In Silico Analysis of MicroRNA Expression Data in Liver Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Nourhan Abu-Shahba, Elsayed Hegazy, Faiz M Khan, Mahmoud Elhefnawi
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引用次数: 0

Abstract

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

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肝癌组织MicroRNA表达数据的计算机分析。
异常miRNA表达已被证明与HCC的发生和发展直接相关。本研究旨在通过mirna表达的计算分析来检测HCC可能的预后、诊断和/或治疗mirna。方法:采用YM500v2服务器对miRNA表达数据集进行meta分析,比较正常和癌变肝组织中miRNA的表达。我们研究中最显著的差异调节mirna使用mirWalk工具进行靶基因分析,以获得其验证和预测的靶标。组合目标预测工具;miRror Suite用于获得共同调控的靶基因。使用DAVID工具对得到的目标进行功能富集分析。基于microrna、它们的靶标和转录因子之间的相互作用,构建了一个网络。使用网络拓扑分析确定了集线器节点和守门人。此外,我们根据确定的枢纽和守门人节点的低表达和高表达进行了患者数据生存分析,将患者分为低和高生存概率组。结果:在YM500v2服务器中使用荟萃分析选项,发现34个mirna受到显著差异调节(p值≥0.05);5个mirna下调,29个mirna上调。得到了每个miRNA的验证和预测的靶基因,以及组合预测的靶基因。DAVID富集分析得出了几个与主要癌症标志直接相关的重要细胞功能。这些功能包括局灶黏附、细胞周期、PI3K-Akt信号通路、胰岛素信号通路、Ras和MAPK信号通路。几个枢纽基因和看门人被发现可以作为肝细胞癌的潜在药物靶点。在HCC患者中,POU2F1和PPARA在低生存率和高生存率之间存在显著差异(p值≤0.05)。我们的研究揭示了肝细胞癌的重要生物标志物mirna及其靶基因和调控功能。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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