Non-targeted metabolomics reveals diagnostic biomarker in the tongue coating of patients with chronic gastritis

Abstract

Analysis of the properties of the tongue has been used in traditional Chinese medicine for disease diagnosis. Notably, tongue analysis, which is non-invasive and convenient compared with gastroscopy and pathological examination, can be used to assess chronic gastritis (CG). In order to find potential diagnostic biomarkers and study the metabolic mechanisms of the endogenous small molecules in the tongue coating related to CG, a non-targeted metabolomic analysis method was developed using ultra high performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS). It was performed using two different columns in positive and negative ion scanning modes separately. The stability of the samples was evaluated and the age and gender factors of the subjects were excluded to ensure the reliability of the data in this study. Finally, under the four analysis models, 130, 229, 113 and 92 differential compounds were found using multivariate statistical methods respectively. 37 potential biomarkers were putatively identified after removing the duplicate compounds and five potential diagnostic biomarkers were putatively identified by receiver operating characteristic (ROC) curve analysis, including inosine, oleamide, adenosine, N-acetylglucosamine (GlcNAc) and xanthine. The main metabolic pathways associated with CG were purine metabolism, amino acid metabolism, sphingolipid metabolism and energy metabolism, which suggested that oxygen free radicals and energy metabolism were altered in patients with CG. These results provided a potential new basis for the quantitative diagnosis and pathogenesis of CG.

Keywords: Chronic gastritis; Diagnostic biomarker; Metabolomics; Tongue coating; UHPLC-Q/TOF-MS.

Fig. 1.

Flowchart of UPLC-QTOF-MS-based tongue coating metabolomics analysis approach for the CG group and HC group. The details of the sample running sequence is shown in “B”: the red, green, blue and yellow color represent the blank solution, the QC1 samples, the real samples and the QC2 samples, respectively.

Fig. 2.

The PCA score plots (A), OPLS-DA score plots (B) and 200 permutation tests (C). A: the PCA score plots for samples of CG group (green), HC group (blue), QC1 samples (yellow) and QC2 samples (red) in each model. B: the OPLS-DA score plots for the CG group (green) and HC group (blue). C: 200 permutation tests to evaluate the quality of the OPLS-DA model.

Fig. 3.

PCA analysis with 37 potential biomarkers as information screened in the positive and negative ion modes of HILIC and HSS T3.

Fig. 4.

The disturbed pathway of CG patients (left) and enrichment of the biomarkers related to CG. The color and size of each circle is based on P value and pathway impact value respectively. 1: Purine metabolism 2:Aminoacyl-tRNA biosynthesis 3: Caffeine metabolism 4: Alanine, aspartate and glutamate metabolism 5: Sphingolipid metabolism 6: Valine, leucine and isoleucine biosynthesis 7: Arginine and proline metabolism.

Fig. 5.

ROC analysis of 5 potential diagnostic biomarkers in diagnosis of CG.

Fig. 6.

Disturbed metabolic pathways related to the tongue coating of patients with CG. Metabolites with red or green words represent significant increase or decrease in CG group compared to HC group.