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.