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accession-icon GSE64826
Expression data from H295R_GR cells following treatment with dexamethasone or RU486
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

The human glucocorticoid receptor (GR) is overexpressed at the molecular and protein level in malignant human adrenocortical cancers. A stable cell line model of GR overexpression was established using the H295R human adrenocortical cancer cell line.

Publication Title

No associated publication

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE12368
Analysis of adrenocortical tumors identify IGF2 and Ki-67 as useful in differentiating carcinomas from adenomas
  • organism-icon Homo sapiens
  • sample-icon 32 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Purpose: The management of adrenocortical tumors (ACTs) is complex, compounded by the difficulty in discriminating benign from malignant tumors using conventional histology. The Weiss score is the current most widely used system for ACT diagnosis but it has limitations, particularly with ACTs with a score of 3. The am of this study was to identify molecular markers whose expression can discriminate adrenocortical carcinomas (ACCs) from adrenocortical adenomas (ACAs) by microarray gene expression profiling and to determine their clinical applicability by using immunohistochemistry (IHC). Experimental design: Microarray gene expression profiling was used to identify 7 molecular markers which were significantly differentially expressed between ACCs and ACAs. These results were confirmed with quantitative PCR for all 7 genes and IHC for 3 protein. Results: Microarray gene expression profiling was able to accurately categorize ACTs into ACCs and ACAs. All 7 genes were strong discriminators of ACCs from ACAs on qPCR. IHC with IGF2, MAD2L1, CCNB1 and Ki-67, but not ACADVL or ALOX15B, had high diagnostic accuracy in differentiating ACCs from ACAs. The best results however were obtained with a combination of IGF2 and Ki-67 with 96% sensitivity and 100% specificity in diagnosing ACCs. Conclusion: Microarray gene expression profiling accurately differentiates ACCs from ACAs. The combination of IGF2 and Ki-67 IHC is also highly accurate in distinguishing between the 2 groups and is particularly helpful in ACTs with Weiss score of 3.

Publication Title

Microarray gene expression and immunohistochemistry analyses of adrenocortical tumors identify IGF2 and Ki-67 as useful in differentiating carcinomas from adenomas.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE10317
A case of primary hyperparathyroidism and undetectable serum PTH due to a truncating PTH gene mutation
  • organism-icon Homo sapiens
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

A investigation of the gene expression of one parathyroid tumour compared to its adjacent normal tissue

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE114384
Expression data of sheep exposed to Mycobacterium avium subsp. Paratuberculosis
  • organism-icon Ovis aries
  • sample-icon 124 Downloadable Samples
  • Technology Badge Icon Affymetrix Bovine Genome Array (bovine)

Description

In this study, consistent gene expression changes were analysed to identify gene-to-gene interaction and functional pathway relationships associated with the MAP exposure outcomes of either a paucibacillary or a multibacillary disease form or sheep resilient to disease. The results suggest that although groups of genes are differentially changed in the MAP exposed sheep regardless of clinical outcome, there are distinct variations in gene expression patterns between the paucibacillary and multibacillary forms of disease and of particular note was the finding that MAP exposed animals with evidence of recovery or resilience respond to MAP exposure with a pattern of gene and molecular pathway interactions distinct from those animals that progress to clinical paratuberculosis.

Publication Title

No associated publication

Sample Metadata Fields

Specimen part, Disease, Treatment

View Samples
accession-icon GSE31130
Non-overlapping progesterone receptor cistromes contribute to cell-specific transcriptional outcomes in breast cells
  • organism-icon Homo sapiens
  • sample-icon 60 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip, Illumina HumanHT-12 V3.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Non-overlapping progesterone receptor cistromes contribute to cell-specific transcriptional outcomes.

Sample Metadata Fields

Specimen part, Cell line

View Samples
accession-icon GSE85998
Expression data from mouse liver
  • organism-icon Mus musculus
  • sample-icon 47 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.1 ST Array (mogene21st)

Description

Livers from 15 month old mice mainatined on one of 25 different diets varying in protein, carbohydrate, fat (P,C,F) and energy content were analysed. Energy content was categorised as low (8kJ/g), medium (13kJ/g) or high (17kJ/g) Mice were placed on diet from 3 weeks of age and a subset culled for various analyses. The rest of the cohort was allowed to live out their natural life to assess lifespan.

Publication Title

Defining the Nutritional and Metabolic Context of FGF21 Using the Geometric Framework.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE31128
Progestin regulation of gene expression in breast cancer and minimally transformed breast cell lines
  • organism-icon Homo sapiens
  • sample-icon 48 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

Time course of response to synthetic progestin ORG2058 in T-47D and ZR-75-1 breast cancer cell lines and in two PR positive clones of the MCF-10A cell line: AB9 and AB32.

Publication Title

Non-overlapping progesterone receptor cistromes contribute to cell-specific transcriptional outcomes.

Sample Metadata Fields

Specimen part, Cell line

View Samples
accession-icon GSE73037
Cross-species Gene Expression Analysis Identifies a Novel Set of Genes Implicated in Human Insulin Sensitivity
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Cross-species gene expression analysis identifies a novel set of genes implicated in human insulin sensitivity.

Sample Metadata Fields

Specimen part, Time

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accession-icon GSE28260
Renal cortex and medulla microRNA and mRNA expression differences between hypertensive and normotensive patients
  • organism-icon Homo sapiens
  • sample-icon 22 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Gene expression profiling reveals renin mRNA overexpression in human hypertensive kidneys and a role for microRNAs.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE106324
Oxidative stress time-course in adipocytes
  • organism-icon Mus musculus
  • sample-icon 32 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.1 ST Array (mogene21st)

Description

3T3-L1 adipocytes were treated inhibitors against the glutathione and thioredoxin cycling pools for several time-points (2-24 h).

Publication Title

The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes.

Sample Metadata Fields

Cell line, Treatment

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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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Developed by the Childhood Cancer Data Lab

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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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