Prajumwongs Piya, Titapun Attapol, Thanasukarn Vasin, Jareanrat Apiwat, Khuntikeo Natcha, Namwat Nisana, Klanrit Poramate, Wangwiwatsin Arporn, Chindaprasirt Jarin, Koonmee Supinda, Sa-Ngiamwibool Prakasit, Muangritdech Nattha, Roytrakul Sittiruk, Loilome Watcharin
Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand.
Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Sci Rep. 2025 Apr 14;15(1):12782. doi: 10.1038/s41598-025-97641-9.
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.
胆管癌（CCA）的复发率很高，严重限制了长期生存率。目前仍缺乏用于准确复发监测和诊断的有效工具。代谢重编程是CCA生长和复发的关键驱动因素，但在癌症筛查和管理中未得到充分利用。本研究旨在识别基于代谢物的生物标志物，以评估复发严重程度、加强疾病管理，并阐明CCA复发的分子机制。我们使用超高效液相色谱与四极杆飞行时间质谱联用进行了全面的非靶向血清代谢组学分析。采用支持向量机（SVM）建模，基于代谢物生物标志物建立了一个预测框架。分析揭示了CCA复发亚型之间代谢组学和脂质组学的显著变化。值得注意的是，氨基酸、脂质衍生的肉碱和甘油磷脂等代谢物的变化通过增强能量产生和脂质重塑与癌症进展相关。基于SVM构建的基于代谢物的预测模型显示出与当前临床诊断标准相当的预测准确性。这些发现为CCA复发的代谢机制提供了新的见解，解决了关键的临床挑战。通过推进早期诊断方法，特别是术前检测，本研究提供了一种预测CCA患者复发的可靠方法。这有助于制定有效的治疗计划，并支持个性化治疗策略的开发，最终改善患者预后。
