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accession-icon GSE15695
Comprehensive Homozygous Deletion Mapping of Myeloma Defines a Poor Prognosis Cell Death Gene Expression Signature
  • organism-icon Homo sapiens
  • sample-icon 244 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Myeloma is a clonal malignancy of plasma cells. Poor prognosis risk is currently identified by clinical and cytogenetic features. However, these indicators do not capture all prognostic information. Gene expression analysis can be used to identify poor prognosis patients and this can be improved by combination with information about DNA level changes. Using SNP-based gene mapping in combination with global gene expression analysis we have identified homozygous deletions in genes and networks that are relevant to myeloma. From these, we have generated an expression-based signature associated with shorter survival in 247 patients and confirmed this signature in data from 2 independent groups totalling 800 patients. We identified 170 genes with homozygous deletions and corresponding loss of expression. Deletion within the Cell Death network was over-represented and cases with these deletions have impaired overall survival. We defined a gene expression signature of 97 cell death genes that reflects prognosis confirmed this in two independent data sets. We developed a simple 6-gene expression signature from the 97-gene signature that can be used to identify poor prognosis myeloma in the clinical environment. The signature can form the basis of future trials aimed at improving the outcome of poor prognosis myeloma.

Publication Title

Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome.

Sample Metadata Fields

Specimen part

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accession-icon GSE21349
A compendium of myeloma associated chromosomal copy number abnormalities and their prognostic value
  • organism-icon Homo sapiens
  • sample-icon 255 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

To obtain a comprehensive genomic profile of presenting multiple myeloma cases we performed high resolution single nucleotide polymorphism (SNP) mapping array analysis in 114 samples alongside 258 samples analysed by U133 Plus 2.0 expression array (Affymetrix). We examined DNA copy number alterations and loss of heterozygosity (LOH) in order to define the spectrum of minimally deleted regions in which relevant genes of interest can be found. The most frequent deletions are located at 1p (30%), 6q (33%), 8q (25%), 12 (22%), 13q (59%), 14q (39%), 16q (35%), 17p (7%), 20 (12%) and 22 (18%). In addition, copy number-neutral LOH, or uniparental disomy, was also prevalent on 1q (8%), 16q (9%), and X (20%), and was associated with regions of gain and loss. Based on fluorescent in situ hybridisation (FISH) and expression quartile analysis, genes of prognostic importance were found to be located at 1p (FAF1, CDKN2C), 1q (ANP32E), and 17p (TP53). In addition, we identified common homozygously deleted genes which have functions relevant to myeloma biology. Taken together, the dysregulated genes from the myeloma genome indicate that the crucial pathways in myeloma pathogenesis include the NF-?B pathway, apoptosis, cell-cycle regulation and Wnt signalling.

Publication Title

Aberrant global methylation patterns affect the molecular pathogenesis and prognosis of multiple myeloma.

Sample Metadata Fields

Specimen part

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accession-icon GSE105777
Major impact of sampling methodology on gene expression in estrogen receptor positive breast cancer
  • organism-icon Homo sapiens
  • sample-icon 426 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

To investigate the impact of sampling methodology on gene expression data from primary estrogen receptor positive (ER+) breast cancer biopsies. Analysis of global gene-expression measured in core-cut biopsies at baseline and surgery from patients randomised to receive either 2-weeks presurgical aromatase inhibitor (AI, n=157) or no presurgical treatment (n=56) revealed genes most markedly altered in the AI group (e.g. FOS, DUSP1, RGS1, FOSB) were equally altered in the no-treatment group; some widely investigated genes that were apparently unaffected in the AI group (e.g. MYC) were counter-altered in the Control group masking actual AI-dependent changes. In the absence of a Control group these artefactual changes would likely lead to the most affected genes being the erroneous focus of research. The findings are likely relevant to all archival collections of ER+ breast cancer.

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part, Disease, Time

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accession-icon GSE114056
Gene expression analysis of cancer-associated fibroblasts (CAFs) from the murine breast cancer model MMTV-PyMT after perturbing the expression of Dickkopf-3 (DKK3)
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.1 ST Array (mogene21st)

Description

Immortalized (hpv-E6) murine fibroblasts isolated from normal mammary glands and carcinoma stages (CAFs) from the MMTV-PyMT model (FVB/N background) were used in this analysis. CAFs were transfected with two independent RNAi targeting Dickkopf-3 (DKK3) or control RNAi and changes in whole-genome gene expression were evaluated by microarray. In addition, the effect of stable ectopic expression of DKK3 in normal fibroblasts (which normally do not express DKK3) was also evaluated. Analysis demonstrated the role of DKK3 in controlling specific transcriptional programs in CAFs.

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE101579
Generation and characterisation of two D2A1 mammary cancer sublines to model spontaneous and experimental metastasis in a syngeneic BALB/c host
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

In vivo and in vitro analyses revealed distinct and shared characteristics of the metastatic D2A1-m1 and D2A1-m2 sublines. In particular, D2A1-m1 cells are more aggressive in experimental metastasis assays, while D2A1-m2 cells are more efficient at disseminating from the primary tumour in spontaneous metastasis assays. Surprisingly, classical metastasis-associated in vitro phenotypes such as enhanced proliferation, migration and invasion are reduced in the sublines compared to the parental cell line. Further, evasion of immune control cannot fully explain their enhanced metastatic properties. By contrast, both sublines show increased resistance to apoptosis when cultured in non-adherent conditions and, for the D2A1-m2 subline, increased 3D tumour spheroid growth. Moreover, the enhanced spontaneous metastatic phenotype of the D2A1-m2 sublines is associated with the ability to recruit an activated tumour stroma and promote directional migration of fibroblasts. Gene expression profiling revealed that the two metastatic sublines are distinct but more closely related to each other than the parental D2A1 cells.

Publication Title

No associated publication

Sample Metadata Fields

Cell line

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accession-icon GSE12320
Differential gene expression in GBS6 cells after EWS-POU5F1 knockdown
  • organism-icon Homo sapiens
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Our objective is to clarify the function of EWS-POU5F1 chimera.

Publication Title

Function of EWS-POU5F1 in sarcomagenesis and tumor cell maintenance.

Sample Metadata Fields

Cell line

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accession-icon GSE94767
CancerMap project prostate cancer microarray dataset
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 241 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)

Description

Microarray expression profiling has currently failed to provide a consistent classification for human prostate cancer. Such classifications are important because they provide a framework for the identification of new biomarkers of clinical behavior and for the development of targeted therapies. We hypothesize that previous studies have been unsuccessful because of their failure to take into account the well documented occurrence of prostate cancer multifocality and genetic heterogeneity. We have invented a novel method for collecting whole RNALater preserved research slices from prostatectomy specimens that, for the first time, allows the mapping of multifocality and of genetic heterogeneity in prostate cancer to be integrated with the selection of samples for expression microarray analysis. For each specimen we will construct a map of the regions of cancer and of their ERG gene rearrangement status from whole mount formalin fixed sections immediately juxtaposed to the research slice. Only foci of cancers containing a homogeneous pattern of ERG gene alteration will be selected for study. A pilot study has already demonstrated the feasibility of this approach, and provides initial evidence that cancers may be stratified into at least two prognostically distinct categories. Novel biomarkers defining distinct prostate cancer categories will be verified and validated in future studies linked to clinical trials.

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part, Subject

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accession-icon GSE37199
Blood mRNA expression signatures derived from unsupervised analyses identify prostate cancers with poor outcome
  • organism-icon Homo sapiens
  • sample-icon 102 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Background: Inter-patient prostate cancer (PrCa) heterogeneity results in highly variable patient outcomes. Multi-purpose biomarkers to dissect this heterogeneity are urgently required to improve treatment and accelerate drug development in PrCa. Circulating biomarkers are most practical for evaluating this disease. We pursued the analytical validation and clinical qualification of blood mRNA expression arrays.

Publication Title

Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study.

Sample Metadata Fields

Subject

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accession-icon GSE12378
Integration of ERG gene mapping and gene expression profiling identifies distinct categories of human prostate cancer
  • organism-icon Homo sapiens
  • sample-icon 39 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)

Description

OBJECTIVE: Previous expression microarray analyses have failed to take into consideration the genetic heterogeneity and complex patterns of ERG gene alteration frequently found in cancerous prostates. The objective of this study is for the first time, to integrate the mapping of ERG gene alterations with the collection of expression microarray data.

Publication Title

Integration of ERG gene mapping and gene-expression profiling identifies distinct categories of human prostate cancer.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE23764
Expression data from actomyosin contractility regulated genes
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Actomyosin contractility regulates cell morphology and movement. The objective of this study was to identify whether actomyosin contractility regulates gene expression in tumour cells and whether such genes are involved in cell morphology and movement. Gene expression analysis was carried out on highly contractile melanoma cell line A375M2 plated on a deformable collagen matrix under conditions where actomyosin contractility could be altered following treatment with blebbistatin, a direct inhibitor of myosin II, or Rho-kinase inhibitors Y27632 or H1152 that interfere with signalling to myosin II.

Publication Title

No associated publication

Sample Metadata Fields

Specimen part, Treatment

View Samples
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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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