Zhenhua Dong,1 Dingliang Zhao,2 Shaopeng Zhang,1 Kai Yu,3 Qirui Chen,4 Gaojun Wang,4 Daguang Wang1 

1Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, 130000, People’s Republic of China; 2Second Urology Department, The First Hospital of Jilin University, Changchun, Jilin Province, 130000, People’s Republic of China; 3Urology Department, The First Hospital of Jilin University, Changchun, Jilin Province, 130000, People’s Republic of China; 4Undergraduate of Clinical Medicine, Jilin University, Changchun, Jilin, 130000, People’s Republic of China

Correspondence: Daguang Wang, Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, 130000, People’s Republic of China, Tel/Fax +86 18743066256, Email dgwang@jlu.edu.cn

Purpose: Gastric cancer (GC) is a disease with high prevalence and mortality, but we lack convenient and accurate methods to screen for this disease. Thus, we aimed to search for some salivary biomarkers and explore changes in metabolites in patients’ saliva after radical gastrectomy.
Patients and Methods: A total of 152 subjects were divided into three groups (healthy group, GC group, and one-week postoperative group). After simple processing, saliva samples were analyzed by liquid chromatography–mass spectrometry. First, we used total ion chromatography and principal component analysis to determine the metabolite profiles. Next, t-test, partial least squares discriminant analysis, support vector machine, and receiver operating characteristics curve analysis were performed to identify biomarkers. Then, Fisher discriminant analysis and hierarchical clustering analysis were performed to determine the discriminating ability of biomarkers. Finally, we established a generalized linear model to predict GC based on biomarkers, and used bootstrapping for internal validation.
Results: Between the healthy and GC groups, we identified four biomarkers: lactic acid, kynurenic acid, 3-hydroxystachydrine, and S-(1,2,2-trichlorovinyl)-L-cysteine. We used stepwise regression to select five metabolites and develop a model with areas under the curve equal to 0.973 in the training dataset and 0.924 in the validation dataset. Between the GC and one-week postoperative groups, we found two differential metabolites: 19-hydroxyprostaglandin E2 and DG (14:0/0:0/18:2n6).
Conclusion: Differential metabolites were observed among the three groups. GC could be initially diagnosed on the basis of detection of these biomarkers. Moreover, changes in salivary metabolites in postoperative patients could provide important insights for basic studies.

Keywords: liquid chromatography–mass spectrometry, saliva, gastric cancer, postoperative patients, clinical prediction model