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accession-icon GSE27083
Expression data from MMTV-PDK1 transgenic mice
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon

Description

The role of PDK1 on mammary tumorigenesis and its interaction with PPARdelta, was assessed. Transgenic mice were generated in which PDK1 was expressed in the mammary epithelium.

Publication Title

PPARĪ“ activation acts cooperatively with 3-phosphoinositide-dependent protein kinase-1 to enhance mammary tumorigenesis.

Sample Metadata Fields

Specimen part, Treatment

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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

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accession-icon GSE34723
Gene Expression Commons: an open platform for absolute gene expression profiling
  • organism-icon Mus musculus
  • sample-icon 1 Downloadable Sample
  • Technology Badge Icon

Description

Gene expression profiling using microarray has been limited to profiling of differentially expressed genes at comparison setting since probesets for different genes have different sensitivities. We overcome this limitation by using a very large number of varied microarray datasets as a common reference, so that statistical attributes of each probeset, such as dynamic range or a threshold between low and high expression can be reliably discovered through meta-analysis. This strategy is implemented in web-based platform named Gene Expression Commons (http://gexc.stanford.edu/ ) with datasets of 39 distinct highly purified mouse hematopoietic stem/progenitor/functional cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, any scientist can explore gene expression of any gene, search by expression pattern of interest, submit their own microarray datasets, and design their own working models.

Publication Title

Gene Expression Commons: an open platform for absolute gene expression profiling.

Sample Metadata Fields

Sex, Age

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accession-icon GSE27563
Expression data from murine PBCs from mice with advanced mammary tumors and their tumor-free counterparts.
  • organism-icon Mus musculus
  • sample-icon 93 Downloadable Samples
  • Technology Badge Icon

Description

Female MMTV/c-MYC transgenic mice expressed the c-MYC proto-oncogene or a more stable point mutation variant (T58A) of the gene under the control of the hormone-responsive MMTV long terminal repeat (LTR) in an FVB/NJ background (Jackson Laboratories, Bar Harbor, ME). The hormones released during pregnancy and lactation have been shown to enhance expression of the oncogene. Thus, the mice were maintained in a continuous breeding program. Mice were monitored twice weekly for tumor development by palpation and tumors were measured twice weekly. Once the tumors reached 3cm3 the animals were sacrificed and tissue was obtained to confirm the tumors by histological analysis. As a control, female mice of the same age and background strain were maintained in the same facility and under the same breeding conditions as their transgenic counterparts.

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 GSE12413
Prediction of left ventricle systolic dysfunction in mice using gene expression profiling
  • organism-icon Mus musculus
  • sample-icon 86 Downloadable Samples
  • Technology Badge Icon

Description

We tested the hypothesis that a set of differentially expressed genes could be used to predict cardiovascular phenotype in mice after prolonged catecholamine stress.

Publication Title

Gene expression profiling: classification of mice with left ventricle systolic dysfunction using microarray analysis.

Sample Metadata Fields

No sample metadata fields

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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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