Department of Chemistry, Fudan University, 200438 Shanghai, China.
Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, 210000 Nanjing, Jiangsu, China.
Proc Natl Acad Sci U S A. 2020 Jul 14;117(28):16167-16173. doi: 10.1073/pnas.2001395117. Epub 2020 Jun 29.
Saliva is a noninvasive biofluid that can contain metabolite signatures of oral squamous cell carcinoma (OSCC). Conductive polymer spray ionization mass spectrometry (CPSI-MS) is employed to record a wide range of metabolite species within a few seconds, making this technique appealing as a point-of-care method for the early detection of OSCC. Saliva samples from 373 volunteers, 124 who are healthy, 124 who have premalignant lesions, and 125 who are OSCC patients, were collected for discovering and validating dysregulated metabolites and determining altered metabolic pathways. Metabolite markers were reconfirmed at the primary tissue level by desorption electrospray ionization MS imaging (DESI-MSI), demonstrating the reliability of diagnoses based on saliva metabolomics. With the aid of machine learning (ML), OSCC and premalignant lesions can be distinguished from the normal physical condition in real time with an accuracy of 86.7%, on a person by person basis. These results suggest that the combination of CPSI-MS and ML is a feasible tool for accurate, automated diagnosis of OSCC in clinical practice.
唾液是一种非侵入性的生物体液，其中可能包含口腔鳞状细胞癌（OSCC）的代谢物特征。导电聚合物喷雾电离质谱（CPSI-MS）可在几秒钟内记录广泛的代谢物种类，这使得该技术成为一种有吸引力的即时护理方法，可用于早期检测 OSCC。收集了 373 名志愿者的唾液样本，其中 124 名是健康的，124 名患有癌前病变，125 名是 OSCC 患者，用于发现和验证失调的代谢物，并确定代谢途径的改变。通过解吸电喷雾电离 MS 成像（DESI-MSI）在原发组织水平上重新确认了代谢物标志物，证明了基于唾液代谢组学的诊断的可靠性。借助机器学习（ML），可以实时以 86.7%的准确率（逐个）区分 OSCC 和癌前病变与正常生理状况。这些结果表明，CPSI-MS 和 ML 的结合是一种可行的工具，可用于临床实践中准确、自动诊断 OSCC。
