A network-biology approach for identification of key genes and pathways involved in malignant peritoneal mesothelioma.

Q2 Agricultural and Biological Sciences
Genomics and Informatics Pub Date : 2021-06-01 Epub Date: 2021-06-30 DOI:10.5808/gi.21019
A M U B Mahfuz, A M Zubair-Bin-Mahfuj, Dibya Joti Podder
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Abstract

Even in the current age of advanced medicine, the prognosis of malignant peritoneal mesothelioma (MPM) remains abysmal. Molecular mechanisms responsible for the initiation and progression of MPM are still largely not understood. Adopting an integrated bioinformatics approach, this study aims to identify the key genes and pathways responsible for MPM. Genes that are differentially expressed in MPM in comparison with the peritoneum of healthy controls have been identified by analyzing a microarray gene expression dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of these differentially expressed genes (DEG) were conducted to gain a better insight. A protein-protein interaction (PPI) network of the proteins encoded by the DEGs was constructed using STRING and hub genes were detected analyzing this network. Next, the transcription factors and miRNAs that have possible regulatory roles on the hub genes were detected. Finally, survival analyses based on the hub genes were conducted using the GEPIA2 web server. Six hundred six genes were found to be differentially expressed in MPM; 133 are upregulated and 473 are downregulated. Analyzing the STRING generated PPI network, six dense modules and 12 hub genes were identified. Fifteen transcription factors and 10 miRNAs were identified to have the most extensive regulatory functions on the DEGs. Through bioinformatics analyses, this work provides an insight into the potential genes and pathways involved in MPM.

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用网络生物学方法识别恶性腹膜间皮瘤的关键基因和通路。
即使在当今医学发达的时代,恶性腹膜间皮瘤(MPM)的预后仍然不容乐观。导致 MPM 发生和发展的分子机制在很大程度上仍不为人所知。本研究采用综合生物信息学方法,旨在确定导致 MPM 的关键基因和通路。通过分析微阵列基因表达数据集,确定了在 MPM 中与健康对照组腹膜相比有差异表达的基因。对这些差异表达基因(DEG)进行了基因本体和京都基因和基因组百科全书通路分析,以获得更深入的了解。利用 STRING 技术构建了 DEGs 所编码蛋白质的蛋白质-蛋白质相互作用(PPI)网络,并通过分析该网络检测了枢纽基因。接着,检测了可能对枢纽基因起调控作用的转录因子和 miRNA。最后,利用 GEPIA2 网络服务器根据枢纽基因进行生存分析。结果发现,有 6006 个基因在骨髓瘤中存在差异表达,其中 133 个基因上调,473 个基因下调。通过分析 STRING 生成的 PPI 网络,确定了 6 个密集模块和 12 个中心基因。15个转录因子和10个miRNA被确定对DEGs具有最广泛的调控功能。通过生物信息学分析,这项研究揭示了参与骨髓瘤的潜在基因和通路。
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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来源期刊
Genomics and Informatics
Genomics and Informatics Agricultural and Biological Sciences-Ecology, Evolution, Behavior and Systematics
CiteScore
1.90
自引率
0.00%
发文量
0
审稿时长
12 weeks
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