Integrating NMR Metabolomics and Glycomics for Early Cancer Detection in Patients with Non-Specific Symptoms

Abstract Background Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1 H-NMR-based metabolomics can identify cancer with high accuracy. SCAN2 tested whether integrating metabolomics with glycomics improves discrimination in a clinically complex, real-world population. Methods Serum from 369 SCAN patients (59 cancers) was analysed using AXINON®lipoFIT® -derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. Findings Integration of glycomics with metabolomics improved discrimination, achieving an AUC of 0.88 in a refined cohort excluding dominant comorbidities. Cancer-associated bi- and tri-antennary glycans, including FA2G2S1, FA2BG1, and M5A1G1S1, differentiated cancer cases. A classifier targeting metastatic disease achieved an AUC of 0.80. Joint probability analysis preserved cancer-associated metabolic signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.8%. Interpretation These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and demonstrate that integrating metabolomics with glycomics enhances cancer detection in patients with non-specific symptoms. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing.