Prioritizing Candidate Disease Metabolites Based on Global Functional Relationships between Metabolites in the Context of Metabolic Pathways

Abstract

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 http://bioinfo.hrbmu.edu.cn/PROFANCY.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

“…We also compared both systems to PROFANCY [5]. PROFANCY has recall of 0.31, a mean ranking of 20.9%, and a median ranking of 16.5%.…”

“…The de novo prediction system MetabolitePredict is different from existing computation-based metabolite prediction systems [5,6], which identify disease metabolites based on known disease-associated metabolites and cannot perform predictions for diseases without known metabolites. Though we demonstrated that MetabolitePredict performs better than PROFANCY in prioritizing RA-associated metabolites, the de novo prediction system has its inherent limitation since it ignores our existing knowlege of disease-associated metabolites.…”

“…MetabolitePredict complements current clinical sample-based metabolomics studies: current human metabolomics characterize clinically significant metabolite profiles from patient samples; MetabolitePredict contextualizes disease metabolite biomarker discovery with vast amounts of existing system-level genetic and molecular data. MetabolitePredict is also different from existing computation-based metabolite prediction systems, including PROFANCY [5] and MetPriCNet [6], which identify additional disease metabolites based on known disease-associated metabolites, therefore cannot perform predictions for diseases without known metabolites. MetabolitePredict is a de novo prediction system that can predict metabolite biomarkers for any diseases without the need of known disease-associated metabolites.…”

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

“…To evaluate the advantage of our strategy of integrating multi-omics information, we compared MetPriCNet with random walk with restart on the metabolite network only (PROFANCY) 9 . After performing leave-one-out cross-validation, we found that MetPriCNet could achieve an AUC value up to 0.918, which is higher than that of PROFANCY (0.903) ( Fig.…”

“…If some information is not available, the integration strategy could use other information to compensate for the missing information. We previously developed a method (PROFANCY) to prioritize disease metabolites based on the context of the metabolite network (or metabolite pathways) 9 . However, some other omics interactions (for example, phenotype-gene interactions, gene-gene interactions and gene-metabolite interactions) and known disease information have been largely ignored.…”