Background@#
Proteomics and
genomics studies have contributed to
understanding the pathogenesis of
chronic obstructive pulmonary disease (
COPD), but previous studies have limitations. Here, using a
machine learning (ML)
algorithm, we attempted to identify pathways in cultured bronchial
epithelial cells of
COPD patients that were significantly affected when the
cells were exposed to a
cigarette smoke extract (CSE). @*
Methods@#Small
airway epithelial cells were collected from
patients with
COPD and those without
COPD who underwent
bronchoscopy. After expansion through
primary cell culture, the
cells were treated with or without CSEs, and the
proteomics of the
cells were analyzed by
mass spectrometry. ML-based feature selection was used to determine the most distinctive patterns in the
proteomes of
COPD and non-
COPD cells after exposure to
smoke extract.Publicly available single-
cell RNA sequencing data from
patients with
COPD (GSE136831) were used to analyze and validate our findings. @*Results@#Five
patients with
COPD and five without
COPD were enrolled, and 7,953
proteins were detected.
Ferroptosis was enriched in both
COPD and non-
COPD epithelial cells after their exposure to
smoke extract. However, the ML-based
analysis identified
ferroptosis as the most dramatically different response between
COPD and non-
COPD epithelial cells, adjusted P value = 4.172 × 10−6 , showing that
epithelial cells from
COPD patients are particularly vulnerable to the effects of
smoke. Single-
cell RNA sequencing data showed that in
cells from
COPD patients,
ferroptosis is enriched in basal, goblet, and club
cells in
COPD but not in other
cell types. @*Conclusion@#Our ML-based feature selection from proteomic data reveals
ferroptosis to be the most distinctive feature of cultured
COPD epithelial cells compared to non-
COPD epithelial cells upon exposure to
smoke extract.