Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study

Background/Objectives: Gastric cancer (GC) is a prevalent malignant tumor worldwide, with its pathological mechanisms largely unknown. Understanding the metabolic reprogramming associated with GC is crucial for the prevention and treatment of this disease. This study aims to identify significant alt...

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Main Authors: Lina Zhou, Benzhe Su, Zexing Shan, Zhenbo Gao, Xingyu Guo, Weiwei Wang, Xiaolin Wang, Wenli Sun, Shuai Yuan, Shulan Sun, Jianjun Zhang, Guowang Xu, Xiaohui Lin
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Metabolites
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Online Access:https://www.mdpi.com/2218-1989/15/4/222
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author Lina Zhou
Benzhe Su
Zexing Shan
Zhenbo Gao
Xingyu Guo
Weiwei Wang
Xiaolin Wang
Wenli Sun
Shuai Yuan
Shulan Sun
Jianjun Zhang
Guowang Xu
Xiaohui Lin
author_facet Lina Zhou
Benzhe Su
Zexing Shan
Zhenbo Gao
Xingyu Guo
Weiwei Wang
Xiaolin Wang
Wenli Sun
Shuai Yuan
Shulan Sun
Jianjun Zhang
Guowang Xu
Xiaohui Lin
author_sort Lina Zhou
collection DOAJ
description Background/Objectives: Gastric cancer (GC) is a prevalent malignant tumor worldwide, with its pathological mechanisms largely unknown. Understanding the metabolic reprogramming associated with GC is crucial for the prevention and treatment of this disease. This study aims to identify significant alterations in metabolites and pathways related to the development of GC. Methods: A liquid chromatography–mass spectrometry-based non-targeted metabolomics data acquisition was performed on paired tissues from 80 GC patients. Differences in metabolic profiles between tumor and adjacent normal tissues were first investigated through univariate and multivariate statistical analyses. Additionally, differential correlation network analysis and a newly proposed network analysis method (NAM) were employed to explore significant metabolite pathways and subnetworks related to tumorigenesis and various TNM stages of GC. Results: Over half of the annotated metabolites exhibited significant alterations. Phosphatidylcholine (PC)_30_0 and fatty acid C20_3 demonstrated strong diagnostic performance for GC, with AUCs of 0.911 and 0.934 in the discovery and validation sets, respectively. Differential correlation network analysis revealed significant fatty acid-related metabolic reprogramming in GC with elevated levels of medium-chain acylcarnitines and increased activity of medium-chain acyl-CoA dehydrogenase, firstly observed in clinical GC tissues. Of note, using NAM, two correlation subnetworks were identified as having significant alterations across different TNM stages, centered with choline and carnitine C4_0-OH, respectively. Conclusions: The identified significant alterations in fatty acid metabolism and TNM-related metabolic subnetworks in GC tissues will facilitate future investigations into the metabolic reprogramming associated with gastric cancer.
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spelling doaj-art-bde02ac2ef41496ba576af9b732e697e2025-08-20T03:13:45ZengMDPI AGMetabolites2218-19892025-03-0115422210.3390/metabo15040222Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics StudyLina Zhou0Benzhe Su1Zexing Shan2Zhenbo Gao3Xingyu Guo4Weiwei Wang5Xiaolin Wang6Wenli Sun7Shuai Yuan8Shulan Sun9Jianjun Zhang10Guowang Xu11Xiaohui Lin12School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaDepartment of Gastric Surgery, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang 110042, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaCAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaCAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaCentral Laboratory, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang110042, ChinaCentral Laboratory, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang110042, ChinaDepartment of Gastric Surgery, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang 110042, ChinaCAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116024, ChinaBackground/Objectives: Gastric cancer (GC) is a prevalent malignant tumor worldwide, with its pathological mechanisms largely unknown. Understanding the metabolic reprogramming associated with GC is crucial for the prevention and treatment of this disease. This study aims to identify significant alterations in metabolites and pathways related to the development of GC. Methods: A liquid chromatography–mass spectrometry-based non-targeted metabolomics data acquisition was performed on paired tissues from 80 GC patients. Differences in metabolic profiles between tumor and adjacent normal tissues were first investigated through univariate and multivariate statistical analyses. Additionally, differential correlation network analysis and a newly proposed network analysis method (NAM) were employed to explore significant metabolite pathways and subnetworks related to tumorigenesis and various TNM stages of GC. Results: Over half of the annotated metabolites exhibited significant alterations. Phosphatidylcholine (PC)_30_0 and fatty acid C20_3 demonstrated strong diagnostic performance for GC, with AUCs of 0.911 and 0.934 in the discovery and validation sets, respectively. Differential correlation network analysis revealed significant fatty acid-related metabolic reprogramming in GC with elevated levels of medium-chain acylcarnitines and increased activity of medium-chain acyl-CoA dehydrogenase, firstly observed in clinical GC tissues. Of note, using NAM, two correlation subnetworks were identified as having significant alterations across different TNM stages, centered with choline and carnitine C4_0-OH, respectively. Conclusions: The identified significant alterations in fatty acid metabolism and TNM-related metabolic subnetworks in GC tissues will facilitate future investigations into the metabolic reprogramming associated with gastric cancer.https://www.mdpi.com/2218-1989/15/4/222gastric cancerfatty acid β-oxidationmetabolic reprogrammingmetabolomics
spellingShingle Lina Zhou
Benzhe Su
Zexing Shan
Zhenbo Gao
Xingyu Guo
Weiwei Wang
Xiaolin Wang
Wenli Sun
Shuai Yuan
Shulan Sun
Jianjun Zhang
Guowang Xu
Xiaohui Lin
Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study
Metabolites
gastric cancer
fatty acid β-oxidation
metabolic reprogramming
metabolomics
title Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study
title_full Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study
title_fullStr Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study
title_full_unstemmed Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study
title_short Metabolic Reprogramming of Gastric Cancer Revealed by a Liquid Chromatography–Mass Spectrometry-Based Metabolomics Study
title_sort metabolic reprogramming of gastric cancer revealed by a liquid chromatography mass spectrometry based metabolomics study
topic gastric cancer
fatty acid β-oxidation
metabolic reprogramming
metabolomics
url https://www.mdpi.com/2218-1989/15/4/222
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