Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatially distinct reprogramming of the tumor microenvironment based on tumor invasion in diffuse-type gastric cancers

ABSTRACT: Spatially distinct reprogramming of the tumor microenvironment based on tumor invasion in diffuse-type gastric cancers

PROVIDER: PRJNA704101 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR13766400.fastq.gz	Fastqsanger.gz
	SRR13766401.fastq.gz	Fastqsanger.gz
	SRR13766402.fastq.gz	Fastqsanger.gz
	SRR13766403.fastq.gz	Fastqsanger.gz
	SRR13766404.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 156

Similar Datasets

Spatially distinct reprogramming of the tumor microenvironment based on tumor invasion in diffuse-type gastric cancers

Project description:The invasive potential of gastric cancers (GCs) defines the hallmarks of malignancies; however, the cellular components and their molecular features associated with tumor invasiveness in GC microenvironments (TME) are poorly understood. In this study, we constructed a comprehensive atlas of 23,060 single cells obtained from surgically dissected superficial and deep layers of five diffuse-type GC along with matched normal. As a result, seven major cell types were identified. Fibroblasts, endothelial cells, and myeloid cells were categorized as being enriched in the deep layers. Cell type-specific clustering further revealed that the superficial-to-deep layer transition is associated with enrichment in inflammatory endothelial cells and fibroblasts with upregulated CCL2 transcripts. This study reveals the spatial reprogramming of the TME that may underlie invasive tumor potential in diffuse-type GC. This TME profiling across tumor layers suggests new targets, such as CCL2, that can modify the TME to inhibit tumor progression in diffuse-type GC.

2021-08-09 | GSE167297 | GEO

Expression data from intestinal-type and diffuse-type gastric cancers

Project description:To identify the specific genes for intestinal-type and diffuse-type gastric cancers. We selected 18 intestinal-type gastric cancers and 12 diffuse-type gastric cancers showing typical characteristics on the form of cell growth (clustered or scattered) and the degree of differentiation (well/moderate or poor), and performed microarray analysis for obtaining genome-wide mRNA expression profiles.

2014-12-25 | E-GEOD-47007 | biostudies-arrayexpress

Expression data from intestinal-type and diffuse-type gastric cancers

Project description:To identify the specific genes for intestinal-type and diffuse-type gastric cancers.

2014-12-25 | GSE47007 | GEO

A single-cell and spatially resolved atlas of human breast cancers

Project description:This study presents a single cell and spatially resolved transcriptomics analysis of human breast cancers. We develop a single cell method of intrinsic subtype classification (scSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using CITE-Seq provides high-resolution immune profiles, including novel PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell surface protein expression through differentiation within 3 major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into anti-tumor immune regulation. Using single cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed ‘ecotypes’, with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.

2021-06-24 | GSE176078 | GEO

Expression profiling of stage IIA/MSS colon cancers from whole tumor and invasion front sampling

Project description:The goal was to identify gene expression signatures predictive of early relapse among stage IIA/MSS colon cancer patients. A secondary question addressed was whether the invasion front profiles could generate a competitive signature. 39 whole tumor and 35 matched invasion front profiles were generated (4 samples failed) and grouped into \\"early relapse\\" (relapse within 5 years) and \\"no relapse\\" (no relapse for at least 6 years) categories.

2024-03-25 | E-MTAB-13695 | biostudies-arrayexpress

A single-cell and spatially resolved atlas of human breast cancers

Project description:A single-cell and spatially resolved atlas of human breast cancers

| PRJNA734812 | ENA

Ataxia-telangiectasia mutated kinase disruption enhances the efficacy of radiation therapy in spatially-directed diffuse midline gliomas

Project description:Diffuse midline gliomas (DMGs) are lethal brain tumors characterized by p53-inactivating mutations and oncohistone H3.3K27M mutations that rewire the cellular response to genotoxic stress. Using RCAS/tv-a retroviruses and Cre recombinase to inactivate p53 and induce native H3.3K27M mutations in a lineage- and spatially-directed manner, we generated primary mouse tumors that recapitulate human DMG. Disrupting ataxia-telangiectasia mutated kinase (ATM) enhanced the efficacy of radiation therapy in murine and patient-derived DMG models, increasing survival. Microscopy-based in situ sequencing was used to spatially resolve transcriptional profiles in >750,000 single cells with or without ATM disruption and radiation therapy, revealing altered immune-neoplastic and endothelial cell interactions after treatment. An allelic series of primary murine DMG models with different p53 mutations confirmed that transactivation-independent p53 activity is a key mediator of radiosensitivity after ATM disruption. Our findings contribute primary DMG mouse models with deep profiling and reveal the mechanisms of treatment response to an actionable therapeutic strategy.

2025-06-16 | GSE284759 | GEO

Spatially confined sub-tumor microenvironments orchestrate pancreatic cancer pathobiology

Project description:Spatially confined sub-tumor microenvironments orchestrate pancreatic cancer pathobiology

| PRJNA701287 | ENA

Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface

Project description:Cancer cells interact with a wide variety of other cell types, but our understanding of microenvironmental heterogeneity and how it influences tumor phenotypes is limited. While single-cell RNA-seq (scRNA-seq) has helped define these TME cell types, it provides limited information on the mechanisms that define how individual tumor cells interact with TME. Here, we integrate spatial transcriptomics with scRNA-seq to define the architecture and nature of nascent tumor and surrounding microenvironment cells as they come into contact through the process of invasion. Using a well-defined transgenic zebrafish model of BRAFV600E-driven melanoma, we identify a transcriptionally unique “interface” cluster localized at the boundary between tumor cells and surrounding tissues. Using an unbiased, data-driven approach, we identify spatially-patterned gene modules specific to the interface and show that the interface is a distinct transcriptional entity that histologically resembles the microenvironment but transcriptionally resembles the tumor. By complementing ST with scRNA-seq, we demonstrate that the interface is composed of specialized tumor and microenvironment cells. Both cell types in the interface upregulate a common set of cilia genes, and we find enrichment of cilia proteins only where the tumor meets the TME. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress their expression outside of this region. This unique ETS-driven interface transcriptional state is conserved across ten different human patient samples, suggesting this is a conserved feature of human melanoma. Taken together, our results demonstrate the power of spatial and single-cell transcriptomics techniques in uncovering novel biological mechanisms that drive tumor invasion into new tissues.

2021-09-21 | GSE159709 | GEO

Periostin, a cell adhesion molecule, facilitates invasion in the tumor microenvironment and annotates a novel tumor invasive signature in esophageal cancer

Project description:Human squamous cell cancers are the most common epithelially derived malignancies. One example is esophageal squamous cell carcinoma (ESCC), which is associated with a high mortality rate that is related to a propensity for invasion and metastasis. We report that periostin, a highly expressed cell adhesion molecule, is a key component of a novel tumor invasive signature obtained from an organotypic culture model of engineered ESCC. This tumor invasive signature classifies with human ESCC microarrays, underscoring its utility in human cancer. Genetic modulation of periostin promotes tumor cell migration and invasion as revealed in gain of and loss of function experiments. Inhibition of EGFR signaling and restoration of wild-type p53 function were each found to attenuate periostin, suggesting interdependence of two common genetic alterations with periostin function. Our studies reveal periostin as an important mediator of ESCC tumor invasion and they indicate that organotypic (3D) culture can offer an important tool to discover novel biologic effectors in cancer. Invading and non-invading genetically engineered human esophageal cells with hTERT and EGFR overexpression and p53 mutations were grown in organotypic culture. These invading and non-invading cells were excised using laser-capture microdissection. RNA was isolated and amplified for Affymetrix U133 microarrays. Subsequent microarray analysis & comparison with 2 independent cohorts of primary ESCC tumors revealed upregulation of periostin as gene with highest upregulation found in novel tumor invasive signature in esophageal cancer.

2010-07-08 | E-GEOD-21293 | biostudies-arrayexpress

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