Identification of Transcriptional Signatures Associated with COPD in Immune and Epithelial–Stromal Cells Using Machine Learning Methods

In the context of chronic obstructive pulmonary disease (COPD), understanding gene expression patterns across various cell types is pivotal for disease comprehension, therapeutic development, and enhancement of patient prognosis. Nevertheless, the intricate interplay between immune and structural cells, along with their significance in COPD pathogenesis, remains only partially understood. This research employed multiple machine learning techniques to pinpoint marker genes exhibiting pronounced alterations between COPD patients and healthy individuals. We utilized six feature selection algorithms (LASSO, LightGBM, MCFS, RF, CATboost, and XGBoost) and three classification methods (DT, KNN, and RF) to analyze single-cell patient data. Cells were bifurcated into two subsets: immune cells and epithelial–stromal cells. Each cell, encompassing 21,237 genes, was labeled either as a healthy control or COPD. We identified a number of promising genes, such as FKBP5, which encodes a protein crucial for stress response regulation, and SCGB3A1, which is predominantly expressed in the respiratory tract and critical for regulating lung function. Both genes exhibit strong correlations with COPD. The study also underscored the potential roles of HLA-DRB1 and HSP90AA1 in COPD pathogenesis. Our findings underscore the unparalleled potential of machine learning in identifying genetic markers for intricate diseases like COPD, paving the way for targeted therapeutic advancements.