This article is part of the supplement: Selected articles from the Ninth Asia Pacific Bioinformatics Conference (APBC 2011)
Research
Integrated metabolome and transcriptome analysis of the NCI60 dataset
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* Corresponding authors: Gang Su sugang@umich.edu - Fan Meng mengf@umich.edu
1 Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA
2 University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109, USA
3 Michigan Center for Translational Pathology, University of Michigan, Ann Arbor MI 48109, USA
4 National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA
5 Psychiatry Department and Molecular Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
BMC Bioinformatics 2011, 12(Suppl 1):S36 doi:10.1186/1471-2105-12-S1-S36
Published: 15 February 2011Abstract
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 mechanisms.
Results
Our analysis reveals that although the NCI-60 metabolome dataset is quite noisy comparing with microarray-based transcriptome data, it does contain tissue origin specific signatures. We also identified biologically meaningful gene-metabolite associations. Most remarkably, several abnormal gene-metabolite relationships identified by our approach can be directly linked to known gene mutations and copy number variations in the corresponding cell lines.
Conclusions
Our results suggest that integrative metabolome and transcriptome analysis is a powerful method for understanding molecular machinery underlying various pathophysiological processes. We expect the availability of large scale metabolome data in the coming years will significantly promote the discovery of novel biomarkers, which will in turn improve the understanding of molecular mechanism underlying diseases.