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生物流体，可以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法（CPSI-MS）用于在几秒钟内记录多种代谢物，使该技术作为OSCC早期检测的即时解决方案具有吸引力。收集了来自373名志愿者，124名健康者，124名癌前病变和125名OSCC患者的唾液样本，以发现和验证失调的代谢产物并确定改变的代谢途径。通过解吸电喷雾离子化MS成像（DESI-MSI）在主要组织水平上再次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），OSCC和恶变前病变可以实时与正常身体状况区分开，准确率达86.7％（逐人）。这些结果表明，CPSI-MS和ML的组合是在临床实践中准确，自动诊断OSCC的可行工具。