E-MTAB-2770
Ontology highlight
ABSTRACT:
PROVIDER: E-MTAB-2770 | ExpressionAtlas | 2023-02-15
REPOSITORIES: ExpressionAtlas
Similar Datasets
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:RNA sequencing of human pancreatic cancer cell lines
| PRJNA316672 | ENA
Project description:RNA seq on two primary pancreatic cancer cell lines (cancer-collagen1 knockout cell line and control pancreatic cancer cell line)
| PRJNA542537 | ENA
Project description:Experiments to test the effect of CtBP2 inhibition on metabolism of breast cell lines. In particular, experiment 1 involves comparison between a normal breast cell line (MCF102A) and a triple-negative breast cancer cell line (MDA-MB231). Experiment 2 is a study between MDA-MB231 silenced for CtBP2 by stable RNA interference (shCtBP2 cells) compared to scramble (shCTRL cells). Experiment 3 is a comparison between a normal breast cell line (MCF102A) and a triple-negative breast cancer cell line (MDA-MB231)in the presence of the absence of small-molecule CtBP inhibitors: HIPP (400 μM) or P4 (300 μM)for 48 hours.
2021-08-31 | ST001941 | MetabolomicsWorkbench
Project description:ATAC-seq data from 3 cancer cell lines and RNA-seq data from 1 cancer cell line
| PRJNA324504 | ENA
Project description:Cultured cell lines are widely used for research in the physiology, pathophysiology, toxicology and pharmacology of the renal proximal tubule. The lines that are most appropriate for a given use depend on the genes expressed. New tools for transcriptomic and proteomic profiling using RNA-sequencing (RNA-Seq) and mass spectrometry make it possible to catalog expressed genes in each cell line. This data set is the protoemic data of Rat NRK-52E cell line. We concludeno cell line fully matched the transcriptome of native proximal tubule cells. However, some of the lines tested are suitable for the study of particular metabolic and transport processes seen in the proximal tubule.
2020-10-15 | PXD021160 | Pride
Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.
2016-12-01 | MSV000080374 | MassIVE
Project description:RNA-seq of 1019 human cancer cell lines from the Broad-Novartis Cancer Cell Line Encyclopedia (http://www.broadinstitute.org/ccle). The original annotation of each cell line to a disease has been checked and manually curated by Expression Atlas (https://www.ebi.ac.uk/gxa/home). Note the original version of the dataset contained 934 cell lines. In the latest update, 2 cell lines were removed (presumed duplicates) and 87 added new.
2013-05-31 | E-MTAB-2770 | biostudies-arrayexpress