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Logo of bmcgenoBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Genomics
 
BMC Genomics. 2009; 10: 137.
Published online Mar 30, 2009. doi:  10.1186/1471-2164-10-137
PMCID: PMC2676301
A signature-based method for indexing cell cycle phase distribution from microarray profiles
Hideaki Mizuno,corresponding author1,2 Yoshito Nakanishi,1 Nobuya Ishii,1 Akinori Sarai,2 and Kunio Kitada1
1Kamakura Research Laboratories, Chugai Pharmaceutical Co Ltd, Kamakura, Kanagawa, Japan
2Department of Biosciences and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
corresponding authorCorresponding author.
Hideaki Mizuno: mizunohda/at/chugai-pharm.co.jp; Yoshito Nakanishi: nakanishiyst/at/chugai-pharm.co.jp; Nobuya Ishii: ishiinby/at/chugai-pharm.co.jp; Akinori Sarai: sarai/at/bse.kyutech.ac.jp; Kunio Kitada: kitadakno/at/chugai-pharm.co.jp
Received October 21, 2008; Accepted March 30, 2009.
Abstract
Background
The cell cycle machinery interprets oncogenic signals and reflects the biology of cancers. To date, various methods for cell cycle phase estimation such as mitotic index, S phase fraction, and immunohistochemistry have provided valuable information on cancers (e.g. proliferation rate). However, those methods rely on one or few measurements and the scope of the information is limited. There is a need for more systematic cell cycle analysis methods.
Results
We developed a signature-based method for indexing cell cycle phase distribution from microarray profiles under consideration of cycling and non-cycling cells. A cell cycle signature masterset, composed of genes which express preferentially in cycling cells and in a cell cycle-regulated manner, was created to index the proportion of cycling cells in the sample. Cell cycle signature subsets, composed of genes whose expressions peak at specific stages of the cell cycle, were also created to index the proportion of cells in the corresponding stages. The method was validated using cell cycle datasets and quiescence-induced cell datasets. Analyses of a mouse tumor model dataset and human breast cancer datasets revealed variations in the proportion of cycling cells. When the influence of non-cycling cells was taken into account, "buried" cell cycle phase distributions were depicted that were oncogenic-event specific in the mouse tumor model dataset and were associated with patients' prognosis in the human breast cancer datasets.
Conclusion
The signature-based cell cycle analysis method presented in this report, would potentially be of value for cancer characterization and diagnostics.
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