Hu Wenmin, Sun Jingjing, Wang Mei, Wang Yaoyao, Mu Chaohui, Yu Xinjuan, Yuan Peng, Han Wei, Li Yongchun, Li Qinghai
Qingdao Key Laboratory of Common Diseases, Qingdao Municipal Hospital, School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong, 266071, People's Republic of China.
Department of Respiratory and Critical Care Medicine, Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences, Qingdao, 266071, People's Republic of China.
J Inflamm Res. 2025 Sep 6;18:12263-12278. doi: 10.2147/JIR.S534626. eCollection 2025.
Chronic obstructive pulmonary disease (COPD) pathogenesis involves persistent airway inflammation and remodeling, yet the role of anoikis resistance remains poorly characterized. This study aimed to identify anoikis resistance-related hub genes and evaluate their clinical utility in COPD phenotyping and prognosis.
Integrated bioinformatics analysis of the GSE11906 dataset identified anoikis resistance-related differentially expressed genes (DEGs). Functional enrichment, LASSO regression, and machine learning (RF, SVM, XGB, GLM) were employed to pinpoint core hub genes. Multi-level validation included external datasets (GSE19407), in vitro (CSE-stimulated 16HBE cells), in vivo (cigarette smoke-exposed mice), and clinical samples (PBMCs). Diagnostic and prognostic models were developed using logistic regression.
Five core hub genes (UCHL1, ME1, SLC2A1, BMP4, CRABP2) were identified, with ME1, SLC2A1, and BMP4 consistently upregulated in COPD across models and strongly correlated with emphysema index (negative, R = -0.41 to -0.45) and airway wall thickness (positive, R = 0.40-0.45). These genes exhibited significant associations with peribronchial immune cell infiltration. Diagnostic models for emphysema-predominant COPD (AUC = 0.860) and disease staging (AUC = 0.882), along with a prognostic model for hospitalization duration (AUC = 0.867), demonstrated robust clinical performance.
ME1, SLC2A1, and BMP4 are pivotal anoikis resistance-related biomarkers in COPD, driving immune dysregulation and structural remodeling. The developed models enable precise phenotyping, severity stratification, and personalized prognosis prediction, advancing precision medicine strategies for COPD management.
慢性阻塞性肺疾病（COPD）的发病机制涉及持续性气道炎症和重塑，但失巢凋亡抗性的作用仍未得到充分描述。本研究旨在鉴定与失巢凋亡抗性相关的核心基因，并评估其在COPD表型分析和预后中的临床应用价值。
对GSE11906数据集进行综合生物信息学分析，以鉴定与失巢凋亡抗性相关的差异表达基因（DEG）。采用功能富集、LASSO回归和机器学习（随机森林、支持向量机、极端梯度提升、广义线性模型）来确定核心基因。多层次验证包括外部数据集（GSE19407）、体外实验（CSE刺激的16HBE细胞）、体内实验（香烟烟雾暴露小鼠）和临床样本（外周血单核细胞）。使用逻辑回归建立诊断和预后模型。
鉴定出五个核心基因（泛素羧基末端水解酶L1、苹果酸酶1、溶质载体家族2成员1、骨形态发生蛋白4、细胞视黄酸结合蛋白2），在各模型中，苹果酸酶1、溶质载体家族2成员1和骨形态发生蛋白4在COPD中持续上调，且与肺气肿指数（负相关，R = -0.41至-0.45）和气道壁厚度（正相关，R = 0.40 - 0.45）密切相关。这些基因与支气管周围免疫细胞浸润显著相关。以肺气肿为主的COPD诊断模型（曲线下面积 = 0.860）和疾病分期模型（曲线下面积 = 0.882），以及住院时间预后模型（曲线下面积 = 0.867）均显示出强大的临床性能。
苹果酸酶1、溶质载体家族2成员1和骨形态发生蛋白4是COPD中关键的失巢凋亡抗性相关生物标志物，驱动免疫失调和结构重塑。所建立的模型能够实现精确的表型分析、严重程度分层和个性化预后预测，推动COPD管理的精准医学策略发展。
