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Integrated metabolome and transcriptome analysis of the NCI60 dataset by Gang Su; Charles F Burant; Christopher W Beecher; Brian D Athey; Fan Meng is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Background Metabolite profiles can be used for identifying molecular signatures and mechanisms underlying diseases since they reflect the outcome of complex upstream genomic, transcriptomic, proteomic and environmental events. The scarcity of publicly accessible large scale metabolome datasets related to human disease has been a major obstacle for assessing the potential of metabolites as biomarkers as well as understanding the molecular events underlying disease-related metabolic changes. The availability of metabolite and gene expression profiles for the NCI-60 cell lines offers the possibility of identifying significant metabolome and transcriptome features and discovering unique molecular processes related to different cancer types. ## Methods We utilized a combination of analytical methods in the R statistical package to evaluate metabolic features associated with cancer cell lines from different tissue origins, identify metabolite-gene correlations and detect outliers cell lines based on metabolome and transcriptome data. Statistical analysis results are integrated with metabolic pathway annotations as well as COSMIC and Tumorscape databases to explore associated molecular

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Author

Gang Su; Charles F Burant; Christopher W Beecher; Brian D Athey; Fan Meng

Publisher

BioMed Central; Springer (Biomed Central Ltd.); [London]: BioMed Central, [2000]-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1471-2105)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)