Statistical Analysis
The unsupervised metabolic profile differentiation between OSCC and HC
groups was first conducted with the t-stochastic neighbor embedding
(t-SNE) in the MATLAB program. Rank sum test was first implemented
separately among the two cohorts to search the OSCC and HC groups for
significantly changed metabolite ions. The false discovery rate (FDR)
was estimated with Benjamini and Hochberg method to adjust p value and
assess the statistical significance.32 The ion will be
selected if its FDR value is lower than 0.05. Only ions that are
significantly changed both in the discovery cohort and validation cohort
will be regarded as potential serum metabolite markers. Finally, a
metabolite with fold change larger than 1.5 or smaller than 0.67 will be
included for further validation at the tissue section by DESI-MSI.
Orthogonal partial least squares discriminant analysis (OPLS-DA) was
used for OSCC staging by aid of SIMCA-P (Umetrics, Umea, Sweden).
Variables with importance in projection (VIP) values higher than 1.5
were considered to contribute strongly to the pattern recognition of
different OSCC stages. Prism (GraphPad Software, USA) was employed for
preparing box plots, heatmaps, and receiver operating characteristic
(ROC) curves.