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Objective: Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways., Design and Methods: Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA)., Results: Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs., Conclusions: We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients., Competing Interests: Conflict of interest: J.C. has provided scientific advice and/or received honoraria or funding for continuous medical education from AAA, Advanz Pharma, Amgen, Bayer, Esteve, Hutchmed, Ipsen, Merck, Novartis, Roche, Sanofi, Lilly, Eisai, Bayer, and ITM, and has received research support from Pfizer, Novartis, Astrazeneca, Eisai, AAA, Amgen, Bayer, Roche, Gilead, and ITM. M.B. reports Advisory from Novartis, Ipsen, and Pfizer. P.J.-F. has received speaker honoraria or consultant honoraria from Adacap, Astellas, BMS, Lilly, and MSD. D.C. has provided scientific advice and/or received honoraria or funding for continuous medical education from Janssen Oncology, Roche/Genetech, Astellas Pharma, AstraZeneca, Pfizer, Novartis, Ipsen, BMS, MSD, Bayer, Lilly, Sanofi, Pierre Fabre, and Boerhinger. F.S. has received honoraria for medical education and research activity from Ipsen, Novartis, Pfizer, Advanced Accelerator Applications, MSD/Merck, and Hutchmed. A.T. has provided scientific advice and/or received honoraria from ADACAP, Ipsen, Novartis, and Pfizer. R.G.-C. has provided scientific advice and/or received honoraria or funding for continuous medical education from ADACAP, Advanz Pharma, Amgen, Bayer, BMS, Boerhinger, Esteve, Hutchmed, Ipsen, Merck, Midatech Pharma, MSD, Novartis, PharmaMar, Pierre Fabre, Roche, Servier, and Sanofi, and has received research support from Pfizer, BMS, and MSD. The remaining authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Society of Endocrinology.)

Purpose: High-throughput "-omic" technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential., Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC-MS, CE-MS and LC-MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential., Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential., Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.