Title:MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links
Volume: 20
Issue: 3
Author(s): Henri Wathieu, Naiem T. Issa, Manisha Mohandoss, Stephen W. Byers and Sivanesan Dakshanamurthy*
Affiliation:
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC 20057,United States
Keywords:
Biomarker, colorectal cancer, esophageal cancer, gene expression analysis, metabolomics, MSD-MAP, prostate
cancer, systems biology.
Abstract: Background: Cancer-associated metabolites result from cell-wide mechanisms of
dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease
biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents.
Objective: This study was undertaken to reliably predict metabolites associated with colorectal,
esophageal, and prostate cancers. Metabolite and disease biological action networks were compared
in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association
Platform).
Methods: Using differential gene expression analysis with patient-based RNAseq data from The
Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were
identified. Relational databases were used to map biological entities including pathways, functions,
and interacting proteins, to those differential disease genes. Similar relational maps were built for
metabolites, stemming from known and in silico predicted metabolite-protein associations. The
hypergeometric test was used to find statistically significant relationships between disease and
metabolite biological signatures at each tier, and metabolites were assessed for multi-scale
association with each cancer. Metabolite networks were also directly associated with various other
diseases using a disease functional perturbation database.
Results: Our platform recapitulated metabolite-disease links that have been empirically verified in
the scientific literature, with network-based mapping of jointly-associated biological activity also
matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers,
using metabolite action networks stemming from both predicted and known functional protein
associations.
Conclusion: By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite
links, and may serve as a predictive tool to streamline conventional metabolomic
profiling methodologies.
