Targeted Metabolomic Profiling of Plasma Samples in Gastric Cancer by Liquid Chromatography-Mass Spectrometry

Taisuke Matsumoto, Masakatsu Fukuzawa, Takao Itoi, Masahiro Sugimoto, Yumi Aizawa, Makoto Sunamura, Takashi Kawai, Daiki Nemoto, Hirokazu Shinohara, Takahiro Muramatsu, Yuka Suzuki, Yasuyuki Kagawa, Maya Suguro, Kumiko Uchida, Yohei Koyama, Akira Madarame, Takashi Morise, Hayato Yamaguchi, Akihiko Sugimoto, Yoshiya YamauchiShin Kono, Sakiko Naito

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Introduction: As the high mortality rate of gastric cancer (GC) is due to delayed diagnosis, early detection is vital for improved patient outcomes. Metabolic deregulation plays an important role in GC. Although various metabolite-level biomarkers for early detection have been assessed, there is still no unified early detection method. We conducted a plasma metabolome study to assess metabolites that may distinguish GC samples from non-GC samples. Methods: Blood samples were collected from 72 GC patients and 29 control participants (non-GC group) at the Tokyo Medical University Hospital between March 2020 and November 2020. Hydrophilic metabolites were identified and quantified using liquid chromatography-time-of-flight mass spectrometry. Differences in metabolite concentrations between the GC and non-GC groups were evaluated using the Mann-Whitney test. The discrimination ability of each metabolite was evaluated by the area under the receiver operating characteristic curve. A radial basis function (RBF) kernel-based support vector machine (SVM) model was developed to assess the discrimination ability of multiple metabolites. The selection of variables used for the SVM utilized a step-wise regression method. Results: Of the 96 quantified metabolites, 8 were significantly different between the GC and non-GC groups. Of these, N1-acetylspermine, succinate, and histidine were used in the RBF-SVM model to discriminate GC samples from non-GC samples. The area under the curve (AUC) of the RBF-SVM model was higher (0.915; 95% CI: 0.865-0.965, p < 0.0001), indicating good performance of the RBF-SVM model. The application of this RBF-SVM to the validation dataset resulted from the AUC of the RBF-SVM model was (0.885; 95% CI: 0.797-0.973, p < 0.0001), indicating the good performance of the RBF-SVM model. The sensitivity of the RBF-SVM model was better (69.0%) than those of the common tumor markers carcinoembryonic antigen (CEA) (10.5%) and carbohydrate antigen 19-9 (CA19-9) (2.86%). The RBF-SVM showed a low correlation with CEA and CA19-9, indicating its independence. Conclusion: We analyzed plasma metabolomics, and a combination of the quantified metabolites showed high sensitivity for the detection of GC. The independence of the RBF-SVM from tumor markers suggested that their complementary use would be helpful for GC screening.

Original languageEnglish
Pages (from-to)97-108
Number of pages12
Issue number2
Publication statusPublished - 2023 Mar 1
Externally publishedYes


  • Gastric cancer
  • Liquid chromatography-mass spectrometry
  • Metabolomics
  • Plasma

ASJC Scopus subject areas

  • Gastroenterology


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