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Cholangiocarcinoma (CCA) has high recurrence rates that severely limit long-term survival. Effective tools for accurate recurrence monitoring and diagnosis remain lacking. Metabolic reprogramming, a key driver of CCA growth and recurrence, is underutilized in cancer screening and management. This study aimed to identify metabolite-based biomarkers to evaluate recurrence severity, enhance disease management, and elucidate the molecular mechanisms underlying CCA recurrence. A comprehensive, non-targeted serum metabolomics analysis using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was conducted. Support Vector Machine (SVM) modeling was employed to develop a predictive framework based on metabolite biomarkers. The analysis revealed significant alterations in metabolomics and lipidomics across CCA recurrence subtypes. Notably, changes in metabolites such as amino acids, lipid-derived carnitines, and glycerophospholipids were associated with cancer progression through enhanced energy production and lipid remodeling. The SVM-constructed metabolite-based predictive model demonstrated predictive accuracy comparable to current clinical diagnostic standards. These findings provide novel insights into the metabolic mechanisms underlying CCA recurrence, addressing critical clinical challenges. By advancing early diagnostic approaches, particularly for preoperative detection, this study offers a reliable method for predicting recurrence in CCA patients. This enables effective treatment planning and supports the development of personalized therapeutic strategies, ultimately improving patient outcomes.
Prajumwongs et al. (Mon,) studied this question.