Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0376358 (prostate cancer)
 59,338 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The tumor-associated Tn antigen has been investigated extensively as a biomarker and therapeutic target. Cancer vaccines containing the Tn antigen as a single tumor antigen or as a component of a polyvalent vaccine have progressed into phase I and II clinical trials. One major focus of Tn-based vaccines is the treatment of prostate cancer patients. Although expression of the antigen on prostate tumors is a critical prerequisite, previous reports investigating Tn expression in prostate tumors have produced conflicting results. Using a combination of immunohistochemistry and carbohydrate microarray profiling, we show that only 4% to 26% of prostate tumors express the Tn antigen. Based on our results, the majority of prostate cancer patients do not express the appropriate antigen. Therefore, efforts to preselect the subset of prostate cancer patients with Tn-positive tumors or apply Tn vaccines to other cancers with higher rates of antigen expression could significantly improve clinical response rates. Because conflicting information on carbohydrate expression is a general problem for the field, the approach described in this article of analyzing antigen expression with multiple antibodies and using carbohydrate microarray profiles to interpret the results will be useful for the development of other carbohydrate-based cancer vaccines and diagnostics.
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PMID:Resolving conflicting data on expression of the Tn antigen and implications for clinical trials with cancer vaccines. 1937 70

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.
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PMID:Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods. 2322 54