Ontology highlight
ABSTRACT:
PROVIDER: PRJNA212322 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR935442.fastq.gz | Fastqsanger.gz | |||
| SRR935443.fastq.gz | Fastqsanger.gz | |||
| SRR935444.fastq.gz | Fastqsanger.gz | |||
| SRR935445.fastq.gz | Fastqsanger.gz | |||
| SRR935446.fastq.gz | Fastqsanger.gz |
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Project description:Using a transcriptional network derived from 2000 breast cancer gene expression profiles we identify the master regulators (MRs) of FGFR2 signalling. To validate the identified regulons, we examined whether there was enrichment of TF binding near the transcription start sites (TSS) of genes found in the regulons of a particular MR.
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Project description:Genome-wide association studies have identified a locus within the second intron of the FGFR2 gene that is consistently the most strongly associated with estrogen receptor-poisive breast cancer risk. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Previously, a systems biology approach was adopted to elucidate the regulatory networks operating in MCF-7 breast cancer cells in order to examine the role of FGFR2 in mediating risk. Here, the same approach has been employed using MCF-7 cells that have been treated with siRNA directed against FGFR2, in order to knock-down FGFR2 expression, to confirm that the differential gene expression that we see when FGF10 signalling is perturbed, on a background of estrogen signalling, is mediated via FGFR2 stimulation.
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Project description:Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways The data consists of 71 microarray samples from MCF-7 cells treated under different conditions, at 3 time points (0, 6 and 24 h) in order to perturb FGFR2 signalling using the iF2 construct system. The data have been pre-processed in R using the beadarray package, and are presented in the form of log2 expression values. The experiment was carried out on 6 Humanv4 BeadChips using 12 samples per BeadChip. The original arrays contain 48324 features, with a mean of 22 beads per feature (Standard Deviation of 5)
2013-07-17 | E-GEOD-48925 | biostudies-arrayexpress
Project description:Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways The data consists of 125 microarray samples from MCF-7 cells treated under different conditions, at 5 time points (0, 3, 6, 12 and 24 h) in order to perturb FGFR2 signalling by overexpressing the full length FGFR2b from a tetracycline-inducible expression vector. The data have been pre-processed in R using the beadarray package, and are presented in the form of log2 expression values. The experiment was carried out on 11 Humanv4 BeadChips using 12 samples per BeadChip. The original arrays contains 48324 features, with a mean of 22 beads per feature (Standard Deviation of 5)
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Project description:Using a transcriptional network derived from 2000 breast cancer gene expression profiles we identify the master regulators (MRs) of FGFR2 signalling. To validate the identified regulons, we examined whether there was enrichment of TF binding near the transcription start sites (TSS) of genes found in the regulons of a particular MR. For ESR1 and SPDEF, ChIP-seq experiments were performed in MCF-7 cells, while existing data was analysed for FOXA1 (Hurtado et al. Nature Genetics, 43:27–33, 2010) and GATA3 (Theodorou, et al., Genome Res 23: 12-22, 2013). ChIP-seq experiments were performed on three biological replicates per each transcription factor. For each sample, 36bp single-end reads were obtained. Peak regions were identified in all ChIP-seq TF data sets using the peak caller algorithm MACS (Zhang et al., Genome Biology, 9(9):R137, 2008) with default parameters.
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Project description:Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways
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