Development of a gene expression database and related analysis programs for evaluation of anticancer compounds. Issue 3 (4th January 2013)

• Record Type: Journal Article
• Authors: Ushijima, Masaru; Mashima, Tetsuo; Tomida, Akihiro; Dan, Shingo; Saito, Sakae; Furuno, Aki; Tsukahara, Satomi; Seimiya, Hiroyuki; Yamori, Takao; Matsuura, Masaaki
• Is Part Of: Cancer science. Volume 104:Issue 3(2013:Mar.)
• Page Start: 360
• Page End: 368
• Publication Date: 2013-01-04
• Subjects: Cancer -- Periodicals; Neoplasms -- Periodicals; Research -- Periodicals; Electronic journals; 616.994005
• DOI: 10.1111/cas.12071
• Languages: English
• ISSNs: 1347-9032

Abstract

<abstract abstract-type="main" id="cas12071-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Genome‐wide transcriptional expression analysis is a powerful strategy for characterizing the biological activity of anticancer compounds. It is often instructive to identify gene sets involved in the activity of a given drug compound for comparison with different compounds. Currently, however, there is no comprehensive gene expression database and related application system that is; (i) specialized in anticancer agents; (ii) easy to use; and (iii) open to the public. To develop a public gene expression database of antitumor agents, we first examined gene expression profiles in human cancer cells after exposure to 35 compounds including 25 clinically used anticancer agents. Gene signatures were extracted that were classified as upregulated or downregulated after exposure to the drug. Hierarchical clustering showed that drugs with similar mechanisms of action, such as genotoxic drugs, were clustered. Connectivity map analysis further revealed that our gene signature data reflected modes of action of the respective agents. Together with the database, we developed analysis programs that calculate scores for ranking changes in gene expression and for searching statistically significant pathways from the Kyoto Encyclopedia of Genes and Genomes database in order to analyze the datasets more easily. Our database and the analysis programs are available online at our website (http://scads.jfcr.or.jp/db/cs/). Using these systems, we successfully showed that proteasome inhibitors are selectively classified as endoplasmic reticulum stress inducers and induce atypical endoplasmic reticulum stress. Thus, our public access database and related analysis programs constitute a set of efficient tools to evaluate the mode of action of novel compounds and identify promising anticancer lead compounds.</p> </abstract>