TCMADR Gene Expression Profiling
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB20302 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR1938020_1.fastq.gz | Fastqsanger.gz | |||
| ERR1938020_2.fastq.gz | Fastqsanger.gz | |||
| ERR1938016_1.fastq.gz | Fastqsanger.gz | |||
| ERR1938016_2.fastq.gz | Fastqsanger.gz | |||
| ERR1938017_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 10 |
Similar Datasets
Project description:Chemotherapy often causes adverse effects including hair loss, however, the molecular basis remains unclear. A common assumption is that by targeting actively dividing cancer cells, chemo-drugs would inevitably damage normal proliferating cells as a “side effect”. Using both mouse hair and avian feather as models, here we show that down-regulation of sonic hedgehog (Shh) signaling is a critical event mediating chemo-drug induced tissue damage. Reduced expression of Shh is also apparent in human patients who experience hair loss after receiving chemotherapy, and in animal models treated with several frontline chemo-drugs. Supplementation of Shh protein or steroid hormones that activate Shh signaling prevented hair loss, but interestingly, not the damage to the pigment system. Together, our results have elucidated a molecular mechanism underlying chemotherapy-induced tissue damage, and suggested novel concepts and methodologies for future clinical interventions and drug development.
2017-01-01 | GSE41287 | GEO
Project description:Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood and its prediction remains challenging. Here, we perform transcriptomic profiling of human heart-derived primary cardiomyocyte cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.
2020-05-13 | GSE146096 | GEO
Project description:Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood and its prediction remains challenging. Here, we perform transcriptomic profiling of human heart-derived primary cardiomyocyte cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.
2020-05-13 | GSE146095 | GEO
Project description:To test whether PD-1 exerts genome-wide gene expression changes in TCM-phenotype CD8 cells, we performed transcriptome analysis on TCM-phenotype CD8 cell subpopulations derived from 7-month old PD-1 KO and WT spleens.
2013-05-06 | E-MTAB-1569 | biostudies-arrayexpress
Project description:At present, the DNA microarray application to study traditional Chinese medicines (TCMs) has received more and more attention. Furthermore, kinds of TCMs were too large, and ingredients in TCMs were also extremely abundant, which afforded an extraordinary source for drug development. To pursue a suitable approach on the research of TCM components, we produced gene expression profiles of 102 TCM ingredients treating MCF7 cells. All selected molecules were main ingredients in Chinese herb and TCM formula. In addition, the gene expression profiles data can be analyzed in combination with public database connectivity map (CMAP) and other bioinformatics methods, which could identify the underlying pharmacological mechanisms and molecular targets/pathway of numerous components. This study indicated that the gene expression profiles data of TCM components was a very convenient and useful approach for researchers engaged in study of TCMs.
2017-03-26 | GSE85871 | GEO
Project description:Gene Expression Profiling from 21 cases: 14 CCND1-negative Mantle Cell Lymphoma and 7 CCND1-positive Mantle Cell Lymphoma
| EGAD00010001582 | EGA
Project description:Age- and sex-related susceptibility to adverse drug reactions is a key concern in understanding drug safety and disease progression. We hypothesize that the underlying suite of hepatic genes expressed at various developmental and life-cycle stages will impact susceptibility to adverse drug reactions. Thus, understanding the basal expression patterns of genes throughout the life span of the rat model species in both sexes will inform our assessments of adverse drug reactions. The liver plays a central role in the metabolism and biotransformation of drugs via key cellular pathways. Untreated, male and female F344 rats were sacrificed at 2, 5, 6, 8, 15, 21, 52, 78, and 104 weeks of age. Liver tissues were collected for histology and gene expression analysis. Whole-genome rat microarrays (44,000 features) were used to query global expression profiles. An initial list of active genes was selected using a 2-way ANOVA with a p-value cutoff of 0.05 and 1.5 fold-change difference from mean expression. Three dimensional principal component analyses revealed notable expression profile divergence between males and females after 5 weeks with greatest differences observed at 21 and 52 weeks before converging again at 104 weeks. Furthermore, k-means clustering identified groups of genes that displayed specific developmental and age-related patterns of expression. Various adult aging-related clusters included genes involved in pathways related to susceptibility to adverse drug effects such as xenobiotic metabolism, DNA damage repair, and oxidative stress. These results suggest an underlying role for genes in these specific clusters in potentiating age- and sex-related susceptibilities to adverse health effects.
2010-06-24 | GSE21335 | GEO
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
2022-11-16 | PXD029891 | JPOST Repository
Project description:Arabidopsis RNA-Seq Differential Gene Expression profiling
| PRJEB19242 | ENA
Project description:Interventions: lesion tissues vs. adjacent tissues of colorectal cancer patients:nil
Primary outcome(s): RNA
Study Design: Factorial
| 2693518 | ecrin-mdr-crc