Fard Ali Amini, Rahimi Hamzeh, Shams Zinat, Ghoraeian Pegah
Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
Department of Molecular Medicine, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.
Microrna. 2022;11(3):227-244. doi: 10.2174/2211536611666220511160502.
Hematologic malignancies are among fatal diseases with different subtypes. Acute myeloid leukemia (AML) is a subtype showing a high invasion rate to different tissues.
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.
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.
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.
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.
血液系统恶性肿瘤是具有不同亚型的致命疾病。急性髓系白血病（AML）是一种对不同组织侵袭率较高的亚型。
AML患者即使在治疗后也表现出较高的复发率，AML的这种复发情况使其成为医学领域的重大挑战。
在本研究中，我们旨在研究AML复发中的关键基因和潜在信号通路。从基因表达综合数据库（GEO）中提取了基因和非编码RNA的两个表达谱。通过生物信息学工具预测已鉴定miRNA的靶基因。进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析以发现共同靶基因和差异表达基因。分别通过STRING在线数据库和Cytoscape软件构建并可视化蛋白质-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的关键基因。
结果发现109个差异表达基因和45个差异表达miRNA，最富集的通路是免疫反应、趋化因子活性、免疫系统和质膜。关键基因是肿瘤坏死因子（TNF）、白细胞介素6（IL6）、Toll样受体4（TLR4）、血管内皮生长因子A（VEGFA）、蛋白酪氨酸磷酸酶受体C（PTPRC）、Toll样受体7（TLR7）、Toll样受体1（TLR1）、CD44分子、半胱天冬酶1（CASP1）和CD68分子。
基于计算机分析和微阵列GEO数据库的本研究可能为AML复发相关机制提供新的认识。
