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accession-icon GSE48391
Concurrent Gene Signatures for Han Chinese Breast Cancers
  • organism-icon Homo sapiens
  • sample-icon 81 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Concurrent gene signatures for han chinese breast cancers.

Sample Metadata Fields

Specimen part

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accession-icon GSE48390
Concurrent Gene Signatures for Han Chinese Breast Cancers [Affymetrix]
  • organism-icon Homo sapiens
  • sample-icon 81 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival. Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer.

Publication Title

Concurrent gene signatures for han chinese breast cancers.

Sample Metadata Fields

Specimen part

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accession-icon GSE73617
Modulation of reversibility by DNA Methyltransferases during hepatogenic differentiation in Mesenchymal Stromal Cells
  • organism-icon Mus musculus
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Mesenchymal stromal cells (MSCs) hold great promise in the field of liver regenerative medicine. However, the mechanisms and reversibility of hepatogenic differentiation in MSCs are poorly understood. Here, we demonstrate that hepatogenic differentiation of MSCs is a reversible process and is modulated by the transforming growth factor beta 1- DNA methyltransferases (TGF-1-Dnmts) axis. Dnmt1 and Dnmt3a differentially regulate hepatogenic differentiation and de-differentiation in response to the alternation of TGF-1 concentration. Knockdown of Dnmt1 accelerates the hepatogenic differentiation in MSCs-derived hepatocyte-like cells (dHeps) whereas Knockdown of Dnmt3a represses hepatogenic differentiation. Conclusions: Our finding first demonstrates that epigenetic regulation by Dnmts in response to stimulation from the surrounding microenvironment controls the reversibility of hepatogenic differentiation in MSCs. Manipulation of Dnmts provides a rapid and efficient differentiation protocol to generate functional dHeps from MSCs that may provide clinical potential for regenerative medicine.

Publication Title

DNA Methyltransferases Modulate Hepatogenic Lineage Plasticity of Mesenchymal Stromal Cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE16567
Genome-wide transcriptome analysis of two maize inbred lines under drought stress during the seedling stage
  • organism-icon Zea mays
  • sample-icon 23 Downloadable Samples
  • Technology Badge Icon Affymetrix Maize Genome Array (maize)

Description

To understand the transcriptome changes during drought tolerance in maize, the drought-tolerant line Han21 and drought-sensitive line Ye478, which show substantial differences in drought tolerance at the seedling stage, were selected for this study. Using the GeneChip Maize Genome Arrays, we applied genome-wide gene expression analysis to the two genotypes under gradual drought stress and re-watering. We identified 2172 common regulated transcripts in both lines under drought stress, with 1084 common up-regulated transcripts and 1088 common down-regulated transcripts. Among the 2172 transcripts, 58 potential protein kinases and 117 potential transcription factors were identified. The potential components of the ABA signaling pathway were identified from the common regulated transcripts. We also identified 940 differentially regulated transcripts between the two lines. Among the 940 transcripts, the differential expression levels of 29 transporters and 15 cell wall-related transcripts may contribute to the different tolerances of the two lines. Additionally, we found that the drought-responsive genes in the tolerant Han21 line recovered more quickly when the seedlings were re-watered, and 311 transcripts in the tolerant Han21 line were exclusively up-regulated at the re-watering stage compared to the control and stress conditions. Our study provides a global characterization of two maize inbred lines during drought stress and re-watering and will be valuable for further study of the molecular mechanisms of drought tolerance in maize.

Publication Title

Genome-wide transcriptome analysis of two maize inbred lines under drought stress.

Sample Metadata Fields

Specimen part

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accession-icon GSE44104
COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer.
  • organism-icon Homo sapiens
  • sample-icon 55 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Biomarkers that predict disease progression might assist the development of better therapeutic strategies for aggressive cancers, such as ovarian cancer. Here, we investigated the role of collagen type XI alpha 1 (COL11A1) in cell invasiveness and tumor formation and the prognostic impact of COL11A1 expression in ovarian cancer. Microarray analysis suggested that COL11A1 is a disease progression-associated gene that is linked to ovarian cancer recurrence and poor survival.

Publication Title

COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer.

Sample Metadata Fields

Specimen part

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accession-icon GSE46892
Generating mouse model with predominant nave or innate memory phenotype CD4+ T cells
  • organism-icon Mus musculus
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Innate memory phenotype (IMP) CD4+ T cells are non-conventional T cells exhibiting features of innate immune cells, characterized as CD44high and CD62Llow in periphery. It is recently reported by our group that bone marrow chimeric mice lacking thymic MHCI expression develop predominantly IMP CD8+ T cells, while those lacking hematopoietic MHCI develop predominantly nave CD8+ T cells. Here we perform hirarchical clustering analysis and found that CD4+ T cells share similar property: chimeras lacking thymic MHCII gave rise to predominantly CD4+ T cells that resemble IMP CD4+ T cells observed in WT mice, and vice versa, chimeras lacking hematopoietic MHCII had a majority of nave-like CD4+ T cells resembling naveCD4+ T cells seen in WT mice.

Publication Title

Dendritic cell-MHC class II and Itk regulate functional development of regulatory innate memory CD4+ T cells in bone marrow transplantation.

Sample Metadata Fields

Age, Specimen part

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accession-icon GSE154789
Gene expression profiles in SATB2-knockdown and control human glioblastoma stem cells
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

Effects of SATB2 knockdown on gene expression were evaluated by microarray analysis in human glioblastoma stem cells

Publication Title

SATB2 drives glioblastoma growth by recruiting CBP to promote FOXM1 expression in glioma stem cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE42091
Mutant TDP-43 in Astrocytes Kills Motor Neurons in Rats through Neurotoxic Gain and Neuroprotective Loss in Astrocytes
  • organism-icon Rattus norvegicus
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina ratRef-12 v1.0 expression beadchip

Description

Mutation in TDP-43 is causative to amyotrophic lateral sclerosis (ALS). TDP-43 is a multifunctional ribonucleoprotein and is reproted to regulate thousands of genes in neurons, but how astrocytes contribute to TDP-43 pathogenesis is not known. This study examined how mutant TDP-43 in astrocytes kills motor neurons and causes ALS phenotypes. Primary astrocytes were isolated from transgenic rats expressing mutant TDP-43 or from control rats without mutant TDP-43 expression. Cultured astrocytes were induced to express mutant human TDP-43 and their gene expression profiles were determined by microarray assays. Microarray analysis revealed that hundreds of genes were altered in astrocytes in response to mutant TDP-43 expression.

Publication Title

Expression of ALS-linked TDP-43 mutant in astrocytes causes non-cell-autonomous motor neuron death in rats.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE35603
Network Biology of Tumor Stem-like Cells Identified a Regulatory Role of CBX5 in Lung Cancer
  • organism-icon Homo sapiens
  • sample-icon 74 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Mounting evidence points to a link between a cancer possessing stem-like properties and a worse prognosis. To understand the biology, a common approach is to integrate network biology with signal processing mechanics. That said, even with the right tools, predicting the risk for a highly susceptible target using only a handful of gene signatures remains very difficult. By compiling the expression profiles of a panel of tumor stem-like cells (TSLCs) originating in different tissues, comparing these to their parental tumor cells (PTCs) and the human embryonic stem cells (hESCs), and integrating network analysis with signaling mechanics, we propose that network topologically-weighted signaling processing measurements under tissue-specific conditions can provide scalable and predicable target identification.

Publication Title

Network biology of tumor stem-like cells identified a regulatory role of CBX5 in lung cancer.

Sample Metadata Fields

Specimen part

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accession-icon GSE40051
Microarray analysis of U87 glioma cells after transient knockdown of FOXM1
  • organism-icon Homo sapiens
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

FOXM1 plays a key role in M phase in normal cells and is overexpressed in human glioma. We found that FOXM1 deprivation could sensitize the glioma cells to TMZ chemotherapy. To find out the mechanistic regulation of FOXM1 in chemo-resistant genes, we used microarrays to select the potential genes regulated by FOXM1 which dominates in glioma chemo-resistance.

Publication Title

FoxM1 inhibition sensitizes resistant glioblastoma cells to temozolomide by downregulating the expression of DNA-repair gene Rad51.

Sample Metadata Fields

Specimen part, Cell line

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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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