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.