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基于肠道菌群的帕金森病早期预测模型建立及生物信息分析

何长颖 韦雨婷 陈佳

何长颖, 韦雨婷, 陈佳. 基于肠道菌群的帕金森病早期预测模型建立及生物信息分析[J]. 中华疾病控制杂志, 2024, 28(9): 1096-1103. doi: 10.16462/j.cnki.zhjbkz.2024.09.016
引用本文: 何长颖, 韦雨婷, 陈佳. 基于肠道菌群的帕金森病早期预测模型建立及生物信息分析[J]. 中华疾病控制杂志, 2024, 28(9): 1096-1103. doi: 10.16462/j.cnki.zhjbkz.2024.09.016
HE Changying, WEI Yuting, CHEN Jia. Establishment and bioinformatics analysis of an early prediction model for parkinson′s disease based on gut microbiota[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1096-1103. doi: 10.16462/j.cnki.zhjbkz.2024.09.016
Citation: HE Changying, WEI Yuting, CHEN Jia. Establishment and bioinformatics analysis of an early prediction model for parkinson′s disease based on gut microbiota[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1096-1103. doi: 10.16462/j.cnki.zhjbkz.2024.09.016

基于肠道菌群的帕金森病早期预测模型建立及生物信息分析

doi: 10.16462/j.cnki.zhjbkz.2024.09.016
基金项目: 

国家自然科学基金 82173621

详细信息
    通讯作者:

    陈佳,E-mail:1805633325@qq.com

  • 中图分类号: R183.1

Establishment and bioinformatics analysis of an early prediction model for parkinson′s disease based on gut microbiota

Funds: 

General Project of National Natural Science Foundation of China 82173621

More Information
  • 摘要:   目的  探究基于肠道菌群对人群未来患帕金森症(parkinson′s disease, PD)的早期预测模型进行构建与评价,并对肠道菌群宏基因KO组进行功能分析,探讨PD的潜在治疗靶点。  方法  基于Zenodo数据库,一方面对肠道菌群相对丰度数据进行标准分数标准化及ZicoSeq降维,采用基于自适应最小绝对收缩和选择算法(least absolute shrinkage and selection operator, LASSO)变量选择的logistic回归算法建立预测模型,使用受试者工作特征(receiver operating characteristic, ROC)曲线下面积(area under curve, AUC)和校准曲线评价模型预测效能,采用临床决策曲线(decision curve analysis, DCA)进行临床使用价值的评价;另一方面,对肠道菌群宏基因KO组数据使用limma包鉴定差异表达基因(differentially expressed genes, DEGs),对DEGs进行基因本体(gene ontology, GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)分析。通过结合蛋白质相互作用网络(protein-protein interaction networks, PPI)、支持向量机-递归特征消除(support vector machine-recursive feature elimination,SVM-RFE)和随机森林(random forest, RF)对DEGs进行筛选。  结果  基于自适应LASSO变量选择的logistic回归分析模型的ROC曲线以及校准曲线显示模型预测效果良好。DCA结果显示模型净收益较大。通过PPI网络分析及机器学习方法,最终筛选出6个核心DEGs,即阿拉伯糖转运系统渗透蛋白(L-arabinose operon Q, araQ)、甘油醛-3-磷酸脱氢酶,Ⅱ型、dCTP脱氨酶(dCTP deaminase, dcd)、颗粒19 kDa蛋白(signal recognition particle 19, SRP19)、加工前体5,核糖核酸酶P/MRP亚基(芽殖酵母)[processing of precursor 5, ribonuclease P/MRP subunit (S. cerevisiae), POP5]、肌醇-3-磷酸合酶1(inositol-3-phosphate synthase 1, ISYNA1)。  结论  基于自适应LASSO变量选择的logistic回归分析模型对PD的预测具有优势,从而实现对PD患者的早发现、早干预、早治疗;相关核心基因的发现为PD的治疗提供科学指导和帮助。
  • 图  1  基于自适应LASSO变量选择的logistic回归的变量选择

    A:基于自适应LASSO变量选择的logistic回归分析模型的log(λ)与自变量数目对应曲线;上横坐标为不同log(λ)对应的模型中非零系数自变量的个数;B:基于自适应LASSO变量选择的logistic回归分析模型中每条曲线代表每个自变量系数的变化轨迹;上横坐标为不同log(λ)对应的模型中非零系数自变量的个数; a: 平均均方误差最小时最优调和系数; b: 平均均方误差一个标准误内最优调和系数。

    Figure  1.  Variable selection based on adaptive LASSO binary logistic

    A: the log (λ) curve corresponding to the number of independent variables in the adaptive LASSO binary logistic regression model; The upper horizontal axis represents the number of non-zero coefficient independent variables in the model corresponding to different logs (λ); B: the change trajectory of each independent variable coefficient represented by each curve in the adaptive LASSO binary logistic regression model; The upper horizontal axis represents the number of non-zero coefficient independent variables in the model corresponding to different logs (λ); a: the optimal harmonic coefficient with the minimum mean square error; b: the mean square error is the best harmonic coefficient within one standard error.

    图  2  帕金森症早期预测模型的评价指标

    A:帕金森症早期预测模型受试者工作特征曲线图;B:帕金森症早期预测模型校准曲线图;C:帕金森症早期预测模型决策分析曲线图。

    Figure  2.  Evaluation indicators for early parkinson′s disease prediction models

    A: the receiver operating characteristic curve of the parkinson′s disease early prediction model; B: the calibration curve of the early parkinson′s disease prediction model; C: the decision analysis curve of the early parkinson′s disease prediction model.

    图  3  差异表达基因蛋白质-蛋白质相互作用网络

    Figure  3.  Protein-protein interaction network of DEGs

    图  4  通过机器学习筛选核心基因及其差异表达

    A: SVM-RFE的十折交叉验证曲线;B: RF的袋外错误率与特征数量关系图;C: 挑选出的所有核心DEGs的差异表达箱线图; a表示组与组之间的显著性差异P值<0.001,b表示组与组之间的显著性差异P值<0.01。

    Figure  4.  Screening core genes through machine learning and their differential expression

    A: the ten fold cross validation curve of SVM-RFE; B: the relationship between out of bag error rate and feature quantity in RF; C: the differential expression box plots of all selected DEGs; a represents a significant difference between groups, with a P < 0.001, b represents a significant difference between groups with a P < 0.01.

    表  1  与帕金森症高度相关的9个协变量

    Table  1.   covariates highly related to parkinson′disease

    变量
    Variable
    帕金森症组
    Parkinson′s group (n=435)
    正常组
    Normal group (n=219)
    t/χ2值 value 帕金森症组与正常组对比
    Parkinson′s group versus normal group
    OR值 value
    (95% CI)
    P值 value
    年龄/岁 Age/years 68.5±8.5 65.8±8.8 3.749 1.1(1.3~4.1) <0.001
    男性 Male 274(63) 65(30) 64.729 4.0(2.8~5.8) <0.001
    饮酒 Drinking 160(37) 112(51) 12.365 0.6(0.4~0.8) <0.001
    泻药 Laxative 131(30) 23(11) 31.124 3.7(2.2~6.2) <0.001
    益生菌 Probiotics 53(12) 41(19) 5.059 0.6(0.4~0.9) 0.033
    疼痛 Pain 100(23) 35(16) 4.366 1.6(1.0~2.5) 0.041
    抑郁 Depression 165(38) 50(23) 15.051 2.1(1.4~3.1) <0.001
    抗组胺药 Antihistamine 80(16) 70(32) 21.810 0.4(0.3~0.6) <0.001
    安眠药 Sleep aid 175(40) 58(26) 12.001 1.9(1.3~2.7) <0.001
    注:①以人数(占比/%)或x±s表示。
    Note:①Number of people(proportion/%) or x±s.
    下载: 导出CSV

    表  2  DEGs在GO分析和KEGG分析中主要富集的前10个通路

    Table  2.   The top ten pathways that DEGs is mainly enriched in GO analysis and KEGG analysis

    GO分析 GO analysis 通路 Pathway
    生物学过程 Biological process 细胞质翻译 Cytoplasmic translation
    基底切除修复、间隙填充 Based-excision repair, gap-filling
    翻译终止 Translation termination
    翻译启动 Translation initiation
    翻译延伸 Translation elongation
    以DNA为模板复制 DNA-templated DNA replication
    细胞核DNA复制 Nuclear DNA replication
    碱基切除修复 Base-excision repair
    细胞周期DNA复制 Cell cycle DNA replication
    DNA复制 DNA replication
    细胞成分 Cellular components 胞质核糖体 Cytosolic ribosome
    胞质核糖体大亚基 Cytosolic large ribosomal subunit
    核糖体亚基 Ribosomal subunit
    核糖体大亚基 Large ribosomal subunit
    核糖体 Ribosome
    黏着斑 Focal adhesion
    细胞-基底结 Cell-substrate junction
    复制叉 Replication fork
    多聚核糖体 Polysome
    线粒体基质 Mitochondrial matrix
    分子功能 Molecular functions 翻译因子活性,RNA结合 Translation factor activity, RNA binding
    翻译调节活性,核酸结合 Translation regulator activity nucleic acid binding
    核糖体的结构成分 Structural constituent of ribosome
    翻译延伸因子 Translation elongation factor activity
    核糖体结合 Ribosome binding
    核糖核蛋白复合物结合 Ribonucleoprotein complex binding
    分子功能 Molecular functions 翻译调节活性Translation regulator activity
    翻译起始因子活性 Translation initiation factor activity
    催化活性,作用于 DNA Catalytic activity, acting on DNA
    镁离子结合 Magnesium ion binding
    KEGG富集分析 KEGG enrichment analysis DNA复制 DNA replication
    核糖体 Ribosome
    新冠肺炎病毒 Cornavirus disease-COVID-19
    碱基切除修复 Base excision repair
    错配修复 Mismatch repair
    核苷酸切除修复 Nucleotide excison repair
    丁酸代谢 Butanoate metabolism
    缬氨酸、亮氨酸和异亮氨酸降解 Valine, lecucine and isoleucine degradation
    非同源末端连接 Non-homologous end-joining
    信使核糖核苷酸检测途径 mRNA surveilance pathway
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-21
  • 修回日期:  2024-05-04
  • 网络出版日期:  2024-10-24
  • 刊出日期:  2024-09-10

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