Identification of Novel Therapeutic Targets in Myelodysplastic Syndrome Using Protein-Protein Interaction Approach and Neural Networks

Dongqing Wei, M. Junaid, Abbas Khan, A. Kaushik, Arif Ali, A. Mehmood, S. Saleem, Asma Sindhoo Nangraj
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引用次数: 5

Abstract

A Myelodysplastic syndrome (MDS) is a disorder characterized by active but ineffective hematopoiesis that leads to pancytopenia. MDS, also termed as myeloid neoplasms, is described by different level of cytopenia that is a different level of blood cells in the body. Various genes mutations have been reported to associate with MDS. To investigate the mechanisms at molecular level underlying MDS patients carrying genetic mutations, the gene expression profiles of MDS the patients were compared to that of healthy individuals and analyzed by bioinformatics tools. In biological networks, genes having important functional roles can be identified by a measure of the node. Networks of genes an in co-expression, candidate hubs also called extremely associated genes have been connected with the key disease-related pathway. Thus, this technique was used to discover the MDS related genes hub. Affymetrix Human Genome U133 plus 2.0 gene expression dataset of microarray GSE58831 was retrieved from GEO (Gene Expression Omnibus) database that contained four 159 diseased samples and 17 samples of control. Based on statistical method and co-expression networking, DEGs gene was detected. DAVID an online tool was employed for Gene ontology (GO) function and KEGG pathway enrichment analysis of DEGs. Besides, PPI (Protein-protein interaction) networks were developed by mapping the DEGs with respect to protein-protein interaction set available in databases for the identification of the pathways involving DEGs. PPI interaction networks were divided into subnetworks via MCODE algorithm and were examined by Cytoscape. Interferon Signaling Pathway, cellular response to zinc ions and negative growth regulation. Immune response, negative regulation of transcription from RNA polymerase II promotor, positive regulation of smooth muscle cell proliferation and cellular response to Dexamethasone stimulus, extracellular matrix, extracellular space, and extracellular region were the main enriched processes and pathways in these DEGs and many of the hub genes’ (UBC, TP53, EGFR, GADPH, CREBBP, HDAC1, STAT1, IL6, ESR1, SMAD4) reported in this study were purposed as novel therapeutic targets against MDS disease.
利用蛋白-蛋白相互作用方法和神经网络鉴定骨髓增生异常综合征的新治疗靶点
骨髓增生异常综合征(MDS)是一种以活跃但无效的造血功能导致全血细胞减少为特征的疾病。MDS也被称为髓系肿瘤,是由不同水平的血细胞减少症(即体内不同水平的血细胞)来描述的。据报道,多种基因突变与MDS有关。为了研究MDS患者携带基因突变的分子机制,我们将MDS患者的基因表达谱与健康个体进行了比较,并利用生物信息学工具进行了分析。在生物网络中,具有重要功能作用的基因可以通过节点的测量来识别。基因网络和共表达,候选中心也被称为极端相关基因,与关键的疾病相关途径有关。因此,该技术被用于发现MDS相关基因中心。从GEO (gene expression Omnibus)数据库中检索到Affymetrix Human Genome U133 plus 2.0基因表达数据集,该数据库包含4个159例患病样本和17个对照组样本。基于统计学方法和共表达网络,检测DEGs基因。利用在线工具DAVID对DEGs进行基因本体(GO)功能和KEGG通路富集分析。此外,通过将deg与数据库中可用的蛋白质-蛋白质相互作用集进行映射,开发了PPI(蛋白质-蛋白质相互作用)网络,以识别涉及deg的途径。通过MCODE算法将PPI相互作用网络划分为子网络,并用Cytoscape进行检测。干扰素信号通路,细胞对锌离子的反应和负生长调控。免疫应答、RNA聚合酶II启动子转录的负调控、平滑肌细胞增殖的正调控和对地塞米松刺激的细胞应答、细胞外基质、细胞外空间和细胞外区域是这些deg的主要富集过程和途径,本研究报道的许多中心基因(UBC、TP53、EGFR、GADPH、CREBBP、HDAC1、STAT1、IL6、ESR1、SMAD4)被认为是MDS疾病的新治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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