Frontiers in immunology · 2026
Identification of plasma lipidomic biomarkers for prognostic stratification in advanced gastric cancer treated with PD-1 inhibitor plus chemotherapy.
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Zhan Zhouwei, Wang Xiaojie, He Muqun, Wei Shenghong, Yu Jiami, Zheng Hanchen, Hu Qiaoting, Chen Bijuan, Huang Sha, Guo Zengqing
Аннотация
BACKGROUND: Immunotherapy combined with chemotherapy has improved outcomes in advanced gastric cancer (GC), but reliable biomarkers to predict clinical benefit remain limited. Metabolomics provides a comprehensive assessment of systemic metabolic changes and may yield prognostic indicators to guide treatment selection.
METHODS: We performed untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics on baseline plasma from 40 patients with advanced GC receiving first-line programmed cell death protein-1 (PD-1) inhibitor plus chemotherapy. Patients were stratified into long-term survivors (LTS) and short-term survivors (STS) based on median overall survival (OS). Differential metabolites were identified using multivariate statistics, followed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis to construct a metabolite-based risk score. Prognostic performance was evaluated using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox models. Comparisons with conventional clinical factors were conducted, and a prognostic nomogram was developed. Proportional-hazards assumptions were assessed with Schoenfeld residuals; discrimination was optimism-corrected using 1,000-bootstrap resampling; Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and bootstrap-corrected calibration curves were reported. Twelve-month decision-curve analysis (DCA) quantified net clinical benefit compared with clinicopathologic baselines.
RESULTS: A total of 4,298 metabolites were detected, including 830 Level 1 and 1,321 Level 2 identifications. Principal component analysis and orthogonal partial least squares discriminant analysis showed clear separation between LTS and STS groups. Differential analysis revealed metabolites enriched in bile acid, amino acid, and retinol metabolism pathways. Cox and LASSO analyses identified six independent prognostic metabolites. The resulting metabolite-based risk score significantly stratified OS and progression-free survival (p < 0.01) and demonstrated stable predictive accuracy over 6-24 months. Compared with age, sex, tumor grade, and programmed death-ligand 1 combined positive score (PD-L1 CPS), the risk score showed superior discrimination. A nomogram integrating risk score, grade, and PD-L1 CPS yielded accurate OS predictions with strong calibration and higher net benefit in DCA. Internal validation supports the robustness of findings within this single-center, 40-patient cohort.
CONCLUSIONS: A plasma metabolite-based risk score derived from six biomarkers independently predicts survival in advanced GC treated with PD-1-based immunotherapy and offers a practical tool for individualized prognosis.
METHODS: We performed untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics on baseline plasma from 40 patients with advanced GC receiving first-line programmed cell death protein-1 (PD-1) inhibitor plus chemotherapy. Patients were stratified into long-term survivors (LTS) and short-term survivors (STS) based on median overall survival (OS). Differential metabolites were identified using multivariate statistics, followed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis to construct a metabolite-based risk score. Prognostic performance was evaluated using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox models. Comparisons with conventional clinical factors were conducted, and a prognostic nomogram was developed. Proportional-hazards assumptions were assessed with Schoenfeld residuals; discrimination was optimism-corrected using 1,000-bootstrap resampling; Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and bootstrap-corrected calibration curves were reported. Twelve-month decision-curve analysis (DCA) quantified net clinical benefit compared with clinicopathologic baselines.
RESULTS: A total of 4,298 metabolites were detected, including 830 Level 1 and 1,321 Level 2 identifications. Principal component analysis and orthogonal partial least squares discriminant analysis showed clear separation between LTS and STS groups. Differential analysis revealed metabolites enriched in bile acid, amino acid, and retinol metabolism pathways. Cox and LASSO analyses identified six independent prognostic metabolites. The resulting metabolite-based risk score significantly stratified OS and progression-free survival (p < 0.01) and demonstrated stable predictive accuracy over 6-24 months. Compared with age, sex, tumor grade, and programmed death-ligand 1 combined positive score (PD-L1 CPS), the risk score showed superior discrimination. A nomogram integrating risk score, grade, and PD-L1 CPS yielded accurate OS predictions with strong calibration and higher net benefit in DCA. Internal validation supports the robustness of findings within this single-center, 40-patient cohort.
CONCLUSIONS: A plasma metabolite-based risk score derived from six biomarkers independently predicts survival in advanced GC treated with PD-1-based immunotherapy and offers a practical tool for individualized prognosis.