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基于代谢组学和机器学习探究胃癌血浆诊断标志物

许楚璇 姜飞 沈伟涛 孙岩 沈孝兵

许楚璇, 姜飞, 沈伟涛, 孙岩, 沈孝兵. 基于代谢组学和机器学习探究胃癌血浆诊断标志物[J]. 中国公共卫生, 2023, 39(2): 164-169. doi: 10.11847/zgggws1139011
引用本文: 许楚璇, 姜飞, 沈伟涛, 孙岩, 沈孝兵. 基于代谢组学和机器学习探究胃癌血浆诊断标志物[J]. 中国公共卫生, 2023, 39(2): 164-169. doi: 10.11847/zgggws1139011
XU Chu-xuan, JIANG Fei, SHEN Wei-tao, . Plasma markers for gastric cancer diagnosis: a metabolomics- and machine learning-based exploratory study[J]. Chinese Journal of Public Health, 2023, 39(2): 164-169. doi: 10.11847/zgggws1139011
Citation: XU Chu-xuan, JIANG Fei, SHEN Wei-tao, . Plasma markers for gastric cancer diagnosis: a metabolomics- and machine learning-based exploratory study[J]. Chinese Journal of Public Health, 2023, 39(2): 164-169. doi: 10.11847/zgggws1139011

基于代谢组学和机器学习探究胃癌血浆诊断标志物

doi: 10.11847/zgggws1139011
详细信息
    作者简介:

    许楚璇(1997 – ),女,浙江台州人,硕士在读,研究方向:流行病与卫生统计学

    通信作者:

    沈孝兵,E-mail:xb.shen@seu.edu.cn

  • 中图分类号: R 735.2

Plasma markers for gastric cancer diagnosis: a metabolomics- and machine learning-based exploratory study

  • 摘要:   目的   基于代谢组学和机器学习算法探究血浆代谢物对胃癌的诊断价值。  方法  分别收集20例胃癌患者(胃癌组)和20名健康自愿者(健康对照组)的血浆样本,用甲醇提取血浆样本中代谢物,对提取的代谢物进行液相色谱串联质谱分析;通过与 mzCloud、mzVault 和 Masslist 数据库比对,对分析后的代谢物进行注释;以变量权重值(variable importance of projection,VIP) > 1、P < 0.05,log2|Fold Change| > 1的标准筛选2组血浆差异代谢物;采用超几何检验将筛选出的差异代谢物富集至京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)数据库进行富集分析,确定2组间的差异代谢通路;通过Boruta算法建立基于特征代谢物的诊断模型和绘制受试者工作特征曲线(receiver operating characteristic,ROC),确定胃癌血浆特征诊断性代谢物,并计算其在2组中的相对含量。  结果  从2组血浆提取物中共筛选出230个差异代谢物;富集到5条代谢差异通路,分别为苯丙氨酸代谢,苯丙氨酸、酪氨酸和色氨酸的生物合成,精氨酸的生物合成,组氨酸代谢,泛酸和辅酶A的生物合成;共确定9个重要的特征诊断性代谢物,分别为天门冬氨酸、苯乙胺、鸟氨酸、马尿酸、瓜氨酸、泛酰巯基乙胺、1 – 甲基组胺、酪氨酸及组氨酸。胃癌组血浆中9种特征诊断性代谢物相对含量均明显低于健康对照组(P < 0.05)。  结论  富集到的5条代谢差异通路可能参与了胃癌的发展过程,确定的9种特征诊断性代谢物可作为胃癌诊断的生物标志物。代谢组学与机器学习算法相结合有助于确定胃癌诊断标志物。
  • 图  1  差异代谢物的确定

    注:A:差异代谢物火山图;B:主成分分析图;C:正交最小偏二乘法分析图;D:最小偏二乘法分析图。

    图  2  富集分析

    注:A:注释到的代谢物富集至KEGG数据库的前25条通路的条状图;B:点状图。

    图  3  9种特征诊断性代谢物ROC曲线图

    表  1  9种特征诊断性代谢物的基本信息及ROC曲线分析结果

    KEGG 编号 中文名称 英文名称 模式精确分子质量AUC95 % CI
    C00049 天门冬氨酸 L- aspartic Acid 阴离子 133.0375 1.000
    C05332 苯乙胺 2-Phenylethylamine 阳离子 121.0891 0.880 0.775~1.000
    C00077 鸟氨酸 Ornithine 阳离子 132.0898 0.835 0.704~0.966
    C01586 马尿酸 Hippuric acid 阴离子 179.0582 0.825 0.695~0.955
    C00327 瓜氨酸 Citrulline 阴离子 175.0957 0.785 0.640~0.930
    C00831 泛酰巯基乙胺 Pantetheine 阳离子 278.1300 0.770 0.618~0.922
    C05127 1 – 甲基组胺 1-Methylhistamine 阳离子 125.0953 0.767 0.617~0.918
    C00082 酪氨酸 L-Tyrosine 阴离子 181.0739 0.762 0.611~0.914
    C00135 组氨酸 L-Histidine 阴离子 155.0695 0.742 0.586~0.899
    下载: 导出CSV

    表  2  9种特征诊断性代谢物在胃癌组和健康对照组中归一化后的相对含量[MP25P75)]

    特征诊断性代谢物胃癌组(n = 20)健康对照组(n = 20)WP
    天门冬氨酸– 0.755(– 0.851,– 0.631)0.432(0.308,0.523)400 < 0.001
    苯乙胺– 0.917(– 1.022,– 0.819)– 0.259(– 0.366,– 0.165)352 < 0.001
    鸟氨酸0.560(0.478,0.635)0.729(0.655,0.837)334 < 0.001
    马尿酸0.121(– 0.207,0.272)0.539(0.330,0.798)330 < 0.001
    瓜氨酸0.065(– 0.002,0.200)0.258(0.128,0.377)314 < 0.01
    泛酰巯基乙胺– 1.046(– 1.242,– 0.832)– 0.501(– 0.823,– 0.284)308 < 0.01
    1 – 甲基组胺– 1.320(– 1.480,– 1.134)– 0.904(– 1.186.– 0.660)307 < 0.01
    酪氨酸0.320(0.264,0.428)0.501(0.360,0.592)305 < 0.01
    组氨酸0.121(0.038,0.169)0.192(0.139,0.246)297 < 0.01
    下载: 导出CSV
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  • 接收日期:  2022-05-05
  • 网络出版日期:  2022-12-26
  • 刊出日期:  2023-02-10

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