PCa-LINES
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001004613 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP & PC346c) and 4 additional patient samples
| EGAD00001006366 | EGA
Project description:Ribo-minus RNA-seq data corresponding to 96x GBM samples from the BELOB trial.
| EGAS00001004570 | EGA
Project description:RNA-Seq with and without RNase treatment in PCa cell lines
| PRJNA450077 | ENA
Project description:ATAC-Seq of MSK-PCa series of Patient Derived Organoids and Xenografts, and prostate cell lines
| PRJNA798395 | ENA
Project description:beta catenin in CCA cell lines and patient samples
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Project description:We describe Ribo Mega-SEC, a powerful approach for the separation and biochemical analysis of mammalian polysomes and ribosomal subunits using Size Exclusion Chromatography and uHPLC, which was achieved within 15 min from sample injection to fraction collection. Ribo Mega-SEC reproducibly shows translating ribosomes exist predominantly in polysome complexes in extracts isolated from human cell lines and mouse liver tissue, which alter in response to starvation. Ribo Mega-SEC provides a rapid, efficient, convenient and highly reproducible method for studying functional translation complexes and is easily combined with high-through put analysis such as proteomics and RNA-Seq, or with structural analysis using electron microscopy. We propose that Ribo Mega-SEC analysis is an accessible alternative to traditional polysome profiling using sucrose density gradients.
2018-08-10 | PXD008913 | Pride
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:In this study, we aimed to investigate the functional roles of miR-33a in PCa.We investigated the relative miR-33a level in normal and tumor prostate samples as well as PCa cell lines. Then, we performed a detailed functional analysis of mir-33a in LNCaP and VCaP PCa cells and evaluated proliferative, invasive and anchorage independent growth potential of cells upon overexpression and knockdown of miR-33a. We next explored the potential direct targets of miR-33a in PCa cells via utilizing gene expression microarray analysis, bioinformatics search, further qRT-PCR, western blot, and luciferase assay confirmation. Our results demonstrated that miR-33a is significantly downregulated in PCa tumor samples and PCa cell lines, pointing its tumor suppressor potential in PCa. Overexpression and knockdown of miR-33a significantly altered the proliferative, invasive and anchorage independent growth potentials of cells through altering the expression of its direct target PIM1. Ectopic induction of MiR-33a expression reversed the impacts of PIM1 overexpression on cellular phenotypes associated with PCa progression. Our results suggest that mir-33a exerts its tumor suppressor potential through targeting its direct target PIM1 and carries crucial roles in PCa tumorigenesis.
2017-10-31 | GSE89034 | GEO