Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The HFE H63D variant in pancreatic cancer promotes epithelial-to-mesenchymal transition at the expense of cell cycle

ABSTRACT: This study examines the biological and clinical relevance of the HFE H63D variant in pancreatic ductal adenocarcinoma (PDAC), a highly aggressive cancer with limited biomarkers. We analyzed its prevalence in PDAC cohorts and explored associations with tumor characteristics and patient outcomes. Functional studies in human cell lines and genetically engineered mouse models (KCh67d vs. KCwt) revealed effects on cell cycle arrest and epithelial-to-mesenchymal transition (EMT), suggesting a role in tumor progression. Spatial transcriptomics was used to dissect the tumor microenvironment, supporting the potential of H63D as a prognostic biomarker and therapeutic stratification factor.

ORGANISM(S): Homo sapiens

PROVIDER: GSE297144 | GEO | 2026/05/08

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

An Epigenetetic Signature for Classifying Pancreatic Ductal Adenocarcinoma Based on their Cellular Origin

Project description:Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, characterized by significant heterogeneity that complicates treatment and diagnosis. While acinar and ductal cells can serve as the origins of PDAC in mice, shaping differently tumor biology, no practical method exists to predict the cell of origin in PDAC models. Here, we leveraged DNA methylation as a powerful tool to trace the cellular origins of PDAC. We identified a cell-of-origin DNA methylation signature comprising 178 CpG sites. This signature remains preserved during tumorigenesis and robustly differentiates acinar-origin from ductal-origin PDAC samples across different experimental settings. It also predicted the cell of origin of PDAC samples from KPC mice, the most commonly used preclinical PDAC model, and revealed the influence of oncogenic mutations on tumor fate. A logistic regression model further confirmed the origin of most tumor samples. Notably, cell of origin influences PDAC characteristics and treatment response, underscoring its potential role in patient stratification, origin-informed molecular subtyping, and development of novel therapies. Our findings also raise important questions about the relevance of KPC mice as a preclinical model and the mechanisms driving PDAC heterogeneity.

2026-02-24 | GSE290346 | GEO

Identification of a ΔNp63-dependent Basal-like A Subtype-specific Transcribed Enhancer Program (B-STEP) in Aggressive Pancreatic Ductal Adenocarcinoma [RNA-seq]

Project description:A major hurdle to the application of precision oncology in pancreatic cancer is the lack of molecular stratification approaches and targeted therapy for defined molecular subtypes. In this work, we sought to gain further insight and identify molecular and epigenetic signatures of the basal-like A pancreatic ductal adenocarcinoma (PDAC) subgroup that can be applied to clinical samples for both patient stratification and for choice of therapy. We generated and integrated global gene expression and epigenome mapping data from patient-derived xenograft (PDX) models to identify subtype-specific enhancer regions that were validated in patient-derived samples. In addition, complementary nascent transcription and chromatin topology (HiChIP) analysis revealed a basal-like A subtype-specific transcribed enhancer program (B-STEP) in PDAC characterized by enhancer RNA (eRNA) production that is associated with higher chromatin interactions and subtype-specific gene activation. Importantly, we successfully confirmed the validity of eRNA detection as a possible histological approach for PDAC patient stratification by performing RNA in situ hybridization analyses for subtype-specific eRNAs on pathological tissue samples. Thus, the finding in this study provides proof-of-concept that subtype-specific epigenetic changes relevant for PDAC progression can be detected at a single cell level in complex, heterogeneous, primary tumor material.

2023-09-19 | GSE213387 | GEO

Identification of a ΔNp63-dependent Basal-like A Subtype-specific Transcribed Enhancer Program (B-STEP) in Aggressive Pancreatic Ductal Adenocarcinoma [HiChIP]

2023-09-19 | GSE213385 | GEO

Identification of a ΔNp63-dependent Basal-like A Subtype-specific Transcribed Enhancer Program (B-STEP) in Aggressive Pancreatic Ductal Adenocarcinoma [ChIP-seq]

2023-09-19 | GSE213378 | GEO

Identification of a ΔNp63-dependent Basal-like A Subtype-specific Transcribed Enhancer Program (B-STEP) in Aggressive Pancreatic Ductal Adenocarcinoma [leChRO-seq]

2023-09-19 | GSE213396 | GEO

Transcription profiling of Hfe-deficient liver and duodenum in mouse strains with differing susceptibilities to iron loading

Project description:Hfe disruption in the mouse leads to experimental hemochromatosis by a mechanism which remains elusive. Evidence for at least five modifier genes has been obtained. These account for the higher iron load of Hfe-deficient D2 mice compared to B6 mice. Gene expression profling was used to clarify the mechanism of Hfe action and to identify potential modifier genes. Experiment Overall Design: Liver and duodenum were obtained from wild-type and Hfe-deficient B6 and D2 mice (three mice per strain/genotype combination).

2008-04-04 | E-GEOD-7357 | biostudies-arrayexpress

Glycolytic heterogeneity drives metabolic-targeted therapy in PDAC

Project description:Background: Pancreatic ductal adenocarcinoma is classically characterized as a glycolytic tumor. However, the extent and implications of metabolic heterogeneity within PDAC remain poorly defined. This study aimed to determine whether glycolytic activity follows a consistent expression pattern across PDAC patients and to explore how metabolic diversity influences therapeutic vulnerability. Methods: We employed spatial transcriptomics in six, ex vivo, primary human PDAC specimens, complemented by single-cell and bulk RNA sequencing, to map glycolytic heterogeneity in PDAC. Patient-derived cell models, representative of distinct glycolytic phenotypes, were used to assess metabolic and antiproliferative responses to glycolytic pathway inhibition. A comprehensive multi-omics strategy—including metabolomics, proteomics, and lipidomics—was integrated through a robust bioinformatics pipeline to dissect pathway-specific variations and integromics. Results: PDAC tumors displayed marked glycolytic heterogeneity, with distinct transcriptional profiles that preserved cell identity and spatial organization. These glycolytic patterns correlated with clinical outcomes, highlighting their potential prognostic value. Functional studies strengthen differential sensitivity to metabolic inhibitors in models with opposite glycolytic activity, demonstrating both the therapeutic relevance of PDAC glycolytic stratification and highlighting the potential of metabolic targeting. Conclusions: Our findings uncover a clinically relevant metabolic heterogeneity in PDAC, offering a novel stratification framework based on glycolytic profiling. This approach may allow the selection of patients for tailored metabolic therapies, advancing precision oncology in pancreatic cancer.

2025-11-28 | GSE303338 | GEO

Expression data from Hfe-deficient liver and duodenum in mouse strains with differing susceptibilities to iron loading

2008-03-22 | GSE7357 | GEO

Targeting Pancreatic Cancer by TAK-981, a SUMOylation Inhibitor that Activates the Immune System and Blocks Tumor Cell Cycle Progression

Project description:The 5-year survival rate of pancreatic ductal adenocarcinoma (PDAC) is lower than 8%. PDAC has the characteristics of high-density stroma and a distinctive immunosuppressive microenvironment and is profoundly resistant to all forms of chemo and immunotherapy. SUMOylation is a reversible post-translational modification required for cell cycle progression. We found that SUMOylation is increased in PDAC patient samples compared to primary pancreatic tissue. TAK-981, a novel highly selective and potent small molecule inhibitor of the SUMO activation enzyme E1 (SAE), selectively decreased SUMOylation in PDAC cells at the nanomolar range, thereby causing a G2/M cell cycle arrest, mitotic failure and chromosomal segregation defects. In vivo TAK-981 efficiently limited the tumor burden in the KPC3 syngeneic mouse model without evidence of general toxicity. Interestingly, we found that TAK-981 modulates the immune system, up-regulating CD8+ T cells, NK cells and down-regulating B cells in peripheral blood, spleen, lymph nodes. Treatment of mouse primary T cells ex vivo with TAK-981 activated STAT1, the key transcription factor induced by interferon signaling. Our findings indicate that inhibition of the SUMO pathway might be a potential clinical strategy to target PDAC by inhibiting tumor cell division and activating anti-tumor immunity.

2022-01-10 | PXD024521 | Pride

Timing and location dictate monocyte fate and their transition to tumor-associated macrophages

Project description:Tumor-associated macrophages (TAMs) are a heterogeneous population of cells whose phenotypes and functions are shaped by factors that are incompletely understood. Herein, we asked when and where TAMs arise from blood monocytes, and how they evolve during tumor development. We initiated pancreatic ductal adenocarcinoma (PDAC) in inducible monocyte fate-mapping mice and combined single-cell transcriptomics and high-dimensional flow cytometry to profile the monocyte-to-TAM transition. We revealed that monocytes differentiate first into a transient intermediate population of TAMs (IntTAM) that generates two longer-lived lineages of terminally differentiated TAMs with distinct gene expression profiles, phenotypes and intra-tumoral localization. Transcriptome datasets and tumor samples from patients with PDAC evidenced parallel TAM populations in humans and their prognostic associations. These insights will support the design of new therapeutic strategies targeting TAMs in PDAC.

2024-08-02 | GSE270793 | 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