Blood DNA Methylation and Breast Cancer: A Prospective Case-Cohort Analysis in the Sister Study

DNA Methylation-Derived Immune Cell Proportions and Cancer Risk in Black Participants

SIGNIFICANCE

This study describes associations between immune cell types and cancer risk in a Black population; elevated regulatory T-cell proportions that were associated with increased overall cancer and lung cancer risk, and elevated memory B-cell proportions that were associated with increased prostate and all cancer risk.

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

CCDC12 gene methylation in peripheral blood as a potential biomarker for breast cancer detection

BACKGROUND

Aberrant DNA methylation has been identified as biomarkers for breast cancer detection. Coiled-coil domain containing 12 gene (CCDC12) implicated in tumorigenesis. This study aims to investigate the potential of blood-based CCDC12 methylation for breast cancer detection.

METHODS

DNA methylation level of CpG sites (Cytosine-phosphate Guanine dinucleotides) in CCDC12 gene was measured by mass spectrometry in 255 breast cancer patients, 155 patients with benign breast nodules and 302 healthy controls. The association between CCDC12 methylation and breast cancer risk was evaluated by logistic regression and receiver operating characteristic curve analysis.

RESULTS

A total of eleven CpG sites were analyzed. The CCDC12 methylation levels were higher in breast cancer patients. Compared to the lowest tertile of methylation level in CpG_6,7, CpG_10 and CpG_11, the highest quartile was associated with 82, 91 and 95% increased breast cancer risk, respectively. The CCDC12 methylation levels were associated with estrogen receptor (ER) and human epidermal growth factor 2 (HER2) status. In ER-negative and HER2-positive (ER-/HER2+) breast cancer subtype, the combination of four sites CpG_2, CpG_5, CpG_6,7 and CpG_11 methylation levels could distinguish ER-/HER2+ breast cancer from the controls (AUC = 0.727).

CONCLUSION

The hypermethylation levels of CCDC12 in peripheral blood could be used for breast cancer detection.

Methylation marks in blood DNA reveal breast cancer risk in patients fulfilling hereditary disease criteria

Less than 15-20% of patients who meet the criteria for hereditary breast and ovarian cancer (HBOC) carry pathogenic coding genetic mutations, implying that other molecular mechanisms may contribute to the increased risk of this condition. DNA methylation in peripheral blood has been suggested as a potential epigenetic marker for the risk of breast cancer (BC). We aimed to discover methylation marks in peripheral blood associated with BC in 231 pre-treatment BC patients meeting HBOC criteria, testing negative for coding pathogenic variants, and 156 healthy controls, through methylation analysis by targeted bisulfite sequencing on 18 tumor suppressor gene promoters (330 CpG sites). We found i) hypermethylation in EPCAM (17 CpG sites; p = 0.017) and RAD51C (27 CpG sites; p = 0.048); ii) hypermethylation in 36 CpG-specific sites (FDR q < 0.05) in the BC patients; iii) four specific CpG sites were associated with a higher risk of BC (FDR q < 0.01, Bonferroni p < 0.001): cg89786999-FANCI (OR = 1.65; 95% CI:1.2-2.2), cg23652916-PALB2 (OR = 2.83; 95% CI:1.7-4.7), cg47630224-MSH2 (OR = 4.17; 95% CI:2.1-8.5), and cg47596828-EPCAM (OR = 1.84; 95% CI:1.5-2.3). Validation of cg47630224-MSH2 methylation in one Australian cohort showed an association with 3-fold increased BC risk (AUC: 0.929; 95% CI: 0.904-0.955). Our findings suggest that four DNA methylation CpG sites may be associated with a higher risk of BC, potentially serving as biomarkers in patients without detectable coding mutations.

Value of altered methylation patterns of genes RANBP3, LCP2 and GRAP2 in cfDNA in breast cancer diagnosis

Background

The purpose of this study was to investigate the potential of plasma cfDNA methylation patterns in reflecting tumour methylation changes, focusing on three candidate sites, cg02469161, cg11528914, and cg20131654. These sites were selected for verification, with a particular emphasis on their association with breast cancer.

Methods

We conducted a comprehensive analysis of 850k whole-methylation sequencing data to identify potential markers for breast cancer detection. Subsequently, we investigated the methylation status of the genes Ran-binding protein 3 (RANBP3), Lymphocyte cytoplasmic protein 2 (LCP2), and GRB2 related adaptor protein 2 (GRAP2), situated at the specified sites, using cancer and canceradjacent tissues from 17 breast cancer patients. We also examined the methylation patterns in different molecular subtypes and pathological grades of breast cancer. Additionally, we compared the methylation levels of these genes in plasma cfDNA to their performance in tissues.

Results

Our analysis revealed that RANBP3, LCP2, and GRAP2 genes exhibited significant methylation differences between cancer and cancer-adjacent tissues. In breast cancer, these genes displayed diagnostic efficiencies of 91.0%, 90.6%, and 92.2%, respectively. Notably, RANBP3 showed a tendency towards lower methylation in HR+ breast cancer, and LCP2 methylation was correlated with tumour malignancy. Importantly, the methylation levels of these three genes in plasma cfDNA closely mirrored their tissue counterparts, with diagnostic efficiencies of 83.3%, 83.9%, and 77.6% for RANBP3, LCP2, and GRAP2, respectively.

Conclusions

Our findings propose that the genes RANBP3, LCP2, and GRAP2, located at the identified methylation sites, hold significant potential as molecular markers in blood for the supplementary diagnosis of breast cancer. This study lays the groundwork for a more in-depth investigation into the changes in gene methylation patterns in circulating free DNA (cfDNA) for the early detection not only of breast cancer but also for various other types of cancer.

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.

Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients

BACKGROUND

There is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.

METHODS

DNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted.

RESULTS

A methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways.

CONCLUSION

This study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.

DNA Methylation-Derived Immune Cell Proportions and Cancer Risk, Including Lung Cancer, in Black Participants

Prior cohort studies assessing cancer risk based on immune cell subtype profiles have predominantly focused on White populations. This limitation obscures vital insights into how cancer risk varies across race. Immune cell subtype proportions were estimated using deconvolution based on leukocyte DNA methylation markers from blood samples collected at baseline on participants without cancer in the Atherosclerosis Risk in Communities (ARIC) Study. Over a mean of 17.5 years of follow-up, 668 incident cancers were diagnosed in 2,467 Black participants. Cox proportional hazards regression was used to examine immune cell subtype proportions and overall cancer incidence and site-specific incidence (lung, breast, and prostate cancers). Higher T regulatory cell proportions were associated with statistically significantly higher lung cancer risk (hazard ratio = 1.22, 95% confidence interval = 1.06-1.41 per percent increase). Increased memory B cell proportions were associated with significantly higher risk of prostate cancer (1.17, 1.04-1.33) and all cancers (1.13, 1.05-1.22). Increased CD8+ naïve cell proportions were associated with significantly lower risk of all cancers in participants ≥55 years (0.91, 0.83-0.98). Other immune cell subtypes did not display statistically significant associations with cancer risk. These results in Black participants align closely with prior findings in largely White populations. Findings from this study could help identify those at high cancer risk and outline risk stratifying to target patients for cancer screening, prevention, and other interventions. Further studies should assess these relationships in other cancer types, better elucidate the interplay of B cells in cancer risk, and identify biomarkers for personalized risk stratification.

Circulating tumor DNA: from discovery to clinical application in breast cancer

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Obesity-associated epigenetic alterations and the obesity-breast cancer axis

Both breast cancer and obesity can regulate epigenetic changes or be regulated by epigenetic changes. Due to the well-established link between obesity and an increased risk of developing breast cancer, understanding how obesity-mediated epigenetic changes affect breast cancer pathogenesis is critical. Researchers have described how obesity and breast cancer modulate the epigenome individually and synergistically. In this review, the epigenetic alterations that occur in obesity, including DNA methylation, histone, and chromatin modification, accelerated epigenetic age, carcinogenesis, metastasis, and tumor microenvironment modulation, are discussed. Delineating the relationship between obesity and epigenetic regulation is vital to furthering our understanding of breast cancer pathogenesis.

Circulating Leukocyte Subsets Before and After a Breast Cancer Diagnosis and Therapy

Importance

Changes in leukocyte composition often precede chronic disease onset. Patients with a history of breast cancer (hereinafter referred to as breast cancer survivors) are at increased risk for subsequent chronic diseases, but the long-term changes in peripheral leukocyte composition following a breast cancer diagnosis and treatment remain unknown.

Objective

To examine longitudinal changes in peripheral leukocyte composition in women who did and did not develop breast cancer and identify whether differences in breast cancer survivors were associated with specific treatments.

Design, Setting, and Participants

In this prospective cohort study, paired blood samples were collected from 2315 women enrolled in The Sister Study, a US-nationwide prospective cohort study of 50 884 women, at baseline (July 2003 to March 2009) and follow-up (October 2013 to March 2015) home visits, with a mean (SD) follow-up interval of 7.6 (1.4) years. By design, approximately half of the included women had been diagnosed and treated for breast cancer after enrollment and before the second blood draw. A total of 410 women were included in the present study, including 185 breast cancer survivors and 225 who remained free of breast cancer over a comparable follow-up period. Data were analyzed from April 21 to September 9, 2022.

Exposures

Breast cancer status and, among breast cancer survivors, cancer treatment type (chemotherapy, radiotherapy, endocrine therapy, or surgery).

Main Outcomes and Measures

Blood DNA methylation data were generated in 2019 using a genome-wide methylation screening tool and deconvolved to estimate percentages of 12 circulating leukocyte subsets.

Results

Of the 410 women included in the analysis, the mean (SD) age at enrollment was 56 (9) years. Compared with breast cancer-free women, breast cancer survivors had decreased percentages of circulating eosinophils (-0.45% [95% CI, -0.87% to -0.03%]; P = .03), total CD4+ helper T cells (-1.50% [95% CI, -2.56% to -0.44%]; P = .01), and memory B cells (-0.22% [95% CI, -0.34% to -0.09%]; P = .001) and increased percentages of circulating naive B cells (0.46% [95% CI, 0.17%-0.75%]; P = .002). In breast cancer survivor-only analyses, radiotherapy was associated with decreases in total CD4+ T cell levels, whereas chemotherapy was associated with increases in naive B cell levels. Surgery and endocrine therapy were not meaningfully associated with leukocyte changes.

Conclusions and Relevance

In this cohort study of 410 women, breast cancer survivors experienced lasting changes in peripheral leukocyte composition compared with women who remained free of breast cancer. These changes may be related to treatment with chemotherapy or radiotherapy and could influence future chronic disease risk.

Epigenetic regulation of mRNA mediates the phenotypic plasticity of cancer cells during metastasis and therapeutic resistance (Review)

Plasticity, the ability of cancer cells to transition between differentiation states without genomic alterations, has been recognized as a major source of intratumoral heterogeneity. It has a crucial role in cancer metastasis and treatment resistance. Thus, targeting plasticity holds tremendous promise. However, the molecular mechanisms of plasticity in cancer cells remain poorly understood. Several studies found that mRNA, which acts as a bridge linking the genetic information of DNA and protein, has an important role in translating genotypes into phenotypes. The present review provided an overview of the regulation of cancer cell plasticity occurring via changes in the transcription and editing of mRNAs. The role of the transcriptional regulation of mRNA in cancer cell plasticity was discussed, including DNA‑binding transcriptional factors, DNA methylation, histone modifications and enhancers. Furthermore, the role of mRNA editing in cancer cell plasticity was debated, including mRNA splicing and mRNA modification. In addition, the role of non‑coding (nc)RNAs in cancer plasticity was expounded, including microRNAs, long intergenic ncRNAs and circular RNAs. Finally, different strategies for targeting cancer cell plasticity to overcome metastasis and therapeutic resistance in cancer were discussed.

Blood-based DNA methylation in advanced Nasopharyngeal Carcinoma exhibited distinct CpG methylation signature

The TNM staging system is currently used to detect cancer stages. Regardless, a small proportion of cancer patients recur even after therapy, suggesting more specific molecular tools are required to justify the stage-specific detection and prompt cancer diagnosis. Thus, we aimed to explore the blood-based DNA methylation signature of metastatic nasopharyngeal carcinoma (NPC) to establish a holistic methylation biomarker panel. For the identification of methylation signature, the EPIC BeadChip-based array was performed. Comparative analysis for identifying unique probes, validation, and functional studies was investigated by analyzing GEO and TCGA datasets. We observed 4093 differentially methylated probes (DMPs), 1232 hydroxymethylated probes, and 25 CpG islands. Gene expression study revealed both upregulated and downregulated genes. Correlation analysis suggested a positive (with a positive r, p ≤ 0.05) and negative (with a negative r, p ≤ 0.05) association with different cancers. TFBS analysis exhibited the binding site for many TFs. Furthermore, gene enrichment analysis indicated the involvement of those identified genes in biological pathways. However, blood-based DNA methylation data uncovered a distinct DNA methylation pattern, which might have an additive role in NPC progression by altering the TFs binding. Moreover, based on tissue-specificity, a variation of correlation between methylation and gene expression was noted in different cancers.

Changes in methylation-based aging in women who do and do not develop breast cancer

BACKGROUND

Breast cancer survivors have increased incidence of age-related diseases, suggesting that some survivors may experience faster biological aging.

METHODS

Among 417 women enrolled in the prospective Sister Study cohort, DNA methylation data were generated on paired blood samples collected an average of 7.7 years apart and used to calculate 3 epigenetic metrics of biological aging (PhenoAgeAccel, GrimAgeAccel, and Dunedin Pace of Aging Calculated from the Epigenome [DunedinPACE]). Approximately half (n = 190) the women sampled were diagnosed and treated for breast cancer between blood draws, whereas the other half (n = 227) remained breast cancer-free. Breast tumor characteristics and treatment information were abstracted from medical records.

RESULTS

Among women who developed breast cancer, diagnoses occurred an average of 3.5 years after the initial blood draw and 4 years before the second draw. After accounting for covariates and biological aging metrics measured at baseline, women diagnosed and treated for breast cancer had higher biological aging at the second blood draw than women who remained cancer-free as measured by PhenoAgeAccel (standardized mean difference [β] = 0.13, 95% confidence interval [CI) = 0.00 to 0.26), GrimAgeAccel (β = 0.14, 95% CI = 0.03 to 0.25), and DunedinPACE (β = 0.37, 95% CI = 0.24 to 0.50). In case-only analyses assessing associations with different breast cancer therapies, radiation had strong positive associations with biological aging (PhenoAgeAccel: β = 0.39, 95% CI = 0.19 to 0.59; GrimAgeAccel: β = 0.29, 95% CI = 0.10 to 0.47; DunedinPACE: β = 0.25, 95% CI = 0.02 to 0.48).

CONCLUSIONS

Biological aging is accelerated following a breast cancer diagnosis and treatment. Breast cancer treatment modalities appear to differentially contribute to biological aging.

Potential diagnostic and prognostic biomarkers for breast cancer: A compiled review

Breast cancer is one of the major reason for death of women worldwide. As per the International Agency for Research on Cancer (IARC) statistics, the number of cases of breast cancer is increasing year by year in many parts of the world. As per the recent global cancer burden figures, in 2020, there were 2.26 million incidences of breast cancer cases and it is one of the main causes of mortality due to cancer in women in the world. Biomarkers of breast cancer would prove to be very beneficial to screen women who are at higher risk and for detection of disease recurrence. Here, studies carried out on biomarkers of breast cancer and susceptibility to the disease have been reviewed. Various databases like Google Scholar, ScienceDirect and PubMed have been used for searching and majorly literature from the last 10 years have been considered. Potential biomarkers of breast cancer including blood based angiogenic factors, glycoprotein-based biomarkers, hormone receptor biomarkers and other biomarkers that were identified from various studies have been summarized.

Air pollution and epigenetic aging among Black and White women in the US

BACKGROUND

DNA methylation-based measures of biological aging have been associated with air pollution and may link pollutant exposures to aging-related health outcomes. However, evidence is inconsistent and there is little information for Black women.

OBJECTIVE

We examined associations of ambient particulate matter <2.5 μm and <10 μm in diameter (PM2.5 and PM10) and nitrogen dioxide (NO2) with DNA methylation, including epigenetic aging and individual CpG sites, and evaluated whether associations differ between Black and non-Hispanic White (NHW) women.

METHODS

Validated models were used to estimate annual average outdoor residential exposure to PM2.5, PM10, and NO2 in a sample of self-identified Black (n=633) and NHW (n=3493) women residing in the contiguous US. We used sampling-weighted generalized linear regression to examine the effects of pollutants on six epigenetic aging measures (primary: DunedinPACE, GrimAgeAccel, and PhenoAgeAccel; secondary: Horvath intrinsic epigenetic age acceleration [EAA], Hannum extrinsic EAA, and skin & blood EAA) and epigenome-wide associations for individual CpG sites. Wald tests of nested models with and without interaction terms were used to examine effect measure modification by race/ethnicity.

RESULTS

Black participants had higher median air pollution exposure than NHW participants. GrimAgeAccel was associated with both PM10 and NO2 among Black participants, (Q4 versus Q1, PM10: β=1.09, 95% CI: 0.16-2.03; NO2: β=1.01, 95% CI 0.08-1.94) but not NHW participants (p-for-heterogeneity: PM10=0.10, NO2=0.20). In Black participants, we also observed a monotonic exposure-response relationship between NO2 and DunedinPACE (Q4 versus Q1, NO2: β=0.029, 95% CI: 0.004-0.055; p-for-trend=0.03), which was not observed in NHW participants (p-for-heterogeneity=0.09). In the EWAS, pollutants were significantly associated with differential methylation at 19 CpG sites in Black women and one in NHW women.

CONCLUSIONS

In a US-wide cohort study, our findings suggest that air pollution is associated with DNA methylation alterations consistent with higher epigenetic aging among Black, but not NHW, women.

Circulating tumor biomarkers in early-stage breast cancer: characteristics, detection, and clinical developments

Breast cancer is the most common form of cancer in women, contributing to high rates of morbidity and mortality owing to the ability of these tumors to metastasize via the vascular system even in the early stages of progression. While ultrasonography and mammography have enabled the more reliable detection of early-stage breast cancer, these approaches entail high rates of false positive and false negative results Mammograms also expose patients to radiation, raising clinical concerns. As such, there is substantial interest in the development of more accurate and efficacious approaches to diagnosing breast cancer in its early stages when patients are more likely to benefit from curative treatment efforts. Blood-based biomarkers derived from the tumor microenvironment (TME) have frequently been studied as candidate targets that can enable tumor detection when used for patient screening. Through these efforts, many promising biomarkers including tumor antigens, circulating tumor cell clusters, microRNAs, extracellular vesicles, circulating tumor DNA, metabolites, and lipids have emerged as targets that may enable the detection of breast tumors at various stages of progression. This review provides a systematic overview of the TME characteristics of early breast cancer, together with details on current approaches to detecting blood-based biomarkers in affected patients. The limitations, challenges, and prospects associated with different experimental and clinical platforms employed in this context are also discussed at length.

Exploring the cross-cancer effect of smoking and its fingerprints in blood DNA methylation on multiple cancers: A Mendelian randomization study

Aberrant smoking-related DNA methylation has been widely investigated as a carcinogenesis mechanism, but whether the cross-cancer epigenetic pathways exist remains unclear. We conducted two-sample Mendelian randomization (MR) analyses respectively on smoking behaviors (age of smoking initiation, smoking initiation, smoking cessation, and lifetime smoking index [LSI]) and smoking-related DNA methylation to investigate their effect on 15 site-specific cancers, based on a genome-wide association study (GWAS) of 1.2 million European individuals and an epigenome-WAS (EWAS) of 5907 blood samples of Europeans for smoking and 15 GWASs of European ancestry for multiple site-specific cancers. Significantly identified CpG sites were further used for colocalization analysis, and those with cross-cancer effect were validated by overlapping with tissue-specific eQTLs. In the genomic MR, smoking measurements of smoking initiation, smoking cessation and LSI were suggested to be casually associated with risk of seven types of site-specific cancers, among which cancers at lung, cervix and colorectum were provided with strong evidence. In the epigenetic MR, methylation at 75 CpG sites were reported to be significantly associated with increased risks of multiple cancers. Eight out of 75 CpG sites were observed with cross-cancer effect, among which cg06639488 (EFNA1), cg12101586 (CYP1A1) and cg14142171 (HLA-L) were validated by eQTLs at specific cancer sites, and cg07932199 (ATXN2) had strong evidence to be associated with cancers of lung (coefficient, 0.65, 95% confidence interval [CI], 0.31-1.00), colorectum (0.90 [0.61, 1.18]), breast (0.31 [0.20, 0.43]) and endometrium (0.98 [0.68, 1.27]). These findings highlight the potential practices targeting DNA methylation-involved cross-cancer pathways.

Potential applications of DNA methylation testing technology in female tumors and screening methods

DNA methylation is a common epigenetic modification, and the current commonly used methods for DNA methylation detection include methylation-specific PCR, methylation-sensitive restriction endonuclease-PCR, and methylation-specific sequencing. DNA methylation plays an important role in genomic and epigenomic studies, and combining DNA methylation with other epigenetic modifications, such as histone modifications, may lead to better DNA methylation. DNA methylation also plays an important role in the development of disease, and analyzing changes in individual DNA methylation patterns can provide individualized diagnostic and therapeutic solutions. Liquid biopsy techniques are also increasingly well established in clinical practice and may provide new methods for early cancer screening. It is important to find new screening methods that are easy to perform, minimally invasive, patient-friendly, and affordable. DNA methylation mechanisms are thought to have an important role in cancer and have potential applications in the diagnosis and treatment of female tumors. This review discussed early detection targets and screening methods for common female tumors such as breast, ovarian, and cervical cancers and discussed advances in the study of DNA methylation in these tumors. Although existing screening, diagnostic, and treatment modalities exist, the high morbidity and mortality rates of these tumors remain challenging.

Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study

BACKGROUND

Epigenome-wide association studies of ambient fine particulate matter (PM2.5) have been reported. However, few have examined PM2.5 components (PMCs) and sources or included repeated measures. The lack of high-resolution exposure measurements is the key limitation. We hypothesized that significant changes in DNA methylation might vary by PMCs and the sources.

METHODS

We predicted the annual average of 14 PMCs using novel high-resolution exposure models across the contiguous U.S., between 2000-2018. The resolution was 50 m × 50 m in the Greater Boston Area. We also identified PM2.5 sources using positive matrix factorization. We repeatedly collected blood samples and measured leukocyte DNAm with the Illumina HumanMethylation450K BeadChip in the Normative Aging Study. We then used median regression with subject-specific intercepts to estimate the associations between long-term (one-year) exposure to PMCs / PM2.5 sources and DNA methylation at individual cytosine-phosphate-guanine CpG sites. Significant probes were identified by the number of independent degrees of freedom approach, using the number of principal components explaining > 95% of the variation of the DNA methylation data. We also performed regional and pathway analyses to identify significant regions and pathways.

RESULTS

We included 669 men with 1,178 visits between 2000-2013. The subjects had a mean age of 75 years. The identified probes, regions, and pathways varied by PMCs and their sources. For example, iron was associated with 6 probes and 6 regions, whereas nitrate was associated with 15 probes and 3 regions. The identified pathways from biomass burning, coal burning, and heavy fuel oil combustion sources were associated with cancer, inflammation, and cardiovascular diseases, whereas there were no pathways associated with all traffic.

CONCLUSIONS

Our findings showed that the effects of PM2.5 on DNAm varied by its PMCs and sources.

A novel cuproptosis-related genes model in breast cancer prognosis

Breast cancer (BRCA) is a highly heterogeneous malignancy with an urgent need to build a proper model to predict its prognosis. Cuproptosis is a recently discovered form of cell death, mediated by protein fatty acylation and tightly associated with mitochondrial metabolism. The role of cuproptosis-related genes (CRGs) in BRCA remains to be explored. We aimed to investigate the applications of CRGs in BRCA prognosis in different clinical contexts, including chemotherapy and immunotherapy, via bioinformatics analysis of the messenger RNA profiles and clinical data obtained from public databases. Molecular subtyping of CRGs was performed through consistent clustering analysis. Differentially expressed genes between different CRG clusters were identified. The differentially expressed genes were then used to build a risk assessment model using least absolute shrinkage and selection operator regression to predict patient survival with BRCA. The model was then validated with the data from the Molecular Taxonomy of Breast Cancer International Consortium, GSE96058, and GSE20685. Differences in somatic mutations, copy number variations, hallmark pathways, drug responses, and prognosis of immunotherapy and chemotherapy were analyzed by comparing the high-risk and low-risk groups. Patients with high-risk scores showed worse overall survival than those with low-risk scores. The results indicated significant differences between the 2 groups immune-related biological pathways and the variable immune status. It also suggests the differential sensitivity to chemotherapy between the 2 groups. The CRGs model showed the promise to predict the prognosis of BRCA patients and shed light on their treatment.

Promoter Methylation-Regulated Differentially Expressed Genes in Breast Cancer

Background

Breast cancer is one of the most common malignancies among women. Recent studies revealed that differentially methylated regions (DMRs) are implicated in regulating gene expression. The goal of this research was to determine which genes and pathways are dysregulated in breast cancer when their promoters are methylated in an abnormal way, leading to differential expression. Whole-genome bisulfite sequencing was applied to analyze DMRs for eight peripheral blood samples collected from five Saudi females diagnosed with stages I and II of breast cancer aligned with three normal females. Three of those patients and three normal samples were used to determine differentially expressed genes (DEG) using Illumina platform NovaSeq PE150.

Results

Based on ontology (GO) and KEGG pathways, the analysis indicated that DMGs and DEG are closely related to associated processes, such as ubiquitin-protein transferase activity, ubiquitin-mediated proteolysis, and oxidative phosphorylation. The findings indicated a potentially significant association between global hypomethylation and breast cancer in Saudi patients. Our results revealed 81 differentially promoter-methylated and expressed genes. The most significant differentially methylated and expressed genes found in gene ontology (GO) are pumilio RNA binding family member 1 (PUM1) and zinc finger AN1-type containing 2B (ZFAND2B) also known as (AIRAPL).

Conclusion

The essential outcomes of this study suggested that aberrant hypermethylation at crucial genes that have significant parts in the molecular pathways of breast cancer could be used as a potential prognostic biomarker for breast cancer.

Buffy coat signatures of breast cancer risk in a prospective cohort study

BACKGROUND

Epigenetic alterations are a near-universal feature of human malignancy and have been detected in malignant cells as well as in easily accessible specimens such as blood and urine. These findings offer promising applications in cancer detection, subtyping, and treatment monitoring. However, much of the current evidence is based on findings in retrospective studies and may reflect epigenetic patterns that have already been influenced by the onset of the disease.

METHODS

Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).

RESULTS

We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.

CONCLUSIONS

Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.

Epigenome-wide DNA methylation in leukocytes and toenail metals: The normative aging study

BACKGROUND

Environmental metal exposures have been associated with multiple deleterious health endpoints. DNA methylation (DNAm) may provide insight into the mechanisms underlying these relationships. Toenail metals are non-invasive biomarkers, reflecting a medium-term time exposure window.

OBJECTIVES

This study examined variation in leukocyte DNAm and toenail arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg) among elderly men in the Normative Aging Study, a longitudinal cohort.

METHODS

We repeatedly collected samples of blood and toenail clippings. We measured DNAm in leukocytes with the Illumina HumanMethylation450 K BeadChip. We first performed median regression to evaluate the effects of each individual toenail metal on DNAm at three levels: individual cytosine-phosphate-guanine (CpG) sites, regions, and pathways. Then, we applied a Bayesian kernel machine regression (BKMR) to assess the joint and individual effects of metal mixtures on DNAm. Significant CpGs were identified using a multiple testing correction based on the independent degrees of freedom approach for correlated outcomes. The approach considers the effective degrees of freedom in the DNAm data using the principal components that explain >95% variation of the data.

RESULTS

We included 564 subjects (754 visits) between 1999 and 2013. The numbers of significantly differentially methylated CpG sites, regions, and pathways varied by metals. For example, we found six significant pathways for As, three for Cd, and one for Mn. The As-associated pathways were associated with cancer (e.g., skin cancer) and cardiovascular disease, whereas the Cd-associated pathways were related to lung cancer. Metal mixtures were also associated with 47 significant CpG sites, as well as pathways, mainly related to cancer and cardiovascular disease.

CONCLUSIONS

This study provides an approach to understanding the potential epigenetic mechanisms underlying observed relations between toenail metals and adverse health endpoints.

Epigenome-Wide Analysis of DNA Methylation and Optimism in Women and Men

OBJECTIVE

Higher optimism is associated with reduced mortality and a lower risk of age-related chronic diseases. DNA methylation (DNAm) may provide insight into mechanisms underlying these relationships. We hypothesized that DNAm would differ among older individuals who are more versus less optimistic.

METHODS

Using cross-sectional data from two population-based cohorts of women with diverse races/ethnicities ( n = 3816) and men (only White, n = 667), we investigated the associations of optimism with epigenome-wide leukocyte DNAm. Random-effects meta-analyses were subsequently used to pool the individual results. Significantly differentially methylated cytosine-phosphate-guanines (CpGs) were identified by the "number of independent degrees of freedom" approach: effective degrees of freedom correction using the number of principal components (PCs), explaining >95% of the variation of the DNAm data (PC-correction). We performed regional analyses using comb-p and pathway analyses using the Ingenuity Pathway Analysis software.

RESULTS

We found that essentially all CpGs (total probe N = 359,862) were homogeneous across sex and race/ethnicity in the DNAm-optimism association. In the single CpG site analyses based on homogeneous CpGs, we identified 13 significantly differentially methylated probes using PC-correction. We found four significantly differentially methylated regions and two significantly differentially methylated pathways. The annotated genes from the single CpG site and regional analyses are involved in psychiatric disorders, cardiovascular disease, cognitive impairment, and cancer. Identified pathways were related to cancer, and neurodevelopmental and neurodegenerative disorders.

CONCLUSION

Our findings provide new insights into possible mechanisms underlying optimism and health.

Cell-Free DNA Fragmentomics: A Promising Biomarker for Diagnosis, Prognosis and Prediction of Response in Breast Cancer

Identifying novel circulating biomarkers predictive of response and informative about the mechanisms of resistance, is the new challenge for breast cancer (BC) management. The integration of omics information will gradually revolutionize the clinical approach. Liquid biopsy is being incorporated into the diagnostic and decision-making process for the treatment of BC, in particular with the analysis of circulating tumor DNA, although with some relevant limitations, including costs. Circulating cell-free DNA (cfDNA) fragmentomics and its integrity index may become a cheaper, noninvasive biomarker that could provide significant additional information for monitoring response to systemic treatments in BC. The purpose of our review is to focus on the available research on cfDNA integrity and its features as a biomarker of diagnosis, prognosis and response to treatments in BC, highlighting new perspectives and critical issues for future applications.

Gene regulation in animal miRNA biogenesis

miRNAs are a class of noncoding RNAs of approximately 19-22 nucleotides that are widely found in animals, plants, bacteria and even viruses. Dysregulation of the expression profile of miRNAs is importantly linked to the development of diseases. Epigenetic modifications regulate gene expression and control cellular phenotypes. Although miRNAs are used as an epigenetic regulation tool, the biogenesis of miRNAs is also regulated by epigenetic events. Here the authors review the mechanisms and roles of epigenetic modification (DNA methylation, histone modifications), RNA modification and ncRNAs in the biogenesis of miRNAs, aiming to deepen the understanding of the miRNA biogenesis regulatory network.

Characterisation of ethnic differences in DNA methylation between UK-resident South Asians and Europeans

Ethnic differences in non-communicable disease risk have been described between individuals of South Asian and European ethnicity that are only partially explained by genetics and other known risk factors. DNA methylation is one underexplored mechanism that may explain differences in disease risk. Currently, there is little knowledge of how DNA methylation varies between South Asian and European ethnicities. This study characterised differences in blood DNA methylation between individuals of self-reported European and South Asian ethnicity from two UK-based cohorts: Southall and Brent Revisited and Born in Bradford. DNA methylation differences between ethnicities were widespread throughout the genome (n = 16,433 CpG sites, 3.4% sites tested). Specifically, 76% of associations were attributable to ethnic differences in cell composition with fewer effects attributable to smoking and genetic variation. Ethnicity-associated CpG sites were enriched for EWAS Catalog phenotypes including metabolites. This work highlights the need to consider ethnic diversity in epigenetic research.

A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: An application to breast cancer time to diagnosis

Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways.

Blood-derived DNAm profiles could be exploited as new biomarkers for cancer risk stratification and possibly, early detection. This is of particular interest since blood is a convenient tissue to assay for constitutional methylation and its collection is non-invasive. Exploiting pre-diagnostic blood DNAm data opens the further opportunity to investigate the association of DNAm at baseline on cancer risk, modeling the relationship between sites’ methylation and the Time to Diagnosis. Previous studies mostly provide inconsistent results likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work we argue that an approach to estimate single CpG sites’ effect while modeling their combined effect on the survival outcome is needed, and we claim that such approach should capture the complex (potentially non-linear) relationships interplaying between sites. We prove this concept by developing a novel approach to analyze a prospective case-control study on breast cancer nested in the EPIC Italy cohort. A weighted gene set enrichment analysis confirms that our approach outperforms standard EWAS in identifying biologically meaningful pathways.

Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies

BACKGROUND

DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer.

METHODS

Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage.

RESULTS

None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics.

CONCLUSION

We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.

Emerging Roles of Long Noncoding RNAs in Breast Cancer Epigenetics and Epitranscriptomics

Breast carcinogenesis is a multistep process that involves both genetic and epigenetic changes. Epigenetics refers to reversible changes in gene expression that are not accompanied by changes in gene sequence. In breast cancer (BC), dysregulated epigenetic changes, such as DNA methylation and histone modifications, are accompanied by epitranscriptomic changes, in particular adenine to inosine modifications within RNA molecules. Factors that trigger these phenomena are largely unknown, but there is evidence for widespread participation of long noncoding RNAs (lncRNAs) that already have been linked to virtually any aspect of BC biology, making them promising biomarkers and therapeutic targets in BC patients. Here, we provide a systematic review of known and possible roles of lncRNAs in epigenetic and epitranscriptomic processes, along with methods and tools to study them, followed by a brief overview of current challenges regarding the use of lncRNAs in medical applications.

Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier

We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643-195636243 and chr6: 29691631-29692475) were differentially methylated in the independent cohort (p = 4.19 × 10^-5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.

Aberrant epigenetic and transcriptional events associated with breast cancer risk

BACKGROUND

Genome-wide association studies have identified several breast cancer susceptibility loci. However, biomarkers for risk assessment are still missing. Here, we investigated cancer-related molecular changes detected in tissues from women at high risk for breast cancer prior to disease manifestation. Disease-free breast tissue cores donated by healthy women (N = 146, median age = 39 years) were processed for both methylome (MethylCap) and transcriptome (Illumina's HiSeq4000) sequencing. Analysis of tissue microarray and primary breast epithelial cells was used to confirm gene expression dysregulation.

RESULTS

Transcriptomic analysis identified 69 differentially expressed genes between women at high and those at average risk of breast cancer (Tyrer-Cuzick model) at FDR < 0.05 and fold change ≥ 2. Majority of the identified genes were involved in DNA damage checkpoint, cell cycle, and cell adhesion. Two genes, FAM83A and NEK2, were overexpressed in tissue sections (FDR < 0.01) and primary epithelial cells (p < 0.05) from high-risk breasts. Moreover, 1698 DNA methylation changes were identified in high-risk breast tissues (FDR < 0.05), partially overlapped with cancer-related signatures, and correlated with transcriptional changes (p < 0.05, r ≤ 0.5). Finally, among the participants, 35 women donated breast biopsies at two time points, and age-related molecular alterations enhanced in high-risk subjects were identified.

CONCLUSIONS

Normal breast tissue from women at high risk of breast cancer bears molecular aberrations that may contribute to breast cancer susceptibility. This study is the first molecular characterization of the true normal breast tissues, and provides an opportunity to investigate molecular markers of breast cancer risk, which may lead to new preventive approaches.

Systematic review of Mendelian randomization studies on risk of cancer

BACKGROUND

We aimed to map and describe the current state of Mendelian randomization (MR) literature on cancer risk and to identify associations supported by robust evidence.

METHODS

We searched PubMed and Scopus up to 06/10/2020 for MR studies investigating the association of any genetically predicted risk factor with cancer risk. We categorized the reported associations based on a priori designed levels of evidence supporting a causal association into four categories, namely robust, probable, suggestive, and insufficient, based on the significance and concordance of the main MR analysis results and at least one of the MR-Egger, weighed median, MRPRESSO, and multivariable MR analyses. Associations not presenting any of the aforementioned sensitivity analyses were not graded.

RESULTS

We included 190 publications reporting on 4667 MR analyses. Most analyses (3200; 68.6%) were not accompanied by any of the assessed sensitivity analyses. Of the 1467 evaluable analyses, 87 (5.9%) were supported by robust, 275 (18.7%) by probable, and 89 (6.1%) by suggestive evidence. The most prominent robust associations were observed for anthropometric indices with risk of breast, kidney, and endometrial cancers; circulating telomere length with risk of kidney, lung, osteosarcoma, skin, thyroid, and hematological cancers; sex steroid hormones and risk of breast and endometrial cancer; and lipids with risk of breast, endometrial, and ovarian cancer.

CONCLUSIONS

Despite the large amount of research on genetically predicted risk factors for cancer risk, limited associations are supported by robust evidence for causality. Most associations did not present a MR sensitivity analysis and were thus non-evaluable. Future research should focus on more thorough assessment of sensitivity MR analyses and on more transparent reporting.

DNA Methylation Abnormalities and Altered Whole Transcriptome Profiles after Switching from Combustible Tobacco Smoking to Heated Tobacco Products

BACKGROUND

The use of heated tobacco products (HTP) has increased exponentially in Japan since 2016; however, their effects on health remain a major concern.

METHODS

Tsuruoka Metabolome Cohort Study participants (n = 11,002) were grouped on the basis of their smoking habits as never smokers (NS), past smokers (PS), combustible tobacco smokers (CS), and HTP users for <2 years. Peripheral blood mononuclear cells were collected from 52 participants per group matched to HTP users using propensity scores, and DNA and RNA were purified from the samples. DNA methylation (DNAm) analysis of the 17 smoking-associated DNAm biomarker genes (such as AHRR, F2RL3, LRRN3, and GPR15), as well as whole transcriptome analysis, was performed.

RESULTS

Ten of the 17 genes were significantly hypomethylated in CS and HTP users compared with NS, among which AHRR, F2RL3, and RARA showed intermediate characteristics between CS and NS; nonetheless, AHRR expression was significantly higher in CS than in the other three groups. Conversely, LRRN3 and GPR15 were more hypomethylated in HTP users than in NS, and GPR15 expression was markedly upregulated in all the groups when compared with that in NS.

CONCLUSIONS

HTP users (switched from CS <2 years) display abnormal DNAm and transcriptome profiles, albeit to a lesser extent than the CS. However, because the molecular genetic effects of long-term HTP use are still unknown, long-term molecular epidemiologic studies are needed.

IMPACT

This study provides new insights into the molecular genetic effects on DNAm and transcriptome profiles in HTP users who switched from CS.

Regulatory Mechanism of Neurotrophin Receptor-Interacting Melanoma Antigen Coding Gene Homolog (NRAGE) Gene Methylation on Apoptosis of Breast Cancer Cell Under Tyrosine Kinases/Methyl Ethyl Ketone/Extracellular Regulated Protein Kinases Signaling Pathway

The aim of this study was to discover the influence of Neurotrophin receptor-interacting MAGE homolog (NRAGE) gene methylation on proliferation (Pro) and apoptosis (Apo) of breast cancer cell (BCC), and its influence on TrkA/MEK/ERK signaling. BCC lines MCF-7, MDA-MB-231, and normal mammary gland cell (MGC) MCF-10 were selected. Expression of NRAGE mRNA and methylation level in cells was analyzed via reverse transcription-polymerase chain reaction (RT-PCR) and methylation-specific PCR. Different concentrations (0, 5, 10 mol/L) of DNA methylase inhibitor 5-aza-2′-deoxycytidine (5-Aza-CdR) were adopted to treat the BCC cell line. With dimethyl sulfoxide (DMSO) treatment as control, cell count, 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, flow cytometry, and Western blot were adopted to detect the Pro, Apo, relative expression (REP) of Apo-related proteins Bcl-2, Bax, and target proteins TrkA, MEK, and ERK1/2 after different treatments. The results showed that NRAGE mRNA level in MDA-MB-231 and MCF-7 was notably reduced versus MCF-10 (P < 0.05), and they could express methylated NRAGE specifically. 5-Aza-CdR can increase unmethylated NRAGE’s expression in BCC. Cell Pro level of the 5 and 10 mol/L treatments was greatly inhibited than DMSO and 0 mol/L treatments (P < 0.05). Apo rate and Apo-related proteins Bcl-2 and Bax increased obviously (P < 0.05). In addition, the phosphorylation levels of TrkA in the 5 and 10 mol/L treatments were considerably reduced (P < 0.05), while that in MEK and ERK1/2 was remarkably increased (P < 0.05). In short, NRAGE methylation can inhibit BCC’s Pro and regulate BCC’s Pro and Apo through TrkA/MEK/ERK signaling.

Short- and intermediate-term exposure to ambient fine particulate elements and leukocyte epigenome-wide DNA methylation in older men: the Normative Aging Study

BACKGROUND

Several epigenome-wide association studies (EWAS) of ambient particulate matter with aerodynamic diameter ≤ 2.5 µm (PM_2.5) have been reported. However, EWAS of PM_2.5 elements (PEs), reflecting different emission sources, are very limited.

OBJECTIVES

We performed EWAS of short- and intermediate-term exposure to PM_2.5 and 13 PEs. We hypothesized that significant changes in DNAm may vary by PM_2.5 mass and its elements.

METHODS

We repeatedly collected blood samples in the Normative Aging Study and measured leukocyte DNA methylation (DNAm) with the Illumina HumanMethylation450K BeadChip. We collected daily PM_2.5 and 13 PEs at a fixed central site. To estimate the associations between each PE and DNAm at individual cytosine-phosphate-guanine (CpG) sites, we incorporated a distributed-lag (0-27 d) term in the setting of median regression with subject-specific intercept and examined cumulative lag associations. We also accounted for selection bias due to loss to follow-up and mortality prior to enrollment. Significantly differentially methylated probes (DMPs) were identified using Bonferroni correction for multiple testing. We further conducted regional and pathway analyses to identify significantly differentially methylated regions (DMRs) and pathways.

RESULTS

We included 695 men with 1,266 visits between 1999 and 2013. The subjects had a mean age of 75 years. The significant DMPs, DMRs, and pathways varied by to PM_2.5 total mass and PEs. For example, PM_2.5 total mass was associated with 2,717 DMPs and 10,470 DMRs whereas Pb was associated with 3,173 DMPs and 637 DMRs. The identified pathways by PM_2.5 mass were mostly involved in mood disorders, neuroplasticity, immunity, and inflammation, whereas the pathways associated with motor vehicles (BC, Cu, Pb, and Zn) were related with cardiovascular disease and cancer (e.g., "PPARs signaling").

CONCLUSIONS

PM_2.5 and PE were associated with methylation changes at multiple probes and along multiple pathways, in ways that varied by particle components.

A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer

Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC).

Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database.

Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset.

Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

Methylation biomarkers of polybrominated diphenyl ethers (PBDEs) and association with breast cancer risk at the time of menopause

BACKGROUND

Exposure to polybrominated diphenyl ethers (PBDEs) may influence risk of developing post-menopausal breast cancer. Although mechanisms are poorly understood, epigenetic regulation of gene expression may play a role.

OBJECTIVES

To identify DNA methylation (DNAm) changes associated with PBDE serum levels and test the association of these biomarkers with breast cancer risk.

METHODS

We studied 397 healthy women (controls) and 133 women diagnosed with breast cancer (cases) between ages 40 and 58 years who participated in the California Teachers Study. PBDE levels were measured in blood. Infinium Human Methylation EPIC Bead Chips were used to measure DNAm. Using multivariable linear regression models, differentially methylated CpG sites (DMSs) and regions (DMRs) associated with serum PBDE levels were identified using controls. For top-ranked DMSs and DMRs, targeted next-generation bisulfite sequencing was used to measure DNAm for 133 invasive breast cancer cases and 301 age-matched controls. Conditional logistic regression was used to evaluate associations between DMSs and DMRs and breast cancer risk.

RESULTS

We identified 15 DMSs and 10 DMRs statistically significantly associated with PBDE levels (FDR < 0.05). Methylation changes in a DMS at BMP8B and DMRs at TP53 and A2M-AS1 were statistically significantly (FDR < 0.05) associated with breast cancer risk.

CONCLUSION

We show for the first time that serum PBDE levels are associated with differential methylation and that PBDE-associated DNAm changes in blood are associated with breast cancer risk.

[Peripheral blood EMR3 gene methylation level is correlated with breast cancer in Chinese women]

OBJECTIVE

To explore the association of methylation levels of C19orf57, MAP9, EMR3, NEK6 and PCOLCE2 genes in peripheral blood with breast cancer (BC) in Chinese women.

METHODS

We collected peripheral blood samples from 258 early-stage BC patients and 272 healthy women. Agena matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was utilized to quantitatively measure the methylation levels of CpG sites in the genes. The association between DNA methylation and BC was analyzed using a logistic regression model adjusted for covariants. Spearman's correlation analysis was performed to analyze the association between the gene methylation levels and age. The methylation levels of the genes in the BC patients with different clinical characteristics were investigated using non-parametric tests.

RESULTS

In stead of EMR3 gene hypermethylation as found in BC patients as found in the Caucasian population, EMR3 gene hypomethylation was found to correlate with BC in Chinese women, but this correlation was significant only in women beyond the age of 50 years (for every 10% reduction of the methylation level, EMR3_CpG_1: OR=1.40; EMR3_CpG_2: OR=2.31; EMR3_CpG_3: OR=2.76, P < 0.05). EMR3 methylation was not or was only weakly correlated with tumor stage, size, lymphatic metastasis, ER, PR, HER2, or Ki67. Our data did not show a correlation between C19orf57 methylation and BC.

CONCLUSION

Peripheral blood EMR3 gene hypomethylation is associated with BC in Chinese women, especially in those at an old age and in postmenopausal women.

Blood DNA methylation score predicts breast cancer risk: applying OPERA in molecular, environmental, genetic and analytic epidemiology

In this issue, Kresovich and colleagues have published a hallmark paper in Molecular, Environmental, Genetic and Analytic Epidemiology. By applying artificial intelligence to the Sister Study they created a new methylation-based breast cancer risk score (mBCRS) based on blood DNA methylation. Using a prospective design and after accounting for age and questionnaire-based breast cancer risk factors, the Odds PER Adjusted standard deviation (OPERA) for mBCRS and polygenic risk score (PRS) was 1.58 (95% CI: 1.38, 1.81) and 1.58 (95% CI: 1.36, 1.83), respectively, and the corresponding area under the receiver operating curve was 0.63 for both. Therefore, mBCRS could be as powerful as the current best PRS in differentiating women of the same age in terms of their breast cancer risk. These risk scores are among the strongest known breast cancer risk-stratifiers, shaded only by new mammogram risk scores based on measures other than conventional mammographic density, such as Cirrocumulus and Cirrus, which when combined have an OPERA as high as 2.3. The combination of PRS and mBCRS with the other measured risk factors gave an OPERA of 2.2. OPERA has many advantages over changes in areas under the receiver operator curve because the latter depend on the order in which risk factors are considered. Although more replication is needed using prospective data to protect against reverse causation, there are many novel molecular and analytic aspects to this paper which uncovers a potential mechanism for how genetic and environmental factors combine to cause breast cancer.

Peripheral Blood-Based DNA Methylation of Long Non-Coding RNA H19 and Metastasis-Associated Lung Adenocarcinoma Transcript 1 Promoters are Potential Non-Invasive Biomarkers for Gastric Cancer Detection

Introduction

The early diagnosis and detection could greatly improve the clinical outcome of gastric cancer (GC) patients. However, the non-invasive biomarkers for GC detection remain to be identified.

Method

We used online databases (GEPIA, UALCAN, Kaplan-Meier plotter, TIMER, and MEXPRESS) to explore the association between H19 or metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) expression in tissues and the occurrence, development, prognosis, the levels of immune cell infiltration, and methylation of GC; the correlation between mRNA expression and DNA methylation levels of genes were also examined. Methylation levels of H19 or MALAT1 in peripheral blood were compared between 150 GC patients and 100 healthy controls (HCs). Predictive nomograms were constructed among female and male groups for GC diagnosis. The calibration curves, Hosmer–Lemeshow test, and decision curve analysis were also used to examine the nomograms’ predictive ability and clinical values.

Results

Using multiple online databases, we found that the mRNA expressions of H19 and MALAT1 in tissues were related to the occurrence of GC, and such expressions were associated with immune cell infiltration of GC and negatively correlated with DNA methylation levels of H19 and MALAT1. H19 gene, H19C island, and MALAT1B island, as well as 20 CpG sites were hypermethylated in peripheral blood of GC patients compared with HCs; similar results were also found in female and male groups (P < .05 for all). The combination of H19c3, H19c4, MALAT1b12, and age, as well as the combination of H19b7, H19c1, H19c5, and age in the nomograms could distinguish GC patients from HCs in the female group and male group, respectively.

Conclusion

We found statistically significant hypermethylation of H19 and MALAT1 promoters in GC patients, and meaningful sensitivity and specificity of MALAT1 and H19 methylation in discriminating GC and HCs were observed in both female and male groups, which indicates that the peripheral blood-based DNA methylation of H19 and MALAT1 could act as potential non-invasive biomarkers for the diagnosis of GC.

The breast is yet to come: current and future utility of circulating tumour DNA in breast cancer

Advances in genomic strategies and the development of targeted therapies have enabled precision medicine to revolutionise the field of oncology. Precision medicine uses patient-specific genetic and molecular information, traditionally obtained from tumour biopsy samples, to classify tumours and treat them accordingly. However, biopsy samples often fail to provide complete tumour profiling, and the technique is expensive and, of course, relatively invasive. Advances in genomic techniques have led to improvements in the isolation and detection of circulating tumour DNA (ctDNA), a component of a peripheral blood draw/liquid biopsy. Liquid biopsy offers a minimally invasive method to gather genetic information that is representative of a global snapshot of both primary and metastatic sites and can thereby provide invaluable information for potential targeted therapies and methods for tumour surveillance. However, a lack of prospective clinical trials showing direct patient benefit has limited the implementation of liquid biopsies in standard clinical applications. Here, we review the potential of ctDNA obtained by liquid biopsy to revolutionise personalised medicine and discuss current applications of ctDNA both at the benchtop and bedside.

Blood DNA methylation profiles improve breast cancer prediction

Although blood DNA methylation (DNAm) profiles are reported to be associated with breast cancer incidence, they have not been widely used in breast cancer risk assessment. Among a breast cancer case–cohort of 2774 women (1551 cases) in the Sister Study, we used candidate CpGs and DNAm estimators of physiologic characteristics to derive a methylation‐based breast cancer risk score, mBCRS. Overall, 19 CpGs and five DNAm estimators were selected using elastic net regularization to comprise mBCRS. In a test set, higher mBCRS was positively associated with breast cancer incidence, showing similar strength to the polygenic risk score (PRS) based on 313 single nucleotide polymorphisms (313 SNPs). Area under the curve for breast cancer prediction was 0.60 for self‐reported risk factors (RFs), 0.63 for PRS, and 0.63 for mBCRS. Adding mBCRS to PRS and RFs improved breast cancer prediction from 0.66 to 0.71. mBCRS findings were replicated in a nested case–control study within the EPIC‐Italy cohort. These results suggest that mBCRS, a risk score derived using blood DNAm, can be used to enhance breast cancer prediction.

Prediagnostic White Blood Cell DNA Methylation and Risk of Breast Cancer in the Prostate Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort

BACKGROUND

White blood cell (WBC) DNA may contain methylation patterns that are associated with subsequent breast cancer risk. Using a high-throughput array and samples collected, on average, 1.3 years prior to diagnosis, a case-cohort analysis nested in the prospective Sister Study identified 250 individual CpG sites that were differentially methylated between breast cancer cases and noncases. We examined five of the top 40 CpG sites in a case-control study nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort.

METHODS

We investigated the associations between prediagnostic WBC DNA methylation in 297 breast cancer cases and 297 frequency-matched controls. Two WBC DNA specimens from each participant were used: a proximate sample collected 1 to 2.9 years and a distant sample collected 4.2-7.3 years prior to diagnosis in cases or the comparable timepoints in controls. WBC DNA methylation level was measured using targeted bisulfite amplification sequencing. We used logistic regression to obtain ORs and 95% confidence intervals (CI).

RESULTS

A one-unit increase in percent methylation in ERCC1 in proximate WBC DNA was associated with increased breast cancer risk (adjusted OR = 1.29; 95% CI, 1.06-1.57). However, a one-unit increase in percent methylation in ERCC1 in distant WBC DNA was inversely associated with breast cancer risk (adjusted OR = 0.83; 95% CI, 0.69-0.98). None of the other ORs met the threshold for statistical significance.

CONCLUSIONS

There was no convincing pattern between percent methylation in the five CpG sites and breast cancer risk.

IMPACT

The link between prediagnostic WBC DNA methylation marks and breast cancer, if any, is poorly understood.

Association of mammographic density with blood DNA methylation

Background: Altered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density.

Methods: White blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings.

Results: On average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density.

Conclusion: Mammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.

ipDMR: identification of differentially methylated regions with interval P-values

SUMMARY

ipDMR is an R software tool for identification of differentially methylated regions (DMRs) using auto-correlated P-values for individual CpGs from epigenome-wide association analysis using array or bisulfite sequencing data. It summarizes P-values for adjacent CpGs, identifies association peaks and then extends peaks to find boundaries of DMRs. ipDMR uses BED format files as input and is easy to use. Simulations guided by real data found that ipDMR outperformed current available methods and provided slightly higher true positive rates and much lower false discovery rates.

AVAILABILITY AND IMPLEMENTATION

ipDMR is available at https://bioconductor.org/packages/release/bioc/html/ENmix.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detection and relevance of epigenetic markers on ctDNA: recent advances and future outlook

Liquid biopsy, a minimally invasive approach, is a highly powerful clinical tool for the real-time follow-up of cancer and overcomes many limitations of tissue biopsies. Epigenetic alterations have a high potential to provide a valuable source of innovative biomarkers for cancer, owing to their stability, frequency, and noninvasive accessibility in bodily fluids. Numerous DNA methylation markers are now tested in circulating tumor DNA (ctDNA) as potential biomarkers, in various types of cancer. DNA methylation in combination with liquid biopsy is very powerful in identifying circulating epigenetic biomarkers of clinical importance. Blood-based epigenetic biomarkers have a high potential for early detection of cancer since DNA methylation in plasma can be detected early during cancer pathogenesis. In this review, we summarize the latest findings on DNA methylation markers in ctDNA for early detection, prognosis, minimal residual disease, risk of relapse, treatment selection, and resistance, for breast, prostate, lung, and colorectal cancer.