BRCA_FDDXZLYY_20190823_ExpChip
Ontology highlight
ABSTRACT:
TISSUE(S): ['breast']
DISEASE(S): ['breast Cancer']
SUBMITTER: Liyun Yuan
PROVIDER: OEX00013133 | NODE | 2021-06-03
REPOSITORIES: NODE
Similar Datasets
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
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and identified by:
BIOMD0000000642.
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2024-09-02 | BIOMD0000000642 | BioModels
Project description:Dynamics of Breast Cancer under Different Rates of Chemoradiotherapy.
Mkango SB1, Shaban N1, Mureithi E1, Ngoma T2.
Author information
1
Department of Mathematics, University of Dar es Salaam, P.O. Box 35062, Dar es Salaam, Tanzania.
2
Department of Clinical Oncology, Muhimbili University of Health and Allied Sciences, P.O. Box 65000, Dar es Salaam, Tanzania.
Abstract
A type of cancer which originates from the breast tissue is referred to as breast cancer. Globally, it is the most common cause of death in women. Treatments such as radiotherapy, chemotherapy, hormone therapy, immunotherapy, and gene therapy are the main strategies in the fight against breast cancer. The present study aims at investigating the effects of the combined radiotherapy and chemotherapy as a way to treat breast cancer, and different treatment approaches are incorporated into the model. Also, the model is fitted to data on patients with breast cancer in Tanzania. We determine new treatment strategies, and finally, we show that when sufficient amount of chemotherapy and radiotherapy with a low decay rate is used, the drug will be significantly more effective in combating the disease while health cells remain above the threshold.
Copyright © 2019 Sara B. Mkango et al.
2020-01-20 | MODEL1912120005 | BioModels
Project description:This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.
Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins
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Project description:Breast cancer development model
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Project description:The ets transcription factor ELF5 specifies the differentiation of mammary progenitor cells to establish the milk-secreting lineage. ER- and poor prognosis basal breast cancers arise from this progenitor cell and these cancers express high levels of Elf5. Knockdown of ELF5 expression in basal breast cancer cell lines, or forced expression in luminal breast cancer cell lines, resulted in reduced cell proliferation. Transcript profiling and chromatin immunoprecipitation revealed that the transcriptional activity of ELF5 specified the gene expression patterns that distinguish basal from luminal breast cancer, including suppression of FOXA1, GATA3 and ER, key estrogen-action genes. Tamoxifen treatment of luminal MCF7 cells upregulated Elf5 expression and cells that acquired resistance to Tamoxifen became dependent on ELF5 for proliferation. ELF5 is a regulator of breast cancer cell proliferation, transcriptionally specifies the basal molecular subtype and is utilised by ER+ breast cancer cells to escape proliferative arrest caused by Tamoxifen. ChIP-Seq using an antibody to ELF5, in T47D breast carcinoma cell lines
2012-12-27 | E-GEOD-31216 | biostudies-arrayexpress
Project description:Tumor characteristics are decisive in the determination of treatment strategy for breast cancer patients. Patients with estrogen receptor a(ERa)-positive breast cancer can benefit from long-term hormonal treatment. Nonetheless, the majority of patients will develop resistance to these therapies. Here, we investigated the role of the nuclear receptor liver receptor homolog-1 (LRH-1, NR5A2) in anti-estrogen (AE) sensitive and resistant breast cancer cells. We identified genome-wide LRH-1 binding sites using ChIP-seq, uncovering preferential binding to regions distal to transcriptional start sites (TSS). We further characterized these LRH-1 binding sites by integrating overlapping layers of specific chromatin marks, revealing that many LRH-1 binding sites are active and could be involved in long-range enhancer-promoter looping. Combined with transcriptome analysis of LRH-1 depleted cells, these results show that LRH-1 regulates specific subsets of genes involved in cell proliferation in AE-sensitive and AE-resistant breast cancer cells. Furthermore, the LRH-1 transcriptional program is highly associated with a signature of poor outcome and high-grade breast cancer tumors in vivo. Herein we report the genome-wide location and molecular function of LRH-1 in breast cancer cells and reveal its therapeutic potential for the treatment of breast cancers, notably for tumors resistant to treatments currently used in therapies. ChIP-seq examination of LRH-1 binding sites with specific chromatin marks in MCF7 breast cancer cells.
2014-02-27 | E-GEOD-47027 | biostudies-arrayexpress
Project description:AS a key medium for intercellular communication, exosome plays an important role in the occurrence and development of various forms of cancers, including breast cancer. However, the RNA from breast cancer-derived exosome remains to be explored. The HR+/HER2− breast cancer is the most common subgroup of breast cancer, we collected and primary cultured breast cancer and normal breast epithelial cells from three HR+/HER2− breast cancer patients, isolated the supernatant and extracted RNA from exosomes, and then performed microarray assays.
2023-10-01 | GSE207304 | GEO
Project description:Evaluation of plasma samples from the CHOICE clinical weight loss study in which obese post menopausal breast cancer survivors lost >15% initial body weight following either a low carbohydrate or a low fat dietary pattern. These samples will be interrogated for changes in metabolic intermediates and lipid metabolites that may be indicative of changes in prognosis for long term survival following treatment for breast cancer. Samples will be subjected to LCMS and shotgun lipidomics. In parallel, plasma and mammary gland fat pad from an obese rat model for breast cancer in which rats were subjected to a parallel level of weight loss will also be interrogated using the same procedures in an effort to inform the interpretation of the clinical data.
2018-08-02 | MSV000082728 | GNPS
Project description:CARM1, a coactivator for various cancer-relevant transcription factors, is overexpressed in breast cancer. To elucidate the functions of CARM1 in tumorigenesis, we knocked out CARM1 from several breast cancer cell lines using Zinc-Finger Nuclease technology, which resulted in drastic phenotypic and biochemical changes. The CARM1 KO cell lines enabled identification of novel CARM1 substrates, notably the SWI/SNF core subunit BAF155. Methylation of BAF155 at R1064 was found to be an independent prognostic biomarker for cancer recurrence and to regulate breast cancer cell migration and metastasis. Further, CARM1-mediated BAF155 methylation affects gene expression by directing methylated BAF155 to unique chromatin regions (e.g., c-Myc pathway genes). Collectively our studies uncover a mechanism by which BAF155 acquires tumorigenic functions via arginine methylation. Examination of methylation of BAF155 (R1064) in breast cancer cells
2014-03-28 | E-GEOD-52964 | biostudies-arrayexpress
Project description:Androgen receptor (AR) is expressed in 60-70% of breast cancers independent of estrogen receptor (ER) expression, however its function in breast cancer is largely unknown. Our study identified the high level of AR in ERâ??/HER2+ breast tumors and andorgen and AR greatly stimulated growth of MDA-MB-453 breast cancer cells. To define the genome-wide AR binding sites, we performed AR ChIP-seq using MDA-MB-453 breast cancer cells followig stimulation of DHT. We also identified FOXA1 is a crucial AR cofactor in MDA-MB-453 cells and the FOXA cistrome showed signaficant overlap with AR at both early and late time points of DHT stimulation. AR ChIP was performed in MDA-MB-453 breast cancer cells following 5a-dihydrotestosterone (DHT) stimulation for 4h and 16h respectively. FOXA1 ChIP-seq was performed after 4h DHT stimulation in MDA-MB-453 cells.
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