Zhang Ya-Da, Chen Yi-Ren, Zhang Wei, Tang Bin-Qing
Department of Pneumology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Pulmonary Disease, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
J Asthma. 2025 Mar;62(3):465-480. doi: 10.1080/02770903.2024.2409991. Epub 2024 Oct 12.
Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlying severe asthma and to detect key molecular biomarkers.
We used Weighted Gene Co-Expression Network Analysis (WGCNA) to detect hub genes in the GSE143303 dataset and indicated their functions and regulatory mechanisms using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) annotations. In the GSE147878 dataset, we used Gene Set Enrichment Analysis (GSEA) to determine the regulatory directions of gene sets. We detected differentially expressed genes in the GSE143303 and GSE64913 datasets, constructed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and validated the model using the GSE147878 dataset and real-time quantitative PCR (RT-qPCR) to confirm the molecular biomarkers.
Using WGCNA, we discovered modules that were strongly correlated with clinical features, specifically the purple module ( = 0.53) and the midnight blue module ( = -0.65). The hub genes within these modules were enriched in pathways related to mitochondrial function and oxidative phosphorylation. GSEA in the GSE147878 dataset revealed significant enrichment of upregulated gene sets associated with oxidative phosphorylation and downregulated gene sets related to asthma. We discovered 12 commonly regulated genes in the GSE143303 and GSE64913 datasets and developed a LASSO regression model. The model corresponding to lambda.min selected nine genes, including TFCP2L1, KRT6A, FCER1A, and CCL5, which demonstrated predictive value. These genes were significantly upregulated or under expressed in severe asthma, as validated by RT-qPCR.
Mitochondrial abnormalities affecting oxidative phosphorylation play a critical role in severe asthma. Key molecular biomarkers like TFCP2L1, KRT6A, FCER1A, and CCL5, are essential for detecting severe asthma. This research significantly enhances the understanding and diagnosis of severe asthma.
重度哮喘与典型哮喘有显著差异，涉及特定的分子生物标志物，有助于加深我们的理解和诊断能力。本研究的目的是评估重度哮喘背后的生物学过程，并检测关键分子生物标志物。
我们使用加权基因共表达网络分析（WGCNA）在GSE143303数据集中检测枢纽基因，并使用京都基因与基因组百科全书（KEGG）通路分析和基因本体（GO）注释来表明它们的功能和调控机制。在GSE147878数据集中，我们使用基因集富集分析（GSEA）来确定基因集的调控方向。我们在GSE143303和GSE64913数据集中检测差异表达基因，构建最小绝对收缩和选择算子（LASSO）回归模型，并使用GSE147878数据集和实时定量PCR（RT-qPCR）验证该模型以确认分子生物标志物。
使用WGCNA，我们发现了与临床特征密切相关的模块，特别是紫色模块（=0.53）和午夜蓝模块（=-0.65）。这些模块中的枢纽基因富集于与线粒体功能和氧化磷酸化相关的通路。GSE147878数据集中的GSEA显示，与氧化磷酸化相关的上调基因集和与哮喘相关的下调基因集有显著富集。我们在GSE143303和GSE64913数据集中发现了12个共同调控的基因，并开发了一个LASSO回归模型。对应于lambda.min的模型选择了9个基因，包括TFCP2L1、KRT6A、FCER1A和CCL5，这些基因具有预测价值。经RT-qPCR验证，这些基因在重度哮喘中显著上调或下调。
影响氧化磷酸化的线粒体异常在重度哮喘中起关键作用。像TFCP2L1、KRT6A、FCER1A和CCL5这样的关键分子生物标志物对于检测重度哮喘至关重要。这项研究显著提高了对重度哮喘的理解和诊断。
