基于机器学习鉴定早产儿支气管肺发育不良的关键基因

doi:10.3877/cma.j.issn.1673-5250.2023.04.010

中华妇幼临床医学杂志(电子版) ›› 2023, Vol. 19 ›› Issue (04) : 446 -454. doi: 10.3877/cma.j.issn.1673-5250.2023.04.010

论著

基于机器学习鉴定早产儿支气管肺发育不良的关键基因

胡诤贇, 史建伟, 申建伟, 王冰, 蒋春苗, 刘冲^†(

)

上海市松江区中心医院儿科，上海　201600
南京医科大学附属脑科医院神经外科，南京　210024

收稿日期:2022-12-18 修回日期:2023-05-10 出版日期:2023-08-01
通信作者: 刘冲

Identification of hub genes associated with bronchopulmonary dysplasia in preterm infants based on machine learning

Zhengyun Hu, Jianwei Shi, Jianwei Shen, Bing Wang, Chunmiao Jiang, Chong Liu^†()

Department of Pediatrics, Shanghai Songjiang District Central Hospital, Shanghai 201600, China
Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, Jiangsu Province, China

Received:2022-12-18 Revised:2023-05-10 Published:2023-08-01
Corresponding author: Chong Liu
Supported by:
National Natural Science Foundation of China(82270068)

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引用本文：

胡诤贇, 史建伟, 申建伟, 王冰, 蒋春苗, 刘冲. 基于机器学习鉴定早产儿支气管肺发育不良的关键基因[J/OL]. 中华妇幼临床医学杂志(电子版), 2023, 19(04): 446-454.

Zhengyun Hu, Jianwei Shi, Jianwei Shen, Bing Wang, Chunmiao Jiang, Chong Liu. Identification of hub genes associated with bronchopulmonary dysplasia in preterm infants based on machine learning[J/OL]. Chinese Journal of Obstetrics & Gynecology and Pediatrics(Electronic Edition), 2023, 19(04): 446-454.

目的

探讨基因于机器学习对早产儿支气管肺发育不良(BPD)关键基因(hub基因)筛选和鉴定，为揭示早产儿BPD发病机制提供理论依据。

方法

从基因表达综合(GEO)数据库，获取68例BPD早产儿(研究组)和43例同期出生的非BPD早产儿(对照组)的血清标本数据集GSE32472基因微阵列，利用加权基因共表达网络分析(WGCNA)，筛选颜色模块hub基因集。通过最小绝对收缩和选择算子(LASSO)回归分析依据惩罚值(λ值)，计算每个基因系数，并筛选早产儿BPD的候选hub基因。通过随机森林分析结果，筛选早产儿BPD的hub基因颜色模块中排名前10的hub基因。经LASSO回归模型和随机森林分析结果所筛选基因取交集后，获得BPD的6个hub基因。

结果

①通过R软件4.1.3的"WGCNA"程序包，得到无标度拓扑拟合指数略高于0.9，设定尺度自由度曲线软阈值为18，识别并聚类BPD的hub基因模块，得出11种特征hub基因颜色模块。进一步分析发现，yellow模块的基因与早产儿BPD发生显著相关性。②通过R软件的"glmnet"程序包，对yellow模块的189个基因进行LASSO回归分析，实现L1正则化项参数估计和变量筛选。模型参数的大部分回归系数趋于0，可有效避免训练数据过拟合。使用十折交叉验证进行数据集的模型验证BPD的候选hub基因，当模型中包含41个候选hub基因时，模型的预测误差达到最小，对应的λ值为0.011 4。③利用R软件的"randomForest"程序包，对yellow模块的189个基因进行随机森林分析的结果显示，SPON1、TMEM204、CD28、ICOS、LOC100996619、NOL9、GCSAM、UBASH3A、CCNI2、AQP3这10个候选基因在分析BPD的候选hub基因中的重要性评分>1.0，明显超过其他候选基因。④数据集GSE32472基因微阵列中，与BPD的yellow模块hub基因，经LASSO回归分析和随机森林分析后，最终得出BPD的6个hub基因为：SPON1、TMEM204、CD28、ICOS、LOC100996619、NOL9。

结论

通过构建BPD共表达hub基因调控网络，并依据机器学习算法筛选出6个BPD相关hub基因，这为探索BPD的发病机制和潜在的治疗靶点奠定了理论基础。

关键词: 支气管肺发育不良, 婴儿, 早产, 加权基因共表达网络, LASSO回归, 随机森林, hub基因

Objective

This study aims to apply gene-based machine learning to screen and identify key genes (hub genes) related to bronchopulmonary dysplasia (BPD) in preterm infants, providing theoretical insights into the pathogenesis of BPD.

Methods

We obtained gene microarray dataset GSE32472, including 68 BPD preterm infants (research group) and 43 non-BPD preterm infants born during the same period (control group) from the Gene Expression Omnibus (GEO) database. Using Weighted Gene Co-expression Network Analysis (WGCNA), we screened the hub gene set from color modules. Through least absolute shrinkage and selection operator(LASSO) regression analysis based on penalty values (λ values), we calculated coefficients for each gene and selected candidate hub genes for BPD in preterm infants. Random forest analysis results were used to screen the top 10 hub genes in the color module of BPD in preterm infants. After taking the intersection of the genes selected through LASSO regression and random forest analysis, we identified 6 hub genes for BPD.

Results

① Through the " WGCNA" package in R software (version 4.1.3), we achieved a scale-free topology fit index slightly above 0.9, setting the soft-thresholding power to 18 for scale independence. This allowed us to identify and cluster hub gene modules of BPD, yielding 11 characteristic hub gene color modules. Further analysis revealed that genes in the yellow module showed significant correlation with the incidence of BPD in preterm infants. ②Using the " glmnet" package in R software, we performed LASSO regression analysis on 189 genes in the yellow module, achieving L1 regularization parameter estimation and variable selection. Most of the model parameter regression coefficients tended to zero, effectively avoiding overfitting of the training data. We employed ten-fold cross-validation to validate the model of the dataset of candidate hub genes for BPD. The prediction error of the model was minimized when it included 41 candidate hub genes, corresponding to a λ value of 0.0114. ③With the " randomForest" package in R software, our random forest analysis of the 189 genes in the yellow module showed that ten candidate genes (SPON1, TMEM204, CD28, ICOS, LOC100996619, NOL9, GCSAM, UBASH3A, CCNI2, AQP3) had an importance score of over 1.0 in the analysis of candidate hub genes for BPD, significantly surpassing other candidate genes. ④In the GSE32472 gene microarray dataset, six hub genes for BPD (SPON1, TMEM204, CD28, ICOS, LOC100996619, NOL9) were identified in the yellow module related to BPD, following LASSO regression analysis and random forest analysis.

Conclusions

By constructing a co-expression hub gene regulatory network for BPD and selecting six BPD-related hub genes based on machine learning algorithms, we have laid a theoretical foundation for exploring the pathogenesis of BPD and potential treatment targets.

Key words: Bronchopulmonary dysplasia, Infant, premature, Weighted gene co-expression network, LASSO regression, Random forest, The hub genes

图1 尺度自由度曲线的软阈值筛选(图1A：尺度自由度曲线的软阈值的无标度拟合指数分析；图1B：尺度自由度曲线的不同软阈值平均连通性分析)注：soft threshold (power)为软阈值功率。scale free topology model fit, signed R²为无标度拓扑模型拟合指数，scale independence为尺度独立性，mean connectivity为平均连通度

图2 GSE32472数据集样本的动态树对离群值的去除(每1条线代表1个样本，GSM开头的一串数字为每个样本的编号，通过聚类分析的方法寻找，并且删除离群样本)注：height为高度

图3 GSE32472数据集中2组BPD早产儿的血清标本hub基因的颜色模块聚类注：height为高度，ME为模块特征基因，BPD为支气管肺发育不良

图4 根据所选mRNA中拓扑重叠的不同，直接创建共表达WGCNA模块的聚类基因树状图(不同的颜色代表不同hub基因模块)注：height为高度，dynamic tree cut为动态树切割，WGCNA为加权基因共表达网络分析

图5 GSE32472数据集中2组早产儿BPD的共表达WGCNA模块与临床性状的关系(每行代表1个颜色模块，每列表示临床性状，单元格包含相关系数及P值。红色表示候选hub基因与BPD发生呈正相关关系，蓝色呈负相关关系，颜色越深说明相关性越大)注：WGCNA为加权基因共表达网络分析，ME为模块特征基因，BPD为支气管肺发育不良

图6 yellow模块hub基因回归系数变化图注：coefficients为系数估计值，fraction deviance explained为模型解释的响应变量的方差比例

图7 交叉验证参数惩罚系数λ的选择图注：binomial deviance为二项式离差(1种衡量二项式响应变量拟合度的统计量)

图8 不同回归树的随机森林回归误差趋势注：error为交叉验证的误差，trees为变量选择时建立的回归树

图9 早产儿BPD的hub候选基因在随机森林中的变量重要性评分注：IncNodePurity为节点纯度增益。BPD为支气管肺发育不良

图10 LASSO回归与随机森林分析相结合后的交集基因韦恩图注：LASSO为最小绝对收缩和选择算子

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Identification of hub genes associated with bronchopulmonary dysplasia in preterm infants based on machine learning

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Identification of hub genes associated with bronchopulmonary dysplasia in preterm infants based on machine learning