Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk: Data From 228 951 Women of European Descent

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.

Cancer-specific risk prediction with a serum microRNA signature

We recently derived and validated a serum-based microRNA risk score (miR-score) that predicted colorectal cancer (CRC) occurrence with very high accuracy within 14 years of follow-up in a population-based cohort study from Germany (ESTHER cohort). Here, we aimed to evaluate associations of the CRC-specific miR-score with the risk of developing other common cancers, including female breast cancer (BC), lung cancer (LC), and prostate cancer (PC), in the ESTHER cohort. MicroRNAs (miRNAs) were profiled by quantitative real-time PCR in serum samples collected at baseline from randomly selected incident cases of BC (n = 90), LC (n = 88), and PC (n = 93) and participants without diagnosis of CRC, LC, BC, or PC (controls, n = 181) until the end of the 17-year follow-up. Multivariate logistic regression models were used to evaluate the associations of the miR-score with BC, LC, and PC incidence. The miR-score showed strong inverse associations with BC and LC incidence [odds ratio per 1 standard deviation increase: 0.60 (95% confidence interval [CI] 0.43-0.82), p = 0.0017, and 0.64 (95% CI 0.48-0.84),p = 0.0015, respectively]. Associations with PC were not statistically significant but pointed in the positive direction. Our study highlights the potential of serum-based miRNA biomarkers for cancer-specific risk prediction. Further large cohort studies aiming to investigate, validate, and optimize the use of circulating miRNA signatures for cancer risk assessment are warranted.

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.

The impact of physical activity on promoter-specific methylation of genes involved in the redox-status and disease progression: A longitudinal study on post-surgery female breast cancer patients undergoing medical treatment

Most anticancer treatments act on oxidative-stress pathways by producing reactive oxygen species (ROS) to kill cancer cells, commonly resulting in consequential drug-induced systemic cytotoxicity. Physical activity (PA) has arisen as an integrative cancer therapy, having positive health effects, including in redox-homeostasis. Here, we investigated the impact of an online supervised PA program on promoter-specific DNA methylation, and corresponding gene expression/activity, in 3 antioxidants- (SOD1, SOD2, and CAT) and 3 breast cancer (BC)-related genes (BRCA1, L3MBTL1 and RASSF1A) in a population-based sample of women diagnosed with primary BC, undergoing medical treatment. We further examined mechanisms involved in methylating and demethylating pathways, predicted biological pathways and interactions of exercise-modulated molecules, and the functional relevance of modulated antioxidant markers on parameters related to aerobic capacity/endurance, physical fatigue and quality of life (QoL). PA maintained levels of SOD activity in blood plasma, and at the cellular level significantly increased SOD2 mRNA (≈+77 %), contrary to their depletion due to medical treatment. This change was inversely correlated with DNA methylation in SOD2 promoter (≈-20 %). Similarly, we found a significant effect of PA only on L3MBTL1 promoter methylation (≈-25 %), which was inversely correlated with its mRNA (≈+43 %). Finally, PA increased TET1 mRNA levels (≈+15 %) and decreased expression of DNMT3B mRNA (≈-28 %). Our results suggest that PA-modulated DNA methylation affects several signalling pathways/biological activities involved in the cellular oxidative stress response, chromatin organization/regulation, antioxidant activity and DNA/protein binding. These changes may positively impact clinical outcomes and improve the response to cancer treatment in post-surgery BC patients.

DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.

Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data

Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls

BACKGROUND

Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated.

METHODS

The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways.

RESULTS

Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified.

CONCLUSIONS

Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby.

PLAIN LANGUAGE SUMMARY

The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models

BACKGROUND

Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets.

METHODS

We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent.

RESULTS

We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment.

CONCLUSIONS

Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data

Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.

Anticancer role of mango (Mangifera indica L.) peel and seed kernel extracts against 7,12- dimethylbenz[a]anthracene-induced mammary carcinogenesis in female rats

Breast cancer is the second leading cause of cancer death among women. The present study is an effort to reveal the antiproliferative and antioxidant actions of mango seed kernel extract (KE), peel extract (PE), and their combination (KEPE) on mammary tumors induced by 7,12 dimethylbenz[a]anthracene (DMBA). Seven groups of adult female Sprague-Dawley rats were prepared, including C: (control), DMBA: (rats were administered with DMBA), (DMBA-KE), (DMBA-PE), and (DMBA-KEPE): rats were administered with DMBA and then treated with KE, PE, and (both KE and PE), respectively, (KE) and (PE): rats were administered with KE and PE, separately. The study focused on the assessment of markers of endocrine derangement [serum 17-β estradiol (E2)], apoptosis [caspase-3 and deoxyribonucleic acid fragmentation (DNAF)], and oxidative stress [lipid peroxidation and antioxidants (glutathione, glutathione-S-transferase, glutathione reductase, glutathione peroxidase, and superoxide dismutase)]. Histopathological examination and immunohistochemical expression of caspase-3 and estrogen receptor-α (ER-α) in mammary gland tissues (MGTs) were determined, as well as the characterization of mango extracts. The results showed that DMBA administration induced mammary tumors by increasing cell proliferation and evading apoptosis. In addition, DMBA administration caused oxidative stress by the production of reactive oxygen species, which increased lipid peroxidation and decreased cellular antioxidants, allowing cancer to progress. In contrast, treatment with DMBA-KE, DMBA-PE, or DMBA-KEPE diminished mammary tumors induced by DMBA, where they reduced oxidative stress via increased antioxidant parameters including reduced glutathione, superoxide dismutase, total glutathione peroxidase, glutathione reductase, and glutathione S-transferase. Also, different treatments decreased proliferation through the reduction of E2, and ER-α expression levels. However, these treatments increased the apoptosis of unwanted cells as they increased caspase-3 activity and DNAF. All these changes led to the prevention of breast injuries and the reduction of mammary tumors. This demonstrates that the contents of mango extracts, especially phenolics and flavonoids, have an important role in mammary tumor treatment through their potential antioxidant, antiproliferative, proapoptotic, and anti-estrogenic effects. KE and PE administration for 4 weeks had no adverse effects. Conclusion: Each of KE, PE, and KEPE has a therapeutic effect against DMBA-induced mammary tumors via induction of apoptosis and reduction of each of the OS, proliferation, and estrogenic effects. So, they can play an important role in the pharmacological tole.

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer

Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and “Boruta” feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.

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.

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N_BrCa = 228,951, N_PrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (N_BrCa = 621, N_PrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (P_cond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)—a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

Methylome-wide association studies identify genetically-influenced CpGs associated with breast and prostate cancer risk and (epi)genome-transcriptome mechanistic relationships, with lipid metabolism genes implicated as potential therapeutic targets.

The Association Between Breast Cancer and Blood-Based Methylation of CD160, ISYNA1 and RAD51B in the Chinese Population

Recent studies have identified DNA methylation signatures in the white blood cells as potential biomarkers for breast cancer (BC) in the European population. Here, we investigated the association between BC and blood-based methylation of cluster of differentiation 160 (CD160), inositol-3-phosphate synthase 1 (ISYNA1) and RAD51 paralog B (RAD51B) genes in the Chinese population. Peripheral blood samples were collected from two independent case-control studies with a total of 272 sporadic early-stage BC cases (76.5% at stage I&amp;II) and 272 cancer-free female controls. Mass spectrometry was applied to quantitatively measure the levels of DNA methylation. The logistic regression and non-parametric tests were used for the statistical analyses. In contrast to the protective effects reported in European women, we reported the blood-based hypomethylation in CD160, ISYNA1 and RAD51B as risk factors for BC in the Chinese population (CD160_CpG_3, CD160_CpG_4/cg20975414, ISYNA1_CpG_2, RAD51B_CpG_3 and RAD51B_CpG_4; odds ratios (ORs) per -10% methylation ranging from 1.08 to 1.67, p &lt; 0.05 for all). Moreover, hypomethylation of CD160, ISYNA1 and RAD51B was significantly correlated with age, BC subtypes including estrogen receptor (ER)-negative BC tumors, triple negative tumors, BC cases with larger size, advanced stages and more lymph node involvement. Our results supported the report in European women that BC is associated with altered methylation of CD160, ISYNA1 and RAD51B in the peripheral blood, although the effects are opposite in the Chinese population. The difference between the two populations may be due to variant genetic background or life styles, implicating that the validations of epigenetic biomarkers in variant ethnic groups are warranted.

Genetic Regulation of DNA Methylation Yields Novel Discoveries in GWAS of Colorectal Cancer

BACKGROUND

Colorectal cancer has a strong epigenetic component that is accompanied by frequent DNA methylation (DNAm) alterations in addition to heritable genetic risk. It is of interest to understand the interrelationship of germline genetics, DNAm, and colorectal cancer risk.

METHODS

We performed a genome-wide methylation quantitative trait locus (meQTL) analysis in 1,355 people, assessing the pairwise associations between genetic variants and lymphocytes methylation data. In addition, we used penalized regression with cis-genetic variants ± 1 Mb of methylation to identify genome-wide heritable DNAm. We evaluated the association of genetically predicted methylation with colorectal cancer risk based on genome-wide association studies (GWAS) of over 125,000 cases and controls using the multivariate sMiST as well as univariately via examination of marginal association with colorectal cancer risk.

RESULTS

Of the 142 known colorectal cancer GWAS loci, 47 were identified as meQTLs. We identified four novel colorectal cancer-associated loci (NID2, ATXN10, KLHDC10, and CEP41) that reside over 1 Mb outside of known colorectal cancer loci and 10 secondary signals within 1 Mb of known loci.

CONCLUSIONS

Leveraging information of DNAm regulation into genetic association of colorectal cancer risk reveals novel pathways in colorectal cancer tumorigenesis. Our summary statistics-based framework sMiST provides a powerful approach by combining information from the effect through methylation and residual direct effects of the meQTLs on disease risk. Further validation and functional follow-up of these novel pathways are needed.

IMPACT

Using genotype, DNAm, and GWAS, we identified four new colorectal cancer risk loci. We studied the landscape of genetic regulation of DNAm via single-SNP and multi-SNP meQTL analyses.

Integrating Genome and Methylome Data to Identify Candidate DNA Methylation Biomarkers for Pancreatic Cancer Risk

BACKGROUND

The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.

METHODS

Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.

RESULTS

We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.

CONCLUSIONS

We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.

IMPACT

A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.

Smoking, DNA Methylation, and Breast Cancer: A Mendelian Randomization Study

Background

Smoking was strongly associated with breast cancer in previous studies. Whether smoking promotes breast cancer through DNA methylation remains unknown.

Methods

Two-sample Mendelian randomization (MR) analyses were conducted to assess the causal effect of smoking-related DNA methylation on breast cancer risk. We used 436 smoking-related CpG sites extracted from 846 middle-aged women in the ARIES project as exposure data. We collected summary data of breast cancer from one of the largest meta-analyses, including 69,501 cases for ER+ breast cancer and 21,468 cases for ER- breast cancer. A total of 485 single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) for smoking-related DNA methylation. We further performed an MR Steiger test to estimate the likely direction of causal estimate between DNA methylation and breast cancer. We also conducted colocalization analysis to evaluate whether smoking-related CpG sites shared a common genetic causal SNP with breast cancer in a given region.

Results

We established four significant associations after multiple testing correction: the CpG sites of cg2583948 [OR = 0.94, 95% CI (0.91-0.97)], cg0760265 [OR = 1.07, 95% CI (1.03-1.11)], cg0420946 [OR = 0.95, 95% CI (0.93-0.98)], and cg2037583 [OR =1.09, 95% CI (1.04-1.15)] were associated with the risk of ER+ breast cancer. All the four smoking-related CpG sites had a larger variance than that in ER+ breast cancer (all p < 1.83 × 10^-11) in the MR Steiger test. Further colocalization analysis showed that there was strong evidence (based on PPH4 > 0.8) supporting a common genetic causal SNP between the CpG site of cg2583948 [with IMP3 expression (PPH4 = 0.958)] and ER+ breast cancer. There were no causal associations between smoking-related DNA methylation and ER- breast cancer.

Conclusions

These findings highlight potential targets for the prevention of ER+ breast cancer. Tissue-specific epigenetic data are required to confirm these results.

Impact of Stilbenes as Epigenetic Modulators of Breast Cancer Risk and Associated Biomarkers

With the recent advancement of genetic screening for testing susceptibility to mammary oncogenesis in women, the relevance of the gene−environment interaction has become progressively apparent in the context of aberrant gene expressions. Fetal exposure to external stressors, hormones, and nutrients, along with the inherited genome, impact its traits, including cancer susceptibility. Currently, there is increasing interest in the role of epigenetic biomarkers such as genomic methylation signatures, plasma microRNAs, and alterations in cell-signaling pathways in the diagnosis and primary prevention of breast cancer, as well as its prognosis. Polyphenols like natural stilbenes have been shown to be effective in chemoprevention by exerting cytotoxic effects that can stall cell proliferation. Besides possessing antioxidant properties against the DNA-damaging effects of reactive oxygen species, stilbenes have also been observed to modulate cell-signaling pathways. With the increasing trend of early-life screening for hereditary breast cancer risks, the potency of different phytochemicals in harnessing the epigenetic biomarkers of breast cancer risk demand more investigation. This review will explore means of exploiting the abilities of stilbenes in altering the underlying factors that influence breast cancer risk, as well as the appearance of associated biomarkers.

A High Epigenetic Risk Score Shapes the Non-Inflamed Tumor Microenvironment in Breast Cancer

Background: Epigenetic dysregulation via aberrant DNA methylation has gradually become recognized as an efficacious signature for predicting tumor prognosis and response to therapeutic targets. However, reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC).

Methods: Whole-genome methylation datasets integrated from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were profiled (n = 1,268). A three-stage selection procedure (discovery, training, and external validation) was utilized to screen out the prominent biomarkers and establish a robust risk score from more than 300,000 CpG sites after quality control, rigorous filtering, and reducing dimension. Moreover, gene set enrichment analyses guided us to systematically correlate this epigenetic risk score with immunological characteristics, including immunomodulators, anti-cancer immunity cycle, immune checkpoints, tumor-infiltrating immune cells and a series of signatures upon modulating components within BC tumor microenvironment (TME). Multi-omics data analyses were performed to decipher specific genomic alterations in low- and high-risk patients. Additionally, we also analyzed the role of risk score in predicting response to several treatment options.

Results: A 10-CpG-based prognostic signature which could significantly and independently categorize BC patients into distinct prognoses was established and sufficiently validated. And we hypothesize that this signature designs a non-inflamed TME in BC based on the evidence that the derived risk score is negatively correlated with tumor-associated infiltrating immune cells, anti-cancer immunity cycle, immune checkpoints, immune cytolytic activity, T cell inflamed score, immunophenoscore, and the vast majority of immunomodulators. The identified high-risk patients were characterized by upregulation of immune inhibited oncogenic pathways, higher TP53 mutation and copy number burden, but lower response to cancer immunotherapy and chemotherapy.

Conclusion: Our work highlights the complementary roles of 10-CpG-based signature in estimating overall survival in BC patients, shedding new light on investigating failed events concerning immunotherapy at present.

An integrative multiomics analysis identifies putative causal genes for COVID-19 severity

Purpose

It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19.

Methods

We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes.

Results

Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity.

Conclusion

We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

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.

Genome-wide DNA methylome and whole-transcriptome landscapes of spontaneous intraductal papilloma in tree shrews

Background

Breast intraductal papilloma (IP) is mainly caused by the abnormal proliferation of ductal epithelial cells. Tree shrews have potential as an animal model for the study of breast tumours; however, little is known regarding the transcriptome and DNA methylome landscapes of breast IP in tree shrews. In this research, we conducted whole-genome DNA methylation and transcriptome analyses of breast IP and normal mammary glands in tree shrews.

Methods

DNA methylation profiles were generated from the whole-genome bisulfite sequencing and whole-transcriptome landscapes of IP and control groups of tree shrews through strand-specific library construction and RNA sequencing. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses and gene set enrichment analysis were performed. Spearman’s correlation analysis was used to identify statistical relationships between gene expression and DNA methylation.

Results

A genome-wide perspective of the epigenetic regulation of protein-coding genes in breast IP in tree shrews was obtained. The methylation levels at CG sites were considerably higher than those at CHG or CHH sites, and were highest in gene body regions. In total, 3,486, 82 and 361 differentially methylated regions (DMRs) were identified in the context of CG, CHG, and CHH, respectively, and 701 differentially methylated genes (DMGs) were found. Further, through transcriptomic analysis, 62 differentially expressed genes, 50 long noncoding RNAs, and 32 circular RNAs were identified in breast IP compared to normal mammary glands. Correlation analysis between the DNA methylation and transcriptome data revealed that 25 DMGs were also differentially expressed genes, among which the expression levels of 9 genes were negatively correlated with methylation levels in gene body regions. Importantly, integrated analysis identified 3 genes (PDZ domain-containing 1, ATPase plasma membrane Ca²⁺ transporting 4 and Lymphocyte cytosolic protein 1) that could serve as candidates for further study of breast IP in tree shrews.

Conclusions

This research has unearthed the comprehensive landscape of the transcriptome and DNA methylome of spontaneous IP in tree shrews, as well as candidate tumorigenesis related genes in IP. These results will contribute to the use of tree shrews in animal models of breast tumours.

Upregulation of LINC00511 expression by DNA hypomethylation promotes the progression of breast cancer

Background

LINC00511 is a newly discovered long intergenic nonprotein-coding RNA (Ribonucleic acid) with unknown.

Method

Differential gene expression analysis was performed on breast cancer microarray data, and the upregulated expression of LINC00511 in breast cancer tissues and breast cancer cell lines was verified by qRT-PCR (quantitative Reverse Transcription-Polymerase Chain Reaction). A cohort study revealed a correlation between the expression of LINC00511 and the clinicopathological features in breast cancer patients. The effects of LINC00511 on breast cancer migration and invasion were studied in vitro. Then, an experiment using the Illumina Infinium Human Methylation450 Beadchip data was conducted to study the role of DNA (Deoxyribonucleic acid) methylation in LINC00511 expression, and DAVID (Database for Annotation, Visualization and Integrated Discovery) Functional Annotation Bioinformatics Microarray Analysis was used to determine the biological functions and potential pathways of LINC00511 in breast cancer. Then, LINC00511 and key genes associated with breast cancer disease progression were further studied in TCGA (The Cancer Genome Atlas), and western blotting was used to verify the results at the protein level. Finally, we further studied the effect of LINC00511 on Panobinostat drug sensitivity in breast cancer and its effect on the prognosis of breast cancer patients.

Results

LINC00511 was upregulated in breast cancer patients. The expression of LINC00511 was closely related to lymph node metastasis, tumor size and molecular subtypes of breast cancer. The in vitro studies revealed that LINC00511 could promote the migration and invasion in MDA-MB-231 and MCF-7 cells. In terms of mechanism, DNA hypomethylation promoted the expression of LINC00511, furthermore LINC00511 promoted the expression of Wnt10A, E2F2, TGFA, and MET, which participate in the progression of breast cancer. In addition, LINC00511 reduced the sensitivity of breast cancer cells to Panobinostat. Moreover, breast cancer patients with a high expression of LINC00511 had a poor prognosis.

Conclusions

DNA hypomethylation promotes the expression of LINC00511 in breast cancer, and LINC00511 promotes the progression of breast cancer by upregulating Wnt10A, E2F2, TGFA and MET. High expression of LINC00511 is associated with poor prognosis. Our study identified the mechanism of LINC00511 upregulation and provides novel information on the progression of breast cancer.

Personalized early detection and prevention of breast cancer: ENVISION consensus statement

The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.

Epigenome-wide DNA methylation and risk of breast cancer: a systematic review

BACKGROUND

DNA methylation is a potential biomarker for early detection of breast cancer. However, robust evidence of a prospective relationship between DNA methylation patterns and breast cancer risk is still lacking. The objective of this study is to provide a systematic analysis of the findings of epigenome-wide DNA methylation studies on breast cancer risk, in light of their methodological strengths and weaknesses.

METHODS

We searched major databases (MEDLINE, EMBASE, Web of Science, CENTRAL) from inception up to 30th June 2019, for observational or intervention studies investigating the association between epigenome-wide DNA methylation (using the HM450k or EPIC BeadChip), measured in any type of human sample, and breast cancer risk. A pre-established protocol was drawn up following the Cochrane Reviews rigorous methodology. Study selection, data abstraction, and risk of bias assessment were performed by at least two investigators. A qualitative synthesis and systematic comparison of the strengths and weaknesses of studies was performed.

RESULTS

Overall, 20 studies using the HM450k BeadChip were included, 17 of which had measured blood-derived DNA methylation. There was a consistent trend toward an association of global blood-derived DNA hypomethylation and higher epigenetic age with higher risk of breast cancer. The strength of associations was modest for global hypomethylation and relatively weak for most of epigenetic age algorithms. Differences in length of follow-up periods may have influenced the ability to detect associations, as studies reporting follow-up periods shorter than 10 years were more likely to observe an association with global DNA methylation. Probe-wise differential methylation analyses identified between one and 806 differentially methylated CpGs positions in 10 studies. None of the identified differentially methylated sites overlapped between studies. Three studies used breast tissue DNA and suffered major methodological issues that precludes any conclusion. Overall risk of bias was critical mainly because of incomplete control of confounding. Important issues relative to data preprocessing could have limited the consistency of results.

CONCLUSIONS

Global DNA methylation may be a short-term predictor of breast cancer risk. Further studies with rigorous methodology are needed to determine spatial distribution of DNA hypomethylation and identify differentially methylated sites associated with risk of breast cancer.

PROSPERO REGISTRATION NUMBER

CRD42020147244.

DNA Methylation and Breast Cancer Risk: An Epigenome-Wide Study of Normal Breast Tissue and Blood

Differential DNA methylation is a potential marker of breast cancer risk. Few studies have investigated DNA methylation changes in normal breast tissue and were largely confounded by cancer field effects. To detect methylation changes in normal breast epithelium that are causally associated with breast cancer occurrence, we used a nested case-control study design based on a prospective cohort of patients diagnosed with a primary invasive hormone receptor-positive breast cancer. Twenty patients diagnosed with a contralateral breast cancer (CBC) were matched (1:1) with 20 patients who did not develop a CBC on relevant risk factors. Differentially methylated Cytosine-phosphate-Guanines (CpGs) and regions in normal breast epithelium were identified using an epigenome-wide DNA methylation assay and robust linear regressions. Analyses were replicated in two independent sets of normal breast tissue and blood. We identified 7315 CpGs (FDR < 0.05), 52 passing strict Bonferroni correction (p < 1.22 × 10^-7) and 43 mapping to known genes involved in metabolic diseases with significant enrichment (p < 0.01) of pathways involving fatty acids metabolic processes. Four differentially methylated genes were detected in both site-specific and regions analyses (LHX2, TFAP2B, JAKMIP1, SEPT9), and three genes overlapped all three datasets (POM121L2, KCNQ1, CLEC4C). Once validated, the seven differentially methylated genes distinguishing women who developed and who did not develop a sporadic breast cancer could be used to enhance breast cancer risk-stratification, and allow implementation of targeted screening and preventive strategies that would ultimately improve breast cancer prognosis.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Determinants of p14/ARF methylation in healthy females: association with reproductive and non-reproductive risk factors of breast cancer

Background

DNA methylation is associated with the risk factors of breast cancer. However, the impact of the reproductive and non-reproductive risk factors of breast cancer on p14/ARF methylation is not well known. Therefore, we investigated the relationships between p14/ARF methylation percentage and risk factors of breast cancer including age, family history, obesity, and reproductive risk factors in 120 breast cancer-free subjects; 60 women with a first-degree family history of breast cancer and 60 age-matched women with no family history of breast cancer. Extracted DNA from the whole blood was bisulfite-treated by EZ DNA modification kit. Quantitative methylation of p14/ARF was analyzed by methylation-specific PCR then methylation percentage of p14/ARF was calculated.

Results

P14/ARF methylation percentage was not related to any of the risk factors of breast cancer except age. Our study showed that p14/ARF methylation percentage was significantly higher in females with age ≥ 40 years than in females with age <  40 years (p=0.029). Also, a positive significant correlation between the p14/ARF methylation percentage and age was detected (r = 0.285, p = 0.014). Furthermore, univariate regression analysis showed that the age is independently associated with high p14/ARF methylation percentage (β = 1. 46, p = 0.029).

Conclusion

Among healthy females, the age is strongly linked to the peripheral p14/ARF methylation percentage. The present study suggests that p14/ARF methylation is not associated with other breast cancer risk factors. These results need oncoming research on a large cohort to define the interactions between p14/ARF methylation and the risk factors of breast cancer.

Individual and joint performance of DNA methylation profiles, genetic risk score and environmental risk scores for predicting breast cancer risk

DNA methylation patterns in the blood, genetic risk scores (GRSs), and environmental risk factors can potentially improve breast cancer (BC) risk prediction. We assessed the individual and joint predictive performance of methylation, GRS, and environmental risk factors for BC incidence in a prospective cohort study. In a cohort of 5462 women aged 50–75 from Germany, 101 BC cases were identified during 14 years of follow‐up and were compared to 263 BC‐free controls in a nested case–control design. Three previously suggested methylation risk scores (MRSs) based on methylation of 423, 248, and 131 cytosine‐phosphate‐guanine (CpG) loci, and a GRS based on the risk alleles from 269 recently identified single nucleotide polymorphisms were constructed. Additionally, multiple previously proposed environmental risk scores (ERSs) were built based on environmental variables. Areas under the receiver operating characteristic curves (AUCs) were estimated for evaluating BC risk prediction performance. MRS and ERS showed limited accuracy in predicting BC incidence, with AUCs ranging from 0.52 to 0.56 and from 0.52 to 0.59, respectively. The GRS predicted BC incidence with a higher accuracy (AUC = 0.61). Adjusted odds ratios per standard deviation increase (95% confidence interval) were 1.07 (0.84–1.36) and 1.40 (1.09–1.80) for the best performing MRS and ERS, respectively, and 1.48 (1.16–1.90) for the GRS. A full risk model combining the MRS, GRS, and ERS predicted BC incidence with the highest accuracy (AUC = 0.64) and might be useful for identifying high‐risk populations for BC screening.

Coupled Genome-Wide DNA Methylation and Transcription Analysis Identified Rich Biomarkers and Drug Targets in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications.

MicroRNAs and Epigenetics Strategies to Reverse Breast Cancer

Breast cancer is a sporadic disease with genetic and epigenetic components. Genomic instability in breast cancer leads to mutations, copy number variations, and genetic rearrangements, while epigenetic remodeling involves alteration by DNA methylation, histone modification and microRNAs (miRNAs) of gene expression profiles. The accrued scientific findings strongly suggest epigenetic dysregulation in breast cancer pathogenesis though genomic instability is central to breast cancer hallmarks. Being reversible and plastic, epigenetic processes appear more amenable toward therapeutic intervention than the more unidirectional genetic alterations. In this review, we discuss the epigenetic reprogramming associated with breast cancer such as shuffling of DNA methylation, histone acetylation, histone methylation, and miRNAs expression profiles. As part of this, we illustrate how epigenetic instability orchestrates the attainment of cancer hallmarks which stimulate the neoplastic transformation-tumorigenesis-malignancy cascades. As reversibility of epigenetic controls is a promising feature to optimize for devising novel therapeutic approaches, we also focus on the strategies for restoring the epistate that favor improved disease outcome and therapeutic intervention.