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accession-icon GSE21656
Expression profiling of cisplatin resistant cells derived from H460 lung cell line.
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Combination of platinum-based chemotherapy and radiation is currently the standard treatment for locally advanced lung cancer patients. However, therapeutic resistance to these therapies may arise from the presence of cancer stem cells (CSCs). To investigate the CSCs hypothesis of chemo-radiation resistance, we used microarray assay to profile CSCs-like cisplatin-resistant lung cancer cells (CDDP-R) versus its parental cells. CDDP-R cells were established by exposing H460 lung cancer cells to 3M cisplatin for 7 days, followed by 0.8% methylcellulose selection over 14 consecutive days.We found that CDDP-R cells expressed higher levels of stem cell markers, including CD133 and ALDH. They are more resistant to cisplatin- and etoposide-induced apoptosis and to high radiation dose (20Gy). Clonogenic assays suggest that CDDP-R cells were more resistant to radiation than parental H460 cells (DER=1.21, p<0.01). Xenograft studies suggest that CDDP-R cells were more tumorigenic (p<0.001). Microarray and comprehensive protein interaction networks analyses revealed IGFBP3 as a highly ranked hub protein which plays an important role in the mechanism of cisplatin resistance. We found reduced level of IGFBP3 and enhanced IGFR-1 activation upon IGF stimulation in CDDP-R cells. The specific targeting of IGF-1R using siRNA resulted in significant sensitization of CDDP-cells (DER=1.17, p<0.05) to radiation compared with the parental H460 cells. Our findings suggest that CDDP-R cells have the characteristics of CSCs and constitute a suitable model to study lung CSCs. Profiling of CSCs-like H460 cells led to the identification of IGF as an important pathway for chemo- and radiotherapy resistance in lung cancer.

Publication Title

Role of insulin-like growth factor-1 signaling pathway in cisplatin-resistant lung cancer cells.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE12000
Obesity study in transgenic and knockout animals
  • organism-icon Mus musculus
  • sample-icon 48 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID), Rosetta/Merck Mouse TOE 75k Array 1 microarray

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11999
Lactb male transgenic liver expression vs FVB male wildtype control
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11996
Gas7 male transgenic liver expression vs FVB male wildtype control
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11998
Gyk female heterozygous liver expression vs C57Bl/6J female wildtype control
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11997
Gpx3 male transgenic liver expression vs B6/DBA male wildtype control
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE78958
Effect of obesity on molecular characteristics of invasive breast tumors: gene expression analysis of 405 tumors by BMI
  • organism-icon Homo sapiens
  • sample-icon 424 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

Background: Obesity is a risk factor for breast cancer in postmenopausal women and is associated with decreased survival and less favorable clinical characteristics such as greater tumor burden, higher grade, and poor prognosis, regardless of menopausal status. Despite the negative impact of obesity on clinical outcome, molecular mechanisms through which excess adiposity influences breast cancer etiology are not well-defined.

Publication Title

Effect of obesity on molecular characteristics of invasive breast tumors: gene expression analysis in a large cohort of female patients.

Sample Metadata Fields

Disease stage

View Samples
accession-icon SRP097793
Genome wide identification of gene sets involved in the regulation of hypothalamic pubertal development
  • organism-icon Rattus norvegicus
  • sample-icon 20 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Gene expression analysis of hypothalami from female animals at different juvenile developmental reproductive stages. Results provide insight into the role of the hypothalamus in controlling the onset of puberty. Overall design: SD rats were housed (8/cage) in a controlled environment and euthanized at different ages (PND=7, PND=14, Early Juvenile: 21 days, Late Juvenile: 28 days, Late Proestus (the day of first ovulation): 30-33 days. Rats were anesthetized and brains were rapidly removed. The medial basal hypothalamus (MBH) was dissected away from the rest of the brain and flash frozen. Total RNA was isolated from each sample using Qiagen''s RNeasy Mini Kit (Valencia, CA). Samples were bioanalyzed on a RNA 6000 Nano chip kit to check for integrity and concentration before sending it to OHSU''s Massively Parallel Sequencing Shared Resource for library preparation and sequencing.

Publication Title

Trithorax dependent changes in chromatin landscape at enhancer and promoter regions drive female puberty.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE10595
Interaction of bone marrow stroma and monocytes: bone marrow stromal cell lines cultured with monocytes
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The hematopoietic microenvironment consists of non-hematopoietic derived stromal elements and hematopoietic derived monocytes and macrophages which interact and function together to control the proliferation and differentiation of early blood-forming cells. Two human stromal cell lines (HS-5 and HS-27a) representing distinct functional components of this microenvironment have been extensively characterized and shown to influence monocyte gene expression. This series of gene expression profiles is intended to extend the previous studies and identify which gene expression changes may require cell-cell contact or occur in the stromal cells as a result of monocyte influence;or in the monocytes as a result of stormal influences.

Publication Title

Functionally and phenotypically distinct subpopulations of marrow stromal cells are fibroblast in origin and induce different fates in peripheral blood monocytes.

Sample Metadata Fields

Sex

View Samples
accession-icon GSE9390
Interaction of bone marrow stroma and monocytes
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The bone marrow microenvironment is a complex mixture of cells that function in concert to regulate hematopoiesis. Cellular components include fixed nonhematopoietic stromal elements as well as monocytes and resident macrophages, which are derived from the hematopoietic stem cells. Although these monocyte-lineage cells are reported to modify stromal cell function, the reverse also occurs. Given the secretory capability of the monocyte/macrophage and their various potential functions, it is not surprising that stromal cells contained within a particular niche can modify monocyte gene expression and functional maturation.

Publication Title

Functionally and phenotypically distinct subpopulations of marrow stromal cells are fibroblast in origin and induce different fates in peripheral blood monocytes.

Sample Metadata Fields

Sex

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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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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