Department of Bioengineering, Marmara University, Istanbul, Turkey.
Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey.
OMICS. 2022 Jul;26(7):392-403. doi: 10.1089/omi.2022.0051. Epub 2022 Jun 28.
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.
急性髓系白血病(AML)是一种常见的、复杂的、多因素的造血系统恶性肿瘤。AML 的诊断和治疗结果表现出明显的异质性和患者间的差异。迄今为止,AML 相关生物标志物的发现研究采用了单一的组学研究。多组学分析可以整合来自细胞层次多个层面的数据流,从基因到蛋白质到代谢物,为 AML 的诊断和治疗创新提供了很大的希望。在本研究中,我们报告了一种系统医学和多组学方法,使用基因组规模的生物网络整合 AML 转录组数据和报告分子在 RNA、蛋白质和代谢物水平上的信息。我们利用了基因表达综合数据库中的两个独立转录组数据集(GSE5122、GSE8970)。我们确定了与 AML 相关的新的多组学分子特征:miRNA(例如,mir-484 和 miR-519d-3p)、受体(ACVR1 和 PTPRG)、转录因子(PRDM14 和 GATA3)和代谢物(特别是氨基酸衍生物)。所有报告分子的差异表达谱在独立的 RNA-Seq 和 miRNA-Seq 数据集中进行了交叉验证。值得注意的是,我们发现 PTPRG 通过 Kaplan-Meier 生存分析评估具有重要的预后预测潜力。本分析中揭示的多组学关系指向 AML 的基因组发病机制。这些多组学分子线索值得进一步研究和开发,作为潜在的诊断和治疗靶点。