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

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      1 , 2 , , 1 , 3 , 2
      BMC Medical Genomics
      BioMed Central

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          Abstract

          Background

          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.

          Description

          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.

          Conclusion

          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.

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          Most cited references46

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          Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.

          Previously undescribed prognostic subclasses of high-grade astrocytoma are identified and discovered to resemble stages in neurogenesis. One tumor class displaying neuronal lineage markers shows longer survival, while two tumor classes enriched for neural stem cell markers display equally short survival. Poor prognosis subclasses exhibit markers either of proliferation or of angiogenesis and mesenchyme. Upon recurrence, tumors frequently shift toward the mesenchymal subclass. Chromosomal locations of genes distinguishing tumor subclass parallel DNA copy number differences between subclasses. Functional relevance of tumor subtype molecular signatures is suggested by the ability of cell line signatures to predict neurosphere growth. A robust two-gene prognostic model utilizing PTEN and DLL3 expression suggests that Akt and Notch signaling are hallmarks of poor prognosis versus better prognosis gliomas, respectively.
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            Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

            The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
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              Gene-expression profiles predict survival of patients with lung adenocarcinoma.

              Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.
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                Author and article information

                Journal
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central
                1755-8794
                2009
                24 April 2009
                : 2
                : 18
                Affiliations
                [1 ]Pharmaceutical Technology Department, Kamakura Research Laboratories, Chugai Pharmaceutical Co Ltd, Kamakura, Kanagawa, Japan
                [2 ]Department of Biosciences and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
                [3 ]Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan
                Article
                1755-8794-2-18
                10.1186/1755-8794-2-18
                2689870
                19393097
                695a48cd-2837-475b-b44b-b28224b5d00f
                Copyright © 2009 Mizuno et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 December 2008
                : 24 April 2009
                Categories
                Database

                Genetics
                Genetics

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