Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Stromal gene signature

ABSTRACT: Stromal gene signature predictive for PCa metastasis

PROVIDER: PRJEB19256 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR1856020_1.fastq.gz	Fastqsanger.gz
	ERR1856020_2.fastq.gz	Fastqsanger.gz
	ERR1856030_1.fastq.gz	Fastqsanger.gz
	ERR1856030_2.fastq.gz	Fastqsanger.gz
	ERR1856040_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 84

Similar Datasets

Dissection of a metastasis expression signature into stromal and tumor cell components for improved predictive accuracy and sample scope

Project description:Experiment performed to investigate the influence of different tumor percentages on a previously identified predictive signature for HNSCC metastasis.

2007-11-02 | E-TABM-152 | biostudies-arrayexpress

Genome-wide analysis of galectin-4 mediated gene signature in PCa

Project description:Prostate cancer (PCa) causes significant mortality among men worldwide. Galectin-4 plays an important roles in regulatiog cancer metastasis and high level of galectin-4 correlated with poor survival in subpopulation of PCa patients. To further understand the galectin-4-mediated signature and develop effective treatment, we froced expressed galectin-4 in 22Rv1 and LNCaP cells, and downregulated galectin-4 in 22Rv1-M4 cells. Next, cDNA microarray were applyed to study galectin-4-mediated gene expression and cancer progression.

2018-06-30 | GSE100301 | GEO

A six-gene signature predicting breast cancer lung metastasis

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

Identification of Predictive Gene Markers for Multipotent Stromal Cell Proliferation

Project description:Identification of Predictive Gene Markers for Multipotent Stromal Cell Proliferation

| PRJNA299639 | ENA

Extraction of a Stromal Metastatic Gene Signature in Breast Cancer via Spatial Profiling

Project description:Extraction of a Stromal Metastatic Gene Signature in Breast Cancer via Spatial Profiling

| PRJNA1141928 | ENA

Development and validation of a secretome-specific prognostic and predictive gene expression signature for colorectal cancer

Project description:Relapse and metastatic progression is a frequent event in colorectal cancer patients detected at early stages. The risk of recurrence requires the development of new biomarkers to correctly predict biological behavior of early stage II and stage III patients and their response to adjuvant chemotherapy. Here, we combined the proteomic quantification of secreted proteins involved in metastasis with a transcriptional analysis to develop a risk score algorithm based on the expression of six genes (SEC6). The SEC6 signature was predictive of survival and recurrence for stage II and III patients in four different datasets including a total of 1534 patients and was also associated with deficient mismatch repair, CpG-island methylator positive status and BRAF mutation. SEC6 was also predictive of beneficial or detrimental effects from 5-Fluorouracil-containing regimes and the improved response to more aggressive chemotherapies based on FOLFOX and FOLFIRI. In summary, the SEC6 risk-score algorithm may constitute a new tool for decision-making in colorectal cancer management.

2022-12-27 | PXD032899 | Pride

Primary cicatricial alopecias are characterized by dysregulation of shared gene expression pathways

Project description:To investigate the molecular signatures of the different sub-types of Primary cicatricial alopecia (PCA). We performed high-throughput RNA-sequencing on scalp biopsies of PCA patient. We developed gene expression-based signatures, trained into machine-learning based predictive models and pathways associated with dysregulated gene expression. We performed morphological and cytokine analysis to define the immune cell populations found in PCA sub types. Using bulk RNA sequencing, we identified a common PCA gene signature that was shared between LPP, FFA, and CCCA which revealed a significant over-representation of mast cell signature genes, as well as down regulation of cholesterogenic pathways and up regulation of fibrosis and immune cell signaling genes. Thw Immunohistological analyses revealed an increased presence of mast cells in PCAs lesions.

2022-07-27 | GSE186075 | GEO

Transcription profiling of human cohort of lymph node-negative breast cancer patients identifies a six-gene signature predicting breast cancer lung metastasis

2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress

Gene Signature for Aggression of Melanoma Metastases - Melanoma Metastasis

Project description:Metastasis is the deadliest phase of cancer progression. Experimental models using immunodeficient mice have been used to gain insights into the mechanisms of metastasis. We report here the identification of a “metastasis aggressiveness gene expression signature” derived using human melanoma cells selected based on their metastatic potentials in a xenotransplant metastasis model. Comparison with expression data from human melanoma patients shows that this metastasis gene signature correlates with the aggressiveness of melanoma metastases in human patients. Many genes encoding secreted and membrane proteins are included in the signature, suggesting the importance of tumor-microenvironment interactions during metastasis. Keywords: disease state

2007-10-02 | GSE8401 | GEO

Glioma MYC gene signature

Project description:Glioma MYC gene signature

| PRJNA852544 | ENA