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

Hypomethylation-associated Sox11 upregulation promotes oncogenesis via the PI3K/AKT pathway in OLP-associated OSCC

Oral lichen planus (OLP) is a particularly prevalent oral disorder with the potential to progress to oral squamous cell carcinoma (OSCC). SRY-box transcription factor 11 (Sox11) has been reported to serve as a prognostic marker for various cancers. However, the role and mechanism of Sox11 in OLP-related OSCC are unknown. Our results indicated that Sox11 was highly expressed, and that Sox11 promoter methylation was significantly reduced in OLP-associated OSCC tissues. High Sox11 expression and Sox11 promoter hypomethylation indicate a poor patient prognosis. According to in vivo and in vitro experiments, the knockdown of Sox11 inhibited proliferation, invasion, and migration while driving its apoptotic death in OSSC cells; Sox11 overexpression exerted the opposite effect as Sox11 knockdown. Mechanistically, knockdown of Sox11 inhibited PI3K/AKT and glycolysis pathway, and overexpression of Sox11 enhanced the PI3K/AKT and glycolysis pathways in OSCC cells. In addition, we demonstrated that Sox11 overexpression accelerated the progression of OSCC, at least in part by promoting PI3K/AKT pathway activation. In conclusion, our data indicated that the DNA hypomethylation-associated upregulation of Sox11 could promote oncogenic transformation via the PI3K/AKT pathway in OLP-associated OSCC. Therefore, Sox11 might be a reliable biomarker for predicting the progression of precancerous oral tissues.

A simple and effective method for smartphone-based detection of polyamines in oral cancer

Oral cancer accounts for 50%-70% of all cancer-related deaths in India and ranks sixth among the most frequent cancers globally. Roughly 90% of oral malignancies are histologically arise from squamous cells and are therefore called oral squamous cell carcinoma. Organic polycations known as biogenic polyamines, for example, putrescine (Put), spermidine (Spd), and spermine (Spm), are vital for cell proliferation, including gene expression control, regulation of endonuclease-mediated fragmentation of DNA, and DNA damage inhibition. Higher Spm and Spd levels have been identified as cancer biomarkers for detecting tumour development in various cancers. The current study utilises tannic acid, a polyphenolic compound, as a reducing and capping agent to fabricate AuNPs via a one-step microwave-assisted synthesis. The fabricated TA@AuNPs were utilised as a nanoprobe for colourimetric sensing of polyamines in PBS. When TA@AuNPs are added to the polyamine, the amine groups in polyamines interact with the phenolic groups of TA@AuNPs via hydrogen bonding or electrostatic interactions. These interactions cause the aggregation of TA@AuNPs, resulting in a red shift of the Surface Plasmon Resonance band of TA@AuNPs from 530 nm to 560 nm. The nanoprobe was found to be highly specific for Spm at low concentrations. TA@AuNPs were able to detect Spm successfully in artificial saliva samples. On recording the RGB values of the sensing process using a smartphone app, it was found that as the nanoparticles aggregated due to the presence of Spm, the intensity of theR-value decreased, indicating the aggregation of TA@AuNPs due to interaction with the polyamine.

Salivary Metabolites Produced by Oral Microbes in Oral Diseases and Oral Squamous Cell Carcinoma: A Review

In recent years, salivary metabolome studies have provided new biological information and salivary biomarkers to diagnose different diseases at early stages. The saliva in the oral cavity is influenced by many factors that are reflected in the salivary metabolite profile. Oral microbes can alter the salivary metabolite profile and may express oral inflammation or oral diseases. The released microbial metabolites in the saliva represent the altered biochemical pathways in the oral cavity. This review highlights the oral microbial profile and microbial metabolites released in saliva and its use as a diagnostic biofluid for different oral diseases. The importance of salivary metabolites produced by oral microbes as risk factors for oral diseases and their possible relationship in oral carcinogenesis is discussed.

Spatially and temporally resolved metabolome of the human oral cavity

Saliva is a complex bodily fluid composed of secretions by major and minor salivary glands. Salivary glands and their secretions are known to be unevenly distributed in the human oral cavity. Moreover, saliva flow rate and composition vary across locations and time of the day. This remarkable heterogeneity of salivary secretions suggests that different subtypes of saliva fulfill different functions. By coupling a non-invasive and facile collection method with comprehensive metabolomic profiling, we investigated the spatial and temporal distributions of salivary components. We identified location-specific metabolite profiles, novel oscillating metabolites, and location-specific diurnal patterns. In summary, our study paves the way for a deeper and more comprehensive understanding of the complex dynamics and functionalities of the salivary metabolome and its integration in multi-omics studies related to oral and systemic (patho-)physiology.

Clinical validation of a saliva-based matrix metalloproteinase-1 rapid strip test for detection of oral cavity cancer

BACKGROUND

We previously identified matrix metalloproteinase-1 (MMP-1) as one of the most promising salivary biomarkers for oral squamous cell carcinoma (OSCC) and developed a sensitive ELISA for MMP-1 with good performance in detection of OSCC using a cohort of 1160 saliva samples.

METHODS

A time-saving rapid strip test (RST) for MMP-1 was developed in this study and its diagnostic performance compared with ELISA using saliva samples from a new cohort of 603 subjects (171 healthy controls, 236 patients with oral potentially malignant disorders, and 196 OSCC patients).

RESULTS

Salivary MMP-1 levels measured using RST and ELISA were highly comparable and both assays could effectively distinguish between OSCC and non-cancerous groups. Similar to ELISA, receiver operating characteristic curve analysis of the MMP-1 RST was effective in identifying patients with OSCC at different oral cavity sites and stages.

CONCLUSIONS

Salivary MMP-1 can be sensitively detected using both RST and ELISA methods. Our newly developed point-of-care MMP-1 RST is a promising in vitro diagnostic device (IVD) that may serve as a novel auxiliary tool in the routine clinical detection and monitoring of OSCC.

Salivary metabolomics in patients with oral lichen planus: a preliminary study based on NMR spectroscopy

OBJECTIVES

The present preliminary study aimed to investigate the salivary metabolic profile in patients with asymptomatic oral lichen planus (OLP) using nuclear magnetic resonance (NMR) spectroscopy.

MATERIAL AND METHODS

Stimulated whole mouth saliva (SWMS) samples were collected from 15 reticular OLP female patients and 15 from age- and sex-matched controls (HCs). A total of 23 metabolites were identified and quantified. Mann-Whitney's U test was used to compare the determined concentration salivary metabolite concentrations between OLP patients and the healthy controls.

RESULTS

The concentration of acetate, methylamine, and pyruvate was elevated, whereas the concentration of tyrosine was decreased in the saliva of OLP patients compared with HCs. To identify a combination of metabolites, multivariate discrimination function analysis (DFA) was conducted. DFA analysis have shown that the most powerful discrimination between the groups was achieved when methylamine and tyrosine were considered as combined biomarkers.

CONCLUSIONS

Salivary tyrosine was of particular interest and a promising finding for the screening of OLP and its progression. Further longitudinal studies are required to establish it as a reliable salivary biomarker in OLP.

CLINICAL RELEVANCE

The salivary metabolic profiling can describe the pathologic characteristics of OLP on non-invasive saliva samples and NMR analysis. Salivary metabolites provide details to considered early detectors and to impact oral health of OLP patients.

Salivary metabolomics in oral potentially malignant disorders and oral cancer patients-a systematic review with meta-analysis

OBJECTIVES

The aim of this systematic review and meta-analysis is to assess the diagnostic potential of salivary metabolomics in the detection of oral potentially malignant disorders (OPMDs) and oral cancer (OC).

MATERIALS AND METHODS

A systematic review was performed in accordance with the 3rd edition of the Centre for Reviews and Dissemination (CRD) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Electronic searches for articles were carried out in the PubMed, Web of Science, and Scopus databases. The quality assessment of the included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS) and the new version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. The effect size was presented using the Forest plot, whereas the presence of publication bias was examined through Begg's funnel plot.

RESULTS

A total of nine studies were included in the systematic review. The metabolite profiling was heterogeneous across all the studies. The expression of several salivary metabolites was found to be significantly altered in OPMDs and OCs as compared to healthy controls. Meta-analysis was able to be conducted only for N-acetylglucosamine. There was no significant difference (SMD = 0.15; 95% CI - 0.25-0.56) in the level of N-acetylglucosamine between OPMDs, OC, and the control group.

CONCLUSION

Evidence for N-acetylglucosamine as a salivary biomarker for oral cancer is lacking. Although several salivary metabolites show changes between healthy, OPMDs, and OC, their diagnostic potential cannot be assessed in this review due to a lack of data. Therefore, further high-quality studies with detailed analysis and reporting are required to establish the diagnostic potential of the salivary metabolites in OPMDs and OC.

CLINICAL RELEVANCE

While some salivary metabolites exhibit significant changes in oral potentially malignant disorders (OPMDs) and oral cancer (OC) compared to healthy controls, the current evidence, especially for N-acetylglucosamine, is inadequate to confirm their reliability as diagnostic biomarkers. Additional high-quality studies are needed for a more conclusive assessment of salivary metabolites in oral disease diagnosis.

Biological biomarkers of oral cancer

The oral squamous cell carcinoma (OSCC) 5 year survival rate of 41% has marginally improved in the last few years, with less than a 1% improvement per year from 2005 to 2017, with higher survival rates when detected at early stages. Based on histopathological grading of oral dysplasia, it is estimated that severe dysplasia has a malignant transformation rate of 7%-50%. Despite these numbers, oral dysplasia grading does not reliably predict its clinical behavior. Thus, more accurate markers predicting oral dysplasia progression to cancer would enable better targeting of these lesions for closer follow-up, especially in the early stages of the disease. In this context, molecular biomarkers derived from genetics, proteins, and metabolites play key roles in clinical oncology. These molecular signatures can help predict the likelihood of OSCC development and/or progression and have the potential to detect the disease at an early stage and, support treatment decision-making and predict treatment responsiveness. Also, identifying reliable biomarkers for OSCC detection that can be obtained non-invasively would enhance management of OSCC. This review will discuss biomarkers for OSCC that have emerged from different biological areas, including genomics, transcriptomics, proteomics, metabolomics, immunomics, and microbiomics.

Identification of salivary metabolic biomarker signatures for oral tongue squamous cell carcinoma

OBJECTIVE

To identify the salivary metabolites associated with squamous cell carcinoma of the tongue to develop easy and non-invasive potential biomarkers for disease diagnosis.

DESIGN

Initially, the study utilized untargeted metabolomics to analyze 20 samples of tongue squamous cell carcinoma and 10 control samples. The objective was to determine the salivary metabolites that exhibited differential expression in tongue squamous cell carcinoma. Then the selected metabolites were validated using targeted metabolomics in saliva samples of 100 patients diagnosed with squamous cell carcinoma of the tongue, as well as 30 healthy control individuals.

RESULTS

From the analysis of untargeted metabolomics, 10 metabolites were selected as potential biomarkers. In the subsequent targeted metabolomics study on these selected metabolites, it was observed that N-Acetyl-D-glucosamine, L-Pipecolic acid, L-Carnitine, Phosphorylcholine, and Deoxyguanosine exhibited significant differences. The receiver operating characteristic curve analysis indicates a combination of three important metabolites such as N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine provided the best prediction with an area under the curve of 0.901.

CONCLUSIONS

The present result reveals that the N-Acetyl-D-glucosamine, L-Pipecolic acid and L-Carnitine are the signature diagnostic biomarkers for oral tongue squamous cell carcinoma. These findings can be used to develop a rapid and non-invasive method for disease monitoring and prognosis in oral tongue cancer.

An Intelligent System to Improve Diagnostic Support for Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most-prevalent cancer types worldwide, and it poses a serious threat to public health due to its high mortality and morbidity rates. OSCC typically has a poor prognosis, significantly reducing the chances of patient survival. Therefore, early detection is crucial to achieving a favorable prognosis by providing prompt treatment and increasing the chances of remission. Salivary biomarkers have been established in numerous studies to be a trustworthy and non-invasive alternative for early cancer detection. In this sense, we propose an intelligent system that utilizes feed-forward artificial neural networks to classify carcinoma with salivary biomarkers extracted from control and OSCC patient samples. We conducted experiments using various salivary biomarkers, ranging from 1 to 51, to train the model, and we achieved excellent results with precision, sensitivity, and specificity values of 98.53%, 96.30%, and 97.56%, respectively. Our system effectively classified the initial cases of OSCC with different amounts of biomarkers, aiding medical professionals in decision-making and providing a more-accurate diagnosis. This could contribute to a higher chance of treatment success and patient survival. Furthermore, the minimalist configuration of our model presents the potential for incorporation into resource-limited devices or environments.

Saliva biomarkers in oral disease

Saliva is a versatile biofluid that contains a wide variety of biomarkers reflecting both physiologic and pathophysiologic states. Saliva collection is noninvasive and highly applicable for tests requiring serial sampling. Furthermore, advances in test accuracy, sensitivity and precision for saliva has improved diagnostic performance as well as the identification of novel markers especially in oral disease processes. These include dental caries, periodontitis, oral squamous cell carcinoma (OSCC) and Sjögren's syndrome (SS). Numerous growth factors, enzymes, interleukins and cytokines have been identified and are the subject of much research investigation. This review highlights current procedures for successful determination of saliva biomarkers including preanalytical factors associated with sampling, storage and pretreatment as well as subsequent analysis. Moreover, it provides an overview of the diagnostic applications of these salivary biomarkers in common oral diseases.

Evaluation of Metabolomics as Diagnostic Targets in Oral Squamous Cell Carcinoma: A Systematic Review

In recent years, high-throughput technologies have facilitated the widespread use of metabolomics to identify biomarkers and targets for oral squamous cell carcinoma (OSCC). As a result, the primary goal of this systematic review is to identify and evaluate metabolite biomarkers and their pathways for OSCC that featured consistently across studies despite methodological variations. Six electronic databases (Medline, Cochrane, Web of Science, CINAHL, ProQuest, and Embase) were reviewed for the longitudinal studies involving OSCC patients and metabolic marker analysis (in accordance with PRISMA 2020). The studies included ranged from the inception of metabolomics in OSCC (i.e., 1 January 2007) to 30 April 2023. The included studies were then assessed for their quality using the modified version of NIH quality assessment tool and QUADOMICS. Thirteen studies were included after screening 2285 studies. The majority of the studies were from South Asian regions, and metabolites were most frequently derived from saliva. Amino acids accounted for more than quarter of the detected metabolites, with glutamate and methionine being the most prominent. The top dysregulated metabolites indicated dysregulation of six significantly enriched pathways including aminoacyl-tRNA biosynthesis, glutathione metabolism and arginine biosynthesis with the false discovery rate (FDR) <0.05. Finally, this review highlights the potential of metabolomics for early diagnosis and therapeutic targeting of OSCC. However, larger studies and standardized protocols are needed to validate these findings and make them a clinical reality.

Oral Sources of Salivary Metabolites

The oral cavity is very diverse, where saliva plays an important role in maintaining oral health. The metabolism of saliva has been used to investigate oral diseases as well as general diseases, mainly to detect diagnostic biomarkers. There are many sources of salivary metabolites in the mouth. The online English language search and PubMed databases were searched to retrieve relevant studies on oral salivary metabolites. The physiological balance of the mouth is influenced by many factors that are reflected in the salivary metabolite profile. Similarly, the dysbiosis of microbes can alter the salivary metabolite profile, which may express oral inflammation or oral diseases. This narrative review highlights the factors to be considered when examining saliva and its use as a diagnostic biofluid for different diseases. Salivary metabolites, mainly small molecular metabolites may enter the bloodstream and cause illness elsewhere in the body. The importance of salivary metabolites produced in the oral cavity as risk factors for general diseases and their possible relationship to the body’s function are also discussed.

Association between Levels of Trimethylamine N-Oxide and Cancer: A Systematic Review and Meta-Analysis

Cancer is the second leading cause of death in the world. Reports on the effect of Trimethylamine-N-oxide (TAMO), a small amine oxide generated by gut microbial metabolism of choline, betaine, and carnitine, on cancer are inconsistent. Therefore, this systematic review and meta-analysis summarize the effect of TAMO on cancer incidence. A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase. Data were pooled using the random-effects method and were expressed as weighted mean difference (WMD) and 95% confidence intervals (CI). The pooled results of 16 studies, including 5930 participants, showed that the association between TMAO levels and cancer incidence is insignificant (Odds Ratio: 0.97, 95% CI: (0.64, 1.46), P-value = 0.871). Subgroup analysis showed that urinary TMAO levels were negatively associated with cancer incidence; in contrast, a direct and positive association was observed between serum TMAO levels and cancer incidence. However, "gender" and the "TMAO measuring method" were the potential sources of discrepancies. Meta-regression analysis did not reveal any significant association between duration of studies, age, female ratio, subjects-control, and subjects-case. The present study demonstrates that serum TAMO levels were insignificantly associated with cancer incidence.

Saliva - a new opportunity for fluid biopsy

Saliva is a complex biological fluid with a variety of biomolecules, such as DNA, RNA, proteins, metabolites and microbiota, which can be used for the screening and diagnosis of many diseases. In addition, saliva has the characteristics of simple collection, non-invasive and convenient storage, which gives it the potential to replace blood as a new main body of fluid biopsy, and it is an excellent biological diagnostic fluid. This review integrates recent studies and summarizes the research contents of salivaomics and the research progress of saliva in early diagnosis of oral and systemic diseases. This review aims to explore the value and prospect of saliva diagnosis in clinical application.

Salivary Metabolomics for Oral Cancer Detection: A Narrative Review

The development of low- or non-invasive screening tests for cancer is crucial for early detection. Saliva is an ideal biofluid containing informative components for monitoring oral and systemic diseases. Metabolomics has frequently been used to identify and quantify numerous metabolites in saliva samples, serving as novel biomarkers associated with various conditions, including cancers. This review summarizes the recent applications of salivary metabolomics in biomarker discovery in oral cancers. We discussed the prevalence, epidemiologic characteristics, and risk factors of oral cancers, as well as the currently available screening programs, in India and Japan. These data imply that the development of biomarkers by itself is inadequate in cancer detection. The use of current diagnostic methods and new technologies is necessary for efficient salivary metabolomics analysis. We also discuss the gap between biomarker discovery and nationwide screening for the early detection of oral cancer and its prevention.

Oral Microbiome Research on Oral Lichen Planus: Current Findings and Perspectives

Simple Summary

Oral lichen planus is a disease of the oral mucosa, which frequently affects women aged 40 years or older. Though the T cell-mediated immune response is involved in the development of oral lichen planus, attempts to identify a microorganism that causes the disease have been unsuccessful. Recent studies on the development of oral lichen planus are focusing on the role of the oral microbiome, which includes oral microbiota and their products, and the host environment. The role of the human microbiome in various diseases has been identified and regulating the microbiome is becoming important in personalized medicine. In this review, we summarized current findings on the role of the oral microbiome in the development of oral lichen planus. The homeostasis of the oral microbiome is disrupted in patients, and functional analysis of oral microbiota and oral mucosa implies that pathways involved in defense against bacterial infection and in the inflammatory response are activated in the oral lichen planus-associated oral microbiome. Though the lack of studies to date makes it difficult to conclude, further studies on the oral microbiome associated with the disease will enable a holistic understanding of the role of the oral microbiome in the development of oral lichen planus and developing a personalized therapy for the disease.

Abstract

Oral lichen planus (OLP) is a chronic inflammatory disease of the oral mucosa with an unknown etiology. The role of oral microbes in the development of OLP has gained researchers’ interest. In this review, we summarized the findings of studies focused on the relationship between OLP and oral microbiome, which includes the composition of oral microbiota, molecules produced by oral microbiota or the host, and the oral environment of the host. According to the studies, the oral microbial community in OLP patients undergoes dysbiosis, and the microbial dysbiosis in OLP patients is more prominent in the buccal mucosa than in the saliva. However, no same microorganisms have been suggested to be associated with OLP in multiple investigations, implying that the functional aspects of the oral microbiota are more important in OLP development than the composition of the oral microbiota. According to studies on host factors that make up the oral environment, signal pathways involved in cellular processes, such as keratinization, inflammation, and T cell responses are triggered in OLP. Studies on the functional aspects of the oral microbiota, as well as interactions between the host and the oral microbiota, are still lacking, and more research is required.

Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma - A data mining approach

Data mining has proven to be a reliable method to analyze and discover useful knowledge about various diseases, including cancer research. In particular, data mining and machine learning algorithms to study oral squamous cell carcinoma (OSCC), the most common form of oral cancer, is a new area of research. This malignant neoplasm can be studied using saliva samples. Saliva is an important biofluid that must be used to verify potential biomarkers associated with oral cancer. In this study, first, we provide an overview of OSSC diagnoses based on machine learning and salivary metabolites. To our knowledge, this is the first study to apply advanced data mining techniques to diagnose OSCC. Then, we give new results of classification and feature selection algorithms used to identify potential salivary biomarkers of OSCC. To accomplish this task, we used the filter feature selection random forest importance algorithm and a wrapper methodology to evaluate the importance of metabolites obtained from gas chromatography mass-spectrometry (GC-MS) in the context of differentiation of OSCC and the control group. Salivary samples (n = 68) were collected for the control group, and the OSCC group were from patients matched for gender, age, and smoking habit. The classification process occurred based on Random Forest (RF) classification algorithm along with 10-cross validation. The results showed that glucuronic acid, maleic acid, and batyl alcohol can classify the samples with an area under the curve (AUC) of 0.91 versus an AUC of 0.76 using all 51 metabolites analyzed. The methodology used in this study can assist healthcare professionals and be adopted to discover diagnostic biomarkers for other diseases.

Salivary Metabolomics for Oral Squamous Cell Carcinoma Diagnosis: A Systematic Review

Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer in which the consumption of tobacco and alcohol is considered to be the main aetiological factor. Salivary metabolome profiling could identify novel biochemical pathways involved in the pathogenesis of various diseases. This systematic review was designed to answer the question “Are salivary metabolites reliable for the diagnosis of oral squamous cell carcinoma?”. Following the inclusion and exclusion criteria, nineteen studies were included (according to PRISMA statement guidelines). In all included studies, the diagnostic material was unstimulated whole saliva, whose metabolome changes were determined by different spectroscopic methods. At the metabolic level, OSCC patients differed significantly not only from healthy subjects but also from patients with oral leukoplakia, lichen planus or other oral potentially malignant disorders. Among the detected salivary metabolites, there were the indicators of the impaired metabolic pathways, such as choline metabolism, amino acid pathways, polyamine metabolism, urea cycle, creatine metabolism, glycolysis or glycerolipid metabolism. In conclusion, saliva contains many potential metabolites, which can be used reliably to early diagnose and monitor staging in patients with OSCC. However, further investigations are necessary to confirm these findings and to identify new salivary metabolic biomarkers.

Identification of Metabolism-Associated Biomarkers for Early and Precise Diagnosis of Oral Squamous Cell Carcinoma

The 5-year survival rate for oral squamous cell carcinoma (OSCC), one of the most common head and neck cancers, has not improved in the last 20 years. Poor prognosis of OSCC is the result of failure in early and precise diagnosis. Metabolic reprogramming, including the alteration of the uptake and utilisation of glucose, amino acids and lipids, is an important feature of OSCC and can be used to identify its biomarkers for early and precise diagnosis. In this review, we summarise how recent findings of rewired metabolic networks in OSCC have facilitated early and precise diagnosis of OSCC.

Salivary Metabolomics for Prognosis of Oral Squamous Cell Carcinoma

This study aimed to identify salivary metabolomic biomarkers for predicting the prognosis of oral squamous cell carcinoma (OSCC) based on comprehensive metabolomic analyses. Quantified metabolomics data of unstimulated saliva samples collected from patients with OSCC (n = 72) were randomly divided into the training (n = 35) and validation groups (n = 37). The training data were used to develop a Cox proportional hazards regression model for identifying significant metabolites as prognostic factors for overall survival (OS) and disease-free survival. Moreover, the validation group was used to develop another Cox proportional hazards regression model using the previously identified metabolites. There were no significant between-group differences in the participants’ characteristics, including age, sex, and the median follow-up periods (55 months [range: 3–100] vs. 43 months [range: 0–97]). The concentrations of 5-hydroxylysine (p = 0.009) and 3-methylhistidine (p = 0.012) were identified as significant prognostic factors for OS in the training group. Among them, the concentration of 3-methylhistidine was a significant prognostic factor for OS in the validation group (p = 0.048). Our findings revealed that salivary 3-methylhistidine is a prognostic factor for OS in patients with OSCC.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part I: Emerging Platforms and Perspectives

1H NMR-based metabolomics analysis of human saliva, other oral fluids, and/or tissue biopsies serves as a valuable technique for the exploration of metabolic processes, and when associated with ’state-of-the-art’ multivariate (MV) statistical analysis strategies, provides a powerful means of examining the identification of characteristic metabolite patterns, which may serve to differentiate between patients with oral health conditions (e.g., periodontitis, dental caries, and oral cancers) and age-matched heathy controls. This approach may also be employed to explore such discriminatory signatures in the salivary 1H NMR profiles of patients with systemic diseases, and to date, these have included diabetes, Sjörgen’s syndrome, cancers, neurological conditions such as Alzheimer’s disease, and viral infections. However, such investigations are complicated in view of quite a large number of serious inconsistencies between the different studies performed by independent research groups globally; these include differing protocols and routes for saliva sample collection (e.g., stimulated versus unstimulated samples), their timings (particularly the oral activity abstention period involved, which may range from one to 12 h or more), and methods for sample transport, storage, and preparation for NMR analysis, not to mention a very wide variety of demographic variables that may influence salivary metabolite concentrations, notably the age, gender, ethnic origin, salivary flow-rate, lifestyles, diets, and smoking status of participant donors, together with their exposure to any other possible convoluting environmental factors. In view of the explosive increase in reported salivary metabolomics investigations, in this update, we critically review a wide range of critical considerations for the successful performance of such experiments. These include the nature, composite sources, and biomolecular status of human saliva samples; the merits of these samples as media for the screening of disease biomarkers, notably their facile, unsupervised collection; and the different classes of such metabolomics investigations possible. Also encompassed is an account of the history of NMR-based salivary metabolomics; our recommended regimens for the collection, transport, and storage of saliva samples, along with their preparation for NMR analysis; frequently employed pulse sequences for the NMR analysis of these samples; the supreme resonance assignment benefits offered by homo- and heteronuclear two-dimensional NMR techniques; deliberations regarding salivary biomolecule quantification approaches employed for such studies, including the preprocessing and bucketing of multianalyte salivary NMR spectra, and the normalization, transformation, and scaling of datasets therefrom; salivary phenotype analysis, featuring the segregation of a range of different metabolites into ‘pools’ grouped according to their potential physiological sources; and lastly, future prospects afforded by the applications of LF benchtop NMR spectrometers for direct evaluations of the oral or systemic health status of patients at clinical ‘point-of-contact’ sites, e.g., dental surgeries. This commentary is then concluded with appropriate recommendations for the conduct of future salivary metabolomics studies. Also included are two original case studies featuring investigations of (1) the 1H NMR resonance line-widths of selected biomolecules and their possible dependence on biomacromolecular binding equilibria, and (2) the combined univariate (UV) and MV analysis of saliva specimens collected from a large group of healthy control participants in order to potentially delineate the possible origins of biomolecules therein, particularly host- versus oral microbiome-derived sources. In a follow-up publication, Part II of this series, we conduct censorious reviews of reported observations acquired from a diversity of salivary metabolomics investigations performed to evaluate both localized oral and non-oral diseases. Perplexing problems encountered with these again include those arising from sample collection and preparation protocols, along with 1H NMR spectral misassignments.

Metabolomics based plasma biomarkers for diagnosis of oral squamous cell carcinoma and oral erosive lichen planus

Backgrounds: To identify diagnostic biomarkers for differentiating oral squamous cell carcinoma (OSCC) from oral erosive lichen planus (OELP) and investigate potential biomarkers associated with malignant transformation. Methods: In this study, 72 patients with OSCC, 75 patients with OELP subjects were recruited. Their plasma samples were analyzed by ultra-high-performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry, (UHPLC/Q-Orbitrap HRMS). Principal component analysis, orthogonal partial least square discrimination analysis, t-test analysis and false discovery rate were used to identify different metabolites in patients with OSCC and OELP. The metabolic pathway analysis was performed by MetaboAnalyst. To further screen and identify the biomarkers of OSCC and establish a diagnostic panel, binary logistic regression analysis and receiver operating characteristic analysis were used. The data were then combined with blood samples from healthy individuals for mass spectrometry analysis to obtain biomarkers related to malignant transformation. Results: A total of 20 kinds of endogenous metabolites were identified from plasma samples of OSCC patients and OELP patients. Metabolic pathway analysis showed that the biomarkers associated with OSCC were closely related to cholic acid metabolism and amino acid metabolism. Finally, a diagnostic panel composed of decanoylcarnitine, cysteine and cholic acid was established. This diagnostic panel had good diagnostic efficiency with the AUC=0.998. Other metabolites including uridine, taurine, glutamate, citric acid and LysoPC(18:1) were identified to be general biomarkers for malignant transformation of OELP. Conclusion: Biomarkers based on plasma metabolomics are of great significance for the prediction of malignant transformation of OELP and early diagnosis of OSCC.

Identification of Possible Salivary Metabolic Biomarkers and Altered Metabolic Pathways in South American Patients Diagnosed with Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) represents 90% of oral malignant neoplasms. The search for specific biomarkers for OSCC is a very active field of research contributing to establishing early diagnostic methods and unraveling underlying pathogenic mechanisms. In this work we investigated the salivary metabolites and the metabolic pathways of OSCC aiming find possible biomarkers. Salivary metabolites samples from 27 OSCC patients and 41 control individuals were compared through a gas chromatography coupled to a mass spectrometer (GC-MS) technique. Our results allowed identification of pathways of the malate-aspartate shuttle, the beta-alanine metabolism, and the Warburg effect. The possible salivary biomarkers were identified using the area under receiver-operating curve (AUC) criterion. Twenty-four metabolites were identified with AUC > 0.8. Using the threshold of AUC = 0.9 we find malic acid, maltose, protocatechuic acid, lactose, 2-ketoadipic, and catechol metabolites expressed. We notice that this is the first report of salivary metabolome in South American oral cancer patients, to the best of our knowledge. Our findings regarding these metabolic changes are important in discovering salivary biomarkers of OSCC patients. However, additional work needs to be performed considering larger populations to validate our results.

Salivary Metabolomics for Diagnosis and Monitoring Diseases: Challenges and Possibilities

Saliva is a useful biological fluid and a valuable source of biological information. Saliva contains many of the same components that can be found in blood or serum, but the components of interest tend to be at a lower concentration in saliva, and their analysis demands more sensitive techniques. Metabolomics is starting to emerge as a viable method for assessing the salivary metabolites which are generated by the biochemical processes in elucidating the pathways underlying different oral and systemic diseases. In oral diseases, salivary metabolomics has concentrated on periodontitis and oral cancer. Salivary metabolites of systemic diseases have been investigated mostly in the early diagnosis of different cancer, but also neurodegenerative diseases. This mini-review article aims to highlight the challenges and possibilities of salivary metabolomics from a clinical viewpoint. Furthermore, applications of the salivary metabolic profile in diagnosis and prognosis, monitoring the treatment success, and planning of personalized treatment of oral and systemic diseases are discussed.

Differential Glycosylation Levels in Saliva from Patients with Lung or Breast Cancer: A Preliminary Assessment for Early Diagnostic Purposes

Glycans play a fundamental role in several biological processes, such as cell-cell adhesion, signaling, and recognition. Similarly, abnormal glycosylation is involved in many pathological processes, among which include tumor growth and progression. Several highly glycosylated proteins found in blood are currently used in clinical practice as cancer biomarkers (e.g., CA125, PSA, and CA19-9). The development of novel non-invasive diagnostic procedures would greatly simplify the screening and discovery of pathologies at an early stage, thus also allowing for simpler treatment and a higher success rate. In this observational study carried out on 68 subjects diagnosed with either breast or lung cancer and 34 healthy volunteers, we hydrolyzed the glycoproteins in saliva and quantified the obtained free sugars (fucose, mannose, galactose, glucosamine, and galactosamine) by using high-performance anion-exchange chromatography with pulsed-amperometric detection (HPAEC-PAD). The glycosidic profiles were compared by using multivariate statistical analysis, showing differential glycosylation patterns among the three categories. Furthermore, Receiver Operating Characteristics (ROC) analysis allowed obtaining a reliable and minimally invasive protocol able to discriminate between healthy and pathological subjects.

Identification of Salivary Proteomic Biomarkers for Oral Cancer Screening

BACKGROUND/AIM

The current study aimed to identify biomarkers for differentiating between patients with oral cancer (OC) and healthy controls (HCs) on the basis of the comprehensive proteomic analyses of saliva samples by using liquid chromatography-mass spectrometry (LC-MS/MS).

PATIENTS AND METHODS

Unstimulated saliva samples were collected from 39 patients with OC and from 31 HCs. Proteins in the saliva were comprehensively analyzed using LC-MS/MS. To differentiate between patients with OC and HCs, a multiple logistic regression model was developed for evaluating the discriminatory ability of a combination of multiple markers.

RESULTS

A total of 23 proteins were significantly differentially expressed between the patients with OC and the HCs. Six out of the 23 proteins, namely α-2-macroglobulin-like protein 1, cornulin, hemoglobin subunit β, Ig k chain V-II region Vk167, kininogen-1 and transmembrane protease serine 11D, were selected using the forward-selection method and applied to the multiple logistic regression model. The area under the curve for discriminating between patients with OC and HCs was 0.957 when the combination of the six metabolites was used (95% confidence interval=0.915-0.998; p<0.001). Furthermore, these candidate proteins did not show a stage-specific difference.

CONCLUSION

The results of the current study showed that six salivary proteins are potential non-invasive biomarkers for OC screening.

Differential Tissue Metabolic Signatures in IgG4-Related Ophthalmic Disease and Orbital Mucosa-Associated Lymphoid Tissue Lymphoma

Purpose

To identify tissue metabolomic profiles in biopsy specimens with IgG4-related ophthalmic disease (IgG4-ROD) and mucosa-associated lymphoid tissue (MALT) lymphoma and investigate their potential implication in the disease pathogenesis and biomarkers.

Methods

We conducted a comprehensive analysis of the metabolomes and lipidomes of biopsy-proven IgG4-ROD (n = 22) and orbital MALT lymphoma (n = 21) specimens and matched adjacent microscopically normal adipose tissues using liquid chromatography time-of-flight mass spectrometry. The altered metabolomic profiles were visualized by heat map and principal component analysis. Metabolic pathway analysis was performed by Metabo Analyst 4.0 using differentially expressed metabolites. The diagnostic performance of the metabolic markers was evaluated using receiver operating characteristic curves. Machine learning algorithms were implemented by random forest using the R environment. Finally, an independent set of 18 IgG4-ROD and 17 orbital MALT lymphoma specimens were used to validate the identified biomarkers.

Results

The principal component analysis showed a significant difference of both IgG4-ROD and orbital MALT lymphoma for biopsy specimens and controls. Interestingly, lesions in IgG4-ROD were uniquely enriched in arachidonic metabolism, whereas those in orbital MALT lymphoma were enriched in tricarboxylic acid cycle metabolism. We identified spermine as the best discriminator between IgG4-ROD and orbital MALT lymphoma, and the area under the receiver operating characteristic curve of the spermine to discriminate between the two diseases was 0.89 (95% confidence interval, 0.803–0.984). A random forest model incorporating a panel of five metabolites showed a high area under the receiver operating characteristic curve value of 0.983 (95% confidence interval, 0.981–0.984). The results of validation revealed that four tissue metabolites: N¹,N¹²-diacetylspermine, spermine, malate, and glycolate, had statistically significant differences between IgG4-ROD and orbital MALT lymphoma with receiver operating characteristic values from 0.708 to 0.863.

Conclusions

These data revealed the characteristic differences in metabolomic profiles between IgG4-ROD and orbital MALT lymphoma, which may be useful for developing new diagnostic biomarkers and elucidating the pathogenic mechanisms of these common orbital lymphoproliferative disorders.

Differences in Spectroscopic Properties of Saliva Taken From Normal Subjects and Oral Cancer Patients: Comparison Studies

Oral cancer disease is among the most common cancers in the world and are associated with mortality and morbidity. The characterization of saliva samples may help to distinguish patients with oral cancer disease from normal subjects. To characterize spectra of saliva samples from normal subjects and oral cancer patients by use of fluorescence, absorption, and 1H-NMR spectroscopy. Whole unstimulated saliva samples were collected from patients with oral cancer disease and normal subjects. The saliva samples were analyzed by absorption, fluorescence and 1H-NMR spectroscopic techniques. The characteristic spectra of saliva samples from patients with oral cancer disease and normal subjects were compared. For fluorescence spectroscopic studies, six fluorophores were found in saliva samples. Autofluorescence emission spectra and synchronous spectra of saliva were different between normal subjects and oral cancer patients. For absorption spectroscopic studies, the typical absorption spectra of saliva samples from normal subjects and oral cancer patients were also different in absorption intensity, 1st and 2nd derivative of absorption spectra values. For 1H-NMR studies, nine metabolites and four metabolites were found in saliva samples taken from normal subjects and oral cancer patients, respectively. The metabolic profiles of saliva samples from normal subjects and oral cancer patients were not similar. The characteristic spectra of saliva samples from normal subjects and oral cancer patients were found. These results showed differences in the spectra of saliva samples between both that groups. The spectra from each spectroscopic techniques could determine a candidate saliva biomarkers for distinguishing patients with oral cancer disease from normal subjects.

Salivary metabolomics for cancer detection

Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.

Quantification of Salivary Charged Metabolites using Capillary Electrophoresis Time-of-flight-mass Spectrometry

Salivary metabolomics have provided the potentials to detect both oral and systemic diseases. Capillary electrophoresis time-of-flight-mass spectrometry (CE-TOFMS) enables the identification and quantification of various charged metabolites. This method has been employed to biomarker discoveries using human saliva samples, especially for various types of cancers. The untargeted analysis contributes to finding new biomarkers. i.e., the analysis of all detectable signals including both known and unknown metabolites extends the coverage of metabolite to be observed. However, the observed data includes thousands of peaks. Besides, non-linear migration time fluctuation and skewed peaks are caused by the sample condition. The presented pretreatment protocols of saliva samples enhance the reproducibility of migration time drift, which facilitates the matching peaks across the samples and also results in reproducible absolute concentrations of the detected metabolites. The described protocols are utilized not only for saliva but for any liquid samples with slight modifications.

Metabolic landscape of oral squamous cell carcinoma

BACKGROUND

Head and neck cancers are the seventh most common type of cancer worldwide, with almost half of the cases affecting the oral cavity. Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, showing poor prognosis and high mortality. OSCC molecular pathogenesis is complex, resulting from a wide range of events that involve the interplay between genetic mutations and altered levels of transcripts, proteins, and metabolites. Metabolomics is a recently developed sub-area of omics sciences focused on the comprehensive analysis of small molecules involved in several biological pathways by high throughput technologies.

AIM OF REVIEW

This review summarizes and evaluates studies focused on the metabolomics analysis of OSCC and oral premalignant disorders to better interpret the complex process of oral carcinogenesis. Additionally, the metabolic biomarkers signatures identified so far are also included. Moreover, we discuss the limitations of these studies and make suggestions for future investigations.

KEY SCIENTIFIC CONCEPTS

Although many questions about the metabolic features of OSCC have already been answered in metabolomic studies, further validation and optimization are still required to translate these findings into clinical applications.

Verification of Saliva Matrix Metalloproteinase-1 as a Strong Diagnostic Marker of Oral Cavity Cancer

Oral squamous cell carcinoma (OSCC) accounts for >90% of cases of oral cancer, including cancer at the lip and oral cavity and cancer at the oropharynx. Most OSCCs develop from oral potentially malignant disorders (OPMDs), which consist of heterogeneous lesions with different malignant transformation potentials that make early detection of OSCC a challenge. Using a targeted mass spectrometry-based assay to compare multiple candidate proteins, we previously identified matrix metalloproteinase-1 (MMP-1) as one of the most promising salivary OSCC biomarkers. To explore the clinical utility of MMP-1 in OSCC detection, we developed an in-house, sensitive enzyme-linked immunosorbent assay (ELISA) for measuring MMP-1 content, and tested it on saliva samples from 1160 subjects (313 healthy controls, and 578 OPMD and 269 OSCC patients) collected at two medical centers. Salivary MMP-1 levels measured by our in-house ELISA significantly discriminated OSCC patients from non-cancerous groups. A receiver operating characteristic curve analysis showed that MMP-1 was effective in separating non-cancer groups from patients with OSCCs at the oral cavity. Additionally, salivary MMP-1 levels in oral cavity cancer patients were highly correlated with tumor progression (tumor size, lymph node metastasis, and overall stage). Collectively, our results indicate that salivary MMP-1 is an effective biomarker for OSCC that can be sensitively detected using our newly developed ELISA. The newly developed MMP-1 ELISA may be used as a new adjunctive tool to aid in detecting and monitoring OSCC.

Gene expression analysis of autofluorescence margins in leukoplakia and oral carcinoma: A pilot study

OBJECTIVES

Autofluorescence is considered a useful technique in the early detection of oral mucosal alterations. However, its efficacy to discriminate tumor margins is still under debate. The purpose of this pilot study was to confirm the existence of molecular divergence from the center of a lesion compared to white light and autofluorescence (VELscope^TM ) visualized margins in leukoplakia and oral carcinoma.

MATERIALS AND METHODS

Molecular divergence from the center of the lesion to white light and VELscope^TM defined margins was compared in patients with leukoplakia (n = 3) and oral carcinoma (n = 4). Expression profiling of 45 selected genes was performed through custom-made TaqMan arrays. Gene Ontology was used for biological pathway analysis.

RESULTS

Irrespective of pathology, the greatest molecular divergence existed between the center of the lesion and both white light and VELscope^TM margins. VELscope^TM and white light margins were also molecularly distinct in oral carcinoma samples. Indeed, the white light margin retained molecular abnormalities observed in the center of the lesion thus suggesting the existence of a "partially transformed" cell population.

CONCLUSION

Despite the limited low number of patients, our data confirm the benefit of combining autofluorescence with conventional oral examination in identifying surgical margins during biopsy procedures for leukoplakia and oral carcinoma.

Unravelling the Potential of Salivary Volatile Metabolites in Oral Diseases. A Review

Fostered by the advances in the instrumental and analytical fields, in recent years the analysis of volatile organic compounds (VOCs) has emerged as a new frontier in medical diagnostics. VOCs analysis is a non-invasive, rapid and inexpensive strategy with promising potential in clinical diagnostic procedures. Since cellular metabolism is altered by diseases, the resulting metabolic effects on VOCs may serve as biomarkers for any given pathophysiologic condition. Human VOCs are released from biomatrices such as saliva, urine, skin emanations and exhaled breath and are derived from many metabolic pathways. In this review, the potential of VOCs present in saliva will be explored as a monitoring tool for several oral diseases, including gingivitis and periodontal disease, dental caries, and oral cancer. Moreover, the analytical state-of-the-art for salivary volatomics, e.g., the most common extraction techniques along with the current challenges and future perspectives will be addressed unequivocally.

Oral squamous cell carcinoma diagnosed from saliva metabolic profiling

Saliva is a noninvasive biofluid that can contain metabolite signatures of oral squamous cell carcinoma (OSCC). Conductive polymer spray ionization mass spectrometry (CPSI-MS) is employed to record a wide range of metabolite species within a few seconds, making this technique appealing as a point-of-care method for the early detection of OSCC. Saliva samples from 373 volunteers, 124 who are healthy, 124 who have premalignant lesions, and 125 who are OSCC patients, were collected for discovering and validating dysregulated metabolites and determining altered metabolic pathways. Metabolite markers were reconfirmed at the primary tissue level by desorption electrospray ionization MS imaging (DESI-MSI), demonstrating the reliability of diagnoses based on saliva metabolomics. With the aid of machine learning (ML), OSCC and premalignant lesions can be distinguished from the normal physical condition in real time with an accuracy of 86.7%, on a person by person basis. These results suggest that the combination of CPSI-MS and ML is a feasible tool for accurate, automated diagnosis of OSCC in clinical practice.

Saliva and Oral Diseases

Saliva is a fascinating biological fluid which has all the features of a perfect diagnostic tool. In fact, its collection is rapid, simple, and noninvasive. Thanks to several transport mechanisms and its intimate contact with crevicular fluid, saliva contains hundreds of proteins deriving from plasma. Advances in analytical techniques have opened a new era-called "salivaomics"-that investigates the salivary proteome, transcriptome, microRNAs, metabolome, and microbiome. In recent years, researchers have tried to find salivary biomarkers for oral and systemic diseases with various protocols and technologies. The review aspires to provide an overall perspective of salivary biomarkers concerning oral diseases such as lichen planus, oral cancer, blistering diseases, and psoriasis. Saliva has proved to be a promising substrate for the early detection of oral diseases and the evaluation of therapeutic response. However, the wide variation in sampling, processing, and measuring of salivary elements still represents a limit for the application in clinical practice.

Salivary Metabolomics: From Diagnostic Biomarker Discovery to Investigating Biological Function

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current knowledge of salivary metabolomics. Current applications of salivary metabolomics have largely focused on diagnostic biomarker discovery and the diagnostic value of the current literature base is explored. There is also a small, albeit promising, literature base concerning the use of salivary metabolomics in monitoring athletic performance. Functional roles of salivary metabolites remain largely unexplored. Areas of emerging knowledge include the role of oral host-microbiome interactions in shaping the salivary metabolite profile and the potential roles of salivary metabolites in oral physiology, e.g., in taste perception. Discussion of future research directions describes the need to begin acquiring a greater knowledge of the function of salivary metabolites, a current research direction in the field of the gut metabolome. The role of saliva as an easily obtainable, information-rich fluid that could complement other gastrointestinal fluids in the exploration of the gut metabolome is emphasized.