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accession-icon GSE34559
Tie-2 expressing monocytes (TEM) expression data
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

TEM differentiated in vitro were exposed to treatments increasing or decreasing their proangiogenic activity. We used microarrays to identify the genes differentially expressed among the treatments and associated to changes in TEM proangiogenic and protumoral functions.

Publication Title

TIE-2 and VEGFR kinase activities drive immunosuppressive function of TIE-2-expressing monocytes in human breast tumors.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon SRP065763
IFN-kappa inhibits HPV31 transcription by inducing Sp100 proteins
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIlluminaGenomeAnalyzerIIx

Description

Using doxycycline-inducible IFN-kappa expression in CIN612-9E cells, which maintain extrachromosomally replicating HPV31 genomes, we demonstrate that IFN-kappa inhibits the growth of these cells and reduces viral transcription and replication. Interestingly, the initiation of viral early transcription was already inhibited 4-6h after IFN-kappa expression. This was also observed with recombinant IFN-beta suggesting a common mechanism of IFNs. RNA-seq analysis identified 1367 IFN-kappa regulated genes of which 221 were modulated >2-fold. The majority of those (71%) matched known ISGs confirming that IFN-kappa acts as a bona fide type I IFN in hr-HPV-positive keratinocytes. RNAi and co-transfection experiments indicate that the inhibition of viral transcription is mainly due to the induction of Sp100 proteins by IFN-kappa. Overall design: CIN612-9E/pInd-IFN-kappa were induced for 4h with 1µg/ml doxycyclin or not. Three biological replicates were analyzed.

Publication Title

Interferon Kappa Inhibits Human Papillomavirus 31 Transcription by Inducing Sp100 Proteins.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE75132
TMEM45A, SERPINB5 and p16INK4A transcript levels are predictive for development of high-grade cervical lesions
  • organism-icon Homo sapiens
  • sample-icon 38 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Women persistently infected with human papillomavirus (HPV) type 16 are at high risk for development of cervical intraepithelial neoplasia grade 3 or cervical cancer (CIN3+). We aimed to identify biomarkers for progression to CIN3+ in women with persistent HPV16 infection. In this prospective study, 11,088 women aged 2029 years were enrolled during 1991-1993, and re-invited for a second visit two years later. Cervical cytology samples obtained at both visits were tested for HPV DNA by Hybrid Capture 2 (HC2), and HC2-positive samples were genotyped by INNO-LiPA. The cohort was followed for up to 19 years via a national pathology register. To identify markers for progression to CIN3+, we performed microarray analysis on RNA extracted from cervical swabs of 30 women with persistent HPV16-infection and 11 HPV-negative women. After further validation, we found that high mRNA expression levels of TMEM45A, SERPINB5 and p16INK4a were associated with increased risk of CIN3+ in persistently HPV16-infected women.

Publication Title

TMEM45A, SERPINB5 and p16INK4A transcript levels are predictive for development of high-grade cervical lesions.

Sample Metadata Fields

Specimen part, Disease, Disease stage

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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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