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accession-icon SRP006719
ChimeraScan: A tool for identifying chimeric transcription in sequencing data
  • organism-icon Homo sapiens
  • sample-icon 3 Downloadable Samples
  • Technology Badge IconIlluminaGenomeAnalyzerII

Description

Next Generation Sequencing technologies have enabled de novo gene fusion discovery that could reveal candidates with therapeutic significance in cancer. Here we present an open-source software package, ChimeraScan, for the discovery of chimeric transcription between two independent transcripts. Overall design: Three cancer cell lines with known gene fusions

Publication Title

ChimeraScan: a tool for identifying chimeric transcription in sequencing data.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE3542
Profiling of MCF-7 cell lines stably overexpressing (ca)Raf-1, (ca)MEK, (ca)erbB-2, or ligand-activatable EGFR.
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Profiling of MCF-7 cell lines stably overexpressing constitutively active Raf-1, constitutively active MEK, constitutively active c-erbB-2, or ligand-activatable EGFR as models of overexpressed growth factor signaling, as well as control vector transfected cells (coMCF-7) and control vector transfected cells long-term adapted for estrogen-independent growth (coMCF-7/lt-E2).

Publication Title

Activation of mitogen-activated protein kinase in estrogen receptor alpha-positive breast cancer cells in vitro induces an in vivo molecular phenotype of estrogen receptor alpha-negative human breast tumors.

Sample Metadata Fields

Cell line

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accession-icon GSE7868
Expression data from LNCaP cell line
  • organism-icon Homo sapiens
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Androgen receptor (AR) is a ligand-dependent transcription factor that plays a key role in the onset and progression of prostate cancer. Surprisingly little is known of AR binding sites and collaborating transcription factors in the human genome. Here we have identified the DNA sequence motifs that are significantly enriched within the authentic 90 AR target regions found on chromosomes 21 and 22 in human prostate cancer cells by combining chromatin immunoprecipitation for AR with chromosome-scale tiled oligonucleotide microarrays. By integrating the DNA sequence motif data with the gene expression profiles from human prostate cancers we identified the transcription factors that recognize each of these motifs. These factors form complexes with AR, bind to specific AR target regions and govern androgen-dependent transcription. Together with AR these collaborating transcription factors form a regulatory network that directs prostate cancer growth and survival and identify potential new opportunities for therapeutic intervention.

Publication Title

A hierarchical network of transcription factors governs androgen receptor-dependent prostate cancer growth.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE17044
Expression data from androgen treated LNCaP cells
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Androgens are required for the development of normal prostate, and they are also linked to the development of prostate cancer.

Publication Title

Proteomic interrogation of androgen action in prostate cancer cells reveals roles of aminoacyl tRNA synthetases.

Sample Metadata Fields

Specimen part, Cell line

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accession-icon SRP051102
Comparison of poly(A) and capture RNA-seq: controlled degradation in vitro
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

We compare the performance of two library preparation protocols (poly(A) and exome capture) in in vitro degraded RNA samples Overall design: VcaP cell were grown, and treated with MDV3100 (enzalutamide) or DHT (dihydrotestosterone), intact RNA was isolated and samples were prepared in technical triplicates using two library preparation protocol. Also cells were subject to in vitro degradation through incubation of the whole cell lysate in 37C for increasing amounts of time. Following incbation paired capture and poly(A) libraries were prepared.

Publication Title

The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE28340
Expression data from mouse dendritic cells
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Dendritic cells (DCs) are pivotal for both recognition of antigens and control of an array of immune responses by recognizing microbes through distinct pattern recognition receptors (PRRs). The first microbial component to be studied in detail and known to cause septic shock is endotoxin (LPS). DCs recognize LPS via Toll-like receptor TLR-47. LPS causes many changes in the DCs, but the elicitation of cytokine production is perhaps the one with clear biologic relevance.

Publication Title

Targeting of microRNA-142-3p in dendritic cells regulates endotoxin-induced mortality.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon GSE3325
Integrative Genomic and Proteomic Analysis of Prostate Cancer Reveals Signatures of Metastatic Progression
  • organism-icon Homo sapiens
  • sample-icon 19 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed revealing only 48-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors.

Publication Title

Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP099450
Differentially expressed genes between SPOPmut and control in mouse prostate organoids
  • organism-icon Mus musculus
  • sample-icon 34 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

To provide further insight to the signaling pathways deregulated by SPOP mutation and determine the relevance of these models to human prostate cancer, we performed RNA-seq on SPOP mutant organoids and controls. RNA-seq reads mapped to human and mouse SPOP confirmed appropriate expression of the F133V transgenic transcript without overexpression compared to endogenous mouse Spop. Quantification of gene expression was performed via RSEQtools using GENCODE as reference gene–annotation set. Both SPOPmut and SPOPwt were done in the same run. S0 was done in first run; S1 and S2 were done in second run. S3, S4 and S5 were done in third run. S5mut and S5wt were excluded from differentially expressed genes analysis, due to the different mouse line. Overall design: Differentially expressed genes between mouse SPOPmut organoids and control by RNA-seq.

Publication Title

SPOP Mutation Drives Prostate Tumorigenesis In Vivo through Coordinate Regulation of PI3K/mTOR and AR Signaling.

Sample Metadata Fields

Specimen part, Subject

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accession-icon SRP004637
Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression (RNA-Seq data)
  • organism-icon Homo sapiens
  • sample-icon 58 Downloadable Samples
  • Technology Badge IconIlluminaGenomeAnalyzerII, IlluminaGenomeAnalyzer

Description

Noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease and to reveal uncharacterized aspects of tumor biology. Here we discover 121 unannotated prostate cancer–associated ncRNA transcripts (PCATs) by ab initio assembly of high-throughput sequencing of polyA+ RNA (RNA-Seq) from a cohort of 102 prostate tissues and cells lines. We characterized one ncRNA, PCAT-1, as a prostate-specific regulator of cell proliferation and show that it is a target of the polycomb repressive complex 2 (PRC2). We further found that patterns of PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1–repressed target genes. Taken together, our findings suggest that PCAT-1 is a transcriptional repressor implicated in a subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes. Overall design: 21 prostate cell lines sequenced on the Illumina Genome Analyzer and GAII. Variable number of replicates per sample. RNA-Seq data from prostate cancer tissues used in this study will be made available on dbGAP.

Publication Title

Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE11428
Expression data from LNCaP and abl cells
  • organism-icon Homo sapiens
  • sample-icon 20 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Androgen receptor (AR) is a ligand-dependent transcription factor that plays a key role in the onset and progression of prostate cancer. We investigated AR-induced gene expression in prostate cancer cells LNCaP and abl by transfecting siAR / siControl or treating cells with androgen (DHT) over a time course.

Publication Title

Androgen receptor regulates a distinct transcription program in androgen-independent prostate cancer.

Sample Metadata Fields

No sample metadata fields

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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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