PrognoScan: a new database for meta-analysis of the prognostic value of genes

In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene ex...

Full description

Saved in:
Bibliographic Details
Published in:BMC medical genomics 2009-04, Vol.2 (1), p.18-18, Article 18
Main Authors: Mizuno, Hideaki, Kitada, Kunio, Nakai, Kenta, Sarai, Akinori
Format: Article
Language:English
Subjects:
Cancer
DNA microarrays
Gene expression
Genetic aspects
Online databases
Prognosis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform. To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets. PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html.
ISSN:1755-8794
1755-8794
DOI:10.1186/1755-8794-2-18