TY - JOUR AB - Biomarkers may be of value for the early detection of gastric cancer (GC) and the preoperative identification of tumor characteristics to guide treatment strategies. The present study analyzed the expression levels of phospholipids in plasma from patients with GC using liquid chromatography/electrospray ionization‑mass spectrometry (LC/ESI‑MS) to detect reliable biomarkers for GC. Furthermore, combining the results with a machine learning strategy, the present study attempted to establish a diagnostic system for GC. A total of 20 plasma samples from preoperative patients with GC and 16 plasma samples from tumor‑free patients (controls) were selected from our biobank named ‘SHINGEN (Yamanashi Biobank of Gastroenterological Cancers)’, which includes a total of 1,592 plasma samples, and were analyzed by LC/ESI‑MS. The obtained data were discriminated using a machine learning‑based diagnostic algorithm, whose discriminant ability was confirmed through leave‑one‑out cross‑validation. Using LC/ESI‑MS, the levels of 236 lipid molecules were determined. Biomarker analysis revealed that a few lipids that were downregulated in the GC group could discriminate between the GC and control groups. Whole lipid composition analysis using partial least squares regression revealed good discrimination ability between the GC and control groups. Integrative analysis of all molecules using the aforementioned machine learning method exhibited a diagnostic accuracy of 94.4% (specificity, 93.8%; sensitivity, 95.0%). In conclusion, the outcomes of the present study suggested the potential future application of the aforementioned system in clinical settings. By accumulating more reliable data, the present system will be able to detect early‑stage cancer and will be capable of predicting the efficacy of each therapeutic strategy. AD - First Department of Surgery, Faculty of Medicine, University of Yamanashi, Chuo, Yamanashi 4093898, Japan Department of Anatomy and Cell Biology, Faculty of Medicine, University of Yamanashi, Chuo, Yamanashi 4093898, Japan MS Business Unit, Life Science Business Department, Analytical and Measuring Instruments Division, Shimadzu Corporation, Kyoto 6048511, Japan AU - Saito,Ryo AU - Yoshimura,Kentaro AU - Shoda,Katsutoshi AU - Furuya,Shinji AU - Akaike,Hidenori AU - Kawaguchi,Yoshihiko AU - Murata,Tasuku AU - Ogata,Koretsugu AU - Iwano,Tomohiko AU - Takeda,Sen AU - Ichikawa,Daisuke DA - 2021/05/01 DO - 10.3892/ol.2021.12666 IS - 5 JO - Oncol Lett KW - gastric cancer lipidomics mass spectrometry machine learning plasma PY - 2021 SN - 1792-1074 1792-1082 SP - 405 ST - Diagnostic significance of plasma lipid markers and machine learning‑based algorithm for gastric cancer T2 - Oncology Letters TI - Diagnostic significance of plasma lipid markers and machine learning‑based algorithm for gastric cancer UR - https://doi.org/10.3892/ol.2021.12666 VL - 21 ER -