Differential diagnosis of lung cancer and benign lung lesion using salivary metabolites: A preliminary study

• Published in: Thoracic cancer 2022-02, Vol.13 (3), p.460-465
• Main Authors: Takamori, Satoshi, Ishikawa, Shigeo, Suzuki, Jun, Oizumi, Hiroyuki, Uchida, Tetsuro, Ueda, Shohei, Edamatsu, Kaoru, Iino, Mitsuyoshi, Sugimoto, Masahiro
• Format: Article
• Language: English
• Subjects: benign lung lesion; Biomarkers; Cancer therapies; Lung cancer; Mass spectrometry; Metabolites; Original; saliva; Scientific imaging; Software; Tomography; Tumors
• ISSN: 1759-7706; 1759-7714
• DOI: 10.1111/1759-7714.14282

Background Saliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non‐invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL). Materials and Methods Unstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite. Results The profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516–0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back‐selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598–0.861, p = 0.003). Conclusion The four salivary metabolites can serve as potential non‐invasive biomarkers for distinguishing LC from BLL. ROC curve of the MLR model based on four metabolites for distinguishing patients with lung cancer from those with benign lung lesion.