OncoTargets and Therapy (Jul 2020)

Discovering Biomarkers in Peritoneal Metastasis of Gastric Cancer by Metabolomics

  • Pan G,
  • Ma Y,
  • Suo J,
  • Li W,
  • Zhang Y,
  • Qin S,
  • Jiao Y,
  • Zhang S,
  • Li S,
  • Kong Y,
  • Du Y,
  • Gao S,
  • Wang D

Journal volume & issue
Vol. Volume 13
pp. 7199 – 7211

Abstract

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Guoqiang Pan,1,* Yuehan Ma,1,* Jian Suo,1,* Wei Li,1 Yang Zhang,1 Shanshan Qin,2 Yan Jiao,3 Shaopeng Zhang,1 Shuang Li,1 Yuan Kong,1 Yu Du,4 Shengnan Gao,4 Daguang Wang1 1Department of Gastrointestinal Surgery, First Hospital of Jilin University, Changchun, Jilin Province 130000, People’s Republic of China; 2Department of Radiology, Affiliated Hospital of Qingdao, Qingdao 266000, People’s Republic of China; 3Department of Hepatobiliary and Pancreatic Surgery, First Hospital of Jilin University, Changchun, Jilin Province 130000, People’s Republic of China; 4Department of First Operation Room, First Hospital of Jilin University, Changchun, Jilin Province 130000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Daguang WangDepartment of Gastrointestinal Surgery, First Hospital of Jilin University, Changchun, Jilin Province 130000, People’s Republic of ChinaTel/ Fax +86 17808068189Email [email protected] and Objective: Metabolomics has recently been applied in the field of oncology. In this study, we aimed to use metabolomics to explore biomarkers in peritoneal metastasis of gastric cancer.Methods: Peritoneal lavage fluid (PLF) of 65 gastric cancer patients and related clinical data were collected from the First Hospital of Jilin University. The metabolic components were identified by liquid chromatography-mass spectrometry (LC-MS). Total ion current (TIC) spectra, principal component analysis (PCA), and the Student’s t-test were used to identify differential metabolites in PLF. A support vector machine (SVM) was used to screen the differential metabolites in PLF with a weight of 100%. Cluster analysis was used to evaluate the similarity between samples. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic ability of the metabolites. Univariate and multivariate logistic regression analyses were used to identify potential risk factors for peritoneal metastasis of gastric cancer.Results: We found the differential levels of PLF metabolites by LC-MS, TIC spectra, PCA and the t-test. Cluster analysis showed the co-occurrence of metabolites in the peritoneal metastasis group (p< 0.05). ROC analysis showed the diagnostic ability of metabolites (p< 0.05). Univariate and multivariate logistic regression analyses showed the potential independent risk factors for peritoneal metastasis in gastric cancer patients (p< 0.05).Conclusion: Through the statistical analysis of metabolomics, we found that TG (54:2), G3P, α-aminobutyric acid, α-CEHC, dodecanol, glutamyl alanine, 3-methylalanine, sulfite, CL (63:4), PE-NMe (40:5), TG (53:4), retinol, 3-hydroxysterol, tetradecanoic acid, MG (21:0/0:0/0:0), tridecanoic acid, myristate glycine and octacosanoic acid may be biomarkers for peritoneal metastasis of gastric cancer.Keywords: gastric cancer, metabolomics, peritoneal metastasis, diagnosis

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