Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26176300_1.fastq.gz | Fastqsanger.gz | |||
| SRR26176300_2.fastq.gz | Fastqsanger.gz | |||
| SRR26176301_1.fastq.gz | Fastqsanger.gz | |||
| SRR26176301_2.fastq.gz | Fastqsanger.gz | |||
| SRR26176302_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 24 |
Similar Datasets
Project description:The goal of this study was to determine IGF2BP3 regulation of RNA targets in human pacreatic ductal adenocarcinoma cell lines Included are iCLIP-seq libraries for IGF2BP3 from PL45 and Panc1 PDAC cell samples, RIP-seq samples from PL45 and Panc1 PDAC cells, RNA-seq data sets from control and IGF2BP3 knockdown in PL45 and Panc1 PDAC cells, and small RNA-seq samples from Panc1 cells
2016-03-12 | E-GEOD-79147 | biostudies-arrayexpress
Project description:We demonstrate that a clinically relevant X-ray hypofractionation regimen (3x8 Gy) of multiple PDAC cel lines effectively induces immunogenic cell death and transactivates Interferon beta-1 in a STING-dependent manner. RNA-seq analyses showed a global and steady upregulation of type I interferon response in PDAC cells following 3x8 Gy.
2024-08-12 | E-MTAB-13096 | biostudies-arrayexpress
Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1707250000 | BioModels
Project description:By screening an epigenetic-related compound library, we identified THZ1, a covalent inhibitor of CDK7, as a promising candidate. Multiple long-established and patient-derived PDAC cell lines (PDC) were used to validate the efficacy of THZ1 in vitro. In addition, patient-derived xenograft (PDX) models and animal models of PDAC were utilized for examining THZ1 efficacy in vivo. Furthermore, RNA-Seq and H3K27Ac-based Super-Enhancers (SEs) analyses were performed to reveal the molecular mechanism of THZ1 treatment. Lastly, PDAC cell lines with primary or acquired resistance to THZ1 were investigated to explore the potential mechanism of THZ1 susceptibility.
2019-02-01 | GSE121273 | GEO
Project description:The canonical NF-κB transcription factor RELA is a master regulator of immune and stress responses and is upregulated in PDAC tumours. Here, we characterised previously unknown endogenous RELA-GFP dynamics in PDAC cell lines by live single cell imaging, which revealed rapid, sustained and non-oscillatory nuclear RELA following TNFα stimulation. Using Bayesian analysis of single cell datasets with variation in nuclear RELA, we predicted that RELA heterogeneity in PDAC cell lines is dependent on F-actin dynamics. Using RNA-seq, we identified the actin regulators NUAK2 and ARHGAP31 as transcriptionally regulated by RELA. In turn, NUAK2 or ARHGAP31 siRNA depletion downregulates TNFα-stimulated RELA nuclear localisation in PDAC cells, establishing a novel negative feedback loop regulating RELA activation by TNFα. We identify an additional actin-independent feedback loop involving RELB, which suppresses TNFα-mediated RELA nuclear localisation following RELA mediated upregulation of RELB. Taken together, we provide computational and experimental support for interdependence between the F-actin network and RELA translocation dynamics in PDAC.
2024-10-25 | GSE268743 | GEO
Project description:Brg1 is a subunit of SWI/SNF chromatin remodeling complex. To clarify the role of Brg1 for the progression and metastasis of pancreatic ductal adenocarcinoma (PDAC), we generated a PDAC mouse model with a dual recombinase system (Brg1 lox/lox; Pdx1-Flp; Kras FSF-G12D; Trp53 frt/frt; Rosa26 FSF-CreERT2) which enabled to ablate Brg1 in established PDAC with tamoxifen treatment. We established cancer cell lines derived from PDAC tumors developed in those mice and investigated the effect of Brg1 ablation on PDAC cell proliferation and metastasis. In order to determine the genes that Brg1 directly regulates, we conducted a chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) analysis for Brg1 and histone modifications (H3K27ac and H3K27me3) using Brg1 wild type and Brg1-deleted mouse PDAC cells.
2022-04-30 | GSE199610 | GEO