Identification of atherosclerosis-related prioritizing metabolites based on a multi-omics composite network.

Metabolites are the final products of cellular regulation processes, their level is the ultimate response of biological systems to environmental and genetic changes. Therefore, the identification of key metabolites is required for the diagnosis and therapy of diseases. In this study, atherosclerosis-related gene expression profile information was extracted from ArrayExpress database (GEOD-57691), and analyzed with limma package. Furthermore, we constructed an intricate multi-omics network involved in genes, phenotypes, metabolites and their associations. To identify the prioritization of atherosclerosis-related metabolites, the relation score of each metabolite in the composite network was computed with the random walk with restart (RWR) method. The top 50 metabolites and top 100 genes were chosen based on the score in the weighted composite network. Consequently, several key metabolites that were ranked in the top 5 of relation score or degree greater than 70 were confirmed. Particularly, metabolites Tretinoin and Estraderm not only have high relation scores, but also contain more degrees. Moreover, we obtained 24 co-expression genes that may be regarded as the targets of atherosclerosis therapy. Therefore, identification of metabolite prioritizations by the composite network integrated the information of genes, phenotypes and metabolites may be available to diagnose atherosclerosis, and can provide the potential therapeutic strategies for atherosclerosis.