Ontology highlight
ABSTRACT:
PROVIDER: PRJNA557488 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Background: Breast cancer stem cells (BCSCs) are considered responsible for cancer relapse and drug-resistance. Understanding the identity of BCSCs may open new avenues in breast cancer therapy. Although several discoveries have been made on BCSCs characterization, the factors critical to BCSCs is largely unclear. This study was aimed to determine whether genomic mutation contributes to the acquisition of cancer stem-like phenotype, and to investigate the genetic and transcriptional features of BCSCs. Methods: We detected the potential mutation hotspot regions by using whole genome sequencing on parental cancer cells and derived serial-generation spheroids in increasing order of BCSC frequency, and then performed target deep DNA sequencing in the level of bulk-cell and single-cell. To identify the transcriptional program associated with BCSCs, bulk-cell and single-cell RNA sequencing were performed. Results: By analyzing whole genome sequencing of bulk cells, potential BCSCs associated mutation hotspot regions were detected. Validation by target deep sequencing, in both bulk-cell and single-cell levels, revealed no genetic changes specifically associated with BCSC phenotype. Moreover, single-cell RNA sequencing showed that cancer cells display profound transcriptional variability at the single-cell level that predicts BCSC features. Notably, this transcriptomic variability is enriched in transcription of a number of genes, revealed as BCSC markers. Individuals with breast cancer in a high-risk recurrence group exhibited higher expression of these transcriptomic variabilities, highlighting their clinical significance. Conclusions: Transcriptional variability, not genetic mutations, distinguish BCSCs from non-BCSCs. The identified BCSCs markers can become novel targets for BCSCs.
2018-10-12 | GSE116180 | GEO
Project description:The underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Here we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450K array) in cancer-free women. We tested the relation of established breast cancer risk factors with DNA methylation adjusting for potential variation in cell-type proportions.
2017-06-29 | GSE74214 | GEO
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:Circulating microRNAs (c-miRNAs) have emerged as measurable biomarkers (liquid biopsies) for cancer detection. The goal of our study was to identify novel biomarkers to predict long-term breast cancer risk in cancer-free women. We evaluated the ability of c-miRNAs to identify women most likely to develop breast cancer by profiling miRNA from serum obtained long before diagnosis. 24 breast cancer cases and controls (matched for risk and age) were identified from women enrolled in the High-Risk Breast Program at the UVM Cancer Center. We used Affymetrix miRNA v4 microarrays to interrogate miRNAs (miRBase v20) in the serum of cancer-free women at high-risk for breast cancer. The 24 cases developed breast cancer at least 6 months (average of 3.2 years) and the 24 controls remain cancer-free.
2018-01-22 | GSE98181 | GEO
Project description:NGS-based multiple gene panel resequencing in combination with a high resolution CGH-array was used to identify genetic risk factors for hereditary breast and/or ovarian cancer in 237 high risk patients who were previously tested negative for pathogenic BRCA1/2 variants. All patients were screened for pathogenic variants in 94 different cancer predisposing genes. We identified 32 pathogenic variants in 14 different genes (ATM, BLM, BRCA1, CDH1, CHEK2, FANCG, FANCM, FH, HRAS, PALB2, PMS2, PTEN, RAD51C and NBN) in 30 patients (12.7%). Two pathogenic BRCA1 variants that were previously undetected due to less comprehensive and sensitive methods were found. Five pathogenic variants are novel, three of which occur in genes yet unrelated to hereditary breast and/or ovarian cancer (FANCG, FH and HRAS). In our cohort we discovered a remarkably high frequency of truncating variants in FANCM (2.1%), which has recently been suggested as a susceptibility gene for hereditary breast cancer. Two patients of our cohort carried two different pathogenic variants each and ten other patients in whom a pathogenic variant was confirmed also harbored a variant of unknown significance in a breast and ovarian cancer susceptibility gene. We were able to identify pathogenic variants predisposing for tumor formation in 12.3% of BRCA1/2 negative breast and/or ovarian cancer patients.
2018-10-31 | GSE121968 | GEO
Project description:Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) associated with human traits and diseases. But because the vast majority of these SNPs are located in the noncoding regions of the genome their risk promoting mechanisms are elusive. Employing a new methodology combining cistromics, epigenomics and genotype imputation we annotate the noncoding regions of the genome in breast cancer cells and systematically identify the functional nature of SNPs associated with breast cancer risk. Our results demonstrate that breast cancer risk-associated SNPs are enriched in the cistromes of FOXA1 and ESR1 and the epigenome of H3K4me1 in a cancer and cell-type-specific manner. Furthermore, the majority of these risk-associated SNPs modulate the affinity of chromatin for FOXA1 at distal regulatory elements, which results in allele-specific gene expression, exemplified by the effect of the rs4784227 SNP on the TOX3 gene found within the 16q12.1 risk locus. Examination of histone modification H3K4me2 in untreated and E2 treated cells
2012-08-17 | E-GEOD-31151 | biostudies-arrayexpress
Project description:Accumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10-9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10-7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.
2020-07-30 | GSE155328 | GEO
Project description:RAD51B, a paralog of RAD51, have been associated with breast cancer risk in genome-wide association studies. The underlying biological mechanism through which germline genetic variation in RAD51B confers susceptibility to breast cancer is not well understood. Here we investigate the molecular function of RAD51B in breast cancer cell lines. We used microarrays to detail the global gene expression to identify classes of genes that are regulated differnetly post DNA damages as a result of RAD51B depeletion.
2014-04-22 | GSE56940 | 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.
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BioModels Database:
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To the extent possible under law, all copyright and related or
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CC0
Public Domain Dedication for more information.
2017-07-26 | BIOMD0000000642 | BioModels