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SRP048707
Mus musculus
15 Downloadable Samples
Illumina HiSeq 1000
Description
Post-transcriptional regulation of mRNA by the RNA binding protein HuR is required in B cells for the germinal centre reaction and for the production of class-switched antibodies in response to T-independent antigens. Transcriptome-wide examination of RNA isoforms, abundance and translation in HuR-deficient B cells, together with direct measurements of HuR-RNA interaction, revealed that HuR-dependent mRNA splicing affects hundreds of transcripts including the dihydrolipoyl succinyltransferase (Dlst), a subunit of the aketoglutaratedehydrogenase (aKGDH) enzyme. In the absence of HuR, defective mitochondrial metabolism results in high levels of reactive oxygen species and B cell death. Our study shows how post-transcriptional processes control the balance of energy metabolism required for B cell proliferation and differentiation. Overall design: Sequencing analysis of B cell transcriptome using Illumina TruSeq mRNA sample prep kit and Illumina platform. RNA was isolated from ex-vivo or LPS-activated (48h) splenic B cells from HuRflox/flox x mb1wt control or HuRflox/flox x mb1cre mice. 3-4 biological replicates per genotype and condition.
Publication Title
The RNA-binding protein HuR is essential for the B cell antibody response.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 16 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Following androgen ablation therapy (AAT), the vast majority of prostate cancer patients develop treatment resistance with a median time of 18-24 months to disease progression. To identify molecular targets that aid in prostate cancer cell survival and contribute to the androgen independent phenotype, we evaluated changes in LNCaP cell gene expression during 12 months of androgen deprivation. At time points reflecting critical growth and phenotypic changes, we performed Affymetrix expression array analysis to examine the effects of androgen deprivation during the acute response, during the period of apparent quiescence, and during the emergence of highly proliferative, androgen-independent prostate cancer cells (LNCaP-AI). We discovered alterations in gene expression for a host of molecules associated with promoting prostate cancer cell growth and survival, regulating cell cycle progression, apoptosis and adrenal androgen metabolism, in addition to AR co-regulators and markers of neuroendocrine disease. These findings illustrate the complexity and unpredictable nature of cancer cell biology and contribute greatly to our understanding of how prostate cancer cells likely survive AAT. The value of this longitudinal approach lies in the ability to examine gene expression changes throughout the cellular response to androgen deprivation; it provides a more dynamic illustration of those genes which contribute to disease progression in addition to specific genes which constitute a malignant androgen-independent phenotype. In conclusion, it is of great importance that we employ new approaches, such as the one proposed here, to continue exploring the cellular mechanisms of therapy resistance and identify promising targets to improve cancer therapeutics.
Publication Title
Longitudinal analysis of androgen deprivation of prostate cancer cells identifies pathways to androgen independence.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 20 Downloadable Samples
- Affymetrix Human Genome U95 Version 2 Array (hgu95av2)
Description
Considerable variation in gene expression data from different DNA microarray platforms has been demonstrated. However, no characterization of the source of variation arising from labeling protocols has been performed. To analyze the variation associated with T7-based RNA amplification/labeling methods, aliquots of the Stratagene Human Universal Reference RNA were labeled using 3 eukaryotic target preparation methods and hybridized to a single array type (Affymetrix U95Av2). Variability was measured in yield and size distribution of labeled products, as well as in the gene expression results. All methods showed a shift in cRNA size distribution, when compared to un-amplified mRNA, with a significant increase in short transcripts for methods with long IVT reactions. Intra-method reproducibility showed correlation coefficients >0.99, while inter-method comparisons showed coefficients ranging from 0.94 to 0.98 and a nearly two-fold increase in coefficient of variation. Fold amplification for each method was positively correlated with the number of present genes. Two factors that introduced significant bias in gene expression data were observed: a) number of labeled nucleotides that introduces sequence dependent bias, and b) the length of the IVT reaction that introduces a transcript size dependent bias. This study provides evidence of amplification method dependent biases in gene expression data.
Publication Title
In vitro transcription amplification and labeling methods contribute to the variability of gene expression profiling with DNA microarrays.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 503 Downloadable Samples
Affymetrix Human Genome U95A Array (hgu95a), Affymetrix Human Genome U95B Array (hgu95b), Affymetrix Human Genome U95C Array (hgu95c)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process.
Sample Metadata Fields
Age, Specimen part, Race
View Samples- Homo sapiens
- 196 Downloadable Samples
- Affymetrix Human Genome U95C Array (hgu95c), Affymetrix Human Genome U95A Array (hgu95a), Affymetrix Human Genome U95B Array (hgu95b)
Description
Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer.
Publication Title
Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 181 Downloadable Samples
- Affymetrix Human Genome U95B Array (hgu95b), Affymetrix Human Genome U95A Array (hgu95a), Affymetrix Human Genome U95C Array (hgu95c)
Description
Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer.
Publication Title
Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 74 Downloadable Samples
- Affymetrix Human Genome U95A Array (hgu95a), Affymetrix Human Genome U95C Array (hgu95c), Affymetrix Human Genome U95B Array (hgu95b)
Description
Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer.
Publication Title
Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 52 Downloadable Samples
- Affymetrix Human Genome U95B Array (hgu95b), Affymetrix Human Genome U95C Array (hgu95c), Affymetrix Human Genome U95A Array (hgu95a)
Description
Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer.
Publication Title
Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process.
Sample Metadata Fields
Age, Specimen part, Race
View Samples- Homo sapiens
- 187 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The tissue of origin form metastatic tumors is sometimes difficult to identify from clinical and histologic information. Gene expression signatures are one potential method for identifying the tissue of origin. In the development of algorithms to identify tissue of origin, a collection of human tumor metastatic specimens with known primary sites or primary tumors with poor differentiation are very useful in identifying gene expressions signatures that can classify unknown specimens as to the tissue of origin. Here we describe a series of 276 such tumor specimens used for this purpose. The specimens are poorly differentiated, undifferentiated and metastatic specimens from tumors of the following types/tissues of origin: breast, liver, non-Hodgkin's lymphoma, non-small cell lung cancer, ovary, testicular germ cell, thyroid, kidney, pancreas, colorectal cancer, soft tissue sarcoma, bladder, gastric cancer, prostate and melanoma. This data combined with other series (GSE2109) was used to validate a proprietary tumor classification algorithm of Pathwork Diagnostics. The results of this validation set (N = 545 CEL files) showed that the algorithm correctly identified the tissue of origin for 89.4% of the specimens.
Publication Title
Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 19 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed revealing only 48-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors.
Publication Title
Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression.
Sample Metadata Fields
No sample metadata fields
View Samples