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通过生物信息学分析整合多个GEO数据集鉴定多发性骨髓瘤中的关键基因并构建微小RNA-信使核糖核酸调控网络

Identification of key genes and construction of microRNA-mRNA regulatory networks in multiple myeloma by integrated multiple GEO datasets using bioinformatics analysis.

作者信息

Gao Hongyu, Wang Huihan, Yang Wei

机构信息

Department of Hematology, Shengjing Hospital of China Medical University, Huaxiang Road 39, Shenyang, 110000, Liaoning, People's Republic of China.

出版信息

Int J Hematol. 2017 Jul;106(1):99-107. doi: 10.1007/s12185-017-2216-2. Epub 2017 Mar 18.

DOI:10.1007/s12185-017-2216-2
PMID:28316065
Abstract

Multiple myeloma (MM) is a common hematological malignancy. To identify key genes and microRNAs in MM, we downloaded two gene expression profiles (GSE16558 and GSE47552) and two microRNA expression profiles (GSE17498 and GSE16558) from the Gene Expression Omnibus (GEO) database. A total of 596 differentially expressed genes (DEGs) and 39 differentially expressed microRNAs (DEMs) were screened out. Pathway analysis showed that upregulated genes were mainly enriched in the "B cell receptor signaling pathway", "Cell cycle" and "NF-kappa B signaling pathway", whereas downregulated genes were mainly enriched in the "Ribosome", "FoxO signaling pathway" and "p53 signaling pathway". We subsequently constructed a protein-protein interaction network of DEGs consisting of 277 genes and 563 interactions. In addition, 32 genes with high degrees in the network were identified as hub genes in MM, e.g. HDAC2, RBBP4, CREB1, and RB1. Additionally, we constructed a microRNA-mRNA regulatory network depicting interactions between DEMs and their targets, including the miR-135b-GADD45A and miR-148a-USPL1 pairs. In conclusion, the results of this data mining and integration help reveal the molecular basis of MM pathogenesis as well as potential biomarkers and therapeutic targets for MM diagnosis and treatment.

摘要

多发性骨髓瘤(MM)是一种常见的血液系统恶性肿瘤。为了鉴定MM中的关键基因和微小RNA,我们从基因表达综合数据库(GEO)下载了两个基因表达谱(GSE16558和GSE47552)以及两个微小RNA表达谱(GSE17498和GSE16558)。共筛选出596个差异表达基因(DEG)和39个差异表达微小RNA(DEM)。通路分析表明,上调基因主要富集于“B细胞受体信号通路”、“细胞周期”和“NF-κB信号通路”,而下调基因主要富集于“核糖体”、“FoxO信号通路”和“p53信号通路”。随后,我们构建了一个由277个基因和563个相互作用组成的DEG蛋白质-蛋白质相互作用网络。此外,网络中32个高连接度基因被鉴定为MM中的枢纽基因,如HDAC2、RBBP4、CREB1和RB1。另外,我们构建了一个微小RNA-信使核糖核酸调控网络,描绘了DEM与其靶标之间的相互作用,包括miR-135b-GADD45A和miR-148a-USPL1对。总之,该数据挖掘与整合结果有助于揭示MM发病机制的分子基础以及MM诊断和治疗的潜在生物标志物和治疗靶点。

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miR-135b, upregulated in breast cancer, promotes cell growth and disrupts the cell cycle by regulating LATS2.
非典型泛素特异性蛋白酶 USPL1 的 SUMO 蛋白酶活性的结构基础。
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