Abstract

Esophageal cancer represents a global health challenge with a notably high incidence and poor prognosis, necessitating the development of rapid, noninvasive diagnostic methodologies. In this study, we present a high-throughput metabolomics platform leveraging a hollow-structured Co 3 O 4 @Au nanocomposite as a matrix for laser desorption/ionization mass spectrometry (LDI-MS) to diagnose esophageal cancer and differentiate it from benign esophagitis. Synthesized via a metal–organic framework (MOF) derivation strategy followed by the in situ reduction of gold nanoparticles, the Co 3 O 4 @Au matrix exhibits strong photoelectric properties, high charge separation efficiency, and robust tolerance to complex biological environments. This enables the direct, rapid extraction of serum metabolic profiles with low background interference. Leveraging this platform, serum metabolic profiles were acquired from a clinical cohort of 278 participants, including 116 healthy controls, 80 patients with esophagitis, and 82 patients with esophageal cancer. Integrated machine learning algorithms, notably the Random Forest model, demonstrated robust diagnostic performance, achieving a high AUC value for distinguishing diseased individuals from healthy controls and an AUC of 0.989 for differentiating esophageal cancer from esophagitis. Furthermore, a streamlined diagnostic panel comprising 10 core m / z features was selected, which sustained a high predictive accuracy (AUC = 0.978) and was biologically validated via SHAP interpretability analysis. This work establishes a high-throughput machine learning-driven strategy, offering a potential noninvasive tool for the mass screening and precision differential diagnosis of esophageal malignancies.