Abstract
BackgroundIn 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.DescriptionTo 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.ConclusionPrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at .
Highlights
In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and can motivate further studies
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
Summary
The association between a gene and clinical outcome suggests the underlying etiology of the disease and can motivate further studies. Many cancer microarray datasets with clinical annotation have become available in the public domain and provide vast opportunities to link gene expression to prognosis. We developed "PrognoScan", a database featuring a large collection of publicly available cancer microarray datasets with clinical annotation and a tool for assessing the relationship between gene expression and prognosis using the minimum P-value approach.
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