A Comprehensive Genomic Analysis Constructs miRNA-mRNA Interaction Network in Hepatoblastoma.
A Comprehensive Genomic Analysis Constructs miRNA-mRNA Interaction Network in Hepatoblastoma.
复制标题
综合基因组分析构建肝母细胞瘤中的 miRNA-mRNA 相互作用网络
DOI：
10.3389/fcell.2021.655703
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发表时间：
2021
影响因子：
5.5
通讯作者：
Lv Z
中科院分区：
文献类型：
--
作者：
Chen T;Tian L;Chen J;Zhao X;Zhou J;Guo T;Sheng Q;Zhu L;Liu J;Lv Z
肝母细胞瘤（HB）是一种罕见疾病，但却是儿科人群中最常见的肝肿瘤。对于晚期 HB 患者，预后很差，治疗选择有限。据报道，多种 microRNA (miRNA) 参与 HB 发育，但 HB 中的 miRNA-mRNA 相互作用网络仍然难以捉摸。通过比较GSE131329数据集中的HB和正常肝脏样本，我们检测到580个上调的差异表达mRNA（DE-mRNA）和790个下调的DE-mRNA。对于GSE153089数据集，在胎儿型肿瘤和正常肝脏组之间检测到第一簇差异表达miRNA（DE-miRNA），而在胚胎型肿瘤和正常肝脏组之间检测到第二簇DE-miRNA。通过这两个DE-miRNA簇的交叉，获得了33个上调的中枢miRNA和12个下调的中枢miRNA。基于各自的hub miRNA，通过TransmiR v2.0检测上游转录因子（TF），同时通过miRNet数据库预测下游靶基因。各个hub miRNA的靶基因与相应的DE-mRNA的交叉导致了250个下调的候选基因和202个上调的候选基因。基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析表明上调的候选基因主要富集于与细胞周期相关的术语和通路。我们构建了蛋白质-蛋白质相互作用（PPI）网络，获得了下调候选基因的211个节点对和上调候选基因的157个节点对。应用 Cytoscape 软件可视化 PPI 网络，并使用 CytoHubba 识别各自的前 10 个枢纽基因。随后通过 Oncopression 数据库验证 PPI 网络中 hub 基因的表达值，然后在 HB 和匹配的正常肝组织中进行定量实时聚合酶链反应 (qRT-PCR)，结果有 6 个显着下调的基因和 7 个显着上调的基因。最终构建了miRNA-mRNA相互作用网络。总之，我们发现各种 miRNA、TF 和 hub 基因是 HB 发病机制的潜在调节因子。此外，miRNA-mRNA 相互作用网络、PPI 模块和通路可能为未来的 HB 治疗诊断提供潜在的生物标志物。
Hepatoblastoma (HB) is a rare disease but nevertheless the most common hepatic tumor in the pediatric population. For patients with advanced HB, the prognosis is dismal and there are limited therapeutic options. Multiple microRNAs (miRNAs) were reported to be involved in HB development, but the miRNA–mRNA interaction network in HB remains elusive. Through a comparison between HB and normal liver samples in the GSE131329 dataset, we detected 580 upregulated differentially expressed mRNAs (DE-mRNAs) and 790 downregulated DE-mRNAs. As for the GSE153089 dataset, the first cluster of differentially expressed miRNAs (DE-miRNAs) were detected between fetal-type tumor and normal liver groups, while the second cluster of DE-miRNAs were detected between embryonal-type tumor and normal liver groups. Through the intersection of these two clusters of DE-miRNAs, 33 upregulated hub miRNAs, and 12 downregulated hub miRNAs were obtained. Based on the respective hub miRNAs, the upstream transcription factors (TFs) were detected via TransmiR v2.0, while the downstream target genes were predicted via miRNet database. The intersection of target genes of respective hub miRNAs and corresponding DE-mRNAs contributed to 250 downregulated candidate genes and 202 upregulated candidate genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated the upregulated candidate genes mainly enriched in the terms and pathways relating to the cell cycle. We constructed protein–protein interaction (PPI) network, and obtained 211 node pairs for the downregulated candidate genes and 157 node pairs for the upregulated candidate genes. Cytoscape software was applied for visualizing the PPI network and respective top 10 hub genes were identified using CytoHubba. The expression values of hub genes in the PPI network were subsequently validated through Oncopression database followed by quantitative real-time polymerase chain reaction (qRT-PCR) in HB and matched normal liver tissues, resulting in six significant downregulated genes and seven significant upregulated genes. The miRNA–mRNA interaction network was finally constructed. In conclusion, we uncover various miRNAs, TFs, and hub genes as potential regulators in HB pathogenesis. Additionally, the miRNA–mRNA interaction network, PPI modules, and pathways may provide potential biomarkers for future HB theranostics.
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DOI：
10.3390/molecules23061479
发表时间：
2018-06-19
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Lambert M;Jambon S;Depauw S;David-Cordonnier MH
通讯作者：
David-Cordonnier MH
拓扑异构酶iialpha和IIBETA的C末端区域确定体内酶的同工型特异性功能。
影响因子：
14.9
作者：
Linka, Rene M;Porter, Andrew C G;Volkov, Arsen;Mielke, Christian;Boege, Fritz;Christensen, Morten O
通讯作者：
Christensen, Morten O
鉴定有助于 HBV 相关 HCC 发病机制的潜在 miRNA-mRNA 调控网络
影响因子：
7.4
作者：
Lou, Weiyang;Liu, Jingxing;Fan, Weimin
通讯作者：
Fan, Weimin
影响因子：
14
作者：
Liu Y;Bailey JT;Abu-Laban M;Li S;Chen C;Glick AB;Hayes DJ
通讯作者：
Hayes DJ
影响因子：
4.2
作者：
Liu, Wuyi;Zhang, Qian;Zhang, Rong
通讯作者：
Zhang, Rong