Laboratory of Molecular Health Science, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh.
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
Biomed Res Int. 2023 Jun 15;2023:1946703. doi: 10.1155/2023/1946703. eCollection 2023.
Acute myeloid leukemia (AML) is a blood cancer caused by the abnormal proliferation and differentiation of hematopoietic stem cells in the bone marrow. The actual genetic markers and molecular mechanisms of AML prognosis are unclear till today. This study used bioinformatics approaches for identifying hub genes and pathways associated with AML development to uncover potential molecular mechanisms. The expression profiles of RNA-Seq datasets, GSE68925 and GSE183817, were retrieved from the Gene Expression Omnibus (GEO) database. These two datasets were analyzed by GREIN to obtain differentially expressed genes (DEGs), which were used for performing the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI), and survival analysis. The molecular docking and dynamic simulation were performed to identify the most effective drug/s for AML from the drug list approved by the Food and Drug Administration (FDA). By integrating the two datasets, 238 DEGs were identified as likely to be affected by AML progression. GO enrichment analyses exhibited that the upregulated genes were mainly associated with inflammatory response (BP) and extracellular region (CC). The downregulated DEGs were involved in the T-cell receptor signalling pathway (BP), an integral component of the lumenal side of the endoplasmic reticulum membrane (CC) and peptide antigen binding (MF). The pathway enrichment analysis showed that the upregulated DEGs were mainly associated with the T-cell receptor signalling pathway. Among the top 15 hub genes, the expression levels of and were associated with the prognosis of AML. Four FDA-approved drugs were selected, and a top-ranked drug was identified for each biomarker through molecular docking studies. The top-ranked drugs were further confirmed by molecular dynamic simulation that revealed their binding stability and confirmed their stable performance. Therefore, the drug compounds, enasidenib and gilteritinib, can be recommended as the most effective drugs against the ALDH1A1 and CFD proteins, respectively.
急性髓细胞白血病（AML）是一种由骨髓造血干细胞异常增殖和分化引起的血液系统恶性肿瘤。目前，AML 预后的实际遗传标志物和分子机制尚不清楚。本研究采用生物信息学方法鉴定与 AML 发生发展相关的枢纽基因和通路，以揭示潜在的分子机制。从基因表达综合数据库（GEO）中检索了 RNA-Seq 数据集 GSE68925 和 GSE183817 的表达谱。通过 GREIN 分析这两个数据集，获得差异表达基因（DEGs），用于进行基因本体论（GO）、京都基因与基因组百科全书（KEGG）通路、蛋白质-蛋白质相互作用（PPI）和生存分析。通过分子对接和动态模拟，从美国食品和药物管理局（FDA）批准的药物清单中确定治疗 AML 的最有效药物/化合物。通过整合这两个数据集，确定了 238 个可能受 AML 进展影响的 DEGs。GO 富集分析表明，上调基因主要与炎症反应（BP）和细胞外区（CC）有关。下调的 DEGs 参与了 T 细胞受体信号通路（BP）、内质网膜内腔面的一个组成部分（CC）和肽抗原结合（MF）。通路富集分析表明，上调的 DEGs 主要与 T 细胞受体信号通路有关。在 15 个主要枢纽基因中，和的表达水平与 AML 的预后相关。选择了四种 FDA 批准的药物，通过分子对接研究为每个生物标志物选择了排名最高的药物。通过分子动力学模拟进一步验证了排名最高的药物，揭示了它们的结合稳定性，并证实了它们的稳定性能。因此，药物化合物enasidenib 和 gilteritinib 可以分别被推荐为针对 ALDH1A1 和 CFD 蛋白的最有效药物。
