计算机模拟分析通过构建非肝硬化性肝癌和肝硬化性肝癌之间的miRNA-mRNA网络来挖掘潜在的生物标志物。
In silico analysis excavates potential biomarkers by constructing miRNA-mRNA networks between non-cirrhotic HCC and cirrhotic HCC.
作者信息
Ding Bisha, Lou Weiyang, Liu Jingxing, Li Ruohan, Chen Jing, Fan Weimin
机构信息
1Program of Innovative Cancer Therapeutics, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, College of Medicine, Key Laboratory of Combined Multi-Organ Transplantation, Ministry of Public Health, Key Laboratory of Organ Transplantation, Zhejiang University, Hangzhou, 310003 Zhejiang Province China.
Department of Intensive Care Unit, Changxing People's Hospital of Zhejiang Province, Huzhou, 313100 Zhejiang, China.
出版信息
Cancer Cell Int. 2019 Jul 18;19:186. doi: 10.1186/s12935-019-0901-3. eCollection 2019.
BACKGROUND
Mounting evidences have demonstrated that HCC patients with or without cirrhosis possess different clinical characteristics, tumor development and prognosis. However, few studies directly investigated the underlying molecular mechanisms between non-cirrhotic HCC and cirrhotic HCC.
METHODS
The clinical information and RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) of HCC with or without cirrhosis were obtained by R software. Functional annotation and pathway enrichment analysis were performed by Enrichr. Protein-protein interaction (PPI) network was established through STRING and mapped to Cytoscape to identify hub genes. MicroRNAs were predicted through miRDB database. Furthermore, correlation analysis between selected genes and miRNAs were conducted via starBase database. MiRNAs expression levels between HCC with or without cirrhosis and corresponding normal liver tissues were further validated through GEO datasets. Finally, expression levels of key miRNAs and target genes were validated through qRT-PCR.
RESULTS
Between 132 non-cirrhotic HCC and 79 cirrhotic HCC in TCGA, 768 DEGs were acquired, mainly involved in neuroactive ligand-receptor interaction pathway. According to the result from gene expression analysis in TCGA, , , , and were renamed as key genes and selected for further investigation. Survival analysis indicated that upregulated correlated with worse OS in cirrhotic HCC. Furthermore, ROC analysis revealed the significant diagnostic values of and in cirrhotic HCC, and , in non-cirrhotic HCC. Next, 517 miRNAs were predicted to target the 5 key genes. Correlation analysis confirmed that 16 of 517 miRNAs were negatively regulated the key genes. By detecting the expression levels of these key miRNAs from GEO database, we found 4 miRNAs have high research values. Finally, potential miRNA-mRNA networks were constructed based on the results of qRT-PCR.
CONCLUSION
In silico analysis, we first constructed the miRNA-mRNA regulatory networks in non-cirrhotic HCC and cirrhotic HCC.
背景
越来越多的证据表明，伴有或不伴有肝硬化的肝癌患者具有不同的临床特征、肿瘤发展过程及预后情况。然而，很少有研究直接探究非肝硬化性肝癌和肝硬化性肝癌之间潜在的分子机制。
方法
临床信息和RNA测序数据从癌症基因组图谱（TCGA）数据库下载。使用R软件获取伴有或不伴有肝硬化的肝癌的差异表达基因（DEG）。通过Enrichr进行功能注释和通路富集分析。通过STRING建立蛋白质-蛋白质相互作用（PPI）网络，并映射到Cytoscape以识别枢纽基因。通过miRDB数据库预测微小RNA（miRNA）。此外，通过starBase数据库对选定基因和miRNA之间进行相关性分析。通过GEO数据集进一步验证伴有或不伴有肝硬化的肝癌及相应正常肝组织之间miRNA的表达水平。最后，通过定量逆转录聚合酶链反应（qRT-PCR）验证关键miRNA和靶基因的表达水平。
结果
在TCGA的132例非肝硬化性肝癌和79例肝硬化性肝癌中，获得了768个DEG，主要参与神经活性配体-受体相互作用通路。根据TCGA中基因表达分析的结果， 、 、 、 和 被重新命名为关键基因并选择进行进一步研究。生存分析表明，在肝硬化性肝癌中上调的 与较差的总生存期相关。此外，ROC分析揭示了 在肝硬化性肝癌中以及 在非肝硬化性肝癌中的显著诊断价值。接下来，预测有517个miRNA靶向这5个关键基因。相关性分析证实，517个miRNA中的16个对关键基因有负调控作用。通过检测来自GEO数据库的这些关键miRNA的表达水平，我们发现4个miRNA具有较高的研究价值。最后，基于qRT-PCR的结果构建了潜在的miRNA-信使核糖核酸（mRNA）网络。
结论
在计算机分析中，我们首次构建了非肝硬化性肝癌和肝硬化性肝癌中的miRNA-mRNA调控网络。
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