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基于可变剪接紊乱的乳腺癌亚型预测分析

许鹏 王兵 方刚 石晓龙 刘文斌

许鹏, 王兵, 方刚, 石晓龙, 刘文斌. 基于可变剪接紊乱的乳腺癌亚型预测分析[J]. 电子与信息学报, 2020, 42(6): 1348-1354. doi: 10.11999/JEIT190871
引用本文: 许鹏, 王兵, 方刚, 石晓龙, 刘文斌. 基于可变剪接紊乱的乳腺癌亚型预测分析[J]. 电子与信息学报, 2020, 42(6): 1348-1354. doi: 10.11999/JEIT190871
Peng XU, Bing WANG, Gang FANG, Xiaolong SHI, Wenbin LIU. Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders[J]. Journal of Electronics and Information Technology, 2020, 42(6): 1348-1354. doi: 10.11999/JEIT190871
Citation: Peng XU, Bing WANG, Gang FANG, Xiaolong SHI, Wenbin LIU. Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders[J]. Journal of Electronics and Information Technology, 2020, 42(6): 1348-1354. doi: 10.11999/JEIT190871

基于可变剪接紊乱的乳腺癌亚型预测分析

doi: 10.11999/JEIT190871
基金项目: 国家重点研发计划(2019YFA0706402),国家自然科学基金(61572367, 61573017, 61972107, 61972109)
详细信息
    作者简介:

    许鹏:男,1986年生,博士后,研究方向为生物信息学

    王兵:男,1993年生,硕士生,研究方向为生物信息学

    方刚:男,1969年生,教授,研究方向为生物信息学

    石晓龙:男,1975年生,教授,研究方向为生物信息学

    刘文斌:男,1969年生,教授,研究方向为生物信息学

    通讯作者:

    刘文斌 wbliu6910@126.com

  • 中图分类号: TP391

Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders

Funds: The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (61572367, 61573017, 61972107, 61972109)
  • 摘要: 可变剪接与多种复杂疾病的发生、发展存在密切的联系,包括肿瘤在内的多种疾病的产生往往伴随着可变剪接的紊乱发生。现有的乳腺癌亚型分析主要是基于单个剪接异构体出发,缺少考虑亚型之间由于可变剪接紊乱造成剪接异构体在整体分布上的差异。因此该文提出了基于可变剪接紊乱的乳腺癌亚型预测方法,主要使用Jensen-Shannon(JS)散度来找寻亚型之间的可变剪接紊乱差异较大的基因,并构建反向传播(BP)神经网络模型对乳腺癌亚型进行分类。结果表明,该方法不仅能有效发现肿瘤异质性分子,在乳腺癌亚型分类方面也有较好的识别结果,其平均F1值达到0.89,且能为患者提供个性化乳腺癌亚型药物推荐。该文的研究将有效促进基于可变剪接紊乱的乳腺癌亚型研究的发展。
  • 图  1  排名靠前100基因的JS散度分布情况

    图  2  不同乳腺癌亚型与正常型之间差异基因的韦恩图

    图  3  JS散度大于0.3的基因个数

    图  4  乳腺癌亚型聚类

    图  5  乳腺癌亚型分类结果

    表  1  不同乳腺癌亚型的样本数

    乳腺癌亚型样本数
    Basal140
    Her267
    LumA432
    LumB194
    Normal117
    下载: 导出CSV

    表  2  乳腺癌亚型分类

    乳腺癌亚型精确率召回率F1值
    Basal0.970.960.97
    Her20.850.750.79
    LumA0.890.920.91
    LumB0.810.790.80
    Normal0.890.910.90
    下载: 导出CSV

    表  3  乳腺癌亚型的药物推荐

    靶基因药物BasalHer2LumALumB
    CHEK1Enzastaurin0.3930.3690.1950.316
    ESR1Melatonin,Homosalate,Estradiol,2-Amino-1-methyl-6-phenylimidazo(4,5-b)pyridine,Danazol,Fulvestrant,Raloxifene,Custirsen,Tamoxifen,Estrone sulfate,Methyltestosterone,Fluoxymesterone,Afimoxifene0.3050.1330.0280.025
    FOLR2Folic acid,Methotrexate0.8420.0250.1080.354
    GPER1Estradiol0.4420.4380.0210.013
    GSNLatrunculin A0.4430.4190.2240.476
    PPARGCurcumin,Isoflavone,Valproic acid,Mesalazine,Nabiximols,Cannabidiol0.6680.6450.0300.637
    AURKBEnzastaurin,AT92830.6400.5690.3520.591
    ABCC11Methotrexate,Folic acid0.4310.0360.0130.040
    下载: 导出CSV
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    曾勇, 舒欢, 胡江平, 等. 基于BP神经网络的自适应伪最近邻分类[J]. 电子与信息学报, 2016, 38(11): 2774–2779. doi: 10.11999/JEIT160133

    ZENG Yong, SHU Huan, HU Jiangping, et al. Adaptive pseudo nearest neighbor classification based on BP neural network[J]. Journal of Electronics &Information Technology, 2016, 38(11): 2774–2779. doi: 10.11999/JEIT160133
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    LIU Wenbin, CHEN Jie, FANG Gang, et al. Prediction of drug synergy and antagonism based on drug-drug interaction network[J]. Journal of Electronics &Information Technology, 2020, 42(6): 1428–1435. doi: 10.11999/JEIT190867
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  • 被引次数: 0
出版历程
  • 收稿日期:  2019-11-01
  • 修回日期:  2020-05-10
  • 网络出版日期:  2020-05-23
  • 刊出日期:  2020-06-22

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