Prioritizing candidate disease metabolites based on global functional relationships between metabolites in the context of metabolic pathways

PLoS One. 2014 Aug 25;9(8):e104934. doi: 10.1371/journal.pone.0104934. eCollection 2014.


Identification of key metabolites for complex diseases is a challenging task in today's medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer's disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Alzheimer Disease / metabolism*
  • Computer Simulation
  • Humans
  • Male
  • Metabolic Networks and Pathways*
  • Metabolomics
  • Prostatic Neoplasms / metabolism*
  • Protein Interaction Mapping
  • Software*

Grant support

This work was supported in part by the Funds for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 81121003), the National Program on Key Basic Research Project (Grant No. 2014CB910504), the National Natural Science Foundation of China (Grant Nos. 61170154, 61073136 and 31200996), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant Nos. 20102307120027 and 20102307110022). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.