Discussion
This study has demonstrated several advantages of CPSI-MS/ML for OSCC
diagnosis from serum samples. From the aspect of data collection
efficiency, CPSI-MS realized quick collection of high-dimension
metabolomic data from each case directly with a timescale of seconds.
The total analytical period for these two cohorts of 819 serum samples
took only 12 hours, which satisfies practical requirements for clinical
screening. CPSI-MS is quite suitable for the rapid, direct metabolomic
profiling from a dried spot of biological fluid such as saliva, serum,
or even whole blood. A basic methodology investigation was conducted in
this study. A series of serum samples were evenly distributed among the
whole test sequence. Then, the variations of the first two principal
components (PC1-PC2) were analyzed. The relative standard deviations
(RSD values) of PC1 and PC2 fall into the acceptable levels at 18.7 %
and 31.2 % (Figure S2 ), respectively, meeting the basic
requirement of qualitative analysis.34 This result is
largely because data acquisition from the whole cohort can be completed
in one working day. The short period of single case analysis by CSPI-MS
could make the large cohort assay conducted more effectively. The number
of QC samples introduced for monitoring and normalizing the MS system
variation was also reduced. This variation is a critical factor that
cannot be ignored, especially compared to data taken from traditional
LC-MS or GC-MS systems. With aid of a pre-trained machine learning
model, the high-dimension metabolome data can be transferred into
accurate diagnostic information almost instantly without biased
interpretation by practitioners, facilitating its practical value in
precision medicine.
From the studies of serum metabolomics reported here and the previous
saliva metabolomics, the OSCC-associated discriminating metabolites were
identified, respectively. The pathway enrichment analysis revealed which
metabolism pathways are influenced in serum and saliva (Table
S9 ). The four representative metabolism pathways (histidine metabolism,
arginine biosynthesis, arginine, and proline metabolism, aminoacyl-tRNA
biosynthesis) discovered in the saliva remained highlighted in the serum
level, whereas their impact or significance did not rank at the top.
Instead, lipids-related metabolism becomes the major pathways including
glycerolipid (GL), glycerophospholipid (GPL), and sphingomyelin (SM)
(Fig. 5 ). According to the fold changes of these metabolites
(Tables S3 and S4 ), the changes of many metabolites
become less obvious in serum, although the 57 discriminating metabolites
discovered in the saliva study still had abnormal abundance in serum.
This was observed mostly among the metabolites located in the histidine,
arginine, and proline metabolism pathways. which were the major changed
pathways in the saliva of the OSCC group. In contrast, the GL, GPL, and
SM molecules in serum become the major discriminating markers
(Figure S3 ).