Ali Amini Fard, Hamzeh Rahimi, Zinat Shams, Pegah Ghoraeian
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引用次数: 1
Abstract
Background: Hematologic malignancies are among fatal diseases with different subtypes. Acute myeloid leukemia (AML) is a subtype showing a high invasion rate to different tissues.
Objective: AML patients, even after treatment, show an increased rate of recurrence, and this relapsed profile of AML has turned this malignancy into big challenges in the medical scope.
Methods: In the current study, we aimed to investigate hub-genes and potential signaling pathways in AML recurrence. Two expression profiles of genes and non-coding RNAs were extracted from the Gene Expression Omnibus (GEO) database. Target genes of identified miRNAs were predicted through bioinformatics tools. GO and KEGG pathway enrichment analyses were conducted to discover common target genes and differentially expressed genes. Protein-protein interaction (PPI) network was constructed and visualized through the STRING online database and Cytoscape software, respectively. Hub-genes of constructed PPI were found through the CytoHubba plugin of Cytoscape software.
Results: As a result, 109 differentially expressed genes and 45 differentially expressed miRNAs were found, and the top enriched pathways were immune response, xhemokine activity, immune System, and plasma membrane. The hub-genes were TNF, IL6, TLR4, VEGFA, PTPRC, TLR7, TLR1, CD44, CASP1, and CD68.
Conclusion: The present investigation based on the in silico analysis and microarray GEO databases may provide a novel understanding of the mechanisms related to AML relapse.
背景:血液恶性肿瘤是具有不同亚型的致死性疾病之一。急性髓性白血病(AML)是一种对不同组织具有高侵袭率的亚型。目的:AML患者即使经过治疗，其复发率仍呈上升趋势，AML的这种复发特征使其成为医学领域的一大挑战。方法:在当前的研究中，我们旨在研究中心基因和潜在的AML复发信号通路。从Gene expression Omnibus (GEO)数据库中提取基因和非编码rna的两个表达谱。通过生物信息学工具预测鉴定的mirna的靶基因。通过GO和KEGG途径富集分析，发现共同靶基因和差异表达基因。通过STRING在线数据库和Cytoscape软件分别构建和可视化蛋白质-蛋白质相互作用(PPI)网络。通过Cytoscape软件的CytoHubba插件找到构建的PPI的中心基因。结果:共发现109个差异表达基因和45个差异表达mirna，富集最多的途径为免疫应答、趋化因子活性、免疫系统和质膜。中心基因为TNF、IL6、TLR4、VEGFA、PTPRC、TLR7、TLR1、CD44、CASP1和CD68。结论:目前基于芯片分析和微阵列GEO数据库的研究可能为AML复发相关机制提供新的理解。
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