Acute myeloid leukemia (AML) is a common, complex, and multifactorial malignancy of the hematopoietic system. AML diagnosis and treatment outcomes display marked heterogeneity and patient-to-patient variations. To date, AML-related biomarker discovery research has employed single omics inquiries. Multiomics analyses that reconcile and integrate the data streams from multiple levels of the cellular hierarchy, from genes to proteins to metabolites, offer much promise for innovation in AML diagnostics and therapeutics. We report, in this study, a systems medicine and multiomics approach to integrate the AML transcriptome data and reporter biomolecules at the RNA, protein, and metabolite levels using genome-scale biological networks. We utilized two independent transcriptome datasets (GSE5122, GSE8970) in the Gene Expression Omnibus database. We identified new multiomics molecular signatures of relevance to AML: miRNAs (e.g., mir-484 and miR-519d-3p), receptors (ACVR1 and PTPRG), transcription factors (PRDM14 and GATA3), and metabolites (in particular, amino acid derivatives). The differential expression profiles of all reporter biomolecules were crossvalidated in independent RNA-Seq and miRNA-Seq datasets. Notably, we found that PTPRG holds important prognostication potential as evaluated by Kaplan-Meier survival analyses. The multiomics relationships unraveled in this analysis point toward the genomic pathogenesis of AML. These multiomics molecular leads warrant further research and development as potential diagnostic and therapeutic targets.
Keywords: acute myeloid leukemia; biomarkers; drug targets; multiomics; personalized medicine; systems medicine.