Preoperative plasma ceramide profiling coupled with machine learning accurately predicts recurrence of hepatocellular carcinoma after resection

作者：Lei Yang, Chen Xie, Xuming Mo, Baoxiong Zhuang, Qingping Li, Cuiting Liu, Leyi Liao, Biao Wang, Minghui Zeng, Shanhua Tang, Haiqing Liu, Yuancan Xiao, Shushu Li, Dongqing Cai, Chuanjiang Li, Jie Zhou, Jieyuan Li, Yiyi Li, Kai Wang · 发表于：Lipids in Health and Disease · 年份：2025 · DOI：10.1186/s12944-025-02749-6 · 被引用次数：3 · 研究领域：Sphingolipid Metabolism and Signaling、Metabolomics and Mass Spectrometry Studies、Sepsis Diagnosis and Treatment

BACKGROUND: Accurate stratification of recurrence risk after curative resection remains a critical challenge in the management of hepatocellular carcinoma (HCC). Dysregulated ceramide (CER) metabolism has been implicated in HCC progression and relapse. This paper evaluates whether preoperative plasma CER profiling coupled with machine learning (ML) enhances the risk prediction of HCC recurrence. METHODS: In this retrospective study, 257 HCC patients undergoing curative resection participated. Preoperative plasma CERs were quantified by targeted Lipidomics. Independent predictors were identified via multivariate Cox regression and incorporated into ten ML models. Using an internal 20% validation cohort, model performance was assessed by the area under the curve (AUC), concordance index (C-index), calibration, and decision curve analysis. Model interpretability employed Shapley additive explanations (SHAP), correlation analysis, and Bayesian network-based causal inference. The model's risk stratification capability was evaluated. This study was registered on clinicaltrials.gov (NCT06623474). RESULTS: Preoperative plasma CERs exhibited significant prognostic value in patients with HCC after curative resections. Multivariate analyses revealed that serum α-fetoprotein (AFP), tumor size, CER(d18:1/20:1), and CER(d18:1/22:1) independently predicted recurrence, and these variables were incorporated into ten ML models. Among them, the gradient boosting machine (GBM) algorithm demonstr...