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鉴定lncRNA/circRNA-miRNA-mRNA ceRNA网络作为肝细胞癌的生物标志物

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma.

作者信息

Chen Shanshan, Zhang Yongchao, Ding Xiaoyan, Li Wei

机构信息

Cancer Center, Beijing Ditan Hospital, Capital Medical University, Beijing, China.

出版信息

Front Genet. 2022 Mar 21;13:838869. doi: 10.3389/fgene.2022.838869. eCollection 2022.

Abstract

Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis. Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively. A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value. These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.

摘要

肝细胞癌(HCC)占肝癌的大多数,其发病率和死亡率逐年上升。尽管临床管理有所改善,但由于其高复发率和短生存期,仍面临重大挑战。本研究旨在通过生物信息学分析确定HCC中潜在的诊断和预后生物标志物。来自GEO和TCGA数据库的数据集用于生物信息学分析。通过WebGestalt网站和R的clusterProfiler包进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析。使用STRING数据库和Cytoscape软件建立蛋白质-蛋白质相互作用(PPI)网络。利用GEPIA网站进行基因表达分析。使用miRDB、miRWalk和TargetScan预测miRNA,并在OncomiR数据库中探索预测的miRNA的表达水平。在StarBase和LncBase中预测lncRNA,而通过circBank进行环状RNA(circRNA)预测。进行ROC曲线分析和Kaplan-Meier(KM)生存分析,分别评估基因表达的诊断和预后价值。在HCC组织和非癌性肝组织之间共鉴定出327个上调和422个下调的重叠差异表达基因(DEG)。构建了包含89个节点和178条边的PPI网络,并选择了8个枢纽基因来预测上游miRNA和竞争性内源RNA(ceRNA)。基于ceRNA假说成功构建了lncRNA/circRNA-miRNA-mRNA网络,包括5个lncRNA(DLGAP1-AS1、GAS5、LINC00665、TYMSOS和ZFAS1)、6个circRNA(hsa_circ_0003209、hsa_circ_0008128、hsa_circ_0020396、hsa_circ_0030051、hsa_circ_0034049和hsa_circ_0082333)、8个miRNA(hsa-miR-150-5p、hsa-miR-19b-3p、hsa-miR-23b-3p、hsa-miR-26a-5p、hsa-miR-651-5p、hsa-miR-10a-5p、hsa-miR-214-5p和hsa-miR-486-5p)以及5个mRNA(CDC6、GINS1、MCM4、MCM6和MCM7)。ceRNA网络可促进HCC进展、细胞周期、DNA复制等途径。临床诊断和生存分析表明,ZFAS1/hsa-miR-150-5p/GINS1 ceRNA调控轴具有较高的诊断和预后价值。这些结果表明,细胞周期和DNA复制途径可能是参与HCC发展的潜在途径。ceRNA网络有望为HCC管理提供潜在的生物标志物和治疗靶点,尤其是ZFAS1/hsa-miR-150-5p/GINS1调控轴。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c40/8977626/9a07835764a8/fgene-13-838869-g001.jpg

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