Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA107071 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Experiment Overall Design: We used microarrays to identify lung metastasis-related genes in a series of 23 patients with breast cancer metastases. No replicate, no reference sample.
2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Keywords: Disease state analysis
2008-08-15 | GSE11078 | GEO
Project description:The microRNAs with potential in predicting lung metastasis of triple negative breast cancer
| PRJNA317671 | ENA
Project description:A 13-gene signature predicting rapid development of brain metastases in breast cancer
| PRJNA167135 | ENA
Project description:A gene expression signature predicting the recurrence of tamoxifen-treated primary breast cancer.
| PRJNA103889 | ENA
Project description:Predicting Features of Breast Cancer with Gene Expression Patterns
| PRJNA95969 | ENA
Project description:Breast cancer relapse free survival and lung metastasis free survival
| PRJNA96341 | ENA
Project description:A Gene Expression Signature to Predict Metastasis and Survival in Non-small Cell Lung Cancer
| PRJNA95773 | ENA
Project description:<p>BRCA1 mutations are a hallmark of hereditary ovarian cancer, strongly linked to deficiencies in homologous recombination (HR) DNA repair and impaired DNA replication fork protection. However, its roles in cancer progression beyond maintaining genomic integrity remain poorly understood. Through metabolomics approaches, we found BRCA1-deficiency strikingly increased choline metabolism. Loss of BRCA1 promotes choline uptake through upregulating choline transporter-like protein 4 (CTL4). BRCA1 directly binds and recruits EZH2-mediated H3K27Me3 deposition to CTL4 promoter. CTL4 was therefore overexpressed in ovarian cancer tissues with BRCA1 mutations. Furthermore, BRCA1-deficiency significantly promotes ovarian cancer invasion, while inhibition of CTL4 reverses the high metastatic potential of BRCA1-deficient ovarian cancer cells, suggesting the functionality and specificity of CTL4 as a therapeutic target. Additionally, we discovered that phosphocholine, the choline metabolite increased by CTL4 overexpression, interacted with and stabilized the epithelial-to-mesenchymal transition inducer FAM3C in BRCA1-deficient ovarian cancer cells. Importantly, we identified a potent CTL4 inhibitor, DT-13, which significantly reduces choline metabolism and effectively suppresses metastasis in BRCA1-deficient ovarian cancers. Therefore, our study uncovers a mechanism underlying metastasis in BRCA1-deficient cancers and identifies CTL4 as a therapeutic target for metastatic ovarian cancer patients with BRCA1 mutations.</p>
2026-05-15 | MTBLS12245 | MetaboLights
Project description:Gene transcription signature of obesity in breast cancer
| PRJNA130113 | ENA