Yang Li-Jie, Song Na-Na, Deng Ni-Shan, Zhu Miao-Juan, Li Qing-Qing, Huang Si-Si, Shi Xiu, Hu Zhen-Hong, Nie Han-Xiang
Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Sci Rep. 2025 Jul 9;15(1):24779. doi: 10.1038/s41598-025-09979-9.
This study aims to explore the association between anoikis-related genes (ARGs) and asthma. The dataset GSE143303 for asthma were sourced from the GEO database, while ARGs were retrieved from the Harmonizome web portal and the GeneCards database. Differentially expressed genes (DEGs) identification and GO, KEGG enrichment analysis were performed to reveal potential biological pathways. To identify hub anoikis-related DEGs (hub ARDEGs), we employed WGCNA and machine learning methods including LASSO and Random Forest. Additionally, we constructed risk prediction nomogram model and ROC curves to evaluate the asthma diagnostic value of hub ARDEGs. SsGSEA immune infiltration analysis was used to analyze the role of hub ARDEGs in the asthma immune microenvironment. Finally, miRNAs and transcription factors (TFs) interacting with these hub ARDEGs were investigated. DEGs of ARGs between asthma and healthy controls, along with WGCNA, led to the identification of six ARDEGs. GO and KEGG analyses revealed that these ARDEGs were primarily involved in the apoptotic signaling pathway and adherens junctions. Machine learning methods further narrowed down the six ARDEGs to two hub ARDEGs: PARP1 and SDCBP, which were significantly upregulated in asthma and validated using the GSE147878 and experimental models. Based on these two hub ARDEGs, a risk prediction model for asthma was developed, demonstrating strong diagnostic potential and tissue specificity in endobronchial biopsies. Immune analysis revealed variations in immune cell infiltration within asthma samples correlated with hub ARDEGs. Additionally, the miRNA-TF-mRNA interaction network of hub ARDEGs highlights the complexity of the regulatory process. The process of anoikis, immune dysregulation, and asthma are closely interconnected. The anoikis-related biomarkers PARP1 and SDCBP may serve as diagnostic markers and therapeutic targets for asthma.
本研究旨在探讨失巢凋亡相关基因（ARGs）与哮喘之间的关联。哮喘数据集GSE143303来源于GEO数据库，而ARGs则从Harmonizome网站门户和GeneCards数据库中检索获得。进行差异表达基因（DEGs）鉴定以及GO和KEGG富集分析，以揭示潜在的生物学途径。为了鉴定核心失巢凋亡相关DEGs（核心ARDEGs），我们采用了加权基因共表达网络分析（WGCNA）以及包括套索回归（LASSO）和随机森林在内的机器学习方法。此外，我们构建了风险预测列线图模型和ROC曲线，以评估核心ARDEGs对哮喘的诊断价值。采用单样本基因集富集分析（SsGSEA）免疫浸润分析来分析核心ARDEGs在哮喘免疫微环境中的作用。最后，研究了与这些核心ARDEGs相互作用的微小RNA（miRNAs）和转录因子（TFs）。哮喘组与健康对照组之间ARGs的DEGs以及WGCNA分析，鉴定出了6个ARDEGs。GO和KEGG分析表明，这些ARDEGs主要参与凋亡信号通路和黏着连接。机器学习方法进一步将这6个ARDEGs缩小至2个核心ARDEGs：聚ADP核糖聚合酶1（PARP1）和硫酸乙酰肝素蛋白聚糖结合蛋白（SDCBP），它们在哮喘中显著上调，并在GSE147878和实验模型中得到验证。基于这两个核心ARDEGs，开发了一种哮喘风险预测模型，该模型在支气管活检中显示出强大的诊断潜力和组织特异性。免疫分析显示，哮喘样本中免疫细胞浸润的变化与核心ARDEGs相关。此外，核心ARDEGs的miRNA-TF-mRNA相互作用网络突出了调控过程的复杂性。失巢凋亡过程、免疫失调和哮喘密切相关。失巢凋亡相关生物标志物PARP1和SDCBP可能作为哮喘的诊断标志物和治疗靶点。
