Effect of Helicobacter Pylori on Plasma Metabolic Phenotype in Patients With Gastric Cancer

Abstract

Background: Although Helicobacter pylori (Hp) as high risk factor for gastric cancer have been investigated from human trial, present data is inadequate to explain the effect of Hp on the changes of metabolic phenotype of gastric cancer in different stages.

Purpose: Herein, plasma of human superficial gastritis (Hp negative and positive), early gastric cancer and advanced gastric cancer analyzed by UPLC-HDMS metabolomics can not only reveal metabolic phenotype changes in patients with gastric cancer of different degrees (30 Hp negative, 30 Hp positive, 20 early gastric cancer patients, and 10 advanced gastric cancer patients), but also auxiliarily diagnose gastric cancer.

Results: Combined with multivariate statistical analysis, the results represented biomarkers different from Hp negative, Hp positive, and the alterations of metabolic phenotype of gastric cancer patients. Forty-three metabolites are involved in amino acid metabolism, and lipid and fatty acid metabolism pathways in the process of cancer occurrence, especially 2 biomarkers glycerophosphocholine and neopterin, were screened in this study. Neopterin was consistently increased with gastric cancer progression and glycerophosphocholine tended to consistently decrease from Hp negative to advanced gastric cancer.

Conclusion: This method could be used for the development of rapid targeted methods for biomarker identification and a potential diagnosis of gastric cancer.

Keywords: Helicobacter pylori; biomarkers; gastric cancer; metabolomics.

Figure 1.

Flowchart of the study strategy in this study.

Figure 2.

Metabolomic profiling of plasma samples from 4 groups identifies metabolites that distinguish patients with Gastric Cancer: (A) 2D PCA score plot in positive ion mode; (B) 2D PCA score plot in negative ion mode. Abbreviations: PCA, principal component analysis.

Figure 3.

Box-and-whisker plot analysis and heatmap analysis based on orthogonal partial least squares-discriminant analysis models of Gastric Cancer patients. (A) Heatmap of 2 significantly altered plasma metabolites (glycerophosphocholine and neopterin); (B) and (C) 2 significantly altered plasma metabolites (glycerophosphocholine and neopterin) in the different stages. The letters of A, B, C1, and C2 represent the Hp negative patients, Hp positive patients, early gastric cancer patients, and advanced gastric cancer patients, respectively. Different lowercase letters show significant differences at p < 0.05. Abbreviations: Hp, Helicobacter pylori.

Figure 4.

ROC analysis based on OPLS-DA models of Gastric Cancer patients biomarkers. ROC curve of the diagnostic performance of the significantly altered plasma metabolites (A) glycerophosphocholine and (B) neopterin. (C) The AUC, 95% confidence interval, sensitivity, and specificity for 2 metabolites were mentioned. Abbreviations: AUC: area under curve; OPLS-DA: orthogonal partial least squares discriminant analysis; ROC, receiver operating characteristic curve.