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影响口腔癌发生潜在MicroRNAs的生物信息学分析

赵之新 卢轩 杨铭 赵冰 唐川

赵之新, 卢轩, 杨铭, 赵冰, 唐川. 影响口腔癌发生潜在MicroRNAs的生物信息学分析[J]. 华南师范大学学报(自然科学版), 2021, 53(6): 68-73. doi: 10.6054/j.jscnun.2021095
引用本文: 赵之新, 卢轩, 杨铭, 赵冰, 唐川. 影响口腔癌发生潜在MicroRNAs的生物信息学分析[J]. 华南师范大学学报(自然科学版), 2021, 53(6): 68-73. doi: 10.6054/j.jscnun.2021095
ZHAO Zhixin, LU Xuan, YANG Ming, ZHAO Bing, TANG Chuan. A Bioinformatics Analysis of Potential MicroRNAs Affecting Oral Cancer Development[J]. Journal of South China normal University (Natural Science Edition), 2021, 53(6): 68-73. doi: 10.6054/j.jscnun.2021095
Citation: ZHAO Zhixin, LU Xuan, YANG Ming, ZHAO Bing, TANG Chuan. A Bioinformatics Analysis of Potential MicroRNAs Affecting Oral Cancer Development[J]. Journal of South China normal University (Natural Science Edition), 2021, 53(6): 68-73. doi: 10.6054/j.jscnun.2021095

影响口腔癌发生潜在MicroRNAs的生物信息学分析

doi: 10.6054/j.jscnun.2021095
基金项目: 

国家自然科学基金项目 21606030

辽宁省教育厅科学研究经费项目 LJKZ1178

大连市高层次人才创新支持计划项目 2019RQ118

大连大学优秀青年创新创业团队项目 XQN202004

详细信息
    通讯作者:

    唐川, Email: jiemotc@gmail.com

  • 中图分类号: R739.85

A Bioinformatics Analysis of Potential MicroRNAs Affecting Oral Cancer Development

  • 摘要: 为获得口腔癌组织和正常组织之间差异表达的miRNAs,从分子水平研究相关的miRNAs在肿瘤发生发展中的作用,从GEO数据库筛选并下载口腔癌及正常组织的基因芯片,运用GEO2R工具分析筛选口腔癌与正常组织间的差异表达miRNAs. 采用FunRich软件对将所得差异miRNAs进行GO功能注释、KEGG信号通路分析. 通过对GSE124566和GSE113956两个芯片数据进行分析,分别筛选得到109、1 079个差异表达miRNAs,分别包括41、673个上调基因和68、406个下调基因,筛选得到共同差异表达miRNAs有30个,其中上调16个,参与的生物过程主要有细胞间通讯等,细胞成分主要有细胞核等,分子功能主要有转录因子活性等;下调14个,参与的生物过程主要有信号转导等,细胞成分主要有细胞质等,分子功能主要有转录因子活性等. 通过对口腔癌芯片数据的生物信息学分析,发现30个差异表达miRNAs是口腔癌发生、发展的重要miRNAs,囊泡介导的转运,核苷酸的代谢等过程. 最后预测出了13 796个靶基因,并通过PPI互作分析筛选出了联系最紧密的10个靶基因.
  • 图  1  口腔癌miRNAs数据集表达水平火山图

    Figure  1.  The volcano map of miRNA expression level in the oral cancer data set

    图  2  共同差异表达miRNAs Venn图

    Figure  2.  The common differential expression miRNAs Venn map

    图  3  共同差异表达上调miRNAs GO分析

    Figure  3.  The GO analysis of up-regulated miRNAs in common differential expression

    图  4  共同差异表达下调miRNAs的GO分析

    Figure  4.  The GO analysis of down-regulated miRNAs in common differential expression

    图  5  共同差异表达miRNAs的KEGG分析

    Figure  5.  The KEGG analysis of common differentially expressed miRNAs

    表  1  16个差异表达上调的miRNAs的P及log2 FC

    Table  1.   The P value and log2 FC of 16 miRNAs with up-regulated differential expression

    miRNA P log2 FC
    hsa-miR-513b 6.330×10-9 2.750 163
    hsa-miR-744-5p 2.233×10-8 2.284 359
    hsa-miR-375 7.553×10-16 4.135 624
    hsa-miR-1471 1.173×10-3 1.197 684
    hsa-miR-598 1.253×10-4 1.090 587
    hsa-miR-4324 1.623×10-5 1.356 905
    hsa-miR-378a-5p 3.103×10-8 3.065 295
    hsa-miR-29c-5p 8.863×10-12 3.915 925
    hsa-miR-1224-5p 2.123×10-4 1.316 750
    hsa-miR-3911 1.783×10-21 2.475 252
    hsa-miR-4647 4.793×10-10 2.891 719
    hsa-miR-3188 1.823×10-18 4.603 102
    hsa-miR-24-1-5p 4.683×10-3 1.151 63
    hsa-miR-204-5p 1.333×10-3 1.574 574
    hsa-miR-338-3p 1.023×10-6 2.736 121
    hsa-miR-513c-5p 2.153×10-17 2.510 716
    下载: 导出CSV

    表  2  14个差异表达下调的miRNAs的P及log2 FC

    Table  2.   The P value and log2 FC of 14 miRNAs with down regulated differential expression

    miRNAs P log2 FC
    hsa-miR-4778-5p 2.593×10-14 -2.410 98
    hsa-miR-31-5p 1.933×10-2 -1.342 41
    hsa-miR-299-3p 1.743×10-6 -2.222 96
    hsa-miR-4419a 1.453×10-11 -3.270 17
    hsa-miR-223-3p 1.583×10-6 -1.155 63
    hsa-miR-142-5p 1.293×10-4 -2.564 01
    hsa-miR-23a-5p 1.753×10-12 -1.997 68
    hsa-miR-374c-5p 7.603×10-4 -1.400 69
    hsa-miR-454-3p 2.263×10-4 -1.954 80
    hsa-miR-625-5p 6.983×10-20 -3.165 28
    hsa-miR-765 1.823×10-3 -1.113 14
    hsa-miR-21-5p 1.523×10-4 -1.207 61
    hsa-miR-142-3p 9.313×10-4 -2.640 01
    hsa-let-7i-5p 6.863×10-8 -1.426 81
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-24
  • 网络出版日期:  2022-01-10
  • 刊出日期:  2021-12-25

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