AUTHOR=Meng Ning , Wang Zhiqiang , Peng Yaqi , Wang Xiaoyan , Yue Wenju , Wang Le , Lv Jingxia , Ma Wenqian 

TITLE=Peripheral blood metabolic composite score based on peripheral blood metabolism can be used as an assessment of recurrence after surgery in patients with locally advanced gastric cancer: a novel and promising index

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1536811

DOI=10.3389/fonc.2025.1536811

ISSN=2234-943X

ABSTRACT=BackgroundPostoperative recurrence remains a major challenge in patients with locally advanced gastric cancer (LAGC). Identifying reliable biomarkers for predicting recurrence can guide clinical decision-making and improve patient outcomes. This study aimed to investigate the association between four peripheral blood metabolic markers and postoperative recurrence in LAGC patients, and to develop a predictive model based on these markers.MethodsThis retrospective cohort study analyzed data from 1,040 patients with LAGC who underwent radical surgical resection between January 2010 and December 2019. Peripheral blood metabolic indicators, including low-density lipoprotein/high-density lipoprotein (LHR), cholesterol/high-density lipoprotein (TCHR), triglycerides/high-density lipoprotein (TGHR), and triglycerides × fasting blood glucose (TyG), were used to assess metabolic status. Multivariable regression and survival analysis were performed to assess the prognostic value of these markers. A nomogram combining metabolic markers and clinical factors was developed and validated for predicting postoperative recurrence.ResultsHigh levels of LHR, TCHR, TGHR, and TyG were significantly associated with increased risk of postoperative recurrence in LAGC patients (P < 0.001). Multivariable analysis identified TNM stage, pathological type, systemic immune inflammation index (SII), and metabolic score as independent predictors of recurrence. A predictive model incorporating these factors demonstrated superior performance compared to clinical features alone, with an area under the curve (AUC) of 0.867 (95% CI: 0.836-0.897) in the training set, 0.887 (95% CI: 0.844-0.929) in internal validation set, 0.859 (95% CI: 0.817-0.899) in the external validation set. Patients with high metabolic scores had significantly worse overall survival (OS) and disease-free survival (DFS), further supporting the model’s prognostic value.ConclusionsPeripheral blood metabolic markers, particularly LHR, TCHR, TGHR, and TyG, are valuable predictors of postoperative recurrence in LAGC patients. The combined predictive model, integrating metabolic markers and clinical features, provides an effective tool for personalized risk stratification and may assist in optimizing postoperative management in LAGC.