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 |
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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:RNA sequence of PDAC cell lines affected by SRSF12
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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:Protein kinase R (PKR) functions both as a promoter and inhibitor in various cancers, yet its role in pancreatic ductal adenocarcinoma (PDAC) remains unclear. This study aimed to investigate the role of PKR in PDAC. PKR expression in PDAC cell lines was assessed using real-time reverse transcriptase polymerase chain reaction and western blot analysis. The MTS assay was employed to evaluate the effect of PKR on cell proliferation. To elucidate the underlying mechanisms of PKR's action on PDAC, RNA-sequencing (RNA-seq) analysis was performed, and flow cytometry was used to examine the effects of PKR knockdown on cell cycle progression and apoptosis in PDAC cells. The results indicated that PDAC cell lines exhibited significantly reduced proliferation when transfected with PKR-targeting siRNAs or treated with PKR inhibitors. RNA-seq analysis revealed a substantial upregulation of GADD45A expression upon inhibition of PKR expression. Following PKR silencing, cell cycle analysis showed a marked accumulation of cells in the G1 phase, which is consistent with GADD45A's known role as a cell cycle regulator. Furthermore, the inhibition of proliferation caused by PKR knockdown was reversed by the downregulation of GADD45A, suggesting an interactive effect between PKR and GADD45A in regulating PDAC cell growth. In conclusion, PKR promotes PDAC cell proliferation by modulating the cell cycle through the regulation of GADD45A expression. These findings suggest that PKR may serve as a potential novel therapeutic target for PDAC.
2025-07-16 | GSE285947 | GEO
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
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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