title: MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC 20057. United States.
authors.forename: Henri
authors.initials: H
authors.lastname: Wathieu
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC 20057. United States.
authors.forename: Naiem T
authors.initials: NT
authors.lastname: Issa
authors.affiliation: Department of Biochemistry & Molecular Biology, Georgetown University, Washington DC 20057. United States.
authors.forename: Manisha
authors.initials: M
authors.lastname: Mohandoss
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC 20057. United States.
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Byers
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC 20057. United States.
authors.forename: Sivanesan
authors.initials: S
authors.lastname: Dakshanamurthy
mesh_terms: D015179:Colorectal Neoplasms / Q000378:metabolism; D019295:Computational Biology; D016208:Databases, Factual; D004938:Esophageal Neoplasms / Q000378:metabolism; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D055432:Metabolomics / Q000379:methods*; D009369:Neoplasms / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000378:metabolism; D049490:Systems Biology
keywords: Biomarker; MSD-MAP; colorectal cancer; esophageal cancer; gene expression analysis; metabolomics; prostate
  cancer; systems biology