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accession-icon GSE43270
Genome wide-DNA methylation analysis of articular chondrocytes reveals a cluster of osteoarthritic patients
  • organism-icon Homo sapiens
  • sample-icon 19 Downloadable Samples
  • Technology Badge IconIllumina HumanMethylation27 BeadChip (HumanMethylation27_270596_v.1.2), Affymetrix Human Gene 1.1 ST Array (hugene11st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Genome-wide DNA methylation analysis of articular chondrocytes reveals a cluster of osteoarthritic patients.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Disease stage

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accession-icon GSE43191
Genome wide-DNA methylation analysis of articular chondrocytes reveals a cluster of osteoarthritic patients (gene expression)
  • organism-icon Homo sapiens
  • sample-icon 19 Downloadable Samples
  • Technology Badge IconIllumina HumanMethylation27 BeadChip (HumanMethylation27_270596_v.1.2), Affymetrix Human Gene 1.1 ST Array (hugene11st)

Description

The aim of this study is to identify, for the first time, the genome-wide DNA methylation profiles of human articular chondrocytes from OA and healtly cartilage samples.

Publication Title

Genome-wide DNA methylation analysis of articular chondrocytes reveals a cluster of osteoarthritic patients.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Disease stage

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accession-icon SRP187300
Disruption of the exocyst induces podocyte loss and dysfunction
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

In this study we plan to compare the profiles of control sample (cultured podocytes) with the Exoc5 knock down in cutured podocytes to examine the differentially expressed genes. Overall design: We hope to identify the genes that are downregulated on knocking down Exoc5 in cultured human podocytes cells

Publication Title

Disruption of the exocyst induces podocyte loss and dysfunction.

Sample Metadata Fields

Subject

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accession-icon GSE21383
Expression data from porcine ovary tissue of sows from two prolificacy levels
  • organism-icon Sus scrofa
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Porcine Genome Array (porcine)

Description

Previous results from a genome scan in a F2 Iberian by Meishan intercross showed several chromosome regions associated with litter size traits. In order to identify candidate genes underlying these QTL we have performed an ovary gene expression analysis during pregnancy. F2 sows were ranked by their estimated breeding values for prolificacy, the six sows with higher EBV (HIGH prolificacy) and the six with lower EBV (LOW prolificacy) were selected. Samples were hybridized to Affymetrix porcine expression microarrays. The statistical analysis with a mixed-model approach identified 221 differentially expressed probes, representing 189 genes. These genes were functionally annotated in order to identify the genetic pathways overrepresented. Among the most represented functional groups the first one was immune system response activation against external stimulus. The second group was made up of genes which regulate the maternal homeostasis by complement and coagulation cascades. The last group was involved on lipid and fatty acid enzymes of metabolic processes, which participate in steroidogenesis pathway. In order to identify powerful candidate genes for prolificacy, the second approach of this study was merging microarray data with position information of QTL affecting litter size, previously detected in the same experimental cross. According to this, we have identified 27 differentially expressed genes co-localized with QTL for litter size traits, which fulfill the biological, positional and functional criteria.

Publication Title

Differential gene expression in ovaries of pregnant pigs with high and low prolificacy levels and identification of candidate genes for litter size.

Sample Metadata Fields

Specimen part

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accession-icon GSE974
Left ventricular apex samples
  • organism-icon Homo sapiens
  • sample-icon 38 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

19 paired human left ventricular apex samples were harvested at the time of implant of a left ventricular assist device (PRE) and at the time of explant (POST). The cohort included patients that were clinically classified as "ischemic" (I) showing evidence of coronary artery disease, "non-ischemic" (N) no evidence of coronary artery disease or "acute Myocardial infarction" (IM) myocardial infarction within 10 days of the implant. Tissue was processed and hybridized to the Affymetrix HG-U133A chip.

Publication Title

Genomic profiling of the human heart before and after mechanical support with a ventricular assist device reveals alterations in vascular signaling networks.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE77540
Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse
  • organism-icon Homo sapiens
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE6691
Gene expression profiling of B lymphocytes and plasma cells from Waldenstrms macroglobulinemia.
  • organism-icon Homo sapiens
  • sample-icon 55 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

The tumoral clone of Waldenstrms macroglobulinemia (WM) shows a wide morphological heterogeneity which ranges from B-lymphocytes (BL) to plasma cells (PC). By means of genome-wide expression profiling we have been able to identify genes exclusively deregulated in BL and PC from WM, but with a similar expression pattern in their corresponding cell-counterparts from CLL and MM, as well as normal individuals. The differentially expressed genes have important functions in B-cell differentiation and oncogenesis. Thus, two of the genes down-regulated in WM-BL were IL4R, which plays a relevant role in CLL B cell survival, and BACH2 that participates in the development of class-switched PC. Interestingly, one of the up-regulated genes in WM-BL was IL6. A set of 4 genes was able to discriminate clonal B-lymphocytes from WM and CLL: LEF1 (WNT/catenin pathway), MARCKS, ATXN1 and FMOD. We also found deregulation of genes involved in plasma cell differentiation such as PAX5 which was overexpressed in WM-PC, and IRF4 and BLIMP1 which were underexpressed. In addition, three of the target genes activated by PAX5 -CD79, BLNK and SYK- were up-regulated in WM-PC. In summary, these results indicate that both PC and BL from WM are genetically different from the MM and CLL cell-counterpart.

Publication Title

Gene expression profiling of B lymphocytes and plasma cells from Waldenström's macroglobulinemia: comparison with expression patterns of the same cell counterparts from chronic lymphocytic leukemia, multiple myeloma and normal individuals.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE1729
Gene expression profile of acute myeloid leukemia
  • organism-icon Homo sapiens
  • sample-icon 43 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Gene expression profile of acute myeloid leukemia.

Publication Title

Gene expression profile reveals deregulation of genes with relevant functions in the different subclasses of acute myeloid leukemia.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE77539
Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse [gene expression]
  • organism-icon Homo sapiens
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Multiple myeloma (MM) remains incurable despite the introduction of novel agents and a relapsing course is observed in the majority of patients. Although the development of genomic technologies has greatly improved our understanding of MM pathogenesis, the mechanisms underlying relapse have been less investigated. In this study, an integrative analysis of DNA copy number, DNA methylation and gene expression was conducted in matched diagnosis and relapse samples from 17 MM patients. Overall, the acquisition of abnormalities at relapse was much more frequent than the lost of lesions present at diagnosis, and DNA losses were significantly more frequent at relapse than in diagnosis samples. Interestingly, copy number abnormalities involving more than 100 Mb of DNA at relapse significantly impact the gene expression of these samples, provoking a particular deregulation of IL-8 pathway. On the contrary, no relevant modifications of gene expression were observed in those samples with less than 100 Mb affected by chromosomal changes. Although different statistical approaches were used to uncover genes whose abnormal expression at relapse was regulated by DNA methylation, only two genes significantly deregulated in relapse samples (SORL1 and GLT1D1) showed a negative methylation-expression correlation. A deeper analysis demonstrated that DNA methylation was involved in regulation of SORL1 expression in MM. Finally, relevant changes in gene expression observed in relapse samples, such us downregulation of CD27 and P2RY8, were not apparently preceded by alterations in corresponding DNA. Taken together, these results showed that genomic heterogeneity, both at the DNA and RNA level, is a hallmark of MM transition from diagnosis to relapse.

Publication Title

Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE16558
MicroRNAs in Myeloma
  • organism-icon Homo sapiens
  • sample-icon 65 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Specific microRNA (miRNA) signatures have been associated with different cytogenetic subtypes in acute leukemias. This finding prompted us to investigate potential associations between genetic abnormalities in multiple myeloma (MM) and singular miRNA expression profiles. Moreover, global gene expression profiling was also analyzed to find correlated miRNA-gene expression and select miRNA target genes that show such correlation. For this purpose, we analyzed the expression level of 365 miRNAs and the gene expression profiling in sixty newly diagnosed MM patients, selected to represent the most relevant recurrent genetic abnormalities. Supervised analysis showed significantly deregulated miRNAs in the different cytogenetic subtypes as compared to normal PC. Interestingly, miR-1 and miR-133a clustered on the same chromosomal loci, were specifically overexpressed in the cases with t(14;16). The analysis of the relationship between miRNA expression and their respective target genes showed a conserved inverse correlation between several miRNAs deregulated in MM cells and CCND2 expression level. These results illustrate, for the first time, that miRNA expression pattern in MM is associated with genetic abnormalities, and that the correlation of the expression profile of miRNA and their putative mRNA targets is useful to find statistically significant protein-coding genes in MM pathogenesis associated to changes in specific miRNAs.

Publication Title

Deregulation of microRNA expression in the different genetic subtypes of multiple myeloma and correlation with gene expression profiling.

Sample Metadata Fields

Specimen part, Disease

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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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