Hierarchically Porous Amino-Functionalized Microporous Organic Networks Coated Fiber for Sensitive Breath Metabolite Profiling: A Noninvasive Strategy for Gastric Cancer Diagnosis

The application of solid-phase microextraction (SPME) in breath metabolomics represents a promising strategy for noninvasive clinical disease diagnostics and comprehensive metabolic monitoring. Despite its potential, SPME faces several challenges when analyzing trace-level metabolites in exhaled breath, primarily due to complex sample matrices, insufficient enrichment efficiency, and slow mass transfer kinetics. Here, we targeted aldehyde and ketone biomarkers associated with gastric cancer and engineered a hierarchically porous, amino-functionalized microporous organic network (HP-NH₂@MONs) as a high-performance SPME fiber coating. This prepared coating architecture enables rapid extraction (≤30 min) and highly selective enrichment of aldehyde and ketone biomarkers, achieving enrichment factors ranging from 185 to 6872. The exceptional adsorption performance of HP-NH₂@MONs is attributed to a synergistic dual-mode mechanism combining hydrophobic interactions with amino-group-mediated hydrogen bonding. This mechanism not only enhances the selective enrichment of aldehyde and ketone biomarkers but also provides strong anti-interference capability, thereby markedly improving the reliability, sensitivity, and robustness of the analytical method for trace biomarker detection. Coupled with gas chromatography-mass spectrometry (GC-MS), the proposed HP-NH₂@MONs-SPME-GC-MS method delivers outstanding analytical performance, characterized by high sensitivity (limits of quantification: 0.067-2.7 ppt), excellent precision (RSD ≤ 9.6%), and robust accuracy (recoveries of 82.2%-116.2%). Applied to exhaled breath samples, the method enabled sensitive detection of target biomarkers and clear differentiation between healthy individuals and gastric cancer patients. This work introduces a selective, noninvasive, and accessible analytical platform for breath metabolomics, offering innovative potential for early disease detection, therapeutic monitoring, and prognostic evaluation in clinical practice.