Statistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice

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• 作者：Mattias Rantalainen ; Olivier Cloarec ; Olaf Beckonert ; I. D. Wilson ; David Jackson ; Robert Tonge ; Rachel Rowlinson ; Steve Rayner ; Janice Nickson ; Robert W. Wilkinson ; Jonathan D. Mills ; Johan Trygg ; Je
• 刊名：Journal of Proteome Research
• 出版年：2006
• 出版时间：October 2006
• 年：2006
• 卷：5
• 期：10
• 页码：2642 - 2655
• 全文大小：1959K
• 年卷期：v.5,no.10(October 2006)
• ISSN：1535-3907

文摘

A novel statistically integrated proteometabonomic method has been developed and applied to a humantumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and ¹H NMR metabolicprofile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograftand from matched control animals. To interpret the xenograft-induced differences in plasma profiles,multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were appliedto generate models characterizing the disease profile. Two approaches to integrating metabonomicdata matrices are presented based on OPLS algorithms to provide a framework for generating modelsrelating to the specific and common sources of variation in the metabolite concentrations and proteinabundances that can be directly related to the disease model. Multiple correlations between metabolitesand proteins were found, including associations between serotransferrin precursor and both tyrosineand 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine andincreased presence of gelsolin was also observed. This approach can provide enhanced recovery ofcombination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of invivo model systems studied by multiple omic technologies