Oral squamous cell carcinoma diagnosed from saliva metabolic profiling

Nondestructive Metabolic Monitoring of Living Organisms by Water-Droplet Extraction and Contact-Free Electrospray Ionization Mass Spectrometry

We present a novel tool for nondestructive in vivo metabolite analysis, which we call MetaTube. This method utilizes a high voltage to facilitate efficient ionization while maintaining electrical isolation from living tissues that are sampled. MetaTube achieves a 2-3 order of magnitude improvement in metabolite signal intensity, giving MetaTube the capacity to identify 10-fold more metabolites. These capabilities allow us to profile metabolites of human skin surfaces and monitor metabolite fluctuations of growing bacterial colonies. MetaTube offers a promising strategy to understand in vivo metabolic dynamics and evaluate the metabolic state of living tissues in real time.

Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large-Scale Multicenter Study

Most lung cancer (LC) patients are diagnosed at advanced stages due to the lack of effective screening tools. This multicenter study analyzes 1043 saliva samples (334 LC cases and 709 non-LC cases) using a novel high-throughput platform for metabolic fingerprint acquisition. Machine learning identifies 35 metabolic features distinguishing LC from non-LC subjects, enabling the development of a classification model named SalivaMLD. In the validation set and test set, SalivaMLD demonstrates strong diagnostic performance, achieving an area under the curve of 0.849-0.850, a sensitivity of 81.69-83.33%, and a specificity of 74.23-74.39%, outperforming conventional tumor biomarkers. Notably, SalivaMLD exhibits superior accuracy in distinguishing early stage LC patients. Hence, this rapid and noninvasive screening method may be widely applied in clinical practice for LC detection.

Saliva metabolomics: a non-invasive frontier for diagnosing and managing oral diseases

Salivary metabolomics represents a powerful noninvasive approach for diagnosing, monitoring, and managing oral diseases, providing valuable insights into the metabolic alterations associated with conditions such as oral cancer, oral precancerous lesions, periodontal diseases, and dental caries. Through the comprehensive analysis of salivary metabolites, this methodology facilitates the identification of disease-specific biomarkers reflective of underlying pathophysiological processes, including inflammation, microbial dysbiosis, and metabolic reprogramming. Despite its promising clinical potential, several significant challenges remain, notably the difficulty in establishing direct associations between specific salivary metabolites and distinct disease mechanisms, considerable inter-individual variability, and the inherent complexity of the oral microenvironment. Furthermore, issues related to data interpretation complexity, technological constraints, and the necessity for rigorous clinical validation continue to impede its broader clinical adoption. Nevertheless, ongoing advancements in analytical technologies and bioinformatics approaches hold considerable promise for addressing these limitations, positioning salivary metabolomics as a transformative tool for precision diagnosis and personalized treatment in oral health care.

Malignant Transformed and Non-Transformed Oral Leukoplakias Are Metabolically Different

Understanding the early molecular events driving oral carcinogenesis is vital for diagnosing oral squamous cell carcinoma (OSCC) promptly. While metabolic differences between oral leukoplakia (OLK), OSCC, and healthy oral mucosa have been reported, the metabolic changes distinguishing malignant transformed OLKs (MT-OLK) from non-transformed OLKs (NT-OLK) remain unexplored. Here, we examine the metabolomic profiles of 5 cases of MT-OLK and 15 of NT-OLK to identify key predictive molecules using untargeted high-performance liquid chromatography-mass spectrometry. The potentially discriminant compounds were highlighted through a robust statistical analysis workflow, and the dysregulated metabolic pathways were illustrated by enrichment analysis. Seventeen molecular features, primarily lipids-including phospholipids, oxidised lipids, cholesteryl esters, and fatty acids-were identified as discriminants between MT-OLK and NT-OLK across statistical and bioinformatic approaches. Pathway enrichment analysis revealed alterations in lipid metabolism, particularly fatty acid synthesis and degradation, steroid hormone biosynthesis, and glycerophospholipid metabolism. Predictive models showed high accuracy (AUC = 0.88) in distinguishing the two groups. This study suggests that metabolomics has the potential to differentiate between MT-OLK and NT-OLK by identifying candidate biomarkers that may contribute to the understanding of malignant transformation. Validation in larger cohorts is warranted to translate these findings into clinical practice.

Evaluation of Peri-Implantitis through Fourier-Transform Infrared Spectroscopy on Saliva

BACKGROUND

Peri-implantitis is characterized as a pathological change in the tissues around dental implants. Fourier-transform infrared spectroscopy (FTIR) provides molecular information from optical phenomena observed by the vibration of molecules, which is used in biological studies to characterize changes and serves as a form of diagnosis.

AIMS

this case-control study evaluated the peri-implant disease by using FTIR spectroscopy with attenuated total reflectance in the fingerprint region.

METHODS

38 saliva samples were evaluated, 17 from the control group and 21 from the peri-implantitis group. Clinical data such as plaque index (PI), gingival index, probing depth (PS), and attachment level were assessed.

RESULTS

The results of clinical parameters showed a statistical difference between the two groups regarding an excess of the PI. In the FTIR-ATR analysis, the main components revealed vibrational modes of fatty acids, histidine, lipid esters, nucleic acids, and tryptophan, with the main molecules contributing to spectral discrimination. The five-component partial least-squares discriminant analysis classification model had an accuracy of 81%, showing differences between healthy and diseased implants.

CONCLUSION

the FTIR spectroscopy provides important molecular characteristics of the samples and the results in association with clinical data show the effectiveness of using this tool for diagnosing the disease.

Enhanced nano-LC-MS for analyzing dansylated oral cancer tissue metabolome dissolved in solvents with high elution strength

BACKGROUND

Tissue metabolomics analysis, alongside genomics and proteomics, offers crucial insights into the regulatory mechanisms of tumorigenesis. To enhance metabolite detection sensitivity, chemical isotope labeling (CIL) techniques, such as dansylation, have been developed to improve metabolite separation and ionization in mass spectrometry (MS). However, the dissolution of hydrophobic derivatized metabolites in solvents with high acetonitrile content limits the use of liquid chromatography (LC) systems with small-volume reversed-phase (RP) columns. In this study, we established a nano-LC-MS system with an online dilution design to address this issue, enabling sensitive analysis of oral cancer tissue metabolomes.

RESULTS

Our nano-LC system features a flow path design with online sample dilution before an RP trap column and backflushing of the trap column before entering the analytical column. Compared to other nano-LC systems, both with and without online dilution designs, our system demonstrates the superiority of the T-connector-based dilution method. Using only 1/20th of the sample required for popular micro-LC systems, our nano-LC detects a larger number of peak pairs with similar recovery rates for both hydrophilic and hydrophobic metabolites, ensuring unbiased results. Thirty-two matched pairs of oral squamous cell carcinoma (OSCC) tissue samples and adjacent noncancerous tissues (ANTs) underwent high-throughput CIL-metabolome analysis using our nano-LC system. Compared to our previous micro-LC methods, the nano-LC-MS system exhibits enhanced detection sensitivity, significantly reducing sample requirements.

SIGNIFICANCE

Our findings highlight the efficacy of our platform for metabolomic analysis with limited sample amounts. The nano-LC system's ability to analyze samples dissolved in strong eluents suggests potential applications for handling other hydrophobic compounds using RPLC or other separation methods facing similar solvent incompatibility issues. This approach holds promise for identifying novel metabolite biomarkers for oral cancers, advancing our understanding of tumorigenesis, and enhancing clinical applications.

Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.

Metabolomic insights into idiopathic xerostomia: The central role of caffeine metabolism in salivary biochemistry

OBJECTIVE

This study aims to delineate the salivary metabolomic profile of patients with idiopathic xerostomia using untargeted metabolomics techniques, with the goal of addressing the lack of clear diagnostic markers and providing insights into the pathophysiological mechanisms of the condition.

DESIGN

In this observational, cross-sectional study, saliva samples from 33 patients with idiopathic xerostomia and 34 healthy controls were analyzed using Ultra Performance Liquid Chromatography Quadrupole Time of Flight Mass Spectrometry (UPLC-QTOF MS). Metabolomic profiling was complemented by multivariate statistical analysis to differentiate between affected individuals and controls.

RESULTS

Metabolomic analysis delineated a pronounced differentiation between patients with idiopathic xerostomia and healthy controls. A total of 195 metabolites displayed significant differential expression, each with a variable importance in projection (VIP) greater than 1 and a P-value less than 0.05. Pathway enrichment analysis, according to the Kyoto Encyclopedia of Genes and Genomes (KEGG), identified 22 metabolites that participated in 18 distinct metabolic pathways. Among these, the caffeine metabolism pathway, characterized by notable alterations in impact values (VIP, P-value, Log2-fold change, Rich factor), emerged as the most significantly disrupted, underscoring its potential role in the pathophysiology of idiopathic xerostomia (P = 0.0000395).

CONCLUSIONS

The salivary metabolomic profiling revealed distinct alterations in idiopathic xerostomia, with a significant reduction in caffeine metabolism pathways, underscoring potential neuropathic involvement. This study advances our understanding of the metabolic alterations in xerostomia, suggesting that salivary metabolomics may offer viable biomarkers for diagnosing and understanding the etiology of idiopathic xerostomia. Future research should focus on therapeutic targeting of these metabolic disturbances and evaluating their reversibility with treatment.

Metabolomic Profiling of Oral Potentially Malignant Disorders and Its Clinical Values

Oral potentially malignant disorders (OPMD) are a group of lesions carrying the risk of developing into cancer. The gold standard to predict which lesions are more likely to undergo malignant transformation is the presence of dysplasia histologically. However, not all dysplastic lesions progress, and non-dysplastic lesions may also undergo malignant transformation. Oral carcinogenesis is a complex molecular process that involves somatic alterations and the deregulation of transcriptions, protein expression, and metabolite levels. Metabolomics, which is the scientific study of metabolites, has emerged as a promising high-throughput approach to investigate the metabolic changes of small molecules in biological pathways. In this review, we summarize the data relating to the metabolomic profiling of OPMDs, which will help elucidate the complex process of oral carcinogenesis. Furthermore, we identify that among all metabolites, citrate, pyruvate, and glutamate may serve as potential biomarkers for oral leukoplakia (OLK). Notably, metformin and gluconate have been shown to target glutamate and citrate, respectively, in cancer cells. Based on these findings, we propose that targeting these metabolites in patients with OPMD could be a promising therapeutic strategy to mitigate OPMD progression and potentially reduce the risk of malignant transformation. We also discuss the limitations and future directions of metabolomics in OPMD. Understanding these important metabolites is crucial for early detection and monitoring of oral cancer progression.

Simultaneous Quantitation of Multiple Biological Thiols Using Reactive Ionization and Derivatization with Charged Mass Tags

The biologically important thiols (cysteine, homocysteine, N-acetyl cysteine, and glutathione) are key species in redox homeostasis, and there is a clinical need to measure them rapidly, accurately, and simultaneously at low levels in complex biofluids. The solution to the challenge presented here is based on a new derivatizing reagent that combines a thiol-selective unit to optimize the chemical transformation and a precharged pyridinium unit chosen to maximize sensitivity in mass spectrometry. Derivatization is performed simultaneously with ionization ("reactive ionization"), and mass spectrometry is used to record and characterize the thiol reaction products. The method is applicable over the concentration range from 1 μM to 10 mM and is demonstrated for 25 blood serum, 1 plasma, and 3 types of tissue samples. The experiment is characterized by limited sample preparation (<4 min) and short analysis time (<1 min). High precision and accuracy (both better than 8%) are validated using independent HPLC-MS analysis. Cystine-cysteine redox homeostasis can be monitored by introducing an additional reduction step, and the accuracy and precision of these results are also validated by HPLC-MS.

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers

Introduction

Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived biomarkers.

Methods

We analyzed a multicenter public dataset comprising 61 OSCC patients and 61 healthy controls. Plasma metabolomics data were processed to extract 29 numerical and 47 ratio features. The Extra Trees (ET) algorithm was applied for feature selection, and the TabPFN model was used for classification and prediction.

Results

The model achieved an area under the curve (AUC) of 93% and an overall accuracy of 76.6% when using top-ranked individual biomarkers. Key metabolic features significantly differentiated OSCC patients from healthy controls, providing a detailed metabolic fingerprint of the disease.

Discussion

Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.

A simplified and efficient extracellular vesicle-based proteomics strategy for early diagnosis of colorectal cancer

Colorectal cancer (CRC) is a major cause of cancer-related death worldwide and an effective screening strategy for diagnosis of early-stage CRC is highly desired. Although extracellular vesicles (EVs) are expected to become some of the most promising tools for liquid biopsy of early disease diagnosis, the existing EV-based proteomics methods for practical application in clinical samples are limited by technical challenges in high-throughput isolation and detection of EVs. In the current study, we have developed a simplified and efficient EV-based proteomics strategy for early diagnosis of CRC. DSPE-functionalized beads were specifically designed that enabled direct capture of EVs from plasma samples in 10 minutes with good reproducibility and comprehensive proteome coverage. The single-pot, solid-phase-enhanced sample-preparation (SP3) technology was then combined with data-independent acquisition mass spectrometry (DIA-MS) for in-depth analysis and quantification of EV proteomes. From a cohort with 30 individuals including 11 healthy controls, 8 patients with adenomatous polyp and 11 patients with early-stage CRC, our streamlined workflow reproducibly quantified over 800 proteins from their plasma-derived EV samples, from which dysregulated protein signatures for molecular diagnosis of CRC were revealed. We selected a panel of 10 protein markers to train a machine learning (ML) model, which resulted in accurate prediction of polyp and early-stage CRC in an independent and single-blind validation cohort with excellent diagnostic ability of 89.3% accuracy. Our simplified and efficient clinical proteomic strategy will serve as a valuable tool for fast, accurate, and cost-effective diagnosis of CRC that can be easily extended to other disease samples for discovery of unique EV-based biomarkers.

Multi-omics reveals lactylation-driven regulatory mechanisms promoting tumor progression in oral squamous cell carcinoma

BACKGROUND

Lactylation, a post-translational modification, is increasingly recognized for its role in cancer progression. This study investigates its prevalence and impact in oral squamous cell carcinoma (OSCC).

RESULTS

Immunohistochemical staining of 81 OSCC cases shows lactylation levels correlate with malignancy grading. Proteomic analyses of six OSCC tissue pairs reveal 2765 lactylation sites on 1033 proteins, highlighting its extensive presence. These modifications influence metabolic processes, molecular synthesis, and transport. CAL27 cells are subjected to cleavage under targets and tagmentation assay for accessible-chromatin with high-throughput sequencing, and transcriptomic sequencing pre- and post-lactate treatment, with 217 genes upregulated due to lactylation. Chromatin immunoprecipitation-quantitative PCR and real-time fluorescence quantitative PCR confirm the regulatory role of lactylation at the K146 site of dexh-box helicase 9 (DHX9), a key factor in OSCC progression. CCK8, colony formation, scratch healing, and Transwell assays demonstrate that lactylation mitigates the inhibitory effect of DHX9 on OSCC, thereby promoting its occurrence and development.

CONCLUSIONS

Lactylation actively modulates gene expression in OSCC, with significant effects on chromatin structure and cellular processes. This study provides a foundation for developing targeted therapies against OSCC, leveraging the role of lactylation in disease pathogenesis.

Saliva metabolomics: concepts and applications in oral disorders

OBJECTIVES

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies.

MATERIAL AND METHODS

This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages.

RESULTS

Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline.

CONCLUSIONS

Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories.

CLINICAL RELEVANCE

Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

Bacteriome and mycobiome dysbiosis in oral mucosal dysplasia and oral cancer

It has long been considered that the oral microbiome is tightly connected to oral health and that dysbiotic changes can be detrimental to the occurrence and progression of dysplastic oral mucosal lesions or oral cancer. Improved understanding of the concepts of microbial dysbiosis together with advances in high-throughput molecular sequencing of these pathologies have charted in greater microbiological detail the nature of their clinical state. This review discusses the bacteriome and mycobiome associated with oral mucosal lesions, oral candidiasis, and oral squamous cell carcinoma, aiming to delineate the information available to date in pursuit of advancing diagnostic and prognostic utilities for oral medicine.

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.

Polyamines in Dysbiotic Oral Conditions of Older Adults: A Scoping Review

Polyamines modulate cellular proliferation and function. Their dysregulation results in inflammatory and oncological repercussions. This study aims to map the current literature and provide an overview of polyamines in dysbiotic oral conditions among older adults. English publications indexed in MEDLINE, Scopus, and Web of Science from January 2000 to May 2024 were screened. Eligibility criteria included clinical and laboratory studies using samples from adults aged 65 or above. This scoping review identified 2725 publications and included 19 publications. Ten studies detected that older adults with oral carcinoma had increased levels of polyamines such as spermidine in saliva and tumour-affected tissues. Eight studies reported older adults suffering from periodontal infection had increased levels of polyamines such as putrescine in saliva, gingival crevicular fluid, and biofilm from the gingival crevice. Two studies showed polyamine levels could reflect the success of periodontal therapy. Three studies found older adults with halitosis had increased levels of polyamines such as cadaverine in saliva and tongue biofilm. Polyamines were suggested as biomarkers for these oral conditions. In conclusion, certain polyamine levels are elevated in older adults with oral cancer, periodontal infections, and halitosis. Polyamines may be used as a simple and non-invasive tool to detect dysbiotic oral conditions and monitor treatment progress in older adults (Open Science Framework registration).

Application of metabolomics in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC.

OBJECTIVE

This review aims to provide a comprehensive overview of recent advancements in metabolic marker identification for early OSCC diagnosis. Additionally, the clinical significance and potential applications of metabolic markers for the management of OSCC are discussed.

RESULTS

This review summarizes metabolic changes during the occurrence and development of oral squamous cell carcinoma and reviews prospects for the clinical application of characteristic, differential metabolites in saliva, serum, and OSCC tissue. In this review, the application of metabolomic technology in OSCC research was summarized, and future research directions were proposed.

CONCLUSION

Metabolomics, detection technology that is the closest to phenotype, can efficiently identify differential metabolites. Combined with statistical data analyses and artificial intelligence technology, it can rapidly screen characteristic biomarkers for early diagnosis, treatment, and prognosis evaluations.

Comprehensive insights into oral squamous cell carcinoma: Diagnosis, pathogenesis, and therapeutic advances

Oral squamous cell carcinoma (OSCC) is considered the most common type of head and neck squamous cell carcinoma (HNSCC) as it holds 90 % of HNSCC cases that arise from multiple locations in the oral cavity. The last three decades witnessed little progress in the diagnosis and treatment of OSCC the aggressive tumor. However, in-depth knowledge about OSCC's pathogenesis, staging & grading, hallmarks, and causative factors is a prime requirement in advanced diagnosis and treatment for OSCC patients. Therefore present review was intended to comprehend the OSCCs' prevalence, staging & grading, molecular pathogenesis including premalignant stages, various hallmarks, etiology, diagnostic methods, treatment (including FDA-approved drugs with the mechanism of action and side effects), and theranostic agents. The current review updates that for a better understanding of OSCC progress tumor-promoting inflammation, sustained proliferative signaling, and growth-suppressive signals/apoptosis capacity evasion are the three most important hallmarks to be considered. This review suggests that among all the etiology factors the consumption of tobacco is the major contributor to the high incidence rate of OSCC. In OSCC diagnosis biopsy is considered the gold standard, however, toluidine blue staining is the easiest and non-invasive method with high accuracy. Although there are various therapeutic agents available for cancer treatment, however, a few only are approved by the FDA specifically for OSCC treatment. The present review recommends that among all available OSCC treatments, the antibody-based CAR-NK is a promising therapeutic approach for future cancer treatment. Presently review also suggests that theranostics have boosted the advancement of cancer diagnosis and treatment, however, additional work is required to refine the role of theranostics in combination with different modalities in cancer treatment.

Salivary biomarkers: Effective diagnostic tool for oral leukoplakia and oral squamous cell carcinoma

OBJECTIVE

To identify potential salivary biomarkers for the diagnosis and monitoring of disease progression in oral squamous cell carcinoma and oral leukoplakia.

MATERIALS AND METHODS

An advance search from PubMed and Hindawi was performed with keywords; oral leukoplakia/oral squamous cell carcinoma, salivary biomarker and diagnosis/prognosis. An additional search of articles was done through a manual search from the Google Scholar database.

RESULTS

Twenty studies involving salivary biomarkers as diagnostic tools for oral squamous cell carcinoma and/or oral leukoplakia were identified. A narrative review was carried out.

CONCLUSION

Single or multiple salivary biomarkers reported by most studies have shown great potential as diagnostic tools for oral squamous cell carcinoma and oral leukoplakia. However, the validation of sensitivity and specificity should be carried out to ensure the accuracy of the biomarkers. Furthermore, a standardised method for saliva collection should be established to prevent variability in the expression of biomarkers.

Connecting the dots: investigating the link between environmental, genetic, and epigenetic influences in metabolomic alterations in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) accounts for around 90% of all oral cancers and is the eighth most common cancer worldwide. Despite progress in managing OSCC, the overall prognosis remains poor, with a survival rate of around 50-60%, largely due to tumor size and recurrence. The challenges of late-stage diagnosis and limitations in current methods emphasize the urgent need for less invasive techniques to enable early detection and treatment, crucial for improving outcomes in this aggressive form of oral cancer. Research is currently aimed at unraveling tumor-specific metabolite profiles to identify candidate biomarkers as well as discover underlying pathways involved in the onset and progression of cancer that could be used as new targets for diagnostic and therapeutic purposes. Metabolomics is an advanced technological approach to identify metabolites in different sample types (biological fluids and tissues). Since OSCC promotes metabolic reprogramming influenced by a combination of genetic predisposition and environmental factors, including tobacco and alcohol consumption, and viral infections, the identification of distinct metabolites through screening may aid in the diagnosis of this condition. Moreover, studies have shown the use of metabolites during the catalysis of epigenetic modification, indicating a link between epigenetics and metabolism. In this review, we will focus on the link between environmental, genetic, and epigenetic influences in metabolomic alterations in OSCC. In addition, we will discuss therapeutic targets of tumor metabolism, which may prevent oral tumor growth, metastasis, and drug resistance.

Salivary metabolomic identification of biomarker candidates for oral melanoma and oral squamous cell carcinoma in dogs

BACKGROUND

Oral melanoma (OM) and oral squamous cell carcinoma (OSCC) are frequently diagnosed in dogs, presenting a challenge in distinguishing them from benign oral tumors (BN). Salivary metabolomic biomarkers offer a practical solution because of saliva's direct contact with tumors and the noninvasive nature of collection.

OBJECTIVE

Assess the diversity and abundance of the salivary metabolome in dogs with BN, OM, and OSCC using amine/phenol submetabolome analysis and high-performance chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS).

ANIMALS

Study included 11 BN, 24 OM, 10 OSCC, and 20 healthy control dogs.

METHODS

Case-control cross-sectional study was conducted to assess salivary submetabolic profiles in dogs with BN, OM, and OSCC and healthy dogs. Samples were labeled with 12C-dansyl chloride and analyzed using CIL LC-MS targeted to amine- and phenol-containing metabolites for amine/phenol submetabolome analysis.

RESULTS

Distinct clusters and significant differences in metabolite concentrations were observed among the oral cancer, BN, and control groups. A total of 154 and 66 metabolites showed significantly altered concentrations, particularly in OM and OSCC, respectively, when compared with BN (Padj < .05). Potential metabolic biomarkers were identified for each cancer, including decreased concentrations of seryl-arginine and sarcosine in OSCC. Moreover, high-confidence putative metabolites were identified, including an increase in tryptophyl-threonine and a decrease in 1,2-dihydroxynapthalene-6-sulfonic acid and hydroxyprolyl-hydroxyproline for OM.

CONCLUSIONS AND CLINICAL IMPORTANCE

We identified high coverage of the amine/phenol submetabolome, including seryl-arginine, and sarcosine, in OSCC. Our findings emphasize the potential of these biomarkers for distinguishing between oral OSCC and BN in dogs.

Salivary metabolomics in early detection of oral squamous cell carcinoma - a meta-analysis

INTRODUCTION

Oral squamous cell carcinoma (OSCC) represents the most prevalent form of oral cancer. Potentially malignant disorders of oral mucosa exhibit an elevated propensity for malignant progression. A substantial proportion of cases are discerned during advanced stages, significantly impacting overall survival. This investigation aims to ascertain salivary metabolites with potential utility in the early detection of OSCC.

METHODS

A search encompassing PubMed, EMBASE, Scopus, Ovid, Science Direct, and Web of Science databases was conducted to identify eligible articles. The search strategy employed precise terms. The quality assessment of the included studies was executed using the QUADAS 2 ROB tool. This was registered with PROSPERO CRD42021278217.

RESULTS

Upon removing duplicate articles and publications that didn't satisfy the inclusion criteria, seven articles were included in the current study. The Random Effects Maximum Likelihood (REML) model adopted for quantitative synthesis identified Nacetyl glucosamine as the sole metabolite in two studies included in this metaanalysis. The pathways significantly influenced by these identified metabolites were delineated.

CONCLUSION

This study highlights Nacetyl glucosamine as a distinctive metabolite with the potential to serve as an early diagnostic marker for OSCC. Nevertheless, further research is warranted to validate its clinical utility.

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.

Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry

The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.

Metabolic landscape of head and neck squamous cell carcinoma informs a novel kynurenine/Siglec-15 axis in immune escape

BACKGROUND

Metabolic reprograming and immune escape are two hallmarks of cancer. However, how metabolic disorders drive immune escape in head and neck squamous cell carcinoma (HNSCC) remains unclear. Therefore, the aim of the present study was to investigate the metabolic landscape of HNSCC and its mechanism of driving immune escape.

METHODS

Analysis of paired tumor tissues and adjacent normal tissues from 69 HNSCC patients was performed using liquid/gas chromatography-mass spectrometry and RNA-sequencing. The tumor-promoting function of kynurenine (Kyn) was explored in vitro and in vivo. The downstream target of Kyn was investigated in CD8+ T cells. The regulation of CD8+ T cells was investigated after Siglec-15 overexpression in vivo. An engineering nanoparticle was established to deliver Siglec-15 small interfering RNA (siS15), and its association with immunotherapy response were investigated. The association between Siglec-15 and CD8+ programmed cell death 1 (PD-1)+ T cells was analyzed in a HNSCC patient cohort.

RESULTS

A total of 178 metabolites showed significant dysregulation in HNSCC, including carbohydrates, lipids and lipid-like molecules, and amino acids. Among these, amino acid metabolism was the most significantly altered, especially Kyn, which promoted tumor proliferation and metastasis. In addition, most immune checkpoint molecules were upregulated in Kyn-high patients based on RNA-sequencing. Furthermore, tumor-derived Kyn was transferred into CD8+ T cells and induced T cell functional exhaustion, and blocking Kyn transporters restored its killing activity. Accroding to the results, mechanistically, Kyn transcriptionally regulated the expression of Siglec-15 via aryl hydrocarbon receptor (AhR), and overexpression of Siglec-15 promoted immune escape by suppressing T cell infiltration and activation. Targeting AhR in vivo reduced Kyn-mediated Siglec-15 expression and promoted intratumoral CD8+ T cell infiltration and killing capacity. Finally, a NH2-modified mesoporous silica nanoparticle was designed to deliver siS15, which restored CD8+ T cell function status and enhanced anti-PD-1 efficacy in tumor-bearing immunocompetent mice. Clinically, Siglec-15 was positively correlated with AhR expression and CD8+PD-1+ T cell infiltration in HNSCC tissues.

CONCLUSIONS

The findings describe the metabolic landscape of HNSCC comprehensively and reveal that the Kyn/Siglec-15 axis may be a novel potential immunometabolism mechanism, providing a promising therapeutic strategy for cancers.

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.

Oral tongue squamous cell carcinoma diagnosis from tissue metabolic profiling

OBJECTIVE

Disease metabolomes have been studied for identifying diagnostic and predictive biomarkers of pathology. Oral tongue squamous cell carcinoma (OTSCC) is one of the most prevalent subtypes of head and neck squamous cell carcinoma, yet the profile and diagnostic value of its tissue metabolite are unclear.

SUBJECTS AND METHODS

Tumor tissue samples and matched normal mucosal tissue samples were collected from 40 OTSCC patients. Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry, in positive and negative ion modes, was used to identify dysregulated metabolites in OTSCC. Further, utilizing LASSO regression and receiver operating characteristic analyses, biomarker metabolites were selected and validated, and a diagnostic model was established.

RESULTS

One hundred and ninety metabolites were detected. The OTSCC had a total of 89 dysregulated metabolites, of which 73 were elevated. A diagnostic panel of nine metabolites was subsequently created that could accurately identify OTSCC with 100% sensitivity of 100%, 100% specificity and an AUC of 1.00.

CONCLUSIONS

This study identified distinct metabolic characteristics of OTSCC and established a diagnostic model. Our research also contributes to the investigation of the pathogenesis of OTSCC.

Integrative analysis reveals associations between oral microbiota dysbiosis and host genetic and epigenetic aberrations in oral cavity squamous cell carcinoma

Dysbiosis of the human oral microbiota has been reported to be associated with oral cavity squamous cell carcinoma (OSCC) while the host-microbiota interactions with respect to the potential impact of pathogenic bacteria on host genomic and epigenomic abnormalities remain poorly studied. In this study, the mucosal bacterial community, host genome-wide transcriptome and DNA CpG methylation were simultaneously profiled in tumors and their adjacent normal tissues of OSCC patients. Significant enrichment in the relative abundance of seven bacteria species (Fusobacterium nucleatum, Treponema medium, Peptostreptococcus stomatis, Gemella morbillorum, Catonella morbi, Peptoanaerobacter yurli and Peptococcus simiae) were observed in OSCC tumor microenvironment. These tumor-enriched bacteria formed 254 positive correlations with 206 up-regulated host genes, mainly involving signaling pathways related to cell adhesion, migration and proliferation. Integrative analysis of bacteria-transcriptome and bacteria-methylation correlations identified at least 20 dysregulated host genes with inverted CpG methylation in their promoter regions associated with enrichment of bacterial pathogens, implying a potential of pathogenic bacteria to regulate gene expression, in part, through epigenetic alterations. An in vitro model further confirmed that Fusobacterium nucleatum might contribute to cellular invasion via crosstalk with E-cadherin/β-catenin signaling, TNFα/NF-κB pathway and extracellular matrix remodeling by up-regulating SNAI2 gene, a key transcription factor of epithelial-mesenchymal transition (EMT). Our work using multi-omics approaches explored complex host-microbiota interactions and provided important insights into genetic and functional basis in OSCC tumorigenesis, which may serve as a precursor for hypothesis-driven study to better understand the causational relationship of pathogenic bacteria in this deadly cancer.

Comparisons of different extraction methods and solvents for saliva samples

INTRODUCTION

Changes in the categories and concentrations of salivary metabolites may be closely related to oral, intestinal or systemic diseases. To study salivary metabolites, the first analytical step is to extract them from saliva samples as much as possible, while reducing interferences to a minimum. Frequently used extraction methods are protein precipitation (PPT), liquid-liquid extraction (LLE) and solid-phase extraction (SPE), with various organic solvents. The types and quantities of metabolites extracted with different methods may vary greatly, but few studies have systematically evaluated them.

OBJECTIVES

This study aimed to select the most suitable methods and solvents for the extraction of saliva according to different analytical targets.

METHODS

An untargeted metabolomics approach based on liquid chromatography-mass spectrometry was applied to obtain the raw data. The numbers of metabolites, repeatability of the data and intensities of mass spectrometry signals were used as evaluation criteria.

RESULTS

PPT resulted in the highest coverage. Among the PPT solvents, acetonitrile displayed the best repeatability and the highest coverage, while acetone resulted in the best signal intensities for the extracted compounds. LLE with the mixture of chloroform and methanol was the most suitable for the extraction of small hydrophobic compounds.

CONCLUSION

PPT with acetonitrile or acetone was recommended for untargeted analysis, while LLE with the mixture of chloroform and methanol was recommended for small hydrophobic compounds.

miR-626 Inhibition Enhanced the Radiosensitivity to Oral Squamous Cell Carcinoma via the Downregulation of Nuclear Factor Kappa-B Signaling

Objective: The effect of miR-626 on the radiosensitivity to oral squamous cell carcinoma (OSCC) was evaluated in this study. Materials and Methods: The level of miR-626 in OSCC patients was determined by analyzing the data of miRNA microarray GSE113956. miR-626 was overexpressed by miR-626 mimics and knockdown were performed by miR-626 inhibitor. The level of miR-626 was detected by quantitative real-time polymerase chain reaction. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and colony formation assays were used to detect the effect of miR-626 on the growth of OSCC cells. Flow cytometry was used to detect the apoptosis of OSCC cells. Western blot and dual luciferase reporter assays were used to explore the underlying mechanism of miR-626 regulating the radiosensitivity to OSCC. The effect of miR-626 on the radiosensitivity to OSCC were examined in an in vivo xenograft model. Results: The serum miR-626 level of OSCC patients was significantly higher than that of healthy controls. miR-626 mimics significantly promoted the OSCC cell growth, but the miR-626 inhibitor significantly suppressed the OSCC cell growth. Radiation combined with the miR-626 inhibitor significantly suppressed the cell proliferation and promoted the apoptosis of SCC-4 and HSC4 cells. Moreover, miR-626 regulates the nuclear factor kappa-B (NF-κB) signaling mediated by TRAF-interacting protein with forkhead-associated domain B. Furthermore, inhibition of miR-626 enhances the radiosensitivity to OSCC in nude mice. Conclusions: miR-626 inhibition enhanced the radiosensitivity to OSCC through the downregulation of NF-κB signaling.

Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment

In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune cells in the tumor microenvironment are demonstrated. Most importantly, significant metabolic reprogramming in OSF-derived OSCC, including abnormal polyamine metabolism, potentially playing a pivotal role in promoting tumorigenesis and immune evasion is discovered. The ST and SM data in this study shed new light on deciphering the mechanisms of OSF-derived OSCC. The work also offers invaluable clues for the prevention and treatment of OSCC.

A novel saliva-based miRNA profile to diagnose and predict oral cancer

Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to discover, develop, and validate a novel saliva-based microRNA signature for early diagnosis and prediction of OC risk in oral potentially malignant disorders (OPMD). The Cancer Genome Atlas (TCGA) miRNA sequencing data and small RNA sequencing data of saliva samples were used to discover differentially expressed miRNAs. Identified miRNAs were validated in saliva samples of OC (n = 50), OPMD (n = 52), and controls (n = 60) using quantitative real-time PCR. Eight differentially expressed miRNAs (miR-7-5p, miR-10b-5p, miR-182-5p, miR-215-5p, miR-431-5p, miR-486-3p, miR-3614-5p, and miR-4707-3p) were identified in the discovery phase and were validated. The efficiency of our eight-miRNA signature to discriminate OC and controls was: area under curve (AUC): 0.954, sensitivity: 86%, specificity: 90%, positive predictive value (PPV): 87.8% and negative predictive value (NPV): 88.5% whereas between OC and OPMD was: AUC: 0.911, sensitivity: 90%, specificity: 82.7%, PPV: 74.2% and NPV: 89.6%. We have developed a risk probability score to predict the presence or risk of OC in OPMD patients. We established a salivary miRNA signature that can aid in diagnosing and predicting OC, revolutionising the management of patients with OPMD. Together, our results shed new light on the management of OC by salivary miRNAs to the clinical utility of using miRNAs derived from saliva samples.

Extensive comparison of salivary collection, transportation, preparation, and storage methods: a systematic review

BACKGROUND

Human saliva as a bodily fluid-similar to blood-is utilized for diagnostic purposes. Unlike blood sampling, collecting saliva is non-invasive, inexpensive, and readily accessible. There are no previously published systematic reviews regarding different collection, transportation, preparation, and storage methods for human saliva.

DESIGN

This study has been prepared and organized according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. This systematic review has been registered at PROSPERO (Registration ID: CRD42023415384). The study question according to the PICO format was as followed: Comparison of the performance (C) of different saliva sampling, handling, transportation, and storage techniques and methods (I) assessed for analyzing stimulated or unstimulated human saliva (P and O). An electronic search was executed in Scopus, Google Scholar, and PubMed.

RESULTS

Twenty-three descriptive human clinical studies published between 1995 and 2022 were included. Eight categories of salivary features and biomarkers were investigated (i.e., salivary flow rate, total saliva quantity, total protein, cortisol, testosterone, DNA quality and quantity, pH and buffering pH). Twenty-two saliva sampling methods/devices were utilized. Passive drooling, Salivette®, and spitting were the most utilized methods. Sampling times with optimum capabilities for cortisol, iodine, and oral cancer metabolites are suggested to be 7:30 AM to 9:00 AM, 10:30 AM to 11:00 AM, and 14:00 PM to 20:00 PM, respectively. There were 6 storage methods. Centrifuging samples and storing them at -70 °C to -80 °C was the most utilized storage method. For DNA quantity and quality, analyzing samples immediately after collection without centrifuging or storage, outperformed centrifuging samples and storing them at -70 °C to -80 °C. Non-coated Salivette® was the most successful method/device for analyzing salivary flow rate.

CONCLUSION

It is highly suggested that scientists take aid from the reported categorized outcomes, and design their study questions based on the current voids for each method/device.

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.

3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy

In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.

Polarity-regulated derivatization-assisted LC-MS method for amino-containing metabolites profiling in gastric cancer

Amino-containing compounds, including amino acids, aliphatic amines, aromatic amines, small peptides and catecholamines, are involved in various biological processes and play vital roles in multiple metabolic pathways. Previous studies indicated that some amino-containing metabolites are significant diagnostic and prognostic biomarkers of gastric cancer. However, the discovery of precise biomarkers for the preoperative diagnosis of gastric cancer is still in an urgent need. Herein, we established a polarity-regulated derivatization method coupled with liquid chromatography-mass spectrometry (LC-MS) for amino-containing metabolites profiling in the serum samples of patients with gastric cancer and healthy controls, based on our newly designed and synthesized derivatization reagent (S)-3-(1-(diisopropoxyphosphoryl) pyrrolidine-2-carboxamido)-N-hydroxysuccinimidyl ester (3-DP-NHS). Enhanced separation efficiency and detection sensitivity for amino-containing metabolites were achieved after derivatization. This method exhibited good linearity, recovery, intra- and inter-day precision and accuracy. Only 5 μL serum is needed for untargeted analysis, enabling 202 amino-containing metabolites to be detected. Statistical analysis revealed altered amino acid metabolisms in patients with gastric cancer. Furthermore, ultra high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS) analysis quantification revealed increased serum levels of tryptamine and decreased concentrations of arginine and tryptophan in patients with gastric cancer. Receiver operating characteristic (ROC) curves indicated that an increased tryptamine/tryptophan ratio could serve as a potential biomarker for gastric cancer diagnosis. This study demostrated the possibility of using serum amino acid biomarkers for gastric cancer diagnosis, providing new avenues for the treatment of gastric cancer.

Preliminary Findings on the Salivary Metabolome of Hookah and Cigarette Smokers

The aim of the study was to evaluate the salivary metabolomic profile of patients who habitually smoke hookah and cigarettes. The groups consisted of 33 regular and exclusive hookah smokers, 26 regular and exclusive cigarette smokers, and 30 nonsmokers. Unstimulated whole saliva was collected for the measurement of salivary metabolites by gas chromatography coupled with tandem mass spectrometry (GC-MS/MS). The MetaboAnalyst software was used for statistical analysis and evaluation of biomarkers. 11 smoking salivary biomarkers were identified using the area under receiving-operator curver criterion and threshold of 0.9. Xylitol and octadecanol were higher in cigarette smokers compared to controls; arabitol and maltose were higher in controls compared to cigarette smokers; octadecanol and tyramine were higher in hookah smokers compared to controls; phenylalanine was higher in controls compared to hookah smokers; and fructose, isocitric acid, glucuronic acid, tryptamine, maltose, tyramine, and 3-hydroxyisolvaleric acid were higher in hookah smokers compared to cigarettes smokers. Conclusions: The evaluation of the salivary metabolome of hookah smokers, showing separation between the groups, especially between the control versus hookah groups and cigarette versus hookah groups, and it seems to demonstrate that the use of hookah tobacco is more damaging to health.

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.

Engineering Escherichia coli Nissle 1917 as a microbial chassis for therapeutic and industrial applications

Genetically engineered microbes, especially Escherichia coli, have been widely used in the biosynthesis of proteins and metabolites for medical and industrial applications. As a traditional probiotic with a well-established safety record, E. coli Nissle 1917 (EcN) has recently emerged as a microbial chassis for generating living therapeutics, drug delivery vehicles, and microbial platforms for industrial production. Despite the availability of genetic tools for engineering laboratory E. coli K-12 and B strains, new genetic engineering systems are still greatly needed to expand the application range of EcN. In this review, we have summarized the latest progress in the development of genetic engineering systems in EcN, as well as their applications in the biosynthesis and delivery of valuable small molecules and biomacromolecules of medical and/or industrial interest, followed by a glimpse of how this rapidly growing field will evolve in the future.

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Biofluid Metabolic Profiling for Lung Cancer Screening via Reactive Matrix-Assisted Laser Desorption Ionization Mass Spectrometry

Lung cancer (LC) has the highest mortality rate among various cancer diseases. Developing an early screening method for LC with high classification accuracy is essential. Herein, 2-hydrazinoquinoline (2-HQ) is utilized as a dual-mode reactive matrix for metabolic fingerprint analysis and LC screening via matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). Metabolites in both positive mode and negative mode can be detected using 2-HQ as the matrix, and derivative analysis of aldehyde and ketone compounds can be achieved simultaneously. Hundreds of serum and urine samples from LC patients and healthy volunteers were analyzed. Combined with machine learning, LC patients and healthy volunteers were successfully distinguished with a high area under the curve value (0.996 for blind serum samples and 0.938 for urine). The MS signal was identified for metabolic profiling, and dysregulated metabolites of the LC group were analyzed. The above results showed that this method has great potential for rapid screening of LC.

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.

Tumor cell-derived spermidine is an oncometabolite that suppresses TCR clustering for intratumoral CD8+ T cell activation

The activation and expansion of T cells that recognize cancer cells is an essential aspect to antitumor immunity. Tumors may escape destruction by the immune system through ectopic expression of inhibitory immune ligands typically exemplified by the PD-L1/PD-1 pathway. Here, we reveal another facet of tumor evasion from T cell surveillance. By secretome profiling of necrotic tumor cells, we identified an oncometabolite spermidine as a unique inhibitor of T cell receptor (TCR) signaling. Mechanistically, spermidine causes the downregulation of the plasma membrane cholesterol levels, resulting in the suppression of TCR clustering. Using syngeneic mouse models, we show that spermidine is abundantly detected in the tumor immune microenvironment (TIME) and that administration of the polyamine synthesis inhibitor effectively enhanced CD8+ T cell-dependent antitumor responses. Further, the combination of the polyamine synthesis inhibitor with anti-PD-1 immune checkpoint antibody resulted in a much stronger antitumor immune response. This study reveals an aspect of immunosuppressive TIME, wherein spermidine functions as a metabolic T cell checkpoint that may offer a unique approach for promoting tumor immunotherapy.