通过生物信息学分析和机器学习识别哮喘和抑郁症的共同潜在诊断标志物
2024/06/26
摘要
背景：越来越多的证据表明，哮喘可能会加重抑郁症。
目的：我们试图调查患有哮喘和抑郁症的患者的候选诊断基因。
方法：从基因表达综合数据库（GEO）下载微阵列数据，用于筛选SA和MDD数据集中的差异表达基因（DEGs）。采用加权基因共表达网络分析（WGCNA）鉴定SA和MDD共表达模块。采用最小绝对值收敛和选择算子（LASSO）和支持向量机（SVM）确定关键生物标志物。免疫细胞浸润分析研究免疫细胞浸润与SA和MDD常见生物标志物的相关性。最后，通过使用体内和体外研究来验证这些分析结果。
结果：MDD数据集中包含的DEG数量为5177，而哮喘数据集为1634 个DEG。SA和MDD的DEGs交集包括351个基因，其中SA和MDD最强的阳性模块为119个基因，在免疫中起作用。DEGs与模块化枢纽基因的交集点为54个，通过机器学习算法进行分析，鉴定出3个枢纽基因，用于列线图和诊断效果评价，具有显著的诊断价值（曲线下面积从0.646到0.979）。此外，免疫细胞紊乱是通过免疫浸润来识别的。体外研究表明，STK11IP缺乏加剧了 LPS/IFN-γ 诱导的 M1 巨噬细胞活化上调。
结论：哮喘和MDD病理生理学可能与炎症过程和免疫途径的改变有关。此外，STK11IP可以作为患有这两种疾病的个体的诊断标志物。
Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.
Hui, Jiang; Chang-Yong, Fu
Abstrast
Background：There is mounting evidence that asthma might exacerbate depression.
Objective: We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression.
Methods: Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies.
Results：The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation.
Conclusions: Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.
背景：越来越多的证据表明，哮喘可能会加重抑郁症。
目的：我们试图调查患有哮喘和抑郁症的患者的候选诊断基因。
方法：从基因表达综合数据库（GEO）下载微阵列数据，用于筛选SA和MDD数据集中的差异表达基因（DEGs）。采用加权基因共表达网络分析（WGCNA）鉴定SA和MDD共表达模块。采用最小绝对值收敛和选择算子（LASSO）和支持向量机（SVM）确定关键生物标志物。免疫细胞浸润分析研究免疫细胞浸润与SA和MDD常见生物标志物的相关性。最后，通过使用体内和体外研究来验证这些分析结果。
结果：MDD数据集中包含的DEG数量为5177，而哮喘数据集为1634 个DEG。SA和MDD的DEGs交集包括351个基因，其中SA和MDD最强的阳性模块为119个基因，在免疫中起作用。DEGs与模块化枢纽基因的交集点为54个，通过机器学习算法进行分析，鉴定出3个枢纽基因，用于列线图和诊断效果评价，具有显著的诊断价值（曲线下面积从0.646到0.979）。此外，免疫细胞紊乱是通过免疫浸润来识别的。体外研究表明，STK11IP缺乏加剧了 LPS/IFN-γ 诱导的 M1 巨噬细胞活化上调。
结论：哮喘和MDD病理生理学可能与炎症过程和免疫途径的改变有关。此外，STK11IP可以作为患有这两种疾病的个体的诊断标志物。
(中日友好医院呼吸与危重症医学科 万静萱 摘译 林江涛 审校)
(Int Immunopharmacol 2024 May 30;133(0):112064; doi: 10.1007/s40259-024-00653-6. IF：3.943)
(Int Immunopharmacol 2024 May 30;133(0):112064; doi: 10.1007/s40259-024-00653-6. IF：3.943)
Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.
Hui, Jiang; Chang-Yong, Fu
Abstrast
Background：There is mounting evidence that asthma might exacerbate depression.
Objective: We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression.
Methods: Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies.
Results：The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation.
Conclusions: Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.
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嗜酸性粒细胞过氧化物酶作为哮喘嗜酸性粒细胞炎症生物标志物的效用
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重度哮喘合并支气管扩张患者痰中TGFβ1、VEGF和IFN-γ升高
