Diagnostic significance of plasma lipid markers and machine learning-based algorithm for gastric cancer

• Published in: Oncology letters Vol. 21; no. 5; p. 405
• Main Authors: Saito, Ryo, Yoshimura, Kentaro, Shoda, Katsutoshi, Furuya, Shinji, Akaike, Hidenori, Kawaguchi, Yoshihiko, Murata, Tasuku, Ogata, Koretsugu, Iwano, Tomohiko, Takeda, Sen, Ichikawa, Daisuke
• Format: Journal Article
• Language: English
• Published: Greece D.A. Spandidos 01.05.2021; Spandidos Publications; Spandidos Publications UK Ltd
• Subjects: Algorithms; Analysis; Biobanks; Biomarkers; Biopsy; Blood lipids; Cancer; Cancer therapies; Care and treatment; Chemotherapy; Colorectal cancer; Diagnosis; Endoscopy; Esophageal cancer; Ethylenediaminetetraacetic acid; Gastric cancer; Instrument industry; Ionization; lipidomics; Lipids; Machine learning; Mass spectrometry; Medical diagnosis; Medical prognosis; Membrane lipids; Patients; Plasma; Precision medicine; Stomach cancer; Tumors; Japan; mass spectrometry; gastric cancer; machine learning; plasma; lipidomics
• ISSN: 1792-1074; 1792-1082; 1792-1082
• DOI: 10.3892/ol.2021.12666

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.