Evidence map›Paper›PMID 35441991›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2022

Serum metabolomic profiling for patients with adenocarcinoma of the esophagogastric junction.

Yinan Chen, Lei Hu, Hexin Lin, Huangdao Yu, Jun You

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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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4citing papers in PubMed
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4 citing papers in PubMed.

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5 · Who and what money

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5 authors.

Yinan Chen *Department of Gastrointestinal Surgery, Cancer Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361000, China.
Lei Hu *Department of General Surgery, The First Affliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Hexin Lin *Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China.
Huangdao YuDepartment of Gastrointestinal Surgery, Cancer Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361000, China.
Jun YouDepartment of Gastrointestinal Surgery, Cancer Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361000, China. youjunxm@163.com.ORCID 0000-0002-4646-1422

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AdenocarcinomaStomach NeoplasmsEsophagogastric JunctionGlutamic AcidHumansLactic AcidMetabolomicsGlutamic AcidLactic AcidAdenocarcinoma of the esophagogastric junctionGas chromatography-mass spectrometryMetabolomicsOutcomeSerumStomach cancer

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.