Evaluation of salivary VOC profile composition directed towards oral cancer and oral lesion assessment

Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011-2014

BACKGROUND

Volatile organic compounds (VOCs) are ubiquitous environmental pollutants which have been suggested to have adverse effects on human health. While the influence of environmental pollutant exposures on periodontitis has attracted elevating attention in recent years, the epidemiological evidence on the association between VOCs exposure and periodontitis was scarce. This study aimed to investigate the potential mediating role of systemic inflammation factors in the complex association between VOCs exposure and periodontitis.

METHODS

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, we examined the impacts of VOCs exposure on periodontitis. Concentrations of urinary metabolites of VOCs (mVOCs) were measured using electrospray tandem mass spectrometry to evaluate internal VOCs exposure. Multivariable logistic regression, restricted cubic spline regression (RCS), Bayesian kernel machine regression (BKMR) and Quantile g-computation (QGC) models were performed to investigate the impacts of VOCs exposure on periodontitis. Mediation models were applied to assess the mediated effects of systemic inflammation on the association between mixed VOCs exposure and periodontitis. Besides, we analyzed the association between mixed VOCs exposure and periodontitis in stratified age, gender, and smoking status subgroups.

RESULTS

1,551 participants were ultimately included for further analyses, of whom 45.20% suffering from periodontitis. Multivariable logistic regression and RCS identified positive associations between single urinary mVOCs and periodontitis (P < 0.05). Notably, BKMR and QGC models suggested that mixed VOCs exposure was significantly associated with periodontitis, with 2-Aminothiazoline-4-carboxylic acid (ATCA) contributing the most (conditional posterior inclusion probability = 0.997). Moreover, systemic inflammation markers (leukocyte and lymphocyte counts) were found to partly mediate the association between VOCs exposure and periodontitis (P < 0.05). No interaction effect was identified between mixed VOCs exposure and periodontitis in age, gender and smoking status subgroups (P > 0.05).

CONCLUSION

This study demonstrated a positive association between VOCs exposure and periodontitis, which was potentially mediated by systemic inflammation factors. Further longitudinal researches are demanded to clarify the underlying mechanisms.

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications

BACKGROUND

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids.

AIM OF REVIEW

This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Advances in Electrochemical Biosensors for the Detection of Common Oral Diseases

Limiting and preventing oral diseases remains a major challenge to the health of populations around the world, so finding ways to detect early-stage diseases (e.g., caries, periodontal disease, and oral cancer) and aiding in their prevention has always been an important clinical treatment concept. The development and application of electrochemical detection technology can provide important support for the early detection and non-invasive diagnosis of oral diseases and make up for the shortcomings of traditional diagnostic methods, which are highly sensitive, non-invasive, cost-effective, and less labor-intensive. It detects specific disease markers in body fluids through electrochemical reactions, discovers early warning signals of diseases, and realizes rapid and reliable diagnosis. This paper comprehensively summarizes the development and application of electrochemical biosensors in the detection and diagnosis of common oral diseases in terms of application platforms, sensing types, and disease detection, and discusses the challenges faced by electrochemical biosensors in the detection of oral diseases as well as the great prospects for future applications, in the hope of providing important insights for the future development of electrochemical biosensors for the early detection of oral diseases.

Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer

Early diagnosis of esophageal cancer (EC) is extremely challenging. The study presented herein aimed to assess whether urinary volatile organic compounds (VOCs) may be emerging diagnostic biomarkers for EC. Urine samples were collected from EC patients and healthy controls (HCs). Gas chromatography-ion mobility spectrometry (GC-IMS) was next utilised for volatile organic compound detection and predictive models were constructed using machine learning algorithms. ROC curve analysis indicated that an 8-VOCs based machine learning model could aid the diagnosis of EC, with the Random Forests having a maximum AUC of 0.874 and sensitivities and specificities of 84.2% and 90.6%, respectively. Urine VOC analysis aids in the diagnosis of EC.

Differences in the Volatilomic Urinary Biosignature of Prostate Cancer Patients as a Feasibility Study for the Detection of Potential Biomarkers

Prostate cancer (PCa) continues to be the second most common malignant tumour and the main cause of oncological death in men. Investigating endogenous volatile organic metabolites (VOMs) produced by various metabolic pathways is emerging as a novel, effective, and non-invasive source of information to establish the volatilomic biosignature of PCa. In this study, headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) was used to establish the urine volatilomic profile of PCa and identify VOMs that can discriminate between the two investigated groups. This non-invasive approach was applied to oncological patients (PCa group, n = 26) and cancer-free individuals (control group, n = 30), retrieving a total of 147 VOMs from various chemical families. This included terpenes, norisoprenoid, sesquiterpenes, phenolic, sulphur and furanic compounds, ketones, alcohols, esters, aldehydes, carboxylic acid, benzene and naphthalene derivatives, hydrocarbons, and heterocyclic hydrocarbons. The data matrix was subjected to multivariate analysis, namely partial least-squares discriminant analysis (PLS-DA). Accordingly, this analysis showed that the group under study presented different volatomic profiles and suggested potential PCa biomarkers. Nevertheless, a larger cohort of samples is required to boost the predictability and accuracy of the statistical models developed.

Hybrid volatilomics in cancer diagnosis by HS-GC-FID fingerprinting

Assessing volatile organic compounds (VOCs) as cancer signatures is one of the most promising techniques toward developing non-invasive, simple, and affordable diagnosis. Here, we have evaluated the feasibility of employing static headspace extraction (HS) followed by gas chromatography with flame ionization detector (GC-FID) as a screening tool to discriminate between cancer patients (head and neck-HNC,n= 15; and gastrointestinal cancer-GIC,n= 19) and healthy controls (n= 37) on the basis of a non-target (fingerprinting) analysis of oral fluid and urine. We evaluated the discrimination considering a single bodily fluid and adopting the hybrid approach, in which the oral fluid and urinary VOCs profiles were combined through data fusion. We used supervised orthogonal partial least squares discriminant analysis for classification, and we assessed the prediction power of the models by analyzing the values of goodness of prediction (Q²Y), area under the curve (AUC), sensitivity, and specificity. The individual models HNC urine, HNC oral fluid, and GIC oral fluid successfully discriminated between healthy controls and positive samples (Q²Y = 0.560, 0.525, and 0.559; AUC = 0.814, 0.850, and 0.926; sensitivity = 84.8, 70.2, and 78.6%; and specificity = 82.3; 81.5; 87.5%, respectively), whereas GIC urine was not adequate (Q²Y = 0.292, AUC = 0.694, sensitivity = 66.1%, and specificity = 77.0%). Compared to the respective individual models, Q²Y for the hybrid models increased (0.623 for hybrid HNC and 0.562 for hybrid GIC). However, sensitivity was higher for HNC urine and GIC oral fluid than for hybrid HNC (75.6%) and hybrid GIC (69.8%), respectively. These results suggested that HS-GC-FID fingerprinting is suitable and holds great potential for cancer screening. Additionally, the hybrid approach tends to increase the predictive power if the individual models present suitable quality parameter values. Otherwise, it is more advantageous to use a single body fluid for analysis.

Advances in the Diagnosis, Monitoring, and Progression of Oral Cancer through Saliva: An Update

The early detection of cancer, and in particular oral cancer, has been a priority objective of study in recent years. Saliva has been proposed as an easy-to-obtain means of providing the necessary information to diagnose malignant lesions in the oral cavity, since it can be obtained very easily and completely noninvasively. There are a number of molecules, known as biomarkers, which may be involved in the malignant transformation of oral lesions, and which have different natures. The involvement of proteins (“proteomics”), metabolites (“metabolomics”), and even certain genes in the structural changes of altered tissue has been investigated in order to establish validated parameters for the early diagnosis of oral cancer. In addition, the development of new analytical assay methods that can reduce costs and obtain better results in terms of sensitivity and specificity has been a key point in recent research in this field. Even though there are numerous biomarkers with results showing high sensitivity and specificity, there is still a need for more studies, with a larger sample and with analytical methods that can constitute a real advance in time and cost. Although salivary biomarkers are a promising new diagnostic tool for oral cancer, for the moment they do not replace biopsy as the “gold standard”.

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.

Objective

The objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.

Methods

Untargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

Results

Metabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).

Conclusions

Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers

Simple Summary

The lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.

Abstract

Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

Applications of Medium-Chain Triglycerides in Foods

In the 1950s, the production of processed fats and oils from coconut oil was popular in the United States. It became necessary to find uses for the medium-chain fatty acids (MCFAs) that were byproducts of the process, and a production method for medium-chain triglycerides (MCTs) was established. At the time of this development, its use as a non-fattening fat was being studied. In the early days MCFAs included fatty acids ranging from hexanoic acid (C6:0) to dodecanoic acid (C12:0), but today their compositions vary among manufacturers and there seems to be no clear definition. MCFAs are more polar than long-chain fatty acids (LCFAs) because of their shorter chain length, and their hydrolysis and absorption properties differ greatly. These differences in physical properties have led, since the 1960s, to the use of MCTs to improve various lipid absorption disorders and malnutrition. More than half a century has passed since MCTs were first used in the medical field. It has been reported that they not only have properties as an energy source, but also have various physiological effects, such as effects on fat and protein metabolism. The enhancement of fat oxidation through ingestion of MCTs has led to interest in the study of body fat reduction and improvement of endurance during exercise. Recently, MCTs have also been shown to promote protein anabolism and inhibit catabolism, and applied research has been conducted into the prevention of frailty in the elderly. In addition, a relatively large ingestion of MCTs can be partially converted into ketone bodies, which can be used as a component of "ketone diets" in the dietary treatment of patients with intractable epilepsy, or in the nutritional support of terminally ill cancer patients. The possibility of improving cognitive function in dementia patients and mild cognitive impairment is also being studied. Obesity due to over-nutrition and lack of exercise, and frailty due to under-nutrition and aging, are major health issues in today's society. MCTs have been studied in relation to these concerns. In this paper we will introduce the results of applied research into the use of MCTs by healthy subjects.

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.