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通过生物信息学分析筛选肝细胞癌预后不良的显著生物标志物。

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis.

作者信息

Sun Quanquan, Liu Peng, Long Bin, Zhu Yuan, Liu Tongxin

机构信息

Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences.

Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Beijing.

出版信息

Medicine (Baltimore). 2020 Aug 7;99(32):e21702. doi: 10.1097/MD.0000000000021702.

Abstract

Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis.A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway.Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.

摘要

肝细胞癌(HCC)是一种预后不佳的恶性肿瘤。异常基因表达与HCC的发生及不良预后显著相关。本研究旨在通过生物信息学分析鉴定与HCC发生发展相关的分子生物标志物,为肝细胞癌的个体化治疗提供一定的分子机制。

使用来自基因表达综合数据库(GEO)的三个数据集(GSE101685、GSE112790和GSE121248)进行生物信息学分析。利用在线工具GEO2R获取HCC和正常肝脏样本的差异表达基因(DEG)。通过在线生物信息学工具注释、可视化和综合发现数据库进行基因本体术语和京都基因与基因组百科全书(KEGG)通路分析。通过检索相互作用基因数据库的搜索工具构建蛋白质-蛋白质相互作用(PPI)网络,并通过Cytoscape可视化枢纽基因。通过UALCAN和基因表达谱交互式分析进行生存分析和RNA测序表达。

共鉴定出115个共享的DEG,其中HCC样本中有30个上调基因和85个下调基因。P53信号通路和细胞周期是上调DEG的主要富集通路,而代谢相关通路是下调DEG的主要富集通路。建立了包含105个节点和249条边的PPI网络,并通过分子复合物检测鉴定出3个显著模块。此外,这3个模块中的17个候选基因与HCC患者生存显著相关,17个基因中的15个在HCC样本中表现出高表达水平。此外,鉴定出4个枢纽基因(CCNB1、CDK1、RRM2、BUB1B)用于KEGG通路的进一步重新分析,并富集在2条通路中,即P53信号通路和细胞周期通路。

HCC样本中CCNB1、CDK1、RRM2和BUB1B的过表达与HCC患者的不良生存相关,它们可能是HCC的潜在治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bf0/7593045/d90e209962ae/medi-99-e21702-g001.jpg

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