Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.

Abstract

Background: Peptidomics is an emerging tool for biomarker discovery. By capturing end products and active peptides generated during protein breakdown, it helps reveal short peptides linked to disease. This narrative review centers on chronic obstructive pulmonary disease (COPD) and synthesizes recent advances in respiratory peptidomics across patient-accessible matrices and laboratory workflows toward treatable-trait translation.

Main body: We conducted a structured literature search of PubMed, Embase, and Web of Science (January 2000-September 2025, with emphasis on the most recent five years), prioritizing mass-spectrometry-based discovery and targeted verification in human biospecimens. We distinguish clinical endogenous peptidomics, immunopeptidomics, and degradomics as complementary approaches in respiratory disease. Six representative peptide classes were compared across pre-analytical handling, enrichment strategies, and MS identification, building an evidence map for COPD, asthma, lung cancer, and pulmonary fibrosis. Using this map, we discuss matrix-technology fit and recurrent biological signals-airway inflammation, extracellular matrix turnover, and host-pathogen interaction-that show promise for disease subtyping and early diagnosis. For translation, we outline a stepwise pathway: (i) harmonized sampling and internal-standard-driven quality control; (ii) transparent modeling with calibration and decision-curve analysis; and (iii) multicenter external validation. We further consider integration with proteomics and breathomics, emerging peptide-drug leads, and open sharing of data and code to improve reproducibility and transferability.

Conclusions: Peptidomics is poised to contribute clinically actionable biomarker panels in respiratory disease, with near-term opportunities in COPD phenotyping and exacerbation risk assessment using sputum and blood. Broad adoption will depend on standardized pre-analytics, feasible targeted assays in routine laboratories, robust external validation, and transparent model calibration.