• Journal of Microbiology and Biotechnology
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• v.32 no.3
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• pp.387-395
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• 2022

Deciphering the metabolites of human diseases is an important objective of biomedical research. Here, we aimed to capture the core metabolites of Fanconi anemia (FA) using the bioinformatics method of a multi-omics composite network. Based on the assumption that metabolite levels can directly mirror the physiological state of the human body, we used a multi-omics composite network that integrates six types of interactions in humans (gene-gene, disease phenotype-phenotype, disease-related metabolite-metabolite, gene-phenotype, gene-metabolite, and metabolite-phenotype) to procure the core metabolites of FA. This method is applicable in predicting and prioritizing disease candidate metabolites and is effective in a network without known disease metabolites. In this report, we first singled out the differentially expressed genes upon different groups that were related with FA and then constructed the multi-omics composite network of FA by integrating the aforementioned six networks. Ultimately, we utilized random walk with restart (RWR) to screen the prioritized candidate metabolites of FA, and meanwhile the co-expression gene network of FA was also obtained. As a result, the top 5 metabolites of FA were tenormin (TN), guanosine 5'-triphosphate, guanosine 5'-diphosphate, triphosadenine (DCF) and adenosine 5'-diphosphate, all of which were reported to have a direct or indirect relationship with FA. Furthermore, the top 5 co-expressed genes were CASP3, BCL2, HSPD1, RAF1 and MMP9. By prioritizing the metabolites, the multi-omics composite network may provide us with additional indicators closely linked to FA.