Abstract
Metabolomics analysis based on body fluids, combined with high-throughput laser desorption and ionization mass spectrometry (LDI-MS), holds great potential and promising prospects for disease diagnosis and screening. On the other hand, chronic obstructive pulmonary disease (COPD) currently lacks innovative and powerful diagnostic and screening methods. In this work, CoFeNMOF-D, a metal-organic framework (MOF)-derived metal oxide nanomaterial, was synthesized and utilized as a matrix to assist LDI-MS for extracting serum metabolic fingerprints of COPD patients and healthy controls (HC). Through machine learning algorithms, successful discrimination between the COPD and HC was achieved. Furthermore, four potential biomarkers significantly downregulated in COPD were screened out. The disease diagnostic models based on the biomarkers demonstrated excellent diagnostic performance across different algorithms, with area under the curve (AUC) values reaching 0.931 and 0.978 in the training and validation sets, respectively. Finally, the potential metabolic pathways and disease mechanisms associated with the identified markers were explored. This work advances the application of LDI-based molecular diagnostics in clinical settings.
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