Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods.
Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods.
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DOI：
10.1186/s12920-023-01552-5
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发表时间：
2023-06-17
影响因子：
2.7
通讯作者：
中科院分区：
文献类型：
--
作者：
本研究的目的是通过机器学习的方法，构建一种用于特应性皮炎(AD)准确诊断的模型。从分子特征数据库(MSigDB)中获取上睑下垂相关基因(PRGS)。从基因表达总表(GEO)数据库下载GSE120721、GSE6012、GSE32924和GSE153007的芯片数据。GSE120721和GSE6012的数据合并为训练组，其他数据作为测试组。随后，从训练组中提取PRGS的表达并进行差异表达分析。CiberSort算法计算免疫细胞的渗透率，并进行差异表达分析。一致性聚类分析根据PRGS的表达水平将AD患者分为不同的模块。然后，运用加权相关网络分析(WGCNA)对关键模块进行筛选。对于关键模块，我们使用了随机森林(RF)、支持向量机(SVM)、极端梯度增强(XGB)和广义线性模型(GLM)来构建诊断模型。对于模型重要性最高的五个PRBM，我们建立了一个诺模图。最后，利用GSE32924和GSE153007数据集对模型结果进行了验证。9个PRG在正常人和AD患者之间有显著差异。免疫细胞浸润显示AD患者活化的CD_4+ 记忆T细胞和树突状细胞(DC)显著高于正常人，而激活的自然杀伤细胞(NK)和静息肥大细胞显著低于正常人。一致性聚类分析将表达矩阵分为2个模块。随后，WGCNA分析表明，绿松石模数差异显著，相关系数较高。然后，建立了机床模型，结果表明XGB模型是最优模型。利用HDAC1、GPALPP1、LGALS3、SLC29A1和RWDD3 5个PRBM构建了诺模图。最后，通过GSE32924和GSE153007两个数据集验证了该结果的可靠性。基于5个PRBM的XGB模型可用于AD患者的准确诊断。网上版载有补充材料，可在10.1186/s12920-023-01552-5查阅。
The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. The pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets. Nine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result. The XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients. The online version contains supplementary material available at 10.1186/s12920-023-01552-5.
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影响因子：
3
作者：
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影响因子：
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DOI：
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发表时间：
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影响因子：
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