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accession-icon GSE85033
A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

Analysis of gene expression in pathologically confirmed glioblastoma (GBM) samples. These data were used to test a classifier that was generated to distinguish GBM tumor samples with loss of neurofibromin 1 (NF1) function

Publication Title

A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE9630
Expression data from mouse liver
  • organism-icon Mus musculus
  • sample-icon 59 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Exposure to high levels of arsenic in drinking water is associated with several types of cancers including lung, bladder and skin, as well as vascular disease and diabetes. Drinking water standards are based primarily on epidemiology and extrapolation from higher dose experiments, rather than measurements of phenotypic changes associated with chronic exposure to levels of arsenic similar to the current standard of 10ppb, and little is known about the difference between arsenic in food as opposed to arsenic in water. Measurement of phenotypic changes at low doses may be confounded by the effect of laboratory diet, in part because of trace amounts of arsenic in standard laboratory chows, but also because of broad metabolic changes in response to the chow itself. Finally, this series contrasts 8hr, 1mg/kg injected arsenic with the various chronic exposures, and also contrasts the acute effects of arsenic, dexamethasone or their combination. Male C57BL/6 mice were fed on two commercially available laboratory diets (LRD-5001 and AIN-76A) were chronically exposed, through drinking water or food, to environmentally relevant concentrations of sodium arsenite, or acutely exposed to dexamethasone.

Publication Title

Laboratory diet profoundly alters gene expression and confounds genomic analysis in mouse liver and lung.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11056
Expression data from mouse lung
  • organism-icon Mus musculus
  • sample-icon 55 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Exposure to high levels of arsenic in drinking water is associated with several types of cancers including lung, bladder and skin, as well as vascular disease and diabetes. Drinking water standards are based primarily on epidemiology and extrapolation from higher dose experiments, rather than measurements of phenotypic changes associated with chronic exposure to levels of arsenic similar to the current standard of 10ppb, and little is known about the difference between arsenic in food as opposed to arsenic in water. Measurement of phenotypic changes at low doses may be confounded by the effect of laboratory diet, in part because of trace amounts of arsenic in standard laboratory chows, but also because of broad metabolic changes in response to the chow itself. Finally, this series contrasts 8hr, 1mg/kg injected arsenic with the various chronic exposures, and also contrasts the acute effects of arsenic, dexamethasone or their combination. Male C57BL/6 mice were fed on two commercially available laboratory diets (LRD-5001 and AIN-76A) were chronically exposed, through drinking water or food, to environmentally relevant concentrations of sodium arsenite, or acutely exposed to dexamethasone.

Publication Title

Chronic exposure to arsenic in the drinking water alters the expression of immune response genes in mouse lung.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE79387
Novel Pulmonary Imaging Biomarkers and Cutaneous Gene Expression Subsetting for Patient Selection and Outcome Assessment in the Dasatinib Treatment of Systemic Sclerosis-associated Interstitial Lung Disease
  • organism-icon Homo sapiens
  • sample-icon 45 Downloadable Samples
  • Technology Badge Icon Affymetrix HT Human Genome U133A Array (hthgu133a)

Description

There are no effective treatments or clinical response markers for systemic sclerosis (SSc). We sought to assess the potential of novel imaging biomarkers and gene expression profiling approaches in a clinical trial of the tyrosine kinase inhibitor dasatinib in SSc patients with interstitial lung disease (SSc-ILD).

Publication Title

Novel lung imaging biomarkers and skin gene expression subsetting in dasatinib treatment of systemic sclerosis-associated interstitial lung disease.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE45744
Whole-genome expression data from normal FVB mouse lung tissue, transgenic cyclin E overexpressing (CEO) normal mouse lung tissue, and transgenic CEO lung adenocarcinomas
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

FVB mice were engineered to express wild-type human cyclin E under control of the human surfactant C promoter (CEO mice; Ma et al, PNAS 2007). These mice develop spontaneous lung tumors, which were shown to be adenocarcinoma by histological analysis. Here we compare whole-genome RNA expression levels between the tumors and normal lung of 4 CEO mice as well as 4 nontransgenic animals.

Publication Title

Evidence for tankyrases as antineoplastic targets in lung cancer.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE67006
Expression data from Pseudomonas aeruginosa wild type and anr grown as biofilms on F508 cystic fibrosis bronchial epithelial cells (CFBEs)
  • organism-icon Pseudomonas aeruginosa
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Pseudomonas aeruginosa Array (paeg1a)

Description

The transcription factor Anr regulates the response to low oxygen in P. aeruginosa and is inhibited by oxygen. We used microarrys to compare gene expression in P. aeruginosa PAO1 wild-type with an isogenic anr mutant in order to determine which transcripts are affected by Anr. We grew P. aeruginosa cells as biofilms on CFBE cells in order to model cystic fibrosis airways infections.

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE67038
Genomic DNA hybridization of Pseudomonas aeruginosa strains PAO1 and PA14
  • organism-icon Pseudomonas aeruginosa
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix Pseudomonas aeruginosa Array (paeg1a)

Description

Genomic DNA from Pseudomonas aeruginosa strains PAO1 and PA14

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE103022
Microarray of S100-v-erbB/p53-/- cells cultured in normoxia, hypoxia, hypoxia with Brefeldin A (0.1uM) and hypoxia EHT-1864 (1uM).
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Clariom S Array (clariomsmouse)

Description

High-grade gliomas (HGGs) include the most common and the most aggressive primary brain tumor of adults and children. Despite multimodality treatment, most high grade gliomas eventually recur and are ultimately incurable. Several studies suggest that the initiation, progression, and recurrence of gliomas are driven, at least partly, by cancer stem-like cells. A defining characteristic of these cancer stem-like cells is their capacity to self-renew. We have identified a hypoxia-induced pathway that utilizes the Hypoxia Inducible Factor 1 (HIF-1) transcription factor and the JAK1/2-STAT3 (Janus Kinase 1/2 - Signal Transducer and Activator of Transcription 3) axis to enhance the self-renewal of glioma stem-like cells. Hypoxia is a commonly found pathologic feature of HGGs. Under hypoxic conditions, HIF-1 levels are greatly increased in glioma stem-like cells. Increased HIF-1 activates the JAK1/2-STAT3 axis and enhances tumor stem-like cell self-renewal. Our data further demonstrate the importance of Vascular Endothelial Growth Factor (VEGF) secretion for this pathway of hypoxia-mediated self-renewal. Brefeldin A and EHT-1864, agents that significantly inhibit VEGF secretion, decreased stem cell self-renewal, inhibited tumor growth, and increased the survival of mice allografted with S100-v-erbB/p53-/- glioma stem-like cells. These agents also inhibit the expression of a hypoxia gene expression signature that is associated with decreased survival of HGG patients. These findings suggest that targeting the secretion of extracellular, autocrine/paracrine mediators of glioma stem-like cell self-renewal could potentially contribute to the treatment of HGG.

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP178159
Clinical study of human mesenchymal stem cells on the treatment of severe liver disease
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 4000

Description

we aimed to explore the potential therapeutic effects of human mesenchymal stem cell on severe liver disease

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part, Cell line

View Samples
accession-icon SRP131607
Compare RNA expression of Old Fibroblast to RNA expression of Young Fbroblast
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

Analyze of RNA expression of Old Fibroblast and Young Fibroblast. Compare RNA expression of Old Fibroblast to RNA expression of Young Fbroblast

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part

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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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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