Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comprehensive molecular classification for adenocarcinoma of gastroesophageal junction between esophageal and gastric adenocarcinomas

ABSTRACT: Background and aims: The aim of this study was to analyze the molecular characteristics of adenocarcinoma of the gastroesophageal junction (AGEJ) compared to esophageal (EAC) and gastric adenocarcinomas (GCFB) from The Cancer Genome Atlas (TCGA) and Seoul National University (SNU) cohorts using next-generation sequencing. Methods: Based on mRNA expression of EAC (n=78) and GCFB (n=102) from the TCGA cohort, a molecular classification model using the Bayesian compound covariate predictor classified AGEJ/cardia (n=48) from the TCGA cohort and AGEJ/upper third gastric adenocarcinoma (n=46) from the SNU cohort into the EAC-like or GCFB-like groups. The molecular characteristics between the EAC-like and GCFB-like groups were compared.

ORGANISM(S): Homo sapiens

PROVIDER: GSE126304 | GEO | 2022/02/06

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Bile acids promote adenocarcinoma in a novel transgenic mouse model of BarrettM-bM-^@M-^Ys esophagus

Project description:Esophageal adenocarcinoma (EAC) has the fastest increase of any cancer in the US and Europe, and arises in the setting of BarrettM-bM-^@M-^Ys esophagus (BE), defined by replacement of normal squamous epithelium with columnar intestinal-like epithelium. BE is thought to result from chronic esophageal inflammation but has been elusive to model in animals. Herein, we have generated the first transgenic mouse model of BarrettM-bM-^@M-^Ys esophagus through overexpression of interleukin-1M-CM-^_ (IL-1M-NM-2). IL-1M-NM-2 overexpression in the mouse esophageal mucosa induces chronic inflammation that progresses to intestinal metaplasia, with characteristic expression of TFF2, Bmp4 and Cdx2. With aging, IL-1b transgenic mice progress to esophageal adenocarcinoma (EAC) but the process is markedly accelerated by exposure to bile acids and/or nitrosamines, resembling the human counterpart. Moreover, progenitor cells present in the gastric cardia, but absent from the esophagus in humans and mice, are increased in BE, suggesting the cell of origin in the gastric cardia Comparison of BE and EAC tissue from the mouse with normal squamous epithelium from the mouse.

2011-01-31 | E-GEOD-24931 | biostudies-arrayexpress

Bile acids promote adenocarcinoma in a novel transgenic mouse model of Barrett’s esophagus

Project description:Esophageal adenocarcinoma (EAC) has the fastest increase of any cancer in the US and Europe, and arises in the setting of Barrett’s esophagus (BE), defined by replacement of normal squamous epithelium with columnar intestinal-like epithelium. BE is thought to result from chronic esophageal inflammation but has been elusive to model in animals. Herein, we have generated the first transgenic mouse model of Barrett’s esophagus through overexpression of interleukin-1ß (IL-1β). IL-1β overexpression in the mouse esophageal mucosa induces chronic inflammation that progresses to intestinal metaplasia, with characteristic expression of TFF2, Bmp4 and Cdx2. With aging, IL-1b transgenic mice progress to esophageal adenocarcinoma (EAC) but the process is markedly accelerated by exposure to bile acids and/or nitrosamines, resembling the human counterpart. Moreover, progenitor cells present in the gastric cardia, but absent from the esophagus in humans and mice, are increased in BE, suggesting the cell of origin in the gastric cardia

2011-01-31 | GSE24931 | GEO

Multi-omic analysis of Esophageal Adenocarcinoma uncovers candidate therapeutic targets and cancer-selective post-transcriptional regulation

Project description:Efforts to address the poor prognosis associated with esophageal adenocarcinoma (EAC) have been hampered by a lack of biomarkers to identify early disease and therapeutic targets. Despite extensive efforts to understand the somatic mutations associated with EAC over the past decade, a gap remains in understanding how the atlas of genomic aberrations in this cancer impacts the proteome and which somatic variants are of importance for the disease phenotype. We performed a quantitative proteomic analysis of 23 EACs and matched adjacent normal esophageal and gastric tissues. We explored the correlation of transcript and protein abundance using tissue-matched RNAseq and proteomic data from 7 patients and further integrated these data with a cohort of EAC RNA-seq data (n=264 patients), EAC whole-genome sequencing (n=454 patients) and external published datasets.

2024-08-10 | PXD042792 | Pride

Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [scRNA-seq]

Project description:Esophageal cancer, a highly lethal tumor, contributes to 5% of all cancer deaths, with its primary subtypes being esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While most studies focus on ESCC, this study investigates EAC using single-cell RNA sequencing (scRNA-seq) to analyze CD45+ immune cells from tumors and matched non-tumor tissues in therapy-naïve patients. By examining the transcriptional profiles of these immune cells and the entire transcriptome in a cohort of 23 patients, the study identifies distinct transcriptional signatures. These signatures were used to stratify a large cohort of TCGA EAC patients, revealing strong associations with prognosis and clinical outcomes. The findings suggest that these transcriptional profiles can improve prognosis accuracy post-surgery and potentially guide effective therapies, including immunotherapy, for EAC patients.

2024-08-02 | GSE273847 | GEO

Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq]

2024-08-02 | GSE273848 | GEO

Affymetrix gene expression array data for cardia and non-cardia gastric cancer samples

Project description:We identified different and common dysregulated genes in cardia and non-cardia gastric cancer in the two type of gastric cancer cardia and non-cardia gastric tumors and normal glands

2013-07-19 | E-GEOD-29272 | biostudies-arrayexpress

Affymetrix gene expression array data for cardia and non-cardia gastric cancer samples

Project description:We identified different and common dysregulated genes in cardia and non-cardia gastric cancer in the two type of gastric cancer

2013-07-19 | GSE29272 | GEO

Exome and whole genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity

Project description:The incidence of esophageal adenocarcinoma (EAC) has risen 600% over the last 30 years. With an extremely poor five-year survival rate of only 15%, identification of new therapeutic targets for EAC is of great importance. Here, we analyze the mutation spectra from the whole exome sequencing of 149 EAC tumors/normal pairs, 15 of which have also been subjected to whole genome sequencing. We identify a novel mutational signature in EACs defined by a high prevalence of A to C transversions at Ap*A dinucleotides. Statistical analysis of the exome data identified 26 genes that are mutated at a significant frequency. Of these 26 genes, only four (TP53, CDKN2A, SMAD4, and PIK3CA) have been previously implicated in EAC. The novel significantly mutated genes include several chromatin modifying factors and candidate contributors to EAC: SPG20, TLR4, ELMO1, and DOCK2. Notably, functional analyses of EAC-derived mutations in ELMO1 increase cellular invasion. Therefore, we suggest a new hypothesis about the potential activation of the RAC1 pathway to be a contributor to EAC tumorigenesis. The study aimed to analyze 150 primary, human esophageal adenocarcinoma samples by whole genome and whole exome sequencing (which will be deposited to dbGAP following the TCGA practice). RNA expression data was used to determine gene expression in 14 of the samples analyzed by whole genome sequencing. No normals were analyzed.

2013-02-11 | E-GEOD-42363 | biostudies-arrayexpress

Epigenome analysis of normal and gastric cardia cancer samples

Project description:Genome wide DNA methylation profiling of normal and gastric cardia cancer samples. The Illumina Infinium 850k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 850,000 CpGs in normal and gastric cardia cancer samples. Samples included 8 normal -gastrica cardia cancer paired tissues.

2023-01-01 | GSE200484 | GEO

Identification of clinical trait–related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma

Project description:Endocervical adenocarcinoma (EAC) is an uncommon aggressive type of endocervical adenocarcinoma that is not associated with human papillomavirus (HPV). At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we systematically explored small RNAs in 14 different subtypes of EAC using small RNA sequencing. miRNAs and tRNA-derived RNAs (tDRs) accounted for the majority of mapped reads the total number of miRNAs and tDRs maintained a relative balance. To explore the correlations between small RNAs and EAC with different clinical characteristics, we performed the weighted gene co-expression network analysis (WGCNA) and screened for hub small RNAs. From the key modules, we identified 9 small RNAs that were significantly related to clinical characteristics in EAC patients. Gene Ontology and pathway analyses revealed that these molecules could have roles in the pathogenesis of EAC. Our work provides new insight into EAC pathogenesis, and identify small RNAs as candidate biomarkers for diagnosis and prognosis of EAC.

2021-06-11 | GSE176579 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data