Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of gastric cancer and precancerous stages

Abstract Background: Gastric cancer (GC) remains one of the most common cancers all over the world. The greatest challenge for GC is that it is often detected at advanced stages, leading to the loss of optimum time for treatment and giving rise to poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: In total, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 early gastric cancer (EGC) and 25 advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry ( UPLC-Q-TOF/MS ). Principal components analysis as well as orthogonal partial least squares-discriminant analysis was utilized to evaluate the variation on endogenous metabolites for GC patients and to screen potential biomarkers. Furthermore, the biomarker panels detected above were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathways were carried out on MetaboAnalyst. Results: Totally 50 metabolites were detected differentially expressed among CSG, EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde to be down-regulated in AGC patients. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disruptions, including amino acid and lipid metabolisms, in GC patients. Conclusions: In this study, a total of six differential metabolites that contributed to GC and precancerous stages were identified, respectively. The biomarker panels further improve diagnostic performance for detecting GC, with AUC values of more than 93.1%. It indicated that the biomarker panels may be sensitive to the early diagnosis of GC disease, which can be used as a promising diagnostic and prognostic tool for disease stratification studies.