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蔡启轩, 赵昕, 王雁冰, 孙钰, 王金成. 骨肉瘤miRNA分子共调控网络的构建[J]. 肿瘤防治研究, 2017, 44(9): 601-606. DOI: 10.3971/j.issn.1000-8578.2017.17.0195
引用本文: 蔡启轩, 赵昕, 王雁冰, 孙钰, 王金成. 骨肉瘤miRNA分子共调控网络的构建[J]. 肿瘤防治研究, 2017, 44(9): 601-606. DOI: 10.3971/j.issn.1000-8578.2017.17.0195
CAI Qixuan, ZHAO Xin, WANG Yanbing, SUN Yu, WANG Jincheng. Construction of Gene-coregulation-network for Osteosarcoma MicroRNA[J]. Cancer Research on Prevention and Treatment, 2017, 44(9): 601-606. DOI: 10.3971/j.issn.1000-8578.2017.17.0195
Citation: CAI Qixuan, ZHAO Xin, WANG Yanbing, SUN Yu, WANG Jincheng. Construction of Gene-coregulation-network for Osteosarcoma MicroRNA[J]. Cancer Research on Prevention and Treatment, 2017, 44(9): 601-606. DOI: 10.3971/j.issn.1000-8578.2017.17.0195

骨肉瘤miRNA分子共调控网络的构建

Construction of Gene-coregulation-network for Osteosarcoma MicroRNA

  • 摘要:
    目的 探讨骨肉瘤潜在的miRNA分子调控网络,为解析骨肉瘤发生发展的分子机制提供理论支撑。
    方法 通过差异表达分析获得骨肉瘤组织表达水平发生显著性改变的miRNA,并找到具有显著差异的miRNA靶基因;再通过KEGG代谢通路富集分析以及GO基因功能注释,探究骨肉瘤组织与正常组织相比表达水平发生显著性改变基因的功能,构建分子共调控网络。
    结果 筛选差异表达miRNA 52例,其中31例miRNA上调表达,21例miRNA下调表达;参与肿瘤通路的miRNA共有5例,其关联靶基因共有314个。KEGG代谢通路富集分析结果与GO基因功能分析结果显示,差异表达基因主要参与肿瘤相关的代谢通路。基于差异表达基因及TRED数据库中所收录的人类转录因子信息,对差异表达基因进行分子偶联,构建了分子共调控网络。
    结论 基于分子共表达网络,对骨肉瘤发生发展的分子作用机制进行了系统性挖掘。

     

    Abstract:
    Objective In this study, we constructed a gene-coregulation-network to demonstrate the molecular mechanism of how microRNA regulated the initiation of osteosarcoma. This study may lay a foundation for underlining the molecular mechanism of the occurrence and development of osteosarcoma.
    Methods By analyzing the differentially-expressed genes, we collected the profiling data of microRNA gene expression of osteosarcoma and found the target genes of miRNA with significant difference. By utilizing KEGG and GO database, we annotated all the differentially-expressed genes and constructed a gene-coregulation-network.
    Results This study screened 52 differentially-expressed microRNA, among which 31 microRNAs were up-regulated and 21 microRNAs were down-regulated; a total of five microRNAs were involved in tumor pathway, and there were a total of 314 target genes. GO annotation and KEGG pathway enrichment analysis results showed that the differentially-expressed genes mainly joined in many cancer-related metabolism pathways. Finally, based on the transcription factor data and Pearson correlation analysis, we constructed a gene-coregulation-network.
    Conclusion Based on gene co-regulation analysis, we systemically mine the molecular mechanism of the occurrence and development of osteosarcoma.

     

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