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accession-icon GSE27628
Expression data from affected skin from psoriasis mouse models and normal skin from control mice
  • organism-icon Mus musculus
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon

Description

Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis.

Publication Title

Genome-wide expression profiling of five mouse models identifies similarities and differences with human psoriasis.

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Specimen part

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accession-icon GSE33253
Transcriptional reprogramming of tumor-associated endothelial cells by disruption of TNF- signaling
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon

Description

Endothelial inflammation contributes to the pathogenesis of numerous human diseases; however, the role of tumor endothelial inflammation in the growth of experimental tumors and its influence on the prognosis of human cancers is less understood. TNF-, an important mediator of tumor stromal inflammation, is known to target the tumor vasculature. In this study, we demonstrate that B16-F1 melanomas grew more rapidly in C57BL/6 wild-type (WT) mice than in syngeneic mice with germline deletions of both TNF- receptors (KO). This enhanced tumor growth was associated with increased COX2 inflammatory expression in WT tumor endothelium compared to endothelium in KO mice. We purified endothelial cells from WT and KO tumors and characterized dysregulated gene expression, which ultimately formed the basis of a 6-gene Inflammation-Related Endothelial-derived Gene (IREG) signature. This inflammatory signature expressed in WT tumor endothelial cells was trained in human cancer datasets and predicted a poor clinical outcome in breast cancer, colon cancer, lung cancer and glioma. Consistent with this observation, conditioned media from human endothelial cells treated with pro-inflammatory cytokines (TNF- and interferons) accelerated the growth of human colon and breast tumors in immune-deprived mice as compared with conditioned media from untreated endothelial cells. These findings demonstrate that activation of endothelial inflammatory pathways contributes to tumor growth and progression in diverse human cancers.

Publication Title

Tumor endothelial inflammation predicts clinical outcome in diverse human cancers.

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Specimen part

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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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