Applications of Mass Spectrometry in Dentistry

Mass Spectrometry (MS) is one of the fastest-developing methods in analytical instrumentation. As a highly sensitive, universal detector, it can identify known and unknown compounds, which can indeed be found in a minimal concentration. This review aims to highlight the significant milestones in MS applications in dentistry during recent decades. MS can be applied in three different fields of dentistry: (1) in research of dental materials and chemical agents, (2) in laboratory analysis of biospecimens, and (3) as a real-time diagnostic tool in service of oral surgery and pathology. MS applications on materials and agents may focus on numerous aspects, such as their clinical behavior, possible toxicity, or antimicrobial properties. MS is also a valuable, non-invasive tool for biomarkers' detection in saliva and has found great application in -omics technologies as it achieves efficient structure-finding in metabolites. As metabolites are located beyond the central dogma, this technique can provide a complete understanding of cellular functions. Thus, it is possible to determine the biological profile in normal and pathological conditions, detect various oral or systematic diseases and conditions, and predict their course. Lastly, some promising advances concerning the surgical approach to potentially oral malignant or malignant disorders exist. This breakthrough method provides a comprehensive approach to dental materials research and biomarker discovery in dental and craniofacial tissues. The current availability of various 'OMIC' approaches paves the way for individualized dentistry and provides suggestions for clinical applications in the point-of-care hubs.

Metabolomics and Transcriptomics Analysis on Metabolic Characteristics of Oral Lichen Planus

Objective

To explore metabolic biomarkers related to erosive and reticulated oral lichen planus (OLP) by non-targeted metabolomics methods and correlate metabolites with gene expression, and to investigate the pathological network pathways of OLP from the perspective of metabolism.

Methods

A total of 153 individuals were enrolled in this study, including 50 patients with erosive oral lichen planus (EOLP), 51 patients with reticulated oral lichen planus (ROLP), and 52 healthy controls (HC). The ultra-high-performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry (UHPLC/Q-Orbitrap HRMS) was used to analyze the metabolites of 40 EOLP, 40 ROLP, and 40 HC samples, and the differential metabolic biomarkers were screened and identified. The regulatory genes were further screened through the shared metabolites between EOLP and ROLP, and cross-correlated with the OLP-related differential genes in the network database. A “gene-metabolite” network was constructed after finding the key differential genes. Finally, the diagnostic efficiency of the biomarkers was verified in the validation set and a diagnostic model was constructed.

Result

Compared with HC group, a total of 19 and 25 differential metabolites were identified in the EOLP group and the ROLP group, respectively. A total of 14 different metabolites were identified between EOLP and ROLP. Two diagnostic models were constructed based on these differential metabolites. There are 14 differential metabolites shared by EOLP and ROLP. The transcriptomics data showed 756 differentially expressed genes, and the final crossover network showed that 19 differential genes were associated with 12 metabolites. Enrichment analysis showed that alanine, aspartate and glutamate metabolism were closely associated with the pathogenesis of OLP.

Conclusion

The metabolic change of different types of OLP were clarified. The potential gene perturbation of OLP was provided. This study provided a strong support for further exploration of the pathogenic mechanism of OLP.

Discrimination of oral squamous cell carcinoma from oral lichen planus by salivary metabolomics

OBJECTIVE

This study was conducted to distinguish salivary metabolites in oral squamous cell carcinoma (OSCC) from those in oral lichen planus (OLP) to identify practical biomarkers for the discrimination of OSCC from OLP.

SUBJECTS AND METHODS

Whole unstimulated saliva samples were collected from patients with OSCC (n = 34) and OLP (n = 26). Hydrophilic metabolites in the saliva samples were comprehensively analysed by capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate OSCC from OLP.

RESULTS

Fourteen metabolites were found to be significantly different between the OSCC and OLP groups. Among them, indole-3-acetate and ethanolamine phosphate were used to develop the MLR model. The combination of these two metabolites showed a high area under the receiver operating characteristic curve (0.856, 95% confidential interval: 0.762-0.950; p < .001) for discriminating OSCC from OLP.

CONCLUSIONS

We identified salivary metabolites for discerning between OSCC and OLP, which is clinically important for detecting the malignant transformation of OLP by both dentists and oral surgery specialists. Our candidate salivary metabolites show potential for non-invasive screening of OSCC versus OLP.