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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 867289, 15 pages
http://dx.doi.org/10.1155/2014/867289
Research Article

Systematic Analysis of Time-Series Gene Expression Data on Tumor Cell-Selective Apoptotic Responses to HDAC Inhibitors

1National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China
2School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
3Department of General Internal Medicine, First Hospital of Jilin University, Changchun 130021, China
4Research Center of Agriculture and Medicine Gene Engineering of Ministry of Education, Northeast Normal University, Changchun 130024, China

Received 13 June 2014; Accepted 7 August 2014; Published 13 October 2014

Academic Editor: Hsueh-Fen Juan

Copyright © 2014 Yun-feng Qi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

SAHA (suberoylanilide hydroxamic acid or vorinostat) is the first nonselective histone deacetylase (HDAC) inhibitor approved by the US Food and Drug Administration (FDA). SAHA affects histone acetylation in chromatin and a variety of nonhistone substrates, thus influencing many cellular processes. In particularly, SAHA induces selective apoptosis of tumor cells, although the mechanism is not well understood. A series of microarray experiments was recently conducted to investigate tumor cell-selective proapoptotic transcriptional responses induced by SAHA. Based on that gene expression time series, we propose a novel framework for detailed analysis of the mechanism of tumor cell apoptosis selectively induced by SAHA. Our analyses indicated that SAHA selectively disrupted the DNA damage response, cell cycle, p53 expression, and mitochondrial integrity of tumor samples to induce selective tumor cell apoptosis. Our results suggest a possible regulation network. Our research extends the existing research.