impact of DNA methylation and demethylation markers in tumor tissues on TNBC patients' response to neoadjuvant chemotherapy (NACT) and analyzed the correlation between 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) and clinicopathological characteristics, offering new insights into the predictive value of these epigenetic markers. Methods: The study included 53 TNBC female patients, 19 of whom received neoadjuvant chemotherapy (NACT) before surgery. Global DNA methylation and demethylation levels were quantified using an ELISA-based method to measure 5-mC and 5-hmC content in DNA isolated from pre-treatment biopsy samples (in patients undergoing NACT) and postoperative tissues (in patients without NACT). Results: In patients who received NACT, those with disease progression had significantly higher pretreatment levels of 5-hmC (p = 0.028) and a trend toward higher 5-mC levels (p = 0.054) compared to those with pathological complete response, partial response, or stable disease. Higher 5-mC and 5-hmC levels were significantly associated with higher tumor grade (p = 0.039 and p = 0.017, respectively). Additionally, a positive correlation was observed between the Ki-67 proliferation marker and both 5-mC (rS = 0.340, p = 0.049) and 5-hmC (rS = 0.341, p = 0.048) levels in postoperative tissues. Conclusions: Our study highlights the potential of global DNA methylation and demethylation markers as predictors of tumor aggressiveness and chemotherapy response in TNBC. Further research in larger cohorts is necessary to validate these markers' prognostic and predictive value.

Hypomethylating agents as emerging therapeutics for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is recognized as the most aggressive subtype of breast cancer. Epigenetic silencing, such as DNA methylation mediated by DNA methyltransferases (DNMTs) plays key roles in TNBC tumorigenesis. Hypomethylating agents (HMAs) such as azacitidine, decitabine, and guadecitabine are key inhibitors of DNMTs, and accumulating evidence has shown their immunogenicity properties. In this review, the efficacy and anti-tumor immune responses triggered by HMAs in TNBC are presented and discussed. Essentially, overexpression of DNMTs is associated with poor prognosis and reduced TNBC survival rates, and these effects are negated by HMAs. In particular, HMAs could reverse epigenetic silencing of tumor suppressor genes and enhance immune recognition of TNBC cells. Clinical trials of HMAs in TNBCs are limited but early-stage trials indicate that HMAs are safe and tolerable. More clinical studies are required to establish the effectiveness of HMAs against the disease, as supported by preclinical data substantiating their effectiveness especially guadecitabine. Future research should focus on optimizing dosing and exploring combinations with immunotherapies to maximize the potential of HMAs in TNBC treatment.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

TYMS Knockdown Suppresses Cells Proliferation, Promotes Ferroptosis via Inhibits PI3K/Akt/mTOR Signaling Pathway Activation in Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) is characterized by a grim prognosis and numerous challenges. The objective of our study was to examine the role of thymidylate synthase (TYMS) in TNBC and its impact on ferroptosis. The expression of TYMS was analyzed in databases, along with its prognostic correlation. TYMS positive expression was identified through immunohistochemistry (IHC), while real-time quantitative PCR (qRTPCR) was employed to measure TYMS mRNA levels in various cell lines. Western blotting was utilized to assess protein expression. Cell proliferation, mobility, apoptosis, and reactive oxygen species (ROS) levels were evaluated using CCK8, wound scratch healing assay, transwell assay, and flow cytometry, respectively. Additionally, a tumor xenograft model was established in BALB/c nude mice for further investigation. Tumor volume and weight were measured, and histopathological analysis using hematoxylin and eosin (H&E) staining was conducted to assess tumor tissue changes. IHC staining was employed to detect the expression of Ki67 in tumor tissues. High expression of TYMS was observed in TNBC and was found to be correlated with poor prognosis in patients. Among various cell lines, TYMS expression was highest in BT549 cells. Knockdown of TYMS resulted in suppression of cell proliferation and mobility, as well as promotion of apoptosis. Furthermore, knockdown of TYMS led to increased accumulation of ROS and Fe2+ levels, along with upregulation of ACLS4 expression and downregulation of glutathione peroxidase 4 (GPX4) expression. In vivo studies showed that knockdown of TYMS inhibited tumor growth. Additionally, knockdown of TYMS was associated with inhibition of mTOR, p-PI3K, and p-Akt expression. Our research showed that the knockdown of TYMS suppressed the TNBC progression by inhibited cells proliferation via ferroptosis. Its underlying mechanism is related to the PI3K /Akt pathway. Our study provides a novel sight for the suppression effect of TYMS on TNBC.

Epigenetic regulation of breast cancer metastasis

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of cancer-related mortality among women worldwide. Recurrent metastasis is associated with poor patient outcomes and poses a significant challenge in breast cancer therapies. Cancer cells adapting to a new tissue microenvironment is the key event in distant metastasis development, where the disseminating tumor cells are likely to acquire genetic and epigenetic alterations during the process of metastatic colonization. Despite several decades of research in this field, the exact mechanisms governing metastasis are not fully understood. However, emerging body of evidence indicates that in addition to genetic changes, epigenetic reprogramming of cancer cells and the metastatic niche are paramount toward successful metastasis. Here, we review and discuss the latest knowledge about the salient attributes of metastasis and epigenetic regulation in breast cancer and crucial research domains that need further investigation.

Epigenetic modulations in triple-negative breast cancer: Therapeutic implications for tumor microenvironment

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen receptors, progesterone receptors and lacks HER2 overexpression. This absence of critical molecular targets poses significant challenges for conventional therapies. Immunotherapy, remarkably immune checkpoint blockade, offers promise for TNBC treatment, but its efficacy remains limited. Epigenetic dysregulation, including altered DNA methylation, histone modifications, and imbalances in regulators such as BET proteins, plays a crucial role in TNBC development and resistance to treatment. Hypermethylation of tumor suppressor gene promoters and the imbalance of histone methyltransferases such as EZH2 and histone deacetylases (HDACs) profoundly influence tumor cell proliferation, survival, and metastasis. In addition, epigenetic alterations critically shape the tumor microenvironment (TME), including immune cell composition, cytokine signaling, and immune checkpoint expression, ultimately contributing to immune evasion. Targeting these epigenetic mechanisms with specific inhibitors such as EZH2 and HDAC inhibitors in combination with immunotherapy represents a compelling strategy to remodel the TME, potentially overcoming immune evasion and enhancing therapeutic outcomes in TNBC. This review aims to comprehensively elucidate the current understanding of epigenetic modulation in TNBC, its influence on the TME, and the potential of combining epigenetic therapies with immunotherapy to overcome the challenges posed by this aggressive breast cancer subtype.

Discovery and technical validation of high-performance methylated DNA markers for the detection of cervical lesions at risk of malignant progression in low- and middle-income countries

BACKGROUND

Cervical cancer remains a leading cause of death, particularly in developing countries. WHO screening guidelines recommend human papilloma virus (HPV) detection as a means to identify women at risk of developing cervical cancer. While HPV testing identifies those at risk, it does not specifically distinguish individuals with neoplasia. We investigated whether a quantitative molecular test that measures methylated DNA markers could identify high-risk lesions in the cervix with accuracy.

RESULTS

Marker discovery was performed in TCGA-CESC Infinium Methylation 450 K Array database and verified in three other public datasets. The panel was technically validated using Quantitative Multiplex-Methylation-Specific PCR in tissue sections (N = 252) and cervical smears (N = 244) from the USA, South Africa, and Vietnam. The gene panel consisted of FMN2, EDNRB, ZNF671, TBXT, and MOS. Cervical tissue samples from all three countries showed highly significant differential methylation in squamous cell carcinoma (SCC) with a sensitivity of 100% [95% CI 74.12-100.00], and specificity of 91% [95% CI 62.26-99.53] to 96% [95% CI 79.01-99.78], and receiver operating characteristic area under the curve (ROC AUC) = 1.000 [95% CI 1.00-1.00] compared to benign cervical tissue, and cervical intraepithelial neoplasia 2/3 with sensitivity of 55% [95% CI 37.77-70.84] to 89% [95% CI 67.20-98.03], specificity of 93% [95% CI 84.07-97.38] to 96% [95% CI 79.01-99.78], and a ROC AUC ranging from 0.793 [95% CI 0.68-0.89] to 0.99 [95% CI 0.97-1.00] compared to CIN1. In cervical smears, the marker panel detected SCC with a sensitivity of 87% [95% CI 77.45-92.69], specificity 95% [95% CI 88.64-98.18], and ROC AUC = 0.925 [95% CI 0.878-0.974] compared to normal, and high-grade squamous intraepithelial lesion (HSIL) at a sensitivity of 70% (95% CI 58.11-80.44), specificity of 94% (95% CI 88.30-97.40), and ROC AUC = 0.884 (95% CI 0.822-0.945) compared to low-grade intraepithelial lesion (LSIL)/normal in an analysis of pooled data from the three countries. Similar to HPV-positive, HPV-negative cervical carcinomas were frequently hypermethylated for these markers.

CONCLUSIONS

This 5-marker panel detected SCC and HSIL in cervical smears with a high level of sensitivity and specificity. Molecular tests with the ability to rapidly detect high-risk HSIL will lead to timely treatment for those in need and prevent unnecessary procedures in women with low-risk lesions throughout the world. Validation of these markers in prospectively collected cervical smear cells followed by the development of a hypermethylated marker-based cervical cancer detection test is warranted.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

DNA Methylation Identifies Epigenetic Subtypes of Triple-Negative Breast Cancers With Distinct Clinicopathologic and Molecular Features

Triple-negative breast cancers (TNBC) include diverse carcinomas with heterogeneous clinical behavior. DNA methylation is a useful tool in classifying a variety of cancers. In this study, we analyzed TNBC using DNA methylation profiling and compared the results to those of mutational analysis. DNA methylation profiling (Infinium MethylationEPIC array, Illumina) and 50-gene panel-targeted DNA sequencing were performed in 44 treatment-naïve TNBC. We identified 3 distinct DNA methylation clusters with specific clinicopathologic and molecular features. Cluster 1 (phosphoinositide 3-kinase/protein kinase B-enriched cluster; n = 9) patients were significantly older (mean age, 71 years; P = .008) with tumors that were more likely to exhibit apocrine differentiation (78%; P < .001), a lower grade (44% were grade 2), a lower proliferation index (median Ki-67, 15%; P = .002), and lower tumor-infiltrating lymphocyte fractions (median, 15%; P = .0142). Tumors carried recurrent PIK3CA and AKT1 mutations and a higher percentage of low HER-2 expression (89%; P = .033). Cluster 3 (chromosomal instability cluster; n = 28) patients were significantly younger (median age, 57 years). Tumors were of higher grade (grade 3, 93%), had a higher proliferation index (median Ki-67, 75%), and were with a high fraction of tumor-infiltrating lymphocytes (median, 30%). Ninety-one percent of the germline BRCA1/2 mutation carriers were in cluster 3, and these tumors showed the highest level of copy number alterations. Cluster 2 represented cases with intermediate clinicopathologic characteristics and no specific molecular profile (no specific molecular profile cluster; n = 7). There were no differences in relation to stage, recurrence, and survival. In conclusion, DNA methylation profiling is a promising tool to classify patients with TNBC into biologically relevant groups, which may result in better disease characterization and reveal potential targets for emerging therapies.

A Prognostic Model for Breast Cancer Based on Cancer Incidence-Related DNA Methylation Pattern

Breast cancer (BC) is the most diagnosed cancer and the leading cause of cancer-related deaths in women. The purpose of this study was to develop a prognostic model based on BC-related DNA methylation pattern. A total of 361 BC incidence-related probes (BCIPs) were differentially methylated in blood samples from women at high risk of BC and BC tissues. Twenty-nine of the 361 BCIPs that significantly correlated with BC outcomes were selected to establish the BCIP score. BCIP scores based on BC-related DNA methylation pattern were developed to evaluate the mortality risk of BC. The correlation between overall survival and BCIP scores was assessed using Kaplan-Meier, univariate, and multivariate analyses. In BC, the BCIP score was significantly correlated with malignant BC characteristics and poor outcomes. Furthermore, we assessed the BCIP score-related gene expression profile and observed that genes with expressions associated with the BCIP score were involved in the process of cancer immunity according to GO and KEGG analyses. Using the ESTIMATE and CIBERSORT algorithms, we discovered that BCIP scores were negatively correlated with both T cell infiltration and immune checkpoint inhibitor response markers in BC tissues. Finally, a nomogram comprising the BCIP score and BC prognostic factors was used to establish a prognostic model for patients with BC, while C-index and calibration curves were used to evaluate the effectiveness of the nomogram. A nomogram comprising the BCIP score, tumor size, lymph node status, and molecular subtype was developed to quantify the survival probability of patients with BC. Collectively, our study developed the BCIP score, which correlated with poor outcomes in BC, to portray the variation in DNA methylation pattern related to BC incidence.

Epigenetic Therapies and Biomarkers in Breast Cancer

Epigenetic therapies remain a promising, but still not widely used, approach in the management of patients with cancer. To date, the efficacy and use of epigenetic therapies has been demonstrated primarily in the management of haematological malignancies, with limited supportive data in solid malignancies. The most studied epigenetic therapies in breast cancer are those that target DNA methylation and histone modification; however, none have been approved for routine clinical use. The majority of pre-clinical and clinical studies have focused on triple negative breast cancer (TNBC) and hormone-receptor positive breast cancer. Even though the use of epigenetic therapies alone in the treatment of breast cancer has not shown significant clinical benefit, these therapies show most promise in use in combinations with other treatments. With improving technologies available to study the epigenetic landscape in cancer, novel epigenetic alterations are increasingly being identified as potential biomarkers of response to conventional and epigenetic therapies. In this review, we describe epigenetic targets and potential epigenetic biomarkers in breast cancer, with a focus on clinical trials of epigenetic therapies. We describe alterations to the epigenetic landscape in breast cancer and in treatment resistance, highlighting mechanisms and potential targets for epigenetic therapies. We provide an updated review on epigenetic therapies in the pre-clinical and clinical setting in breast cancer, with a focus on potential real-world applications. Finally, we report on the potential value of epigenetic biomarkers in diagnosis, prognosis and prediction of response to therapy, to guide and inform the clinical management of breast cancer patients.

Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

Single-cell analysis has revolutionised genomic science in recent years. However, due to cost and other practical considerations, single-cell analyses are impossible for studies based on medium or large patient cohorts. For example, a single-cell analysis usually costs thousands of euros for one tissue sample from one volunteer, meaning that typical studies using single-cell analyses are based on very few individuals. While single-cell genomic data can be used to examine the phenotype of individual cells, cell-type deconvolution methods are required to track the quantities of these cells in bulk-tissue genomic data. Hormone receptor negative breast cancers are highly aggressive, and are thought to originate from a subtype of epithelial cells called the luminal progenitor. In this paper, we show how to quantify the number of luminal progenitor cells as well as other epithelial subtypes in breast tissue samples using DNA and RNA based measurements. We find elevated levels of cells which resemble these hormone receptor negative luminal progenitor cells in breast tumour biopsies of hormone receptor negative cancers, as well as in healthy breast tissue samples from BRCA1 (FANCS) mutation carriers. We also find that breast tumours from carriers of heterozygous mutations in non-BRCA Fanconi Anaemia pathway genes are much more likely to be hormone receptor negative. These findings have implications for understanding hormone receptor negative breast cancers, and for breast cancer screening in carriers of heterozygous mutations of Fanconi Anaemia pathway genes.

DNA methylation and breast cancer: A way forward (Review)

The current management of breast cancer (BC) lacks specific non‑invasive biomarkers able to provide an early diagnosis of the disease. Epigenetic‑sensitive signatures are influenced by environmental exposures and are mediated by direct molecular mechanisms, mainly guided by DNA methylation, which regulate the interplay between genetic and non‑genetic risk factors during cancerogenesis. The inactivation of tumor suppressor genes due to promoter hypermethylation is an early event in carcinogenesis. Of note, targeted tumor suppressor genes are frequently hypermethylated in patient‑derived BC tissues and peripheral blood biospecimens. In addition, epigenetic alterations in triple‑negative BC, as the most aggressive subtype, have been identified. Thus, detecting both targeted and genome‑wide DNA methylation changes through liquid‑based assays appears to be a useful clinical strategy for early detection, more accurate risk stratification and a personalized prediction of therapeutic response in patients with BC. Of note, the DNA methylation profile may be mapped by isolating the circulating tumor DNA from the plasma as a more accessible biospecimen. Furthermore, the sensitivity to treatment with chemotherapy, hormones and immunotherapy may be altered by gene‑specific DNA methylation, suggesting novel potential drug targets. Recently, the use of epigenetic drugs administered alone and/or with anticancer therapies has led to remarkable results, particularly in patients with BC resistant to anticancer treatment. The aim of the present review was to provide an update on DNA methylation changes that are potentially involved in BC development and their putative clinical utility in the fields of diagnosis, prognosis and therapy.

Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer

Triple-negative breast cancer, the poorest-prognosis breast cancer subtype, lacks clinically approved biomarkers for patient risk stratification and treatment management. Prior literature has shown that interrogation of the tumor-immune microenvironment may be a promising approach to fill these gaps. Recently developed high-dimensional tissue imaging technology, such as multiplexed ion beam imaging, provide spatial context to protein expression in the microenvironment, allowing in-depth characterization of cellular processes. We demonstrate that profiling the functional proteins involved in cell-to-cell interactions in the microenvironment can predict recurrence and overall survival. We highlight the immunological relevance of the immunoregulatory proteins PD-1, PD-L1, IDO, and Lag3 by tying interactions involving them to recurrence and survival. Multivariate analysis reveals that our methods provide additional prognostic information compared to clinical variables. In this work, we present a computational pipeline for the examination of the tumor-immune microenvironment using multiplexed ion beam imaging that produces interpretable results, and is generalizable to other cancer types.

The epigenetics of breast cancer - Opportunities for diagnostics, risk stratification and therapy

The stage and molecular pathology-dependent prognosis of breast cancer, the limited treatment options for triple-negative carcinomas, as well as the development of resistance to therapies illustrate the need for improved early diagnosis and the development of new therapeutic approaches. Increasing data suggests that some answers to these challenges could be found in the area of epigenetics. In this study, we focus on the current research of the epigenetics of breast cancer, especially on the potential of epigenetics for clinical application in diagnostics, risk stratification and therapy. The differential DNA methylation status of specific gene regions has been used in the past to differentiate breast cancer cells from normal tissue. New technologies as detection of circulating nucleic acids including microRNAs to early detect breast cancer are emerging. Pattern of DNA methylation and expression of histone-modifying enzymes have been successfully used for risk stratification. However, all these epigenetic biomarkers should be validated in larger clinical studies. Recent preclinical and clinical studies show a therapeutic benefit of epigenetically active drugs for breast cancer entities that are still difficult to treat (triple negative, UICC stage IV). Remarkably, epigenetic therapies combined with chemotherapies or hormone-based therapies represent the most promising strategy. At the current stage, the integration of epigenetic substances into established breast cancer therapy protocols seems to hold the greatest potential for a clinical application of epigenetic research.

Methylated markers accurately distinguish primary central nervous system lymphomas (PCNSL) from other CNS tumors

BACKGROUND

Definitive diagnosis of primary central nervous system lymphoma (PCNSL) requires invasive surgical brain biopsy, causing treatment delays. In this paper, we identified and validated tumor-specific markers that can distinguish PCNSL from other CNS tumors in tissues. In a pilot study, we tested these newly identified markers in plasma.

RESULTS

The Methylation Outlier Detector program was used to identify markers in TCGA dataset of 48 diffuse large B-cell lymphoma (DLBCL) and 656 glioblastomas and lower-grade gliomas. Eight methylated markers clearly distinguished DLBCL from gliomas. Marker performance was verified (ROC-AUC of ≥ 0.989) in samples from several GEO datasets (95 PCNSL; 2112 other primary CNS tumors of 11 types). Next, we developed a novel, efficient assay called Tailed Amplicon Multiplexed-Methylation-Specific PCR (TAM-MSP), which uses two of the methylation markers, cg0504 and SCG3 triplexed with ACTB. FFPE tissue sections (25 cases each) of PCNSL and eight types of other primary CNS tumors were analyzed using TAM-MSP. TAM-MSP distinguished PCNSL from the other primary CNS tumors with 100% accuracy (AUC = 1.00, 95% CI 0.95-1.00, P < 0.001). The TAM-MSP assay also detected as few as 5 copies of fully methylated plasma DNA spiked into 0.5 ml of healthy plasma. In a pilot study of plasma from 15 PCNSL, 5 other CNS tumors and 6 healthy individuals, methylation in cg0504 and SCG3 was detectable in 3/15 PCNSL samples (20%).

CONCLUSION

The Methylation Outlier Detector program identified methylated markers that distinguish PCNSL from other CNS tumors with accuracy. The high level of accuracy achieved by these markers was validated in tissues by a novel method, TAM-MSP. These studies lay a strong foundation for a liquid biopsy-based test to detect PCNSL-specific circulating tumor DNA.

A Novel Promoter CpG-Based Signature for Long-Term Survival Prediction of Breast Cancer Patients

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.