Genome-wide single base resolution profiling of 5-methylcytosine in human breast tissue
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE74214 | GEO | 2017/06/29
SECONDARY ACCESSION(S): PRJNA299572
REPOSITORIES: GEO
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Project description:The underlying biology through which established breast cancer risk factors contribute to disease risk is not well characterized. One key risk factor for breast cancer is age, and age-related DNA methylation alterations may contribute to increased risk of disease. Here we assessed normal breast tissues and tested the relation of DNA methylation with known breast cancer risk factors. Cancer-free women donated breast tissue biopsy specimens through the Susan G. Komen Foundation and provided detailed risk factor data (n=100). Bisulfite modified DNA was profiled for DNA methylation genome-wide using the Infinium 450K DNA methylation array. We tested the relation of known breast cancer risk factors such as age, BMI, parity, and family history of disease with DNA methylation adjusted for variation in cell type proportions using a novel cellular mixture deconvolution algorithm. We identified 787 CpGs that exhibited significant (FDR adjusted, q-value < 0.01) differential DNA methylation associated with subject age, but not with other breast cancer risk factors. We observed an enrichment among the risk factor-related CpGs for Polycomb group target genes (Fisher’s Exact test, P = 1.74E-06), and breast myoepithelial cell enhancer regions (H3K4me1; Fisher’s Exact test, P = 7.1E-20). We validated our risk factor-related findings in two independent populations of normal breast tissue (n=18 and n=97). In addition, age-related CpGs were further deregulated in both pre-invasive (DCIS, n=40) and invasive breast cancers (TCGA, n=731). Overall, our results suggest that the breast cancer risk factor age contributes to epigenetic dysregulation in normal breast tissue that exhibit progressive changes in cancer.
2017-06-20 | GSE88883 | GEO
Project description:Prior candidate gene studies have shown tumor suppressor DNA methylation in breast milk related with history of breast biopsy, an established risk factor for breast cancer. To further establish the utility of breast milk as a tissue-specific biospecimen for investigations of breast carcinogenesis we measured genome-wide DNA methylation in breast milk from women with and without a diagnosis of breast cancer in two independent cohorts. In epigenome-wide analyses we identified 58 differentially methylated CpG sites associated with breast cancer diagnosis in the prospectively collected milk samples from the breast that would develop cancer compared with women without a diagnosis of breast cancer (q-value < 0.05), using linear mixed effects models adjusted for history of breast biopsy, age, age of the baby, cell type proportion estimates, array chip, and subject as random effect. Nearly all sites associated with breast cancer diagnosis were hypomethylated in cases compared with controls, and CpG sites were enriched for CpG islands. In addition, inferred repeat element methylation was lower in breast milk DNA from cases compared to controls, and cases exhibited increased estimated epigenetic mitotic tick rate as well as DNA methylation age compared with controls. Breast milk has promise as a biospecimen for prospective assessment of disease risk, and for understanding the underlying molecular basis of breast cancer risk factors and improving primary and secondary prevention of breast cancer.
2019-12-06 | GSE133918 | GEO
Project description:Mufudza2012 - Estrogen effect on the dynamics
of breast cancer
This deterministic model shows the
dynamics of breast cancer with immune response. The effects of
estrogen are incorporated to study its effects as a risk factor for
the disease.
This model is described in the article:
Assessing the effects of
estrogen on the dynamics of breast cancer.
Mufudza C, Sorofa W, Chiyaka
ET.
Comput Math Methods Med 2012; 2012:
473572
Abstract:
Worldwide, breast cancer has become the second most common
cancer in women. The disease has currently been named the most
deadly cancer in women but little is known on what causes the
disease. We present the effects of estrogen as a risk factor on
the dynamics of breast cancer. We develop a deterministic
mathematical model showing general dynamics of breast cancer
with immune response. This is a four-population model that
includes tumor cells, host cells, immune cells, and estrogen.
The effects of estrogen are then incorporated in the model. The
results show that the presence of extra estrogen increases the
risk of developing breast cancer.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000642.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000642 | BioModels
Project description:Epigenetic mechanisms such as DNA methylation are important regulators of gene expression and are frequently dysregulated early in breast carcinogenesis. The relationship between DNA methylation aberrations in normal breast tissue and breast cancer risk remains unclear. Disease-free breast tissue cores donated by high-risk (Tyrer-Cuzick lifetime risk ≥20%) and 78 average-risk women were obtained from the Susan G. Komen Tissue Bank and processed for whole methylome and whole transcriptome profiling. We identified 1355 CpGs that were differentially methylated between high- and average-risk breast tissues (ΔZ>0.5, FDR<0.05). Hypomethylated CpGs were overrepresented in high-risk tissue and were found predominantly (68%) in non-coding regions. Hypermethylated CpG sites were found equally in the gene body and non-coding regions. Transcriptomic analysis identified 67 differentially expressed genes (fold change≥2, FDR<0.05, 17 down 50 up), involved in chemokines signaling, metabolism and estrogen biosynthesis. Methylation-expression correlations revealed 6 epigenetically regulated genes in high-risk breast. This is the first gene expression/DNA methylation analysis of normal breasts from women at either high or average risk of breast cancer. Our discovery of epigenetically regulated genes associated with breast cancer risk using a unique resource of normal breast samples opens the doors to a deeper understanding of the process of cancer initiation and provides an opportunity to mechanistically dissect breast cancer susceptibility and risk-associated molecular alterations.
2021-09-30 | GSE164641 | GEO
Project description:Epigenetic mechanisms such as DNA methylation are important regulators of gene expression and are frequently dysregulated early in breast carcinogenesis. The relationship between DNA methylation aberrations in normal breast tissue and breast cancer risk remains unclear. Disease-free breast tissue cores donated by high-risk (Tyrer-Cuzick lifetime risk ≥20%) and 78 average-risk women were obtained from the Susan G. Komen Tissue Bank and processed for whole methylome and whole transcriptome profiling. We identified 1355 CpGs that were differentially methylated between high- and average-risk breast tissues (ΔZ>0.5, FDR<0.05). Hypomethylated CpGs were overrepresented in high-risk tissue and were found predominantly (68%) in non-coding regions. Hypermethylated CpG sites were found equally in the gene body and non-coding regions. Transcriptomic analysis identified 67 differentially expressed genes (fold change≥2, FDR<0.05, 17 down 50 up), involved in chemokines signaling, metabolism and estrogen biosynthesis. Methylation-expression correlations revealed 6 epigenetically regulated genes in high-risk breast. This is the first gene expression/DNA methylation analysis of normal breasts from women at either high or average risk of breast cancer. Our discovery of epigenetically regulated genes associated with breast cancer risk using a unique resource of normal breast samples opens the doors to a deeper understanding of the process of cancer initiation and provides an opportunity to mechanistically dissect breast cancer susceptibility and risk-associated molecular alterations.
2021-09-30 | GSE164640 | GEO
Project description:Breast cancer risk factors are associated with DNA methylation in non-diseased breast tissue independent of cell type
| PRJNA349021 | ENA
Project description:Despite known age-related DNA methylation (aDNAm) changes in breast tumors, little is known about aDNAm in normal breast tissues. Breast tissues from a cross-sectional study of 121 cancer-free women, were assayed for genome-wide DNA methylation. mRNA expression was assayed by microarray technology. Analysis of covariance was used to identify aDNAm’s. Altered methylation was correlated with expression of the corresponding gene and with DNA methyltransferase protein DNMT3A, assayed by immunohistochemistry. Publically-available TCGA data were used for replication. 1,214 aDNAm’s were identified; 97% with increased methylation, and all on autosomes. Sites with increased methylation were predominantly in CpG lslands and non-enhancers. aDNAm’s with decreased methylation were generally located in intergenic regions, non-CpG Islands, and enhancers. Of the aDNAm’s identified, 650 are known to be involved in cancer, including ESR1 and beta-estradiol responsive genes. Expression of DNMT3A was positively associated with age. Two aDNAm’s showed significant associations with DNMT3A expression; KRR1 (OR 6.57, 95% CI: 2.51-17.23) and DHRS12 (OR 6.08, 95% CI: 2.33-15.86). A subset of aDNAm’s co-localized within vulnerable regions for somatic mutations in breast cancer. Expression of C19orf48 was inversely and significantly correlated with its methylation level. In the TCGA dataset, 84% and 64% of the previously identified aDNAm’s were correlated with age in both normal-adjacent and tumor breast tissues, with differential associations by histological subtype. Given the similarity of findings in the breast tissues of healthy women and breast tumors, and the effects on gene expression, aDNAm’s may be one pathway for increased breast cancer risk with age. Obesity is a risk factor for breast cancer, but the underlying mechanisms for this are only partially understood, and there has been only limited study of breast tissues molecularly before cancer develops. Breast tissues from healthy women undergoing reduction mammoplasty and epidemiologic interviews (n=121) were profiled for the whole genome transcriptome (Affymetrix Human Transcriptome Arrays) and genome-wide DNA methylation (Infinium HumanMethylation 450 BeadChip array). After adjusting for confounding by age and race, 12,210 CpG dinucleotides with altered methylation levels correlated with body mass index (BMI) (10,808 positive correlations and1,402 negative correlations, FDR<0.05). Among them 4,170 BMI-associated hypermethylated CpG dinucleotides (-1.5 kb from transcription start site, 1st Exon, and 5’UTR) and 443 BMI-associated hypomethylated CpG dinucleotides were located in promoter regions. By integrating DNA methylation and mRNA expression data, we identified 310 methylated genes that correlated with gene expression (FDR<0.05). Of these, 242 genes had higher methylation status showing concurrent down-regulation in obese women, and 68 genes had lower methylation status showing concurrent up-regulation in obese women. Among the affected genes involved in diseases and disorders for inflammatory response, hereditary disorder, and immunological disease. This study provides evidence that obesity epigenetically deregulates genes potentially involved in breast cancer that have functional relationships to gene expression.
2017-08-01 | GSE102088 | GEO
Project description:Human breast cancer tissue samples from 18 patients with high-risk or low-risk MammaPrint breast cancer were analyzed. The membrane fractions were enriched, and the extracted proteins were reduced, alkylated, digested with Lys-C and Trypsin, and labelled with iTRAQ4plex: 114; high risk; 115; low risk. Identification results were filtered at 1% false discovery rate (FDR) thresholds by searching against a randomized decoy database using Mascot at peptide and protein levels.
2016-10-17 | PXD005163 | JPOST Repository