Literature DB >> 11507037

Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer.

J B Welsh1, L M Sapinoso, A I Su, S G Kern, J Wang-Rodriguez, C A Moskaluk, H F Frierson, G M Hampton.   

Abstract

Detection, treatment, and prediction of outcome for men with prostate cancer increasingly depend on a molecular understanding of tumor development and behavior. We characterized primary prostate cancer by monitoring expression levels of more than 8900 genes in normal and malignant tissues. Patterns of gene expression across tissues revealed a precise distinction between normal and tumor samples, and revealed a striking group of about 400 genes that were overexpressed in tumor tissues. We ranked these genes according to their differential expression in normal and cancer tissues by selecting for highly and specifically overexpressed genes in the majority of cancers with correspondingly low or absent expression in normal tissues. Several such genes were identified that act within a variety of biochemical pathways and encode secreted molecules with diagnostic potential, such as the secreted macrophage inhibitory cytokine, MIC-1. Other genes, such as fatty acid synthase, encode enzymes known as drug targets in other contexts, which suggests new therapeutic approaches.

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Year:  2001        PMID: 11507037

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  244 in total

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4.  Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer.

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5.  Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

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Review 7.  Novel translational strategies in colorectal cancer research.

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Journal:  Am J Pathol       Date:  2009-01-29       Impact factor: 4.307

9.  Collagen-binding proteoglycan fibromodulin can determine stroma matrix structure and fluid balance in experimental carcinoma.

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Journal:  Proc Natl Acad Sci U S A       Date:  2007-08-22       Impact factor: 11.205

10.  Systematic analysis and validation of differential gene expression in ovarian serous adenocarcinomas and normal ovary.

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Journal:  J Cancer Res Clin Oncol       Date:  2012-10-23       Impact factor: 4.553

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