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accession-icon GSE17718
Expression data from CD4-positive HTLV-positive cell lines and from CD4-positive HTLV-negative primary cells.
  • organism-icon Homo sapiens
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

We used microarrays to compare the global programme of gene expression in HTLV-positive, ATL-derived and HTLV-positive in vitro-transformed cell lines with that of uninfected primary CD4 T cells.

Publication Title

Elevated cyclic AMP levels in T lymphocytes transformed by human T-cell lymphotropic virus type 1.

Sample Metadata Fields

Specimen part

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accession-icon GSE10508
Transcriptome analysis of HTLV-1 Tax-transformed T cell line Tesi in absence and presence of Tax
  • organism-icon Homo sapiens
  • sample-icon 3 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The Tesi system allows analysis of HTLV-1 Tax's impact on the transcriptome of a human CD4+ T-cell which is not derived from leukemia but directly from normal human lymphocytes. By comparing cells with and without Tax, one can specifically filter for celluar genes that are either activated or repressed in the presence of Tax.

Publication Title

Strong induction of 4-1BB, a growth and survival promoting costimulatory receptor, in HTLV-1-infected cultured and patients' T cells by the viral Tax oncoprotein.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP031477
Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation
  • organism-icon Drosophila melanogaster
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer IIx

Description

Genome-wide transcriptome analyses have allowed for systems- level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post-transcriptional gene regulation and its effects on protein complex stoichiometry are lagging behind. Here, we employ deep sequencing and iTRAQ technology to determine transcript and protein expression changes of a Drosophila brain tumour model at near genome-wide resolution. In total, we quantify more than 6,200 tissue-specific proteins, corresponding to about 70% of all transcribed protein-coding genes. Using our integrated data set, we demonstrate that post-transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein-protein interaction data and show that post-transcriptional mechanisms significantly enhance co-regulation of protein complex subunits beyond transcriptional co-regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co-regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analysing the co-regulation of potential subunits. Our comprehensive transcriptome and proteome data provide a rich resource for quantitative biology and offer novel insights into understanding post- transcriptional gene regulation in a tumour model. Overall design: Transcriptomes of 1-3 day old adult female Drosophila melanogaster heads of control and brat mutant were generated by deep sequencing, in triplicate, using Illumina GAIIx.

Publication Title

Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation.

Sample Metadata Fields

Subject

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accession-icon E-TABM-544
Transcription profiling of yeast mutants to determine gene regulation by sterol and sphingolipid composition
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 31 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

determination of gene regulation by sterol and sphingolipid composition

Publication Title

Functional interactions between sphingolipids and sterols in biological membranes regulating cell physiology.

Sample Metadata Fields

Sex

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accession-icon GSE102722
Organotypic three-dimensional cancer cell cultures mirror drug responses in vivo: Lessons learned from the inhibition of EGFR signaling
  • organism-icon Homo sapiens
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)

Description

Complex three-dimensional (3D) in vitro model systems that recapitulate human tumor biology are essential to better understand the pathophysiology of the disease and to aid in the discovery of novel anti-cancer therapies. 3D organotypic cultures exhibit intercellula communication, nutrient and oxygen gradients, and cell polarity that is lacking in traditional two-dimensional (2D) monolayer cultures. In the present study, we could demonstrate that 2D and 3D cancer models exhibit different drug sensitivities towards both targeted inhibitors of EGFR signaling and broad acting cytotoxic agents. Changes in the kinase activities of Erb family members and differential expression of apoptosis- and survival-associated genes before and after drug treatment may account for the differential drug sensitivities. Importantly, EGFR oncoprotein addiction was evident only in the 3D cultures mirroring the effect of EGFR inhibition in the clinic. Furthermore, targeted drug efficacy was strongly increased when incorporating cancer-associated fibroblasts into the 3D cultures. Taken together, we could provide conclusive evidence that complex 3D cultures are more predictive of the clinical outcome than their 2D counterparts. In the future, 3D cultures will be instrumental for understanding the mode of action of drugs, identifying genotype-drug response relationships and developing patient-specific and personalized cancer treatments.

Publication Title

Organotypic three-dimensional cancer cell cultures mirror drug responses <i>in vivo</i>: lessons learned from the inhibition of EGFR signaling.

Sample Metadata Fields

Cell line

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accession-icon GSE86575
MicroRNA-196b-5p is a prognostic factor in colorectal cancer patients and influences cancer cell migration and metastases formation through regulation of HOXB7 and GalNT5
  • organism-icon Homo sapiens
  • sample-icon 3 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

Background: MicroRNA-196b-5p (miR-196b-5p) has been previously involved in carcinogenesis, though its role in colorectal cancer (CRC) patients and biology remains controversially. In our current study, we systematically explored the clinical significance and biological relevance of miR-196b-5p, as well as the underlying molecular mechanisms regulated by miR-196b-5p in colorectal cancer.

Publication Title

miR-196b-5p Regulates Colorectal Cancer Cell Migration and Metastases through Interaction with HOXB7 and GALNT5.

Sample Metadata Fields

Cell line

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accession-icon SRP120487
Trnascriptome analysis of HeLa cells infected with rTHOV-wt, -dML, -SW mutant or mock-treated
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

The goal of the study was to compare transcriptome changes in HeLa cells after infection with recombinant Thogoto virus (wild-type, ML deletioin mutant or ML SW mutant not able to interact wiith TFIIB. While wild-type virus is able to inhibit inflammatory genes, ML deletion mutant and TFIIB-non-interacting mutant lose this effect on gene transcription. Overall design: Examination of transcriptome changes in HeLa cells under steady state or after THOV infection using Illumina HiSeq.

Publication Title

Viral targeting of TFIIB impairs de novo polymerase II recruitment and affects antiviral immunity.

Sample Metadata Fields

Cell line, Subject

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accession-icon GSE87431
H19 Noncoding RNA, an independent prognostic factor, regulates essential Rb-E2F and CDK8/-catenin signaling in colorectal cancer
  • organism-icon Homo sapiens
  • sample-icon 17 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

H19 Noncoding RNA, an Independent Prognostic Factor, Regulates Essential Rb-E2F and CDK8-β-Catenin Signaling in Colorectal Cancer.

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE87430
Expression data from HCT116 cells following H19 knockdown
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

Knockdown of H19 leads to cell cycle arrest, reduced cell proliferation, and reduced cell migration in HCT116 cells.

Publication Title

H19 Noncoding RNA, an Independent Prognostic Factor, Regulates Essential Rb-E2F and CDK8-β-Catenin Signaling in Colorectal Cancer.

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE87429
Expression data from HCT116 cells following CTNNB1 knockdown
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

We used microarrays to detail the global programme of gene expression following CTNNB1 knockdown in HCT116 cells

Publication Title

H19 Noncoding RNA, an Independent Prognostic Factor, Regulates Essential Rb-E2F and CDK8-β-Catenin Signaling in Colorectal Cancer.

Sample Metadata Fields

Cell line, Treatment

View Samples
...

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)

fund-icon Fund the CCDL

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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