ArticleMetabolomics : Official journal of the Metabolomic Society2022
Serum metabolomic profiling for patients with adenocarcinoma of the esophagogastric junction.
Article in Metabolomics : Official journal of the Metabolomic Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning-based prediction of gastroesophageal junction cancer using electronic medical records.Clinical and experimental medicine · 2025Article
- Preliminary Metabolomics Study Suggests Favorable Metabolic Changes in the Plasma of Breast Cancer Patients after Surgery and Adjuvant Treatment.Biomedicines · 2024Article
- Novel metabolic biomarker for early detection and diagnosis to the patients with gastric cardia adenocarcinoma.Cancer medicine · 2024Article
- Effect of two surgical approaches on the lung function and prognosis of patients with combined esophagogastric cancer.World journal of gastrointestinal surgery · 2023Article
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5 authors.
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Abstract
introductionThe incidence of adenocarcinoma in the esophagogastric junction (AEG) has increased in the recent years. AEG is reported to have a worse prognosis compared with tumor confined to the stomach (non-AEG). Although the metabolic changes of non-AEG have been investigated in extensive studies, little effort focused on the metabolic profiling of AEG serum.
objectivesHere we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to explore the abnormal metabolism underlying AEG.
methodsGC-MS-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of serum samples from AEG patients (n = 70), non-AEG patients (n = 70) and health controls (n = 71).
resultsA novel serum metabolic profiling of 18 metabolites from patients of AEG and non-AEG was indicated, in comparison with health controls. Moreover, AEG and non-AEG were also well-classified with 9 distinguishing metabolites including hypoxanthine, alanine, proline, pyroglutamate, glycine, lactate, succinic acid, glutamate and kynurenine, which produced a discriminatory model with an area under the Receiver Operating Characteristic (ROC) curve of 0.852, suggesting a distinct metabolic signature of AEG. Importantly, lactate and glutamate disclosed outcome-prediction values by multivariate cox-proportional hazard model and Kaplan-Meier method based on follow-up information for 2-5 years.
conclusionThis is the first metabolomics study to identify serum metabolic signature of AEG. The distinguishing metabolites show a promising application on clinical diagnose and outcome prediction, and allow us to unveil several key metabolic variations coexisting in AEG, which may aid to understand the underlying metabolic mechanisms.
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