Figure 5. The OSCC-associated metabolism pathways and the
involved major metabolites.
Because OSCC occurs in the oral cavity, cancer cells might scavenge
nutrient supply either endogenously from the local blood circulation or
exogenously from the excretion of the salivary gland. In turn, the OSCC
cells’ metabolic products will also be exchanged with the extracellular
environment and transported through the circulation
system.35 Therefore, this inter-specimen derived
difference in dysregulated metabolism pathways might be attributed to
the complex biomass transport and exchange differences between the oral
environment and endogenous circulation environment. Another possibility
for explaining why salivary discriminating metabolites have diminished
significance in serum might be caused by dilution in the global blood
circulation. This suggests the possible value of employing serum
metabolome data complementary with the salivary metabolome data for OSCC
diagnosis based on serum lipid features.
It is known that cancer cells can utilize massive nutrients to support
their uncontrolled proliferation.36 Carbohydrates,
amino acids, nucleotides as well as fatty acids were all their target
biomass not only as the basic building blocks for proteins, glycans,
nucleic acids, and bilayer lipids of membranes but also as the
functional agents such as energy fuels, signaling factors, and transport
intermediates.37-39 The dysregulation in aminoacyl
tRNA biosynthesis pathway hints at enhancing protein
synthesis.40, 41 The excessive energy consumption was
also observed by the abnormal levels of glucose, lactic acid, free fatty
acids (e.g., palmitic acid, palmitoleic acid, caprylic acid, linolenic
acid), mono-acyl glycerol [e.g., MG(14:0), MG(16:1), MG(16:0)], and
acyl carnitine (e.g., acetyl carnitine, nonanoyl carnitine,
6-keto-decanoylcarnitine, pentadecenoyl carnitine) for glycolysis and
β-oxidation in the mitochondrion. GL, GPL and SPL are not only the
critical constituents for building the bilayer membrane systems but also
served as the regulators for signaling.42 The abnormal
SPL metabolism (e.g., sphingosine 1-phosphate, sphinganine, and
phytosphingosine) suggests cell proliferation
dysregulation.43 These dysregulated metabolite markers
could not only serve as potential markers for OSCC diagnosis but also
might assist in roughly evaluating the OSCC stages.
Serum metabolome-based profiling and metabolites panel-based detection
can serve as the molecular diagnosis approach complementary with the
traditional tissue-based histopathology and routine visual examination.
More than half of serum discriminating metabolites can be traced to the
primary lesion site of OSCC tissue by the DESI-MSI confirmation, proving
the feasibility of using serum metabolome information for OSCC
detection. As for the rest of the discriminating metabolites that failed
in DESI-MSI detection, it might be attributed to the limited sensitivity
of DESI-MSI especially in these species with low abundance and
ionization efficiency. This possible explanation needs to be confirmed
in the future with the aid of an LC-MS or GC-MS system after tissue
extraction and analyte enrichment process.
It was also found that the serum metabolome has the potential for
roughly assessing OSCC stages (Fig. 4A ). Although so far, no
inter-stage statistical significance was found among any single serum
metabolite (Fig. 4B ), a clear pattern difference appears
especially when the OSCC stage was developed from T1 or T2 into T3 or T4
(Fig. 4C ). This result also emphasizes the fact that a single
metabolite signature is neither specific nor sensitive enough to
indicate the OSCC occurrence and development compared to the combination
of characteristic metabolites in the form of a diagnostic panel. The
criteria of traditional hypothetical tests in univariate analysis
(conventionally referred to P or FDR < 0.05) might be too
cautious to pick out important features in profiling-based prediction
methods. In another aspect, it also implies the importance of
multivariate analytical method or even machine learning method in
discerning these critical feature variables for the profile pattern
recognition.