Conclusion
We find that the serum metabolic profile obtained by CPSI-MS and analyzed using machine learning can reflect oral cancer development. Most discovered significant metabolites in serum were also founded in saliva and cancer tissue, demonstrating the potential of serum for in vitro molecular diagnosis of OSCC. By cohort analysis using CPSI-MS, we found that histidine metabolism, arginine and proline metabolism, sphingolipid metabolism, and aminoacyl-tRNA biosynthesis were present in serum. These findings provide potential clinical markers for indicating OSCC tumorigenesis. We have demonstrated that CPSI-MS is a promising ambient ionization mass spectrometry tool that offers cost-effective performance in monitoring hundreds of biofluidic metabolites only with minor sample pretreatment. The combination of CPSI-MS with ML enabled excellent molecular diagnosis (89.6 % accuracy). All these findings indicate that CPSI-MS/ML can be a very useful tool for providing a simple, fast, affordable diagnostic method for OSCC screening.