Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

[miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

ABSTRACT: To identify biomarkers predictive of biochemical recurrence, we isolated the RNA from 70 formalin-fixed paraffin-embedded (FFPE) radical prostatectomy specimens with known long term outcome to perform DASL expression profiling with a custom-designed panel of 522 prostate cancer relevant genes that we designed. We identified a panel of ten protein-coding genes and two miRNA genes that could be used to separate patients with and without biochemical recurrence (p < 0.001), as well as for the subset of 42 Gleason score 7 patients (p < 0.001). We performed an independent validation analysis on 40 samples and found that the biomarker panel was also significant at prediction of recurrence for all cases (p = 0.013) and for a subset of 19 Gleason score 7 cases (p = 0.010), both of which were adjusted for relevant clinical information including T-stage, PSA and Gleason score. Importantly, these biomarkers could significantly predict clinical recurrence for Gleason 7 patients. These biomarkers may increase the accuracy of prognostication following radical prostatectomy using formalin-fixed specimens.

ORGANISM(S): Homo sapiens

PROVIDER: GSE26247 | GEO | 2011/06/01

SECONDARY ACCESSION(S): PRJNA142319

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

[Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | GSE26242 | GEO

[Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | GSE26022 | GEO

[miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | GSE26245 | GEO

[miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

Project description:To identify biomarkers predictive of biochemical recurrence, we isolated the RNA from 70 formalin-fixed paraffin-embedded (FFPE) radical prostatectomy specimens with known long term outcome to perform DASL expression profiling with a custom-designed panel of 522 prostate cancer relevant genes that we designed. We identified a panel of ten protein-coding genes and two miRNA genes that could be used to separate patients with and without biochemical recurrence (p < 0.001), as well as for the subset of 42 Gleason score 7 patients (p < 0.001). We performed an independent validation analysis on 40 samples and found that the biomarker panel was also significant at prediction of recurrence for all cases (p = 0.013) and for a subset of 19 Gleason score 7 cases (p = 0.010), both of which were adjusted for relevant clinical information including T-stage, PSA and Gleason score. Importantly, these biomarkers could significantly predict clinical recurrence for Gleason 7 patients. These biomarkers may increase the accuracy of prognostication following radical prostatectomy using formalin-fixed specimens. Total RNA prepared from FFPE cores from prostatectomy samples of 70 patients were used for the training phase (29 with biochemical recurrence and 41 controls). All samples were analyzed by both custom Prostate DASL of 522 genes and by Illumina miRNA microarray. Subsequently in the validation phase, samples from 40 patients were used on the same platforms (13 with biochemical recurrence and 27 controls). For the training set, 45 cases were from Sunnybrook Health Science Center (Toronto, ON), and 25 patients from Emory University. For the validation set, all samples were from Emory University. Relevant clinical metadata included are PSA, T-stage, and Gleason Score.

2011-06-01 | E-GEOD-26245 | biostudies-arrayexpress

[miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | E-GEOD-26247 | biostudies-arrayexpress

[Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | E-GEOD-26242 | biostudies-arrayexpress

[Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | E-GEOD-26022 | biostudies-arrayexpress

RNAseq Analysis of Formalin-Fixed Paraffin-Embedded Prostate Cancer Tissues

Project description:In an effort to identify biomarkers of recurrence, we have performed global RNA-sequencing on 106 formalin-fixed, paraffin-embedded (FFPE) prostatectomy samples from 100 patients at three independent sites, and identified a new set of biomarkers of biochemical recurrence composed of a 24-gene panel. We validated this 24-gene panel on an independent publicly available dataset of 140 patients and this new panel outperformed previously published markers based on cell proliferation gene sets in terms of prediction of biochemical recurrence (BCR). In addition we identified differences in gene expression between Gleason Pattern 4+3 and Gleason Pattern 3+4 tumors. 106 samples from 100 patients were sequenced. Six samples were sequenced from two independent RNA preps and libraries. All samples were taken from FFPE Radical Prostatectomies. Samples were obtained from Atlanta VA Medical Center, U. Toronto Sunnybrook Research Centre, and Moffitt Cancer Center.

2014-04-11 | E-GEOD-54460 | biostudies-arrayexpress

RNAseq Analysis of Formalin-Fixed Paraffin-Embedded Prostate Cancer Tissues

2014-04-11 | GSE54460 | GEO

DNA Methylation Sequencing of Prostate Tumors Reveals Possible Molecular Mechanisms for Cancer Aggressiveness

Project description:A major challenge in the clinical management of prostate cancer is the inability to definitively diagnose indolent versus aggressive cases. Contributing to this challenge is a lack of basic science understanding of the molecular basis behind aggressiveness subtypes in prostate cancer. DNA methylation is the epigenetic addition of a methyl group to the DNA base cytosine and has been found to regulate cell proliferation and environmental adaptation. We hypothesized that DNA methylation changes are a mechanism by which an aggressive cancer attains phenotypes that distinguish it from indolent cases via disruption of regulatory networks. This hypothesis was tested by comparing DNA methylation between benign prostate and both low grade (Gleason score 6) and high grade (Gleason score 8 to 10) groups. Methylome-wide next generation sequencing was performed on formalin-fixed paraffin embedded (FFPE) samples from radical prostatectomy cases using MBD-isolated genome sequencing (MiGS). This technique uses a DNA methylation binding protein (MBD) to purify fragments from a genomic library with a high level of CpG DNA methylation. These fragments were then sequenced via next generation sequencing, the reads were aligned to a reference genome, and then the reads were counted within non-overlapping 50bp windows genome wide. Statistical analysis was then performed on these windowed counts to produce differentially methylated regions (DMRs). MBD-isolated Genome Sequencing (MiGS) for groups of benign prostate (from cystoprostatectomy), low grade prostate cancer (from radical prostatectomy with Gleason Score 6), and high grade prostate cancer (from radical prostatectomy with Gleason Scores 8 to 10) in both European Americans and African Americans

2015-11-01 | E-GEOD-66505 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data