Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA177217 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Hypoxia transcriptomic time-series data in three different cancer cell lines
| PRJNA141219 | ENA
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:Time course data of normoxia- and hypoxia-treated prostate tumor cell lines (DU145, PC3, LNCaP, 22RV1) and primary prostate epithelial cells (four different donors) in three biological replicates.
2011-11-09 | E-TABM-948 | biostudies-arrayexpress
Project description:Analysis and visualization of time-series transcriptomic data revealed Sudden TRanscriptomic Irregular Profile Expression changes (STRIPEs), characterized by anomalous transcript level values across the transcriptome in a given time point. We developed quantitative methods for detection and correction of STRIPEs in time-series. Correction of STRIPEs improves the quality of downstream analyses and reproducibility.Here we present cell-cycle transcriptomic time-series experiments containing STRIPEs from two different organisms/experimental conditions: Saccharomyces cerevisiae (BF264-15D background) in YEP 2% Dextrose at 38.5°C (mild chronic heat stress) and the K562 human cell line in RPMI + 10% BCS (bovine calf serum) + P/S.
2025-06-19 | GSE270332 | GEO
Project description:Analysis and visualization of time-series transcriptomic data revealed Sudden TRanscriptomic Irregular Profile Expression changes (STRIPEs), characterized by anomalous transcript level values across the transcriptome in a given time point. We developed quantitative methods for detection and correction of STRIPEs in time-series. Correction of STRIPEs improves the quality of downstream analyses and reproducibility.Here we present cell-cycle transcriptomic time-series experiments containing STRIPEs from two different organisms/experimental conditions: Saccharomyces cerevisiae (BF264-15D background) in YEP 2% Dextrose at 38.5°C (mild chronic heat stress) and the K562 human cell line in RPMI + 10% BCS (bovine calf serum) + P/S.
2025-06-19 | GSE270331 | GEO
Project description:Purpose: To study differential mRNA and precursor miRNA expression under hypoxia exposure in cancer cells. We treated ovarian cancer cell line A2780 under hypoxia condition at different time points. Normoxi acultured cells at corresponding time point were used as controls.
2016-10-20 | GSE52695 | GEO
Project description:Inflammatory tissues are characterized by low oxigen concentrations (hypoxia). These conditions are very different from that usually present in tissue cultures where transcriptomic profiles of human fibroblasts from inflammatory tissues have been previously analysed. The aim of this study was to characterize the changes on gene expression induced by hypoxia in human synovial fibroblasts. We used microarray expression profiling in paired normoxic and hypoxic cultures of healthy and rheumatoid arthritis (RA) synovial fibroblasts (HSF and RASF). Hypoxia induces significant changes on the expression of large groups of genes in both HSF and RASF. The hypoxic and normoxic profiles are also different between both groups. These data demonstrate that hypoxia induces significant changes on gene expression in HSF and RASF and identify differences between RASF and HSF. Synovial fibroblasts obtained from 6 patients with rheumatoid arthritis (RASF) and 6 sex and age matched adult healthy donors (HSF) were used. SF cultures were incubated for 22 hours under normoxic or hypoxic (0.5% O2) conditions. Nine experiments per group were performed, single experiments with three SF lines, and duplicated in other three lines per group. All 18 normoxia-hypoxia experiments (36 microarray data) were used for paired analysis of the changes induced by hypoxia in HSF or RASF.
2010-12-10 | E-GEOD-21959 | biostudies-arrayexpress
Project description:We compared the transcriptome at gene expression level in hypoxic and normoxic conditions. The transcriptome of cells cultured at three different timepoints, 2h, 24 h and 7 days with three different cell lines in normoxia and hypoxia was compared
2014-01-02 | GSE35819 | GEO
Project description:Asbestos has been shown to cause chromosomal damage and DNA aberrations. The fiber is associated with many different lung diseases such as asbestosis, malignant mesothelioma, and lung cancer, but the disease-related processes are still largely unknown. Our aim was to identify specific gene expression profiles by using Affymetrix arrays, in human cell lines A549, Beas-2B, and MeT5A exposed to asbestos in a time-dependent manner. The hybridization data was analyzed using an algorithm specifically designed for clustering short time series expression data, a canonical correlation analysis (CCA) for identifying correlations between the cell lines, and a Gene Ontology (GO) analysis method for the identification of enriched differentially expressed biological processes. Keywords: time course, gene expression, cell line, asbestos
2007-03-05 | GSE6013 | GEO