Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A signature predicting poor prognosis in gastric and ovarian cancer represents a coordinated macrophage and stromal-response.

ABSTRACT: Genome wide mRNA expression profiling of 94 gastric tumours derived from Australian based cohort was performed. . From this data we identified a cluster of co-expressed genes termed the stromal response cluster which almost perfectly differentiates tumor from its non-malignant gastric tissue and hence can be regarded as a highly tumor-specific gene expression signature. We show that these genes are consistently co-expressed across a range of independent gastric datasets as well as other cancer types suggesting a conserved functional role in cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE51105 | GEO | 2014/04/01

SECONDARY ACCESSION(S): PRJNA221194

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

A signature predicting poor prognosis in gastric and ovarian cancer represents a coordinated macrophage and stromal-response.

Project description:Genome wide mRNA expression profiling of 94 gastric tumours derived from Australian based cohort was performed. . From this data we identified a cluster of co-expressed genes termed the stromal response cluster which almost perfectly differentiates tumor from its non-malignant gastric tissue and hence can be regarded as a highly tumor-specific gene expression signature. We show that these genes are consistently co-expressed across a range of independent gastric datasets as well as other cancer types suggesting a conserved functional role in cancer. Profiling of 94 primary gastric tumors on Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays. All tumors were collected with approvals from Peter MacCallum Cancer Center, Australia; the Research Ethics Review Committee; and signed informed patient consent.

2014-04-01 | E-GEOD-51105 | biostudies-arrayexpress

Gastric Cancer Subtyping (Australian Patient Cohort)

Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort.

2012-07-05 | GSE35809 | GEO

Expression profiling in samples with gastric intestinal metaplasia without concurrent gastric cancer

Project description:Genome wide mRNA expression profiling of 14 gastric IM samples derived from an Australian based cohort was performed.

2021-02-28 | GSE160116 | GEO

Gastric Cancer Subtyping (Australian Patient Cohort)

2012-07-04 | E-GEOD-35809 | biostudies-arrayexpress

DNA methylation represents one of the key regulator that transcriptional control of highly expressed genes in gastric cancer.

Project description:We used microarray approach to establish demethylated specific genes profiles in early gastric cancer. We analyzed 11 gastric cancer cell lines and 32 pairs of gastric cancer and normal samples to discover methylation signatures specific to gastric cancer cells.

2011-05-31 | E-GEOD-25869 | biostudies-arrayexpress

Syndecan-4 is a maestro of gastric cancer cell invasion and communication that underscores poor survival

Project description:Gastric cancer is a dominating cause of cancer-associated mortality with limited therapeutic options. Here, we show that syndecan-4 (SDC4), a plasma membrane proteoglycan, is highly expressed in intestinal subtype gastric tumours and that this signature associates with patients’ poor survival. Further, we mechanistically demonstrate that SDC4 is a master regulator of gastric cancer motility and invasion. We also find that SDC4 conjugated with heparan sulfate chains is efficiently sorted in extracellular vesicles (EVs). Interestingly, SDC4 in EVs regulates gastric cancer cell-derived EV organ distribution, uptake and functional effects in recipient cells. Specifically, we show that SDC4 knockout disrupts the tropism of EVs for the common gastric cancer metastatic sites. Our findings set the basis of the molecular implications of SDC4 expression in gastric cancer cells and open new perspectives on the development of therapeutic strategies targeting the glycan-EV axis to limit tumour progression.

2023-05-05 | PXD035679 | Pride

Homo sapiens

Project description:Gastric Cancer Subtyping (Australian Patient Cohort)

| PRJNA152559 | ENA

YangziChen2024 - Metabolic Machine Learning Predictor Model for Diagnosis of Gastric Cancer

Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models. I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.

2024-07-23 | MODEL2407230001 | BioModels

Progression risk of gland-forming gastric neoplasms assessed by DNA copy-number profiling

Project description:We tried progression risk prediction of individual gland-forming gastric cancers using genomic DNA copy number profile as a genetic lineage marker. The unsupervised clustering of DNA copy-number profiles of 107 gastric cancer samples, using large-sized (≥ 6 probes) genes, were divided into the loss-rich (A) and gain-rich (B) clusters. The T1/T2-4 ratio was significantly higher in cluster B and in cluster A (P < 0.0007). Small cancers (≤ 2 cm in diameter) were more frequent in cluster B than in cluster A. These 2 clusters were not linked to the frequency of metastasis but to the liability to progression from early to advanced stage; the cluster A may more readily become advanced than cluster B. Our approach suggested that the genetic lineages of early and advanced gland-forming gastric cancers are largely different; the eradication of small cluster B tumors (≤ 2 cm) by the present ESD indication may be valid, but not be effective for reduction of large, aggressive cluster A tumors.

2017-12-27 | GSE108507 | GEO