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accession-icon SRP181605
Identifying Tumor Progression by Genome-Wide Characterization of Immature Myeloid Cells In the Peripheral Blood
  • organism-icon Mus musculus
  • sample-icon 23 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

characterize the molecular signature of PB-IMC in different stages of tumor development, thus possibly leading to a novel, sensitive and elegant approach for early cancer detection and surveillance. Overall design: Two types of cancer. For each type 4 groups (day 0, day 4, day 8, day 11), for each group 3 biological repeats

Publication Title

The transcriptional profile of circulating myeloid derived suppressor cells correlates with tumor development and progression in mouse.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE44285
Atxn1L is a novel regulator of Hematopoietic Stem Cell Quiescence
  • organism-icon Mus musculus
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We compared gene expression differences in Atxn1L knockout vs wildtype HSCs

Publication Title

Ataxin1L is a regulator of HSC function highlighting the utility of cross-tissue comparisons for gene discovery.

Sample Metadata Fields

Specimen part

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accession-icon GSE145974
Global transcriptome analysis identifies a signature for early disseminated Lyme disease and its resolution
  • organism-icon Homo sapiens
  • sample-icon 86 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U219 Array (hgu219)

Description

Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (Acute LD) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels six months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly acurate in distinvuishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infecitons. This computational approach offers the potential for more accurate diagnosis of early dissminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.

Publication Title

Global Transcriptome Analysis Identifies a Diagnostic Signature for Early Disseminated Lyme Disease and Its Resolution.

Sample Metadata Fields

Disease, Disease stage

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accession-icon GSE38681
Lyl-1 knockout vs wildtype Lymphoid Primed Multipotent Progenitors (LMPPs)
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We compared gene expression differences in Lyl-1 knockout vs wildtype LMPPs

Publication Title

The transcription factor Lyl-1 regulates lymphoid specification and the maintenance of early T lineage progenitors.

Sample Metadata Fields

Specimen part

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accession-icon GSE24870
Gene expression profiling of CD34+ subsets in Multiple Myeloma and healthy individuals
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

Multiple myeloma (MM) is a clonal plasma cell disorder frequently accompanied by hematopoietic impairment. Genomic profiling of distinct HSPC subsets revealed a consistent deregulation of signaling cascades, including TGF beta signaling, p38MAPK signaling and pathways involved in cytoskeletal organization, migration, adhesion and cell cycle regulation in MM patients.

Publication Title

Multiple myeloma-related deregulation of bone marrow-derived CD34(+) hematopoietic stem and progenitor cells.

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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