Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network

Sci Rep. 2015 Nov 24;5:17201. doi: 10.1038/srep17201.

Abstract

The identification of disease-related metabolites is important for a better understanding of metabolite pathological processes in order to improve human medicine. Metabolites, which are the terminal products of cellular regulatory process, can be affected by multi-omic processes. In this work, we propose a powerful method, MetPriCNet, to predict and prioritize disease candidate metabolites based on integrated multi-omics information. MetPriCNet prioritized candidate metabolites based on their global distance similarity with seed nodes in a composite network, which integrated multi-omics information from the genome, phenome, metabolome and interactome. After performing cross-validation on 87 phenotypes with a total of 602 metabolites, MetPriCNet achieved a high AUC value of up to 0.918. We also assessed the performance of MetPriCNet on 18 disease classes and found that 4 disease classes achieved an AUC value over 0.95. Notably, MetPriCNet can also predict disease metabolites without known disease metabolite knowledge. Some new high-risk metabolites of breast cancer were predicted, although there is a lack of known disease metabolite information. A predicted disease metabolic landscape was constructed and analyzed based on the results of MetPriCNet for 87 phenotypes to help us understand the genetic and metabolic mechanism of disease from a global view.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biomarkers, Tumor / metabolism*
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / metabolism*
  • Female
  • Gene Regulatory Networks
  • Humans
  • Male
  • Metabolic Networks and Pathways
  • Metabolome
  • Phenotype
  • Prostatic Neoplasms / diagnosis
  • Prostatic Neoplasms / metabolism*
  • ROC Curve
  • Risk
  • Software

Substances

  • Biomarkers, Tumor