[Identification of salivary biomarkers in breast cancer patients with thick white or thick yellow tongue fur using isobaric tags for relative and absolute quantitative proteomics]

Automated tongue diagnosis on the smartphone and its applications

Tongue features are important objective basis for clinical diagnosis and treatment in both western medicine and Chinese medicine. The need for continuous monitoring of health conditions inspires us to develop an automatic tongue diagnosis system based on built-in sensors of smartphones. However, tongue images taken by smartphone are quite different in color due to various lighting conditions, and it consequently affects the diagnosis especially when we use the appearance of tongue fur to infer health conditions. In this paper, we captured paired tongue images with and without flash, and the color difference between the paired images is used to estimate the lighting condition based on the Support Vector Machine (SVM). The color correction matrices for three kinds of common lights (i.e., fluorescent, halogen and incandescent) are pre-trained by using a ColorChecker-based method, and the corresponding pre-trained matrix for the estimated lighting is then applied to eliminate the effect of color distortion. We further use tongue fur detection as an example to discuss the effect of different model parameters and ColorCheckers for training the tongue color correction matrix under different lighting conditions. Finally, in order to demonstrate the potential use of our proposed system, we recruited 246 patients over a period of 2.5 years from a local hospital in Taiwan and examined the correlations between the captured tongue features and alanine aminotransferase (ALT)/aspartate aminotransferase (AST), which are important bio-markers for liver diseases. We found that some tongue features have strong correlation with AST or ALT, which suggests the possible use of these tongue features captured on a smartphone to provide an early warning of liver diseases.

In search of the altering salivary proteome in metastatic breast and ovarian cancers

Breast and ovarian cancers, the most common cancers in women in India, are expected to rise in the next decade. Metastatic organotropism is a nonrandom, predetermined process which represents a more lethal and advanced form of cancer with increased mortality rate. In an attempt to study organotropism, salivary proteins were analyzed by mass spectrometry indicative of pathophysiology of breast and ovarian cancers and were compared to healthy and ovarian chemotherapy subjects. Collectively, 646 proteins were identified, of which 409 proteins were confidently identified across all four groups. Network analysis of upregulated proteins such as coronin-1A, hepatoma-derived growth factor, vasodilator-stimulated phosphoprotein (VASP), and cofilin in breast cancer and proteins like coronin-1A, destrin, and HSP90α in ovarian cancer were functionally linked and were known to regulate cell proliferation and migration. Additionally, proteins namely VASP, coronin-1A, stathmin, and suprabasin were confidently identified in ovarian chemotherapy subjects, possibly in response to combined paclitaxel and carboplatin drug therapy to ovarian cancer. Selected representative differentially expressed proteins (eg, gelsolin, VASP) were validated by western blot analysis. Results of this study provide a foundation for future research to better understand the organotropic behavior of breast and ovarian cancers, as well as neoadjuvant drug response in ovarian cancer.

Cancer Salivary Biomarkers for Tumours Distant to the Oral Cavity

The analysis of saliva as a diagnostic approach for systemic diseases was proposed just two decades ago, but recently great interest in the field has emerged because of its revolutionary potential as a liquid biopsy and its usefulness as a non-invasive sampling method. Multiple molecules isolated in saliva have been proposed as cancer biomarkers for diagnosis, prognosis, drug monitoring and pharmacogenetic studies. In this review, we focus on the current status of the salivary diagnostic biomarkers for different cancers distant to the oral cavity, noting their potential use in the clinic and their applicability in personalising cancer therapies.

[Metabolite changes in the greasy tongue coating of patients with chronic gastritis]

OBJECTIVE

To explore the changes in metabolites in the greasy tongue coating in patients with chronic gastritis.

METHODS

Forty chronic gastritis patients presenting with greasy tongue coating, 30 chronic gastritis patients presenting with non-greasy tongue coating, and 20 healthy control persons presenting with light red tongues and thin white coating were enrolled, and the tongue coating was detected by combining artificial diagnosis and the Z-BOX Tongue Digital Analyzer's diagnosis. Samples of all the tongue coatings were collected before treatment. The metabolic fingerprinting of the tongue coating samples was obtained using ultra-performance liquid chromatography and mass spectrometry (UPLC-MS), and the metabolic components in the tongue coating samples were detected. After this, principal component analysis, partial least squares discriminant analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to identify potential metabolic markers. Finally, the components were identified using the Chemspider and HMDB searching.

RESULTS

UPLC-MS results were analyzed by OPLS-DA and showed that the metabolites among the three groups were distributed in different regions. The different potential metabolic markers between the patients with or without greasy coating were 3-ketolactose, 2-deoxy-D-ribose, UDP-D-galactose metarhodopsin, ascorbate, picolinate and histidine. The different potential metabolic markers between the greasy coating group and the normal group were 3-ketolactose, UDP-D-galactose, leukotriene A4 and vitamin D(2).

CONCLUSION

The metabolites of the greasy coating group, the non-greasy coating group and the normal group show significant differences in energy metabolism, mainly of glucose metabolism. This demonstrated that glucose metabolism may be one of the mechanisms leading to the formation of greasy coating.

The emerging landscape of salivary diagnostics

Saliva contains a variety of biomolecules, including DNA, coding and noncoding RNA, proteins, metabolites and microbiota. The changes in the salivary levels of these molecular constituents can be used to develop markers for disease detection and risk assessment. Use of saliva as an early-detection tool is a promising approach because collection of saliva is easy and noninvasive. Here, we review recent developments in salivary diagnostics, accomplished using salivaomics approaches, including genomic, transcriptomic, proteomic, metabolomic and microbiomic technologies. Additionally, we illustrate the mechanisms of how diseases distal from the oral cavity can lead to the appearance of discriminatory biomarkers in saliva, and discuss the relevance of these markers for translational and clinical applications.

Salivary biomarker development using genomic, proteomic and metabolomic approaches

The use of saliva as a diagnostic sample provides a non-invasive, cost-efficient method of sample collection for disease screening without the need for highly trained professionals. Saliva collection is far more practical and safe compared with invasive methods of sample collection, because of the infection risk from contaminated needles during, for example, blood sampling. Furthermore, the use of saliva could increase the availability of accurate diagnostics for remote and impoverished regions. However, the development of salivary diagnostics has required technical innovation to allow stabilization and detection of analytes in the complex molecular mixture that is saliva. The recent development of cost-effective room temperature analyte stabilization methods, nucleic acid pre-amplification techniques and direct saliva transcriptomic analysis have allowed accurate detection and quantification of transcripts found in saliva. Novel protein stabilization methods have also facilitated improved proteomic analyses. Although candidate biomarkers have been discovered using epigenetic, transcriptomic, proteomic and metabolomic approaches, transcriptomic analyses have so far achieved the most progress in terms of sensitivity and specificity, and progress towards clinical implementation. Here, we review recent developments in salivary diagnostics that have been accomplished using genomic, transcriptomic, proteomic and metabolomic approaches.