Salivary biomarkers in the diagnosis of breast cancer: A review

Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung cancer.

Free Salivary Amino Acid Profile in Breast Cancer: Clinicopathological and Molecular Biological Features

The study of salivary amino acid profiles has attracted the attention of researchers, since amino acids are actively involved in most metabolic processes, including breast cancer. In this study, we analyzed the amino acid profile of saliva in a sample including all molecular biological subtypes of breast cancer to obtain a more complete picture and evaluate the potential utility of individual amino acids or their combinations for diagnostic purposes. This study included 116 patients with breast cancer, 24 patients with benign breast disease, and 25 healthy controls. From all patients, strictly before the start of treatment, saliva samples were collected, and the quantitative content of 26 amino acids was determined. Statistically significant differences between the three groups are shown in the content of Asp, Gly, Leu + Ile, Orn, Phe, Pro, Thr, and Tyr. To differentiate the three groups from each other, a decision tree was built. To construct it, we selected those amino acids for which the change in concentrations in the subgroups was multidirectional (GABA, Hyl, Arg, His, Pro, and Car). For the first time, it is shown that the amino acid profile of saliva depends on the molecular biological subtype of breast cancer. The most significant differences are shown for the luminal B HER2-positive and TNBC subgroups. In our opinion, it is critically important to consider the molecular biological subtype of breast cancer when searching for potential diagnostic markers.

High sensitivity saliva-based biosensor in detection of breast cancer biomarkers: HER2 and CA15-3

The prevalence of breast cancer in women underscores the urgent need for innovative and efficient detection methods. This study addresses this imperative by harnessing salivary biomarkers, offering a noninvasive and accessible means of identifying breast cancer. In this study, commercially available disposable based strips similar to the commonly used glucose detection strips were utilized and functionalized to detect breast cancer with biomarkers of HER2 and CA15-3. The results demonstrated limits of detection for these two biomarkers reached as low as 1 fg/ml much lower than those of conventional enzyme-linked immunosorbent assay in the range of 1∼4 ng/ml. By employing a synchronized double-pulse method to apply 10 of 1.2 ms voltage pulses to the electrode of sensing strip and drain electrode of the transistor for amplifying the detected signal, and the detected signal was the average of 10 digital output readings corresponding to those 10 voltage pulses. The sensor sensitivities were achieved approximately 70/dec and 30/dec for HER2 and CA15-3, respectively. Moreover, the efficiency of this novel technique is underscored by its swift testing time of less than 15 ms and its minimal sample requirement of only 3 μl of saliva. The simplicity of operation and the potential for widespread public use in the future position this approach as a transformative tool in the early detection of breast cancer. This research not only provides a crucial advancement in diagnostic methodologies but also holds the promise of revolutionizing public health practices.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Characterization of Salivary and Plasma Metabolites as Biomarkers for HCC: A Pilot Study

(1) Background: The incidence of hepatocellular carcinoma (HCC) is rising, and current screening methods lack sensitivity. This study aimed to identify distinct and overlapping metabolites in saliva and plasma that are significantly associated with HCC. (2) Methods: Saliva samples were collected from 42 individuals (HCC = 16, cirrhosis = 12, healthy = 14), with plasma samples from 22 (HCC = 14, cirrhosis = 2, healthy = 6). We performed untargeted mass spectrometry on blood and plasma, tested metabolites for associations with HCC or cirrhosis using a logistic regression, and identified enriched pathways with Metaboanalyst. Pearson's correlation was employed to test for correlations between salivary and plasma metabolites. (3) Results: Six salivary metabolites (1-hexadecanol, isooctanol, malonic acid, N-acetyl-valine, octadecanol, and succinic acid) and ten plasma metabolites (glycine, 3-(4-hydroxyphenyl)propionic acid, aconitic acid, isocitric acid, tagatose, cellobiose, fucose, glyceric acid, isocitric acid, isothreonic acid, and phenylacetic acid) were associated with HCC. Malonic acid was correlated between the paired saliva and plasma samples. Pathway analysis highlighted deregulation of the 'The Citric Acid Cycle' in both biospecimens. (4) Conclusions: Our study suggests that salivary and plasma metabolites may serve as independent sources for HCC detection. Despite the lack of correlation between individual metabolites, they converge on 'The Citric Acid Cycle' pathway, implicated in HCC pathogenesis.

Salivary biomarkers: novel noninvasive tools to diagnose chronic inflammation

Several chronic disorders including type 2 diabetes (T2D), obesity, heart disease and cancer are preceded by a state of chronic low-grade inflammation. Biomarkers for the early assessment of chronic disorders encompass acute phase proteins (APP), cytokines and chemokines, pro-inflammatory enzymes, lipids and oxidative stress mediators. These substances enter saliva through the blood flow and, in some cases, there is a close relation between their salivary and serum concentration. Saliva can be easily collected and stored with non-invasive and cost-saving procedures, and it is emerging the concept to use it for the detection of inflammatory biomarkers. To this purpose, the present review aims to discuss the advantages and challenges of using standard and cutting-edge techniques to discover salivary biomarkers which may be used in diagnosis/therapy of several chronic diseases with inflammatory consequences with the pursuit to possibly replace conventional paths with detectable soluble mediators in saliva. Specifically, the review describes the procedures used for saliva collection, the standard approaches for the measurement of salivary biomarkers and the novel methodological strategies such as biosensors to improve the quality of care for chronically affected patients.

A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions

The human body has a unique way of saying when something is wrong with it. The molecules in the body fluids can be helpful in the early detection of diseases by enabling health and preventing disease progression. These biomarkers enabling better healthcare are becoming an extensive area of research interest. Biosensors that detect these biomarkers are becoming the future, especially Point Of Care (POC) biosensors that remove the need to be physically present in the hospital. Detection of complex and systemic diseases using biosensors has a long way to go. Saliva-based biosensors are gaining attention among body fluids due to their non-invasive collection and ability to detect periodontal disease and identify systemic diseases. The possibility of saliva-based diagnostic biosensors has gained much publicity, with companies sending home kits for ancestry prediction. Saliva-based testing for covid 19 has revealed effective clinical use and relevance of the economic collection. Based on universal biomarkers, the detection of systemic diseases is a booming research arena. Lots of research on saliva-based biosensors is available, but it still poses challenges and limitations as POC devices. This review paper talks about the relevance of saliva and its usefulness as a biosensor. Also, it has recommendations that need to be considered to enable it as a possible diagnostic tool.

Graphical Abstract

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part III: Implementations for the Diagnosis of Non-Cancerous Disorders, Both Oral and Systemic

This communication represents Part III of our series of reports based on the applications of human saliva as a useful and conveniently collectable medium for the discovery, identification and monitoring of biomarkers, which are of some merit for the diagnosis of human diseases. Such biomarkers, or others reflecting the dysfunction of specific disease-associated metabolic pathways, may also be employed for the prognostic pathological tracking of these diseases. Part I of this series set the experimental and logistical groundwork for this report, and the preceding paper, Part II, featured the applications of newly developed metabolomics technologies to the diagnosis and severity grading of human cancer conditions, both oral and systemic. Clearly, there are many benefits, both scientific and economic, associated with the donation of human saliva samples (usually as whole mouth saliva) from humans consenting to and participating in investigations focused on the discovery of biomolecular markers of diseases. These include usually non-invasive collection protocols, relatively low cost when compared against blood sample collection, and no requirement for clinical supervision during collection episodes. This paper is centred on the employment and value of 'state-of-the-art' metabolomics technologies to the diagnosis and prognosis of a wide range of non-cancerous human diseases. Firstly, these include common oral diseases such as periodontal diseases (from type 1 (gingivitis) to type 4 (advanced periodontitis)), and dental caries. Secondly, a wide range of extra-oral (systemic) conditions are covered, most notably diabetes types 1 and 2, cardiovascular and neurological diseases, and Sjögren's syndrome, along with a series of viral infections, e.g., pharyngitis, influenza, HIV and COVID-19. Since the authors' major research interests lie in the area of the principles and applications of NMR-linked metabolomics techniques, many, but not all, of the studies reviewed were conducted using these technologies, with special attention being given to recommended protocols for their operation and management, for example, satisfactory experimental model designs; sample collection and laboratory processing techniques; the selection of sample-specific NMR pulse sequences for saliva analysis; and strategies available for the confirmation of resonance assignments for both endogenous and exogenous molecules in this biofluid. This article also features an original case study, which is focussed on the use of NMR-based salivary metabolomics techniques to provide some key biomarkers for the diagnosis of pharyngitis, and an example of how to 'police' such studies and to recognise participants who perceive that they actually have this disorder but do not from their metabolic profiles and multivariate analysis pattern-based clusterings. The biochemical and clinical significance of these multidimensional metabolomics investigations are discussed in detail.

A Flexible Wearable Thermography System Based on Bioheat Microsensors Network for Early Breast Cancer Detection: IoT Technology

Many women diagnosed by traditional screening methods, such as palpation, mammography, and MRI, do not discover their cancer until it is relatively advanced. This makes treatment more difficult and reduces the chance of a cure. To deal with this issue, we have developed a noninvasive embedded thermography system that allows for an early detection of breast cancer. Indeed, the surface temperature distribution of the breast, which we will call the thermal image, can be used as a preventive indicator of the subsequent development of a cancerous tumor. This is due to the metabolic activity of the immune system, which induces, in the presence of cancer cells, a local increase of temperature even before the tumor tissue is differentiated and detectable by conventional imaging systems. The proposed system is designed as a network of bioheat microsensors applied to the breast, to measure periodically the temperature gradients on the surface. All the bioheat microsensors are addressed by a microcontroller via the I2C protocol. To calibrate and evaluate the proposed system, we have proposed an experimental model of the breast, inside which we have placed Joule effect heating elements.

Zincon-Modified CNTs Electrochemical Tool for Salivary and Urinary Zinc Detection

Recently, the abnormal level of zinc emerged as a powerful indicator or risk factor for metabolic, endocrine, neurodegenerative and cardiovascular diseases, including cancer. Electrochemical detection has been explored to quantify zinc in a precise, rapid, and non-expensive way; however, most of the current electrochemical systems lack in specificity. In this work we studied a highly selective and sensitive electrochemical method to detect quickly and reliably free zinc ions (Zn²⁺). The surface of the working electrode was modified with zincon electropolymerized on carbon nanotube (CNT) to enable the binding of zinc in complex body fluids. After being physicochemically characterized, the performances of the zincon-CNT complex was electrochemically assessed. Square Wave Voltammetry (SWV) was used to determine the calibration curve and the linear range of zinc quantification in artificial saliva and urine. This zincon- CNT system could specifically quantify mobile Zn²⁺ in salivary and urinary matrices with a sensitivity of ~100 ng·mL^-1 and a limit of detection (LOD) of ~20 ng·mL^-1. Zincon-modified CNT presented as a desirable candidate for the detection and quantification of free zinc in easily body fluids that potentially can become a diagnostic non-invasive testing platform.

Saliva diagnostics: emerging techniques and biomarkers for salivaomics in cancer detection

INTRODUCTION

The pursuit of easy-to-use, non-invasive and inexpensive diagnostics is an urgent task for clinicians and scientists. Saliva is an important component of body fluid with regular changes of contents under various pathophysiological conditions, and the biomarkers identified from saliva shows high application potentials and values in disease diagnostics. This review introduces the latest developments in saliva research, with an emphasis on the detection and application of salivary biomarkers in cancer detection.

AREAS COVERED

Detection of disease-specific biomarkers in saliva samples by existing salivaomic methods can be used to diagnose various human pathological conditions and was introduced in details. This review also covers the saliva collection methods, the analytical techniques as well as the corresponding commercial products, with an aim to describe an holistic process for saliva-based diagnostics.

EXPERT OPINION

Saliva, as a non-invasive and collectable body fluid, can reflect the pathophysiological changes of the human body to a certain extent. Identification of reliable saliva biomarkers can provide a convenient way for cancer detection in clinical applications.

Salivary noncoding RNA in the diagnosis of pancreatic cancer: Systematic review and meta-analysis

BACKGROUND

Pancreatic cancer is considered one of the most deadly malignancies, primarily because of its diagnostic challenges. We performed a systematic review and diagnostic meta-analysis to evaluate the diagnostic value of noncoding salivary RNAs in pancreatic cancer diagnosis.

METHODS

Our investigation involved pertinent studies published in PubMed, Scopus, Web of Science, LIVIVO, Ovid and also the Google Scholar search engine. Specificity and sensitivity were calculated, as were positive and negative likelihood ratios (PLR and NLR), and the diagnostic odds ratio (DOR). The summary receiver-operating characteristics and area under the curve were plotted and assessed.

RESULTS

This meta-analysis and systematic review involved and examined five studies that contained 145 study units with a total of 2731 subjects (1465 pancreatic cancer patients versus 1266 noncancer controls). The pooled specificity, sensitivity, NLR, PLR and DOR were 0.783 (95% CI: 0.759-0.805), 0.829 (95% CI: 0.809-0.848), 0.309 (95% CI: 0.279-0.343), 3.386 (95% CI: 2.956-3.879) and 18.403 (95% CI: 14.753-22.954), respectively, with the area under the curve (AUC) equal to 0.882. Subgroup analyses were conducted based on the saliva type (unstimulated and stimulated), mean age of patients, sample size, type of control, serum carbohydrate antigen 19-9 (CA19-9) level and type of salivary noncoding RNA (microRNA (miRNA) and long noncoding RNA (lncRNA)).

CONCLUSIONS

The results of our systematic review and meta-analysis suggest that noncoding RNA biomarkers in the stimulated saliva could be a promising approach for accurate pancreatic cancer diagnosis in the early stages.

Evaluation of Salivary KCNJ3 mRNA Levels in Breast Cancer: A Case–control Study and in silico Analysis

Background:

Breast cancer (BC) is considered the most malignant and central cancer-related death among women worldwide. There is an essential need to discover new methods for developing noninvasive and low-cost diagnoses. The present study examines the expression of KCNJ3 which acts as a biomarker for detecting BC in the saliva of BC patients compared to controls.

Methods:

The mRNA expression level of KCNJ3 has been evaluated. Forty-three unstimulated whole saliva samples from BC patients and forty-three salivary samples from healthy controls were collected. The mRNA level was measured using quantitative real-time polymerase chain reaction (qRT-PCR). Furthermore, the protein-protein interaction network in which KCNJ3 is involved was obtained. In silico analysis was applied to predict the possible molecular mechanisms of KCNJ3 in BC development.

Results:

Differentially expressed KCNJ3 was statistically significant between BC patients and controls (p<0.001). The sensitivity and specificity of KCNJ3 mRNA in BC detection were 76.74% and 94.95%, respectively. Receiver operating characteristic (ROC) curve analysis of KCNJ3 mRNA revealed that Area under the curve (AUC) was 0.923 (95% Confidence Interval (CI): 0.866-0.979). AUCs of ROC curve analysis were 0.743 (95% CI: 0.536-0.951), 0.685 (95% CI: 0.445-0.925), and 0.583(95% CI: 0.343-0.823) for differentiation stage I from stage III, stage I to stage II and finally stage II from stage III, respectively. Furthermore, the GABAergic synapse signaling pathway was suggested as a potential pathway involved in BC development.

Conclusion:

Salivary levels of KCNJ3 could be considered a potential diagnostic biomarker with high sensitivity and specificity for BC detection.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part II: Applications to the Diagnosis and Prognostic Monitoring of Oral and Systemic Cancers

Human saliva offers many advantages over other biofluids regarding its use and value as a bioanalytical medium for the identification and prognostic monitoring of human diseases, mainly because its collection is largely non-invasive, is relatively cheap, and does not require any major clinical supervision, nor supervisory input. Indeed, participants donating this biofluid for such purposes, including the identification, validation and quantification of surrogate biomarkers, may easily self-collect such samples in their homes following the provision of full collection details to them by researchers. In this report, the authors have focused on the applications of metabolomics technologies to the diagnosis and progressive severity monitoring of human cancer conditions, firstly oral cancers (e.g., oral cavity squamous cell carcinoma), and secondly extra-oral (systemic) cancers such as lung, breast and prostate cancers. For each publication reviewed, the authors provide a detailed evaluation and critical appraisal of the experimental design, sample size, ease of sample collection (usually but not exclusively as whole mouth saliva (WMS)), their transport, length of storage and preparation for analysis. Moreover, recommended protocols for the optimisation of NMR pulse sequences for analysis, along with the application of methods and techniques for verifying and resonance assignments and validating the quantification of biomolecules responsible, are critically considered. In view of the authors’ specialisms and research interests, the majority of these investigations were conducted using NMR-based metabolomics techniques. The extension of these studies to determinations of metabolic pathways which have been pathologically disturbed in these diseases is also assessed here and reviewed. Where available, data for the monitoring of patients’ responses to chemotherapeutic treatments, and in one case, radiotherapy, are also evaluated herein. Additionally, a novel case study featured evaluates the molecular nature, levels and diagnostic potential of ¹H NMR-detectable salivary ‘acute-phase’ glycoprotein carbohydrate side chains, and/or their monomeric saccharide derivatives, as biomarkers for cancer and inflammatory conditions.

Types of spectroscopy and microscopy techniques for cancer diagnosis: a review

Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis.

Identification of early biomarkers in saliva in genetically engineered mouse model C(3)1-TAg of breast cancer

Breast cancer is one of leading causes of death worldwide in the female population. Deaths from breast cancer could be reduced significantly through earlier and more efficient detection of the disease. Saliva, an oral fluid that contains an abundance of protein biomarkers, has been recognized as a promising diagnostic biofluid that is easy to isolate through non-invasive techniques. Assays on saliva can be performed rapidly and are cost-effective. Therefore, our work aimed to identify salivary biomarkers present in the initial stages of breast cancer, where cell alterations are not yet detectable by histopathological analysis. Using state-of-the-art techniques, we employed a transgenic mouse model of mammary cancer to identify molecular changes in precancerous stage breast cancer through protein analysis in saliva. Through corroborative molecular approaches, we established that proteins related to metabolic changes, inflammatory process and cell matrix degradation are detected in saliva at the onset of tumor development. Our work demonstrated that salivary protein profiles can be used to identify cellular changes associated with precancerous stage breast cancer through non-invasive means even prior to biopsy-evident disease.

«Salivaomics» of Different Molecular Biological Subtypes of Breast Cancer

The aim of the study was to determine the metabolic characteristics of saliva depending on the molecular biological subtype of breast cancer, as well as depending on the expression levels of HER2, estrogen receptors (ER), and progesterone receptors (PR). The study included 487 patients with morphologically verified breast cancer and 298 volunteers without breast pathologies. Saliva samples were obtained from all patients strictly before the start of treatment and the values of 42 biochemical indicators were determined. It has been established that the saliva of healthy volunteers and patients with various molecular biological subtypes of breast cancer differs in 12 biochemical indicators: concentrations of protein, urea, nitric oxide, malondialdehyde, total amino acid content, and activity of lactate dehydrogenase, alkaline phosphatase, gamma-glutamyltransferase, catalase, amylase, superoxide dismutase, and peroxidases. The saliva composition of patients with basal-like breast cancer differs from other subtypes in terms of the maximum number of indicators. Changes in biochemical indicators indicated an increase in the processes of lipid peroxidation and endogenous intoxication and a weakening of antioxidant protection, which correlates with the severity of the disease and the least favorable prognosis for this subtype of breast cancer. An analysis was made of the individual contribution of the expression level of HER2, estrogen, and progesterone receptors to changes in the biochemical composition of saliva. The HER2 (−)/HER2 (+) group, which should be considered as a single group, as well as ER-positive breast cancer, differ statistically significantly from the control group. For ER/PR-positive breast cancer, a more favorable ratio of saliva biochemical indicators was also noted compared to ER/PR-negative breast cancer.

Salivary biomarkers in breast cancer diagnosis: A systematic review and diagnostic meta-analysis

BACKGROUND

Salivary diagnostics and their utility as a nonaggressive approach for breast cancer diagnosis have been extensively studied in recent years. This meta-analysis assesses the diagnostic value of salivary biomarkers in differentiating between patients with breast cancer and controls.

METHODS

We conducted a meta-analysis and systematic review of studies related to salivary diagnostics published in PubMed, EMBASE, Scopus, Ovid, Science Direct, Web of Science (WOS), and Google Scholar. The articles were chosen utilizing inclusion and exclusion criteria, as well as assessing their quality. Specificity and sensitivity, along with negative and positive likelihood ratios (NLR and PLR) and diagnostic odds ratio (DOR), were calculated based on random- or fixed-effects model. Area under the curve (AUC) and summary receiver-operating characteristic (SROC) were plotted and evaluated, and Fagan's Nomogram was evaluated for clinical utility.

RESULTS

Our systematic review and meta-analysis included 14 papers containing 121 study units with 8639 adult subjects (4149 breast cancer patients and 4490 controls without cancer). The pooled specificity and sensitivity were 0.727 (95% CI: 0.713-0.740) and 0.717 (95% CI: 0.703-0.730), respectively. The pooled NLR and PLR were 0.396 (95% CI: 0.364-0.432) and 2.597 (95% CI: 2.389-2.824), respectively. The pooled DOR was 7.837 (95% CI: 6.624-9.277), with the AUC equal to 0.801. The Fagan's nomogram showed post-test probabilities of 28% and 72% for negative and positive outcomes, respectively. We also conducted subgroup analyses to determine specificity, sensitivity, DOR, PLR, and NLR based on the mean age of patients (≤52 or >52 years old), saliva type (stimulated and unstimulated saliva), biomarker measurement method (mass spectrometry [MS] and non-MS measurement methods), sample size (≤55 or >55), biomarker type (proteomics, metabolomics, transcriptomics and proteomics, and reagent-free biophotonic), and nations.

CONCLUSION

Saliva, as a noninvasive biomarker, has the potential to accurately differentiate breast cancer patients from healthy controls.

Prognostic Value of Salivary Biochemical Indicators in Primary Resectable Breast Cancer

Despite the fact that breast cancer was detected in the early stages, the prognosis was not always favorable. In this paper, we examined the impact of clinical and pathological characteristics of patients and the composition of saliva before treatment on overall survival and the risk of recurrence of primary resectable breast cancer. The study included 355 patients of the Omsk Clinical Oncology Center with a diagnosis of primary resectable breast cancer (T_1-3N_0-1M₀). Saliva was analyzed for 42 biochemical indicators before the start of treatment. We have identified two biochemical indicators of saliva that can act as prognostic markers: alkaline phosphatase (ALP) and diene conjugates (DC). Favorable prognostic factors were ALP activity above 71.7 U/L and DC level above 3.93 c.u. Additional accounting for aspartate aminotransferase (AST) activity allows for forming a group with a favorable prognosis, for which the relative risk is reduced by more than 11 times (HR = 11.49, 95% CI 1.43-88.99, p = 0.01591). Salivary AST activity has no independent prognostic value. Multivariate analysis showed that tumor size, lymph nodes metastasis status, malignancy grade, tumor HER2 status, and salivary ALP activity were independent predictors. It was shown that the risk of recurrence decreased with menopause and increased with an increase in the size of the primary tumor and lymph node involvement. Significant risk factors for recurrence were salivary ALP activity below 71.7 U/L and DC levels below 3.93 c.u. before treatment. Thus, the assessment of biochemical indicators of saliva before treatment can provide prognostic information comparable in importance to the clinicopathological characteristics of the tumor and can be used to identify a risk group for recurrence in primary resectable breast cancer.

Salivary Metabolomics for Oral Cancer Detection: A Narrative Review

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

Potential Diagnostic Significance of Salivary Copper Determination in Breast Cancer Patients: A Pilot Study

Determination of the copper content in the saliva of breast cancer patients was carried out to assess the potential diagnostic and prognostic value. The malignant group included 75 breast cancer patients; the benign group included 87 patients with fibroadenomas; and the control group included 20 volunteers without breast pathology. All participants had 1 ml of saliva collected prior to treatment. The determination of copper in saliva was carried out by the stripping voltammetric method. Overall survival was assessed using the Kaplan-Meier method with the presentation of survival curves and the calculation of the significance of differences by Log-rank. The average copper content in the saliva of breast cancer patients is 49.3% higher than in patients with fibroadenomas and 60.4% higher than in volunteers without breast pathologies. Within the group of breast cancer patients, the content of copper in saliva is heterogeneous, which limits the possibilities of using this indicator for diagnostic purposes. The copper content increases when comparing the initial stages and locally widespread (+ 22.4%) and then sharply increases by 3.5 times at the T_3-4N_0-2M₀ stage. For HER2-positive breast cancer, the copper content in saliva is 51.9% higher than for HER2-negative, a similar pattern was observed for luminal A and B breast cancer subtypes. The content of copper in saliva less than 1.14 mg/l is a prognostically unfavorable sign, while the relative risk of dying from breast cancer more than doubles. Thus, the content of copper in saliva can be used in clinical practice for planning treatment tactics.

Basics for the potential use of saliva to evaluate stress, inflammation, immune system, and redox homeostasis in pigs

The use of saliva as a biological sample has many advantages, being especially relevant in pigs where the blood collection is highly stressful and painful, both for the animal and the staff in charge of the sampling. Currently one of the main uses of saliva is for diagnosis and detection of infectious diseases, but the saliva can also be used to measure biomarkers that can provide information of stress, inflammation, immune response and redox homeostasis. This review will be focused on the analytes that can be used for such evaluations. Emphasis will be given in providing data of practical use about their physiological basis, how they can be measured, and their interpretation. In addition, some general rules regarding sampling and saliva storage are provided and the concept of sialochemistry will be addressed. There is still a need for more data and knowledge for most of these biomarkers to optimize their use, application, and interpretation. However, this review provides updated data to illustrate that besides the detection of pathogens in saliva, additional interesting applicative information regarding pigs´ welfare and health can be obtained from this fluid. Information that can potentially be applied to other animal species as well as to humans.

Metabolic Features of Saliva in Breast Cancer Patients

The aim of the work was to study the metabolic characteristics of saliva in breast cancer and the subsequent assessment of the potential information content of its individual biochemical indicators. The study included 487 patients of the Omsk Clinical Oncology Center with morphologically verified breast cancer and 298 volunteers without breast pathologies. Saliva samples were obtained from all patients before the start of treatment, and the values of 34 biochemical indicators were determined. It has been shown that concentration of total protein, urea, uric acid (UA), the total content of α-amino acids and lipid peroxidation products, and the activity of metabolic and antioxidant enzymes (in particular catalase—CAT) of saliva changed significantly in breast cancer. Biochemical indicators characterizing early breast cancer have been identified, which can be used for timely diagnosis in addition to existing methods. The coefficients UA/Urea and UA·CAT/Urea are proposed, for which the maximum deviation from the norm was observed in the early stages of the disease. It was shown that for ductal breast cancer, changes in the activity of metabolic enzymes of saliva were more pronounced, while, for lobular breast cancer, the indicators of enzymatic and non-enzymatic components of antioxidant protection changed. The results confirmed the potential importance of saliva in the diagnosis of breast cancer.

SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer

Identifying secretory proteins from blood, saliva or other body fluids has become an effective method of diagnosing diseases. Existing secretory protein prediction methods are mainly based on conventional machine learning algorithms and are highly dependent on the feature set from the protein. In this article, we propose a deep learning model based on the capsule network and transformer architecture, SecProCT, to predict secretory proteins using only amino acid sequences. The proposed model was validated using cross-validation and achieved 0.921 and 0.892 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively. Meanwhile, the proposed model was validated on an independent test set and achieved 0.917 and 0.905 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively, which are better than conventional machine learning methods and other deep learning methods for biological sequence analysis. The main contributions of this article are as follows: (1) a deep learning model based on a capsule network and transformer architecture is proposed for predicting secretory proteins. The results of this model are better than the those of existing conventional machine learning methods and deep learning methods for biological sequence analysis; (2) only amino acid sequences are used in the proposed model, which overcomes the high dependence of existing methods on the annotated protein features; (3) the proposed model can accurately predict most experimentally verified secretory proteins and cancer protein biomarkers in blood and saliva.

Indicators of L-arginine metabolism in saliva: A focus on breast cancer

OBJECTIVES

To analyze the arginase activity, the level of nitric oxide (NO) and the cytokine profile of saliva in patients with breast cancer.

METHODS

A total of 114 volunteers took part in this case-control study, and were divided into three groups as follows: The main group (breast cancer, n = 43), the comparison group (fibroadenomas, n = 32), and the control group (conditionally healthy, n = 39). All participants underwent biochemical examination of saliva and histological verification of the diagnosis.

RESULTS

We found that the arginase activity in the saliva of breast cancer patients was significantly higher, and the level of NO was lower than in the control group. The 'arginase:NO' ratio was 0.22 for the control group, 0.98 for the comparison group (p = 0.0040), and 1.48 for the breast cancer group (p < 0.0001). The maximum increase in the 'arginase:NO' is characteristic of the early stages of the disease, which makes this ratio potentially applicable for the diagnosis of breast cancer with sensitivity and specificity of 81.1% and 81.0%, respectively. It was not possible to establish an unambiguous relationship between the level of cytokines and the metabolic parameters of L-arginine. However, we found an increase in salivary cytokine levels in breast cancer, and thus may represent an independent direction of research.

CONCLUSIONS

Saliva can be used as a substrate to determine L-arginine metabolic parameters.

Using an Untargeted Metabolomics Approach to Identify Salivary Metabolites in Women with Breast Cancer

Metabolic alterations are a hallmark of the malignant transformation in cancer cells, which is characterized by multiple changes in metabolic pathways that are linked to macromolecule synthesis. This study aimed to explore whether salivary metabolites could help discriminate between breast cancer patients and healthy controls. Saliva samples from 23 breast cancer patients and 35 healthy controls were subjected to untargeted metabolomics using liquid chromatography-quadrupole time-of-flight mass spectrometry and a bioinformatics tool (XCMS Online), which revealed 534 compounds, characterized by their retention time in reverse-phase liquid chromatography and by the m/z ratio detected, that were shared by the two groups. Using the METLIN database, 31 compounds that were upregulated in the breast cancer group (p < 0.05) were identified, including seven oligopeptides and six glycerophospholipids (PG14:2, PA32:1, PS28:0, PS40:6, PI31:1, and PI38:7). In addition, pre-treatment and post-treatment saliva samples were analyzed for 10 patients who experienced at least a partial response to their treatment. In these patients, three peptides and PG14:2 were upregulated before but not after treatment. The area under the curve, sensitivity, and specificity for PG14:2 was 0.7329, 65.22%, and 77.14%, respectively. These results provide new information regarding the salivary metabolite profiles of breast cancer patients, which may be useful biomarkers.

Variability of salivary metabolite levels in patients with Sjögren's syndrome

PURPOSE

To investigate inter- and intra-individual variation in the levels and outputs (concentration multiplied by salivary flow rate) of salivary metabolites in patients with primary Sjögren's syndrome (pSS).

METHODS

A total of 56 samples of stimulated saliva were collected from 14 female pSS patients during four laboratory visits within 20 weeks and analyzed using proton nuclear magnetic resonance spectroscopy. Single saliva samples from each of 15 controls were also analyzed.

RESULTS

Among 21 quantified metabolites, choline was significantly elevated in the pSS patients at each time point (P ≤ 0.015), taurine at the last three time points (P ≤ 0.013), alanine at the last two time points (P ≤ 0.007) and glycine at the last time point (P = 0.005). Inter-individual variation in metabolite concentrations was generally larger among the patients than among the controls, and significantly large variations were observed for glycine (P ≤ 0.007, all time points), choline (P ≤ 0.033, three last time points) and alanine (P = 0.028, baseline). Metabolite output analysis showed that choline had the lowest intra-patient variation.

CONCLUSION

In spite of considerable intra- and inter-individual variation, levels and outputs of specific metabolites in patients with pSS differ from those in controls, and may be potentially applicable as new biological markers for monitoring of the response to treatment.

A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development

Traditional techniques for cancer diagnosis, such as nuclear magnetic resonance, ultrasound and tissue analysis, require sophisticated devices and highly trained personnel, which are characterized by elevated operation costs. The use of biomarkers has emerged as an alternative for cancer diagnosis, prognosis and prediction because their measurement in tissues or fluids, such as blood, urine or saliva, is characterized by shorter processing times. However, the biomarkers used currently, and the techniques used for their measurement, including ELISA, western-blot, polymerase chain reaction (PCR) or immunohistochemistry, possess low sensitivity and specificity. Therefore, the search for new proteomic, genomic or immunological biomarkers and the development of new noninvasive, easier and cheaper techniques that meet the sensitivity and specificity criteria for the diagnosis, prognosis and prediction of this disease has become a relevant topic. The purpose of this review is to provide an overview about the search for new cancer biomarkers, including the strategies that must be followed to identify them, as well as presenting the latest advances in the development of biosensors that possess a high potential for cancer diagnosis, prognosis and prediction, mainly focusing on their relevance in lung, prostate and breast cancers.

Splicing Factor Transcript Abundance in Saliva as a Diagnostic Tool for Breast Cancer

Breast cancer is the second leading cause of death in women above 60 years in the US. Screening mammography is recommended for women above 50 years; however, 22% of breast cancer cases are diagnosed in women below this age. We set out to develop a test based on the detection of cell-free RNA from saliva. To this end, we sequenced RNA from a pool of ten women. The 1254 transcripts identified were enriched for genes with an annotation of alternative pre-mRNA splicing. Pre-mRNA splicing is a tightly regulated process and its misregulation in cancer cells promotes the formation of cancer-driving isoforms. For these reasons, we chose to focus on splicing factors as biomarkers for the early detection of breast cancer. We found that the level of the splicing factors is unique to each woman and consistent in the same woman at different time points. Next, we extracted RNA from 36 healthy subjects and 31 breast cancer patients. Recording the mRNA level of seven splicing factors in these samples demonstrated that the combination of all these factors is different in the two groups (p value = 0.005). Our results demonstrate a differential abundance of splicing factor mRNA in the saliva of breast cancer patients.

Salivary biomarkers in breast cancer: a cross-sectional study

AIM

The objective was to evaluate the diagnostic value of salivary biomarkers in breast cancer.

MATERIALS AND METHODS

A prospective study was carried out of 91 consecutive breast cancer patients and 60 controls without neoplastic disease. Demographic data were recorded, along with the Hospital Anxiety Depression (HAD), risk for development periodontal diseases questionnaire and Mini-Nutritional Assessment (MNA) scores, and the TNM classification and stage of the primary tumor. Unstimulated basal saliva samples were collected and stored at - 80 °C until analysis.

RESULTS

The mean salivary CA125 concentration was 102.1 pg/ml in the control group and 267.6 pg/ml in the breast cancer group (p = 0.005). The soluble Fas (sFas) concentration in turn was 84.1 pg/ml and 145.9 pg/ml, respectively (p = 0.008). None of the biomarkers was related to questionnaire-assessed risk for development of periodontal diseases. The area under the ROC curve was 0.68 for CA125 (95% CI, 0.05-0.56) and 0.67 for sFas (95% CI, 0.08-0.55).

CONCLUSIONS

The salivary biomarkers CA125 and sFas appear to be promising tools in the diagnosis of breast cancer.

CapsNet-SSP: multilane capsule network for predicting human saliva-secretory proteins

Background

Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins.

Results

We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer.

Conclusions

The main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/. We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.

Early diagnosis of breast and ovarian cancers by body fluids circulating tumor-derived exosomes

Exosomes (EXs) are small extracellular vesicles, a size range of 40-100 nm in diameter, actively secreted by most eukaryotic cells into surrounding body fluids like blood, saliva, urine, bile, breast milk and etc. These endosomal-derived vesicles mediate cell-cell communication between various cell populations through transmitting different signaling molecules such as lipids, proteins, and nucleic acids, and participate in a wide range of physiological and pathological body processes. Tumor-derived EXs (TDEs) are vehicles for intercellular communications by transferring bioactive molecules; they deliver oncogenic molecules and contain different molecular cargoes compared to EXs delivered from normal cells, therefore, they can be used as non-invasive invaluable biomarkers for early diagnosis and prognosis of most cancers, including breast and ovarian cancers. Their presence and stability in different types of body fluids highlight them as a suitable diagnostic biomarker for distinguishing various cancer stages. In addition, EXs can predict the therapeutic efficacy of chemotherapy agents and drug resistance in cancer cells, as well as determine the risk of metastasis in different disease stages. In this study, the recent literature on the potential role of TDEs in the diagnosis and prognosis of ovarian and breast cancers is summarized, and then exosome isolation techniques including traditional and new approaches are briefly discussed.

Serum and Saliva Concentrations of Biochemical Parameters in Men with Prostate Cancer and Benign Prostate Hyperplasia

OBJECTIVES

To find suitable biomarkers for diagnosis of prostate cancer (PC) in serum and saliva; also, to evaluate the diagnostic efficacy of saliva in patients with PC.

METHODS

This case-control study included 20 patients with PC and 20 patients with benign prostatic hyperplasia (BPH). Blood and saliva were collected from the participants and centrifuged. Serum and supernatant saliva were used for biochemical analysis. We evaluated serum and salivary levels of urea, creatinine, prostate-specific antigen (PSA), creatine kinase BB (CK-BB), zinc, β-2 microglobulin (B2M), and melatonin. Also, we used Mann-Whitney U testing, Spearman correlation coefficients, and receiver operating characteristic (ROC) analysis to evaluate the data.

RESULTS

Serum and salivary concentrations of urea, creatinine, PSA, CK-BB, zinc, and B2M were significantly higher in patients with PC, compared with the BPH group (P <.05). However, serum and salivary concentrations of melatonin were significantly lower in patients with PC, compared with BPH group (P <.05). In both groups, salivary concentrations of all markers were lower (P <.05), compared with those values in serum. We observed positive correlation between serum and salivary concentrations of all markers studied (P <.05).

CONCLUSION

From the data, we conclude that investigation using saliva specimens is a noninvasive, simple, and effective tool for screening of biochemical parameters.

Liquid Biopsy as Novel Tool in Precision Medicine: Origins, Properties, Identification and Clinical Perspective of Cancer's Biomarkers

In recent years, there has been an increase in knowledge of cancer, accompanied by a technological development that gives rise to medical oncology. An instrument that allows the implementation of individualized therapeutic strategies is the liquid biopsy. Currently, it is the most innovative methodology in medical oncology. Its high potential as a tool for screening and early detection, the possibility of assessing the patient’s condition after diagnosis and relapse, as well as the effectiveness of real-time treatments in different types of cancer. Liquid biopsy is capable of overcoming the limitations of tissue biopsies. The elements that compose the liquid biopsy are circulating tumor cells, circulating tumor nucleic acids, free of cells or contained in exosomes, microvesicle and platelets. Liquid biopsy studies are performed on various biofluids extracted in a non-invasive way, and they can be performed both from the blood and in urine, saliva or cerebrospinal fluid. The development of genotyping techniques, using the elements that make up liquid biopsy, make it possible to detect mutations, intertumoral and intratumoral heterogeneity, and provide molecular information on cancer for application in medical oncology in an individualized way in different types of tumors. Therefore, liquid biopsy has the potential to change the way medical oncology could predict the course of the disease.

Blood contamination in salivary diagnostics: current methods and their limitations

The use of saliva samples in clinical studies has increased. However, the diagnostic value of whole saliva is compromised in the presence of blood contamination, owing to the higher levels of analytes in blood compared with those in saliva. The aim of this study was to review the existing methods and their limitations for measuring the levels of blood contamination in saliva. A literature search was performed using Web of Science, SCOPUS, and PubMed databases and 49 articles dealing with salivary diagnostics and measurements of blood contamination were included. Five methods for measuring the degree of blood components in saliva were discussed, including "visual inspection", use of "strip for urinalysis", and detection of plasma proteins such as "hemoglobin", "albumin", and "transferrin". Each method has its limitations, and transferrin has been regarded as the most reliable and valid marker for blood contamination in saliva. However, transferrin in whole saliva may not be solely a product of blood, and its level in whole saliva can be influenced by several factors such as age, gonadal hormones, salivary flow rate, chewing performance, and oral microorganisms. In conclusion, when quantitatively analyzing whole saliva samples, the influence of blood contamination should be considered.

[Oral cavity as a target and a marker of environmental exposures: diseases diagnosed during adulthood]

The oral cavity is one of the main route for environmental contaminations associated to many chronic diseases via alimentation, medications and respiration. Other factors may also impact the oral environment, some of them are endogenous, like microbiota, hormones and saliva, and others are exogenous, like dental materials and pathogens.

Saliva as a diagnostic tool for dental caries, periodontal disease and cancer: is there a need for more biomarkers?

Introduction: A biomarker is a biological indicator of normal or pathogenic processes. Identification of biomarkers is useful for the prevention, diagnosis and prognosis of diseases as well as for monitoring the progression of pathological disorders. Several types of molecules present in biological fluids can act as biomarkers such as DNA, coding and non-coding RNA, lipids, metabolites, proteins and even microbes. In this context, saliva emerges as a useful diagnostic tool for the detection of biomarkers involved with oral and systemic diseases, since it reflects the pathophysiological conditions of the organism and allows early, rapid, practical and noninvasive detection of biomarkers.Areas covered: This review discusses the properties of saliva as a diagnostic tool and addresses the main identified biomarkers related to dental caries, periodontal disease, head and neck cancer and other types of cancer of considerable incidence among the world population.Expert commentary: Despite extensive efforts which have been directed toward the identification of one or a combination of biomarkers with good predictive values for the early detection of dental caries, periodontal disease and cancer, these biomarkers still need validation before chairside point-of-care devices can be widely used in the clinic.

Salivary metabolites to detect patients with cancer: a systematic review

Novel adjunctive screening aids are needed to reduce the morbidity and mortality related to cancer, and every effort should be made for early diagnosis. This systematic review aimed to evaluate salivary metabolites and their diagnostic value in patients with cancer.The systematic review was performed in two phases and included studies that focused on the diagnostic value of salivary metabolites in humans with solid malignant neoplasms. Five electronic databases were searched, and the risk of bias in individual studies was evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies criteria (QUADAS-2). All procedures were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.Of the 1151 studies retrieved, 25 were included; 13 studies used targeted and 12 untargeted metabolomics approaches. Most studies included patients with breast and oral cancer. Except for one, all studies had case-control designs, and none fulfilled all quality assessments. Overall, 140 salivary metabolites were described. The most frequently reported metabolites were alanine, valine, and leucine. Among the 11 studies that reported diagnostic test accuracy (DTA) values, proline, threonine, and histidine in combination and monoacylglycerol alone demonstrated the highest DTA for breast cancer. Combined choline, betaine, pipecolinic acid, and L-carnitine showed better discriminatory performance for early oral cancer.This systematic review highlights the current evidence on salivary metabolites that may be used as a future strategy to diagnose cancer. Further studies including larger sample sizes with confirmation of the results by untargeted analysis are warranted.

Salivary Redox Biomarkers in Selected Neurodegenerative Diseases

Neurodegenerative diseases (NDDs), such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, are disorders, which cause irreversible and progressive deterioration of the central nervous system. The pathophysiology of NDDs is still not fully explained; nevertheless, oxidative stress is considered as a critical mediator of cerebral degeneration, brain inflammation, as well as neuronal apoptosis. Therefore, it is not surprising that redox biomarkers are increasingly used in the diagnosis of neurodegenerative diseases. As saliva is a very easy to obtain bioliquid, it seems promising to use this biomaterial in the diagnosis of NDDs. Saliva collection is easy, cheap, stress-free, and non-infectious, and it does not require the help of a specialised medical personnel. Additionally, the concentrations of many salivary redox biomarkers correlate with their content in blood serum as well as the degree of disease progression, which makes them non-invasive indicators of NDDs. This paper reviews the latest knowledge concerning the use of salivary redox biomarkers in the diagnosis and prognosis of selected neurodegenerative diseases.

Salivary Gland Function, Antioxidant Defence and Oxidative Damage in the Saliva of Patients with Breast Cancer: Does the BRCA1 Mutation Disturb the Salivary Redox Profile?

Oxidative stress plays a key role in breast cancer progression. However, little is still known about the relationship between the BRCA1 mutation, the incidence of breast cancer and oral homeostasis. This is the first study to evaluate the secretory function of salivary glands, biomarkers of redox balance, and oxidative damage to proteins and lipids in the saliva of subjects with the BRCA1 mutation. Ninety eight women were enrolled in the study and allocated to four groups based on molecular DNA testing: generally healthy patients without the BRCA1 mutation, patients with breast cancer but without the BRCA1 mutation, generally healthy patients with the BRCA1 mutation, and patients with both breast cancer and the BRCA1 mutation. We demonstrated that saliva from breast cancer patients with the BRCA1 mutation is characterized by enhanced antioxidant capacity and a higher degree of oxidative damage to proteins and lipids. The BRCA1 mutation can cause a predisposition to early salivary gland dysfunction, both in patients with breast cancer and in healthy individuals, leading to a decrease in salivary proteins. Using cluster analysis, we showed that salivary peroxidase, advanced glycation end-products (AGE), total antioxidant status (TAS) and malondialdehyde (MDA) may have particular clinical significance in non-invasive diagnostics of breast cancer.

Breast Cancer Interaction Network Concept from Mostly Related Components

BACKGROUND

Management of breast cancer (BC) as a heterogeneous disease is very challenging. Biomarker discovery has been shown promising for this aim. Protein interaction mapping could provide further knowledge of the vital roles of these markers.

MATERIALS AND METHODS

Cytoscape and its plug-ins are used for network construction and evaluation. The plug-ins used in this study are STRING, Network Analyzer, GeneMANIA, and CluePedia.

RESULTS

The central proteins are enriched in transcription regulatory region DNA binding, regulatory region nucleic acid binding, regulatory region DNA binding, Fc receptor signaling pathway, cell cycle arrest, and immune response-regulating cell surface receptor signaling pathway.

CONCLUSION

The introduced biomarkers and their related biological processes may show useful for the breast cancer diagnosis and monitoring; however, has to encounter more validation studies to be clinically applicable.

Identification of abnormal fucosylated-glycans recognized by LTL in saliva of HBV-induced chronic hepatitis, cirrhosis, and hepatocellular carcinoma

The hepatitis B virus (HBV)-induced chronic liver diseases are serious health threats worldwide. There is evidence to display the alterations of salivary N-linked glycans related to the development of HBV-infected liver diseases. Here, we further investigated the alterations of fucosylated N/O-glycans recognized by LTL in saliva from 120 subjects (30 healthy volunteers (HV), 30 patients with hepatitis B (HB), 30 patients with hepatic cirrhosis (HC), and 30 patients with hepatocellular carcinoma (HCC)) using salivary microarrys and MALDI-TOF/TOF-MS. The results showed that the expression level of fucosylated glycans recognized by LTL was significantly increased in HCC compared with other subjects (P < 0.0001). Besides, the fucosylated glycoproteins were isolated from pooled saliva of HV, HB, HC, and HCC by LTL-magnetic particle conjugates. Then, N/O- glycans were released from the isolated glycoproteins with PNGase F and NaClO, and were identified by MALDI-TOF-MS, respectively. Totally, there were 21/20, 25/18, 29/19, and 28/24 N/O-glycan peaks that were identified and annotated with proposed structures in saliva of HV, HB, HC, and HCC. Among the total, there were 8 N-glycan peaks (e.g., m/z 1905.634, 2158.777 and 2905.036) and 15 O-glycan peaks (e.g., 1177.407, 1308.444 and 1322.444) that only presented in patients with HBV-induced liver diseases. One N-glycan peak (m/z 2205.766) was unique in HC, and 9 O-glycan peaks (e.g., m/z 1157.420, 1163.417 and 1193.402) were unique in HCC. This study could facilitate the discovery of biomarkers for HC and HCC based on precise alterations of fucosylated N/O-glycans in saliva.

The Advent of Salivary Breast Cancer Biomarker Detection Using Affinity Sensors

Breast Cancer is one of the world's most notorious diseases affecting two million women in 2018 worldwide. It is a highly heterogeneous disease, making it difficult to treat. However, its linear progression makes it a candidate for early screening programs, and the earlier its detection the higher the chance of recovery. However, one key hurdle for breast cancer screening is the fact that most screening techniques are expensive, time-consuming, and cumbersome, making them impractical for use in several parts of the world. One current trend in breast cancer detection has pointed to a possible solution, the use of salivary breast cancer biomarkers. Saliva is an attractive medium for diagnosis because it is readily available in large quantities, easy to obtain at low cost, and contains all the biomarkers present in blood, albeit in lower quantities. Affinity sensors are devices that detect molecules through their interactions with biological recognition molecules. Their low cost, high sensitivity, and selectivity, as well as rapid detection time make them an attractive alternative to traditional means of detection. In this review article, we discuss the current status of breast cancer diagnosis, its salivary biomarkers, as well as the current trends in the development of affinity sensors for their detection.

Morphology of Dried Drop Patterns of Saliva from a Healthy Individual Depending on the Dynamics of Its Surface Tension

Background: The study of processes in the drying drops of biological fluids (dried drop patterns) and the method of dynamic surface tensiometry can be attributed to integral methods of assessing the state of the organism. Research objective: to establish the relationship between the type of crystallization patterns and the surface tension of human saliva in normal conditions. Methods: 100 volunteers (40 males, 60 females) that were aged 30–59 participated in the study. In all saliva samples, the parameters of dynamic tensiometry, types of crystallization patterns and 11 biochemical parameters were determined. Results: No statistically significant differences in the saliva crystallization patterns were observed, depending on the age and gender characteristics of the volunteers. A negative correlation of the area of the crystallization zone and the surface tension of saliva is shown. When considering the crystallization patterns, their considerable variability was noted; on this basis, the entire studied sample was divided into four clusters by surface tension. Conclusion: In general, the crystallization patterns that are inside the selected groups remain quite heterogeneous. This increases the likelihood of making an incorrect diagnosis when using visual methods to evaluate the crystallization patterns, which significantly limits the use of such diagnostic methods in clinical practice.

Identification of volatile metabolites in human saliva from patients with oral squamous cell carcinoma via zeolite-based thin-film microextraction coupled with GC-MS

In recent years, volatile organic compounds (VOCs) discharged from the human body, of which some compounds exhibit strong correlations with pathological conditions, have attracted attention as a new means of disease diagnosis technology. The aim of this study was to establish the salivary metabolomic profiles of oral squamous cell carcinoma (OSCC) patients and healthy volunteers (control group) and to investigate VOCs as potential biomarkers in the diagnosis of oral cancer. We have demonstrated a method combining thin-film microextraction based on a ZSM-5/polydimethylsiloxane hybrid film coupled with gas chromatography-mass spectrometry and carried out a comparative analysis of salivary VOC profiles between OSCC patients and healthy controls. The results depicted that 42 and 73 VOCs were detected and identified in samples from the healthy control group (n = 50) and oral cancer group (n = 24), respectively. Among them, twenty-seven VOCs (ten were decreased, seven disappeared, and ten were newly produced in the oral cancer group) depict significant differences between both the sample groups, and they have relevance as candidate biomarkers for OSCC. Twelve salivary VOCs that were characteristic of oral cancer patients were finally extracted and used for pattern recognition analyses for oral cancer diagnosis. The proposed TFME approach for analyzing human saliva on the basis of a ZSM-5-loaded PDMS hybrid thin film has been performed for the very first time in the field of dentistry.