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accession-icon GSE102089
Evf2 enhancer long non-coding RNA regulation of gene expression in embryonic mouse brain
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge Icon

Description

The Evf2 long non-coding RNA regulates genes adjacent and overlapping genes (Dlx5 and Dlx6), but it was not known if long range gene regulation occurs. In this study, we find that Evf2 regulates genes across a 27Mb region on mouse chromosome 6, and additional genes outside of mosue chromosome 6.

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE11862
Early Gene expression changes after axonal injury
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon

Description

We used optic nerve injury as a model to study early signaling events in the neuronal soma following axonal injury. Optic nerve injury results in the selective death of retinal ganglion cells (RGCs). The time course of cell death takes place over a period of days with the earliest detection of RGC death at about 48 hr post injury. We hypothesized that in the period immediately following axonal injury, there are changes in the soma that signal surrounding glia and neurons and that start programmed cell death. In the current study, we investigated early changes in cellular signaling and gene expression that occur within the first 6 hrs post optic nerve injury. We detected differences in phosphoproteins and gene expression within this time period that we used to interpret temporal events. Our studies revealed that the entire retina has been signaled by the RGC soma within 30 min after optic nerve injury and that pathways that modulate cell death are likely to be active in RGCs within 6 hrs following axonal injury

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE10965
Comparison of the transcriptional profiles of the retinal pigmental epithelium/choroid from young and old mice
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
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Description

To characterize underlying changes in the retinal pigment epithelium (RPE)/choroid with age, we produced gene expression profiles for the RPE/choroid and compared the transcriptional profiles of the RPE/choroid from young and old mice. The changes in the aged RPE/choroid suggest that the tissue has become immunologically active. Such phenotypic changes in the normal aged RPE/choroid may provide a background for the development of age-related macular degeneration.

Publication Title

The aged retinal pigment epithelium/choroid: a potential substratum for the pathogenesis of age-related macular degeneration.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE21154
Gene array data for Fas knock-out human cancer cell line and mouse liver tissue
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 7 Downloadable Samples
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Description

CD95 (also called FAS and APO-1) is a prototypical death receptor that

Publication Title

CD95 promotes tumour growth.

Sample Metadata Fields

Sex, Age, Specimen part, Cell line

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accession-icon GSE72088
Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity
  • organism-icon Mus musculus
  • sample-icon 54 Downloadable Samples
  • Technology Badge Icon

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity.

Sample Metadata Fields

Specimen part, Compound

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accession-icon GSE27567
Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 94 Downloadable Samples
  • Technology Badge Icon

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors.

Sample Metadata Fields

Specimen part

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accession-icon GSE17538
Experimentally Derived Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 231 Downloadable Samples
  • Technology Badge Icon

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer.

Sample Metadata Fields

Sex, Age, Disease stage, Race

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accession-icon GSE72081
Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity (mRNA)
  • organism-icon Mus musculus
  • sample-icon 54 Downloadable Samples
  • Technology Badge Icon

Description

The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.

Publication Title

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity.

Sample Metadata Fields

Specimen part, Compound

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accession-icon GSE57132
Evaluating mRNA and microRNA profiles reveals discriminative and compound-specific responses following genotoxic or non-genotoxic carcinogen exposure in primary mouse hepatocytes
  • organism-icon Mus musculus
  • sample-icon 57 Downloadable Samples
  • Technology Badge Icon

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Evaluating microRNA profiles reveals discriminative responses following genotoxic or non-genotoxic carcinogen exposure in primary mouse hepatocytes.

Sample Metadata Fields

Specimen part, Compound

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accession-icon GSE11128
Expression data from single cells from mouse primordial germ cell lineage (E6.25-E8.25, wild type and Blimp1KO)
  • organism-icon Mus musculus
  • sample-icon 106 Downloadable Samples
  • Technology Badge Icon

Description

Specification of germ cell fate is fundamental in development. With a highly representative single-cell microarray and rigorous quantitative-PCR analysis, we defined the genome-wide transcription dynamics that create primordial germ cells (PGCs) from the epiblast, a process that exclusively segregates them from their somatic neighbors. We also analyzed the effect of the loss of Blimp1, a key transcriptional regulator, on these dynamics. Our analysis revealed that PGC specification involves complex, yet highly ordered regulation of a large number of genes, proceeding under the strong influence of mesoderm induction with active repression of specific programs such as epithelial-mesenchymal transition, Hox gene activation, cell-cycle progression and DNA methyltransferase machinery. Remarkably, Blimp1 is essential for repressing nearly all the genes normally down-regulated in PGCs relative to their somatic neighbors, whereas it is dispensable for the activation of approximately half of the genes up-regulated in PGCs.

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

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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