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

Gastric cancer (GC) faces a great challenge in clinical diagnosis, that it often is detected at advanced stages and there is a loss of optimum time for treatment. Thus, it is necessary to develop effective strategies for diagnosis of GC. In this study, 82 participants were enrolled, 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. Furthermore, the proposed biomarkers were used to create random forest models, in which discrimination efficiency and accuracy were ascertained by receiver operating characteristic (ROC) curve analysis. 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, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be upregulated in AGC patients, whereasl-proline, l-valine, adrenic acid, and pyruvaldehyde downregulated. Pathway analysis revealed several metabolism disorders, involving amino acids and lipid metabolism. ROC analysis demonstrated a high diagnostic performance in disease diagnosis between CSG and GC. The above results indicate that the biomarker panels are sensitive to early diagnosis of GC disease, which is expected to be a promising diagnostic tool for disease stratification studies. In this study, the authors performed plasma metabolites profiling in gastric cancer and chronic superficial gastritis patients. Pathway analysis revealed several metabolism disorders, involved in amino acids and lipid metabolisms. Based on differential metabolites signatures, biomarker panels were defined for the diagnosis of early gastric cancer with satisfactory discrimination performance, as well as for advanced gastric cancer. image