Salivary metabolomic biomarkers for non-invasive lung cancer detection.

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

Salivary metabolomics for oral leukoplakia with and without dysplasia.

PURPOSE: Oral leukoplakia (OL) is a common potentially malignant oral disorder. Therefore, there is a need for simple screening methods for OL before its transformation into oral cancer. Furthermore, because invasive open biopsy is the sole method to determine if an OL lesion is dysplastic, there is also a clinical need for non-invasive methods to differentiate dysplastic OL from non-dysplastic OL. This study aimed to identify salivary metabolites that can help differentiate patients with OL from healthy controls (HC) and also dysplastic OL from non-dysplastic OL. MATERIAL & METHODS: Whole unstimulated saliva samples were collected from patients with OL (n = 30) and HCs (n = 29). The OL group included nine patients with dysplastic OL and 20 with non-dysplastic OL. Hydrophilic metabolites in the saliva samples were comprehensively analyzed through capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate patients with OL from HCs and dysplastic OL from non-dysplastic OL. RESULTS: Twenty-eight metabolites were evidently different between patients with OL and HCs. Finally, three metabolites (guanine, carnitine, and N-acetylputrescine) were selected to develop the MLR model, which resulted in a high area under curve (AUC) of the receiver operating characteristic (ROC) to differentiate patients with OL from HCs (AUC = 0.946, p < 0.001, 95% confidential interval [CI] = 0.889- 1.000). Similarly, two metabolites were evidently different between patients with dysplastic and non-dysplastic OL. Finally, only one metabolite (7-methylguanine) was selected in the MLR model, which revealed a moderate discrimination ability for dysplastic and non-dysplastic OL (AUC = 0761, p = 0.027, 95% CI = 0.551-0.972). CONCLUSION: Our candidate salivary metabolites showed potential not only to discriminate OL from HC, but also to discriminate dysplastic OL from non-dysplastic OL.

Effect of Underwater Exercise on Salivary Metabolites of Older Persons With Disability.

BACKGROUND/AIM: Underwater exercise is aimed at preventing aging, maintaining, and improving motor function, and improving physical function. However, its rehabilitation effects have not been well evaluated. In order to gain insight into the molecular basis of its rehabilitation effects, possible changes in the salivary metabolites of four older persons with disability (mean age: 72.5 years) during underwater exercise were investigated. MATERIALS AND METHODS: Halitosis was measured by Breathtron; salivary bacterial number by bacterial counter; amino acids by amino acid analyzer; 8-oxoguanine by ELISA; and intracellular metabolites by capillary electrophoresis, time-of-flight mass spectrometry, liquid chromatography, and triode quadrupole mass spectrometry. RESULTS: Underwater exercise induced apparent declines in two major salivary amino acids (glycine and proline) and bacterial numbers in the cheek mucosa and salivary, without apparent changes in the halitosis and urine 8-oxoguanine concentration. Older subjects showed higher concentrations of most of 166 metabolites compared to young volunteers (mean age: 38.8 years old). Fifteen compounds were significantly reduced with the progression of underwater exercise. CONCLUSION: Improvement of upright balance function with underwater exercise is correlated with several salivary components.

Salivary metabolomics with machine learning for colorectal cancer detection.

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva-based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry. An alternative decision tree (ADTree)-based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross-validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver-operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828-0.891) for CV and 0.870 (95% CI: 0.837-0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851-0.907) and 0.870 (95% CI: 0.838-0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Time-Course of Salivary Metabolomic Profiles during Radiation Therapy for Head and Neck Cancer.

Oral mucositis (OM) is one of the most frequently observed adverse oral events in radiation therapy for patients with head and neck cancer. Thus, objective evaluation of OM severity is needed for early and timely intervention. Here, we analyzed the time-course of salivary metabolomic profiles during the radiation therapy. The severity of OM (National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events v3.0) of nine patients with head and neck cancer was evaluated. Partial least squares regression-discriminant analysis, using samples collected before radiation therapy, showed that histidine and tyrosine highly discriminated high-grade OM from low-grade OM before the start of radiation therapy (significant difference, p = 0.048 for both metabolites). Further, the pretreatment concentrations of gamma-aminobutyric acid and 2-aminobutyric acids were higher in the high-grade OM group. Although further validations are still necessary, this study showed potentially associated metabolites with worse radiotherapy-related OM among patients with head and neck cancer.

High-throughput screening of salivary polyamine markers for discrimination of colorectal cancer by multisegment injection capillary electrophoresis tandem mass spectrometry.

Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking. Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS), which allows sequential 40-sample injection followed by electrophoretic separation and specific mass detection. To achieve consecutive analysis of polyamine samples, 1 M formic acid was used as the background electrolyte (BGE). The BGE spacer volume had an apparent effect on peak resolution among samples, and 20 nL was selected as the optimal volume. The use of polyamine isotopomers as the internal standard enabled the correction of matrix effects in MS detection. This method is sensitive, selective and quantitative, and its utility was demonstrated by screening polyamines in 359 salivary samples within 360 min, resulting in discrimination of colorectal cancer patients from noncancer controls.

Multi-Omics Analysis of Anti-Inflammatory Action of Alkaline Extract of the Leaves of Sasa sp.

Efficient utilization of alkaline extracts of several plants for the treatment of oral diseases has been reported. To investigate the mechanism of anti-inflammatory activity of alkaline extract of the leaves of Sasa sp. (SE), multi-omics analysis using metabolomics and DNA array was performed. Human gingival fibroblasts (HGFs) were treated for IL-1ß to induce inflammation (detected by PGE2 production in culture medium) in the presence or absence of SE. Both IL-1ß and SE showed slight hormetic growth stimulation against HGF. SE inhibited PGE2 production dose- and time-dependently. Its inhibitory action was more pronounced by first treating the cells with SE, rather than with IL-1ß. At 3 h after IL-1ß treatment, 18 amino acids (except cysteine and glutamic acid), total glutathione (GSH, GSSG, Cys-GSH disulfide), Met-sulfoxide, 5-oxoproline, and SAM declined, whereas DNA expressions of AKT, CASP3, and CXCL3 were elevated. These changes were reversed by simultaneous treatment with SE. The present study suggests that the anti-inflammatory action of SE is mediated via various metabolic pathways for cell survival, apoptosis, and leukocyte recruitment.

Sensory properties and metabolomic profiles of dry-cured ham during the ripening process.

Dry-cured ham with a long ripening period is a valuable product worldwide. Ripening time is a key determinant of the endogenous metabolites that characterize the flavor and taste of ham products. While various studies have analyzed the relationship between ripening duration and sensory characteristics, no studies have evaluated ham products produced in Japan. Here, we conducted time-course metabolomic profiling, taste sensor-based analyses, and sensory evaluations by non-trained consumers during ripening. Capillary electrophoresis-mass spectrometry was used to quantify non-volatile metabolites, such as amino acids, organic acids, and nucleotides. In an analysis of eight time-points during 680 days of ripening, the highest score for the after-taste of umami was observed on day 540, despite subtle changes in the scores for other properties. The concentrations of aspartic acid and glutamic acid relative to those of total amino acids were the highest at this point. This approach can contribute to the understanding of the relationship between the metabolite profile and ripening duration.

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

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

Metabolomic profiling reveals salivary hypotaurine as a potential early detection marker for medication-related osteonecrosis of the jaw.

Medication-related osteonecrosis of the jaw (MRONJ) is a rare but serious adverse event of bone-modifying agents used to prevent bone complications in cancer patients with bone metastasis. Currently, early treatment is the only way to prevent further progression, as the pathogenesis of MRONJ has not yet been elucidated, and a standard treatment has not been established. The aim of this study was to identify a marker for early detection marker of MRONJ by exploring substances in saliva specific to MRONJ at an early stage. We collected salivary samples from 17 patients with MRONJ and conducted metabolomic analyses using capillary electrophoresis mass spectrometry for non-targeted analysis of hydrophilic metabolites. In the screening cohort, we compared the saliva of patients with stage ≥1 advanced MRONJ (n = 9) with that of controls without MRONJ before chemotherapy (n = 9). The top 5 most elevated salivary metabolites were histamine, 3-(4-hydroxyphenyl)propionate, malonate, carnosine, and hypotaurine. In the validation cohort, we analyzed additional patients with stage ≥1 advanced MRONJ (n = 8) and controls without MRONJ after chemotherapy (n = 9), confirming a significant 2.28-fold elevation in the salivary concentration of hypotaurine. These results revealed elevated salivary hypotaurine concentration as a potential marker for the early detection of MRONJ.

Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination.

PURPOSE: The aim of this study is to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls. METHODS: Saliva samples were collected after 9 h fasting and were immediately stored at - 80 °C. Capillary electrophoresis and liquid chromatography with mass spectrometry were used to quantify hundreds of hydrophilic metabolites. Conventional statistical analyses and artificial intelligence-based methods were used to assess the discrimination abilities of the quantified metabolites. A multiple logistic regression (MLR) model and an alternative decision tree (ADTree)-based machine learning method were used. The generalization abilities of these mathematical models were validated in various computational tests, such as cross-validation and resampling methods. RESULTS: One hundred sixty-six unstimulated saliva samples were collected from 101 patients with invasive carcinoma of the breast (IC), 23 patients with ductal carcinoma in situ (DCIS), and 42 healthy controls (C). Of the 260 quantified metabolites, polyamines were significantly elevated in the saliva of patients with breast cancer. Spermine showed the highest area under the receiver operating characteristic curves [0.766; 95% confidence interval (CI) 0.671-0.840, P < 0.0001] to discriminate IC from C. In addition to spermine, polyamines and their acetylated forms were elevated in IC only. Two hundred each of two-fold, five-fold, and ten-fold cross-validation using different random values were conducted and the MLR model had slightly better accuracy. The ADTree with an ensemble approach showed higher accuracy (0.912; 95% CI 0.838-0.961, P < 0.0001). These prediction models also included spermine as a predictive factor. CONCLUSIONS: These data indicated that combinations of salivary metabolomics with the ADTree-based machine learning methods show potential for non-invasive screening of breast cancer.

Effects of inter-day and intra-day variation on salivary metabolomic profiles.

BACKGROUND: Salivary secretion is an important parameter reflecting the health status of an individual and has been used clinically for the diagnosis of various oral diseases, such as xerostomia. Salivary metabolomic profiling is considered an emerging potential tool for the detection of various systemic diseases. To our knowledge, this is the first study to investigate the quantitative relationship between salivary secretion volume and salivary metabolomic profile. METHODS: To evaluate inter- and intra-day variations in salivary secretion, 234 saliva samples were collected three times per day for three days from 13 subjects and analyzed. Capillary electrophoresis-mass spectrometry was used for non-targeted quantification of water-soluble metabolites. RESULTS: No significant inter- or intra-day variations were observed in salivary secretion volume. No significant inter-day variations were observed in metabolomic patterns. In contrast, significant intra-day variations were observed in salivary metabolomic profiles. The difference was more obvious for stimulated saliva than for unstimulated saliva. These profile changes were independent of salivary secretion volume. CONCLUSIONS: Our results indicated that diurnal change had a greater effect on salivary metabolomic profiles than the other factors. Hence, sampling time should be tightly controlled to minimize unexpected bias in the clinical use of salivary metabolomics.

Elevated Polyamines in Saliva of Pancreatic Cancer.

Detection of pancreatic cancer (PC) at a resectable stage is still difficult because of the lack of accurate detection tests. The development of accurate biomarkers in low or non-invasive biofluids is essential to enable frequent tests, which would help increase the opportunity of PC detection in early stages. Polyamines have been reported as possible biomarkers in urine and saliva samples in various cancers. Here, we analyzed salivary metabolites, including polyamines, using capillary electrophoresis-mass spectrometry. Salivary samples were collected from patients with PC (n = 39), those with chronic pancreatitis (CP, n = 14), and controls (C, n = 26). Polyamines, such as spermine, N1-acetylspermidine, and N1-acetylspermine, showed a significant difference between patients with PC and those with C, and the combination of four metabolites including N1-acetylspermidine showed high accuracy in discriminating PC from the other two groups. These data show the potential of saliva as a source for tests screening for PC.

Effect of timing of collection of salivary metabolomic biomarkers on oral cancer detection.

The aim of this study is to evaluate the effect of duration after meals for saliva collections for oral cancer detection using metabolomics. Saliva samples were collected from oral cancer patients (n = 22) and controls (n = 44). Saliva from cancer patients was collected 12 h after dinner, and 1.5 and 3.5 h after breakfast. Control subjects fasted >1.5 h prior to saliva collection. Hydrophilic metabolites were analyzed using capillary electrophoresis mass spectrometry. Levels of 51 metabolites differed significantly in controls vs. oral cancer patients at the 12-h fasting time point (P < 0.05). Fifteen and ten metabolites differed significantly at the 1.5- and 3.5-h time points, respectively. The area of under receiver operating characteristic curve for discriminating oral cancer patients from controls was greatest at the 12-h fasting time point. The collection time after meals affects levels of salivary metabolites for oral cancer screening. The 12-h fasting after dinner time point is optimal. This study contributes to design of saliva collection protocols for metabolomics-based biomarker discovery.

Identification of salivary metabolomic biomarkers for oral cancer screening.

The objective of this study was to explore salivary metabolite biomarkers by profiling both saliva and tumor tissue samples for oral cancer screening. Paired tumor and control tissues were obtained from oral cancer patients and whole unstimulated saliva samples were collected from patients and healthy controls. The comprehensive metabolomic analysis for profiling hydrophilic metabolites was conducted using capillary electrophoresis time-of-flight mass spectrometry. In total, 85 and 45 metabolites showed significant differences between tumor and matched control samples, and between salivary samples from oral cancer and controls, respectively (P < 0.05 correlated by false discovery rate); 17 metabolites showed consistent differences in both saliva and tissue-based comparisons. Of these, a combination of only two biomarkers yielded a high area under receiver operating characteristic curves (0.827; 95% confidence interval, 0.726-0.928, P < 0.0001) for discriminating oral cancers from controls. Various validation tests confirmed its high generalization ability. The demonstrated approach, integrating both saliva and tumor tissue metabolomics, helps eliminate pseudo-molecules that are coincidentally different between oral cancers and controls. These combined salivary metabolites could be the basis of a clinically feasible method of non-invasive oral cancer screening.

Decreased miR122 in hepatocellular carcinoma leads to chemoresistance with increased arginine.

Reduced expression of microRNA122 (miR122), a liver-specific microRNA, is frequent in hepatocellular carcinoma (HCC). However, its biological significances remain poorly understood. Because deregulated amino acid levels in cancers can affect their biological behavior, we determined the amino acid levels in miR122-silenced mouse liver tissues, in which intracellular arginine levels were significantly increased. The increased intracellular arginine levels were through upregulation of the solute carrier family 7 (SLC7A1), a transporter of arginine and a direct target of miR122. Arginine is the substrate for nitric oxide (NO) synthetase, and intracellular NO levels were increased in miR122-silenced HCC cells, with increased resistance to sorafenib, a multikinase inhibitor. Conversely, maintenance of the miR122-silenced HCC cells in arginine-depleted culture media, as well as overexpression of miR122 in miR122-low-expressing HCC cells, reversed these effects and rendered the cells more sensitive to sorafenib. Using a reporter knock-in construct, chemical compounds were screened, and Wee1 kinase inhibitor was identified as upregulators of miR122 transcription, which increased the sensitivity of the cells to sorafenib. These results provide an insight into sorafenib resistance in miR122-low HCC, and suggest that arginine depletion or a combination of sorafenib with the identified compound may provide promising approaches to managing this HCC subset.

Effects of 3-styrylchromones on metabolic profiles and cell death in oral squamous cell carcinoma cells.

4H-1-benzopyran-4-ones (chromones) are important naturally-distributing compounds. As compared with flavones, isoflavones and 2-styrylchromones, there are only few papers of 3-styrylchromones that have been published. We have previously reported that among fifteen 3-styrylchromone derivatives, three new synthetic compounds that have OCH3 group at the C-6 position of chromone ring, (E)-3-(4-hydroxystyryl)-6-methoxy-4H-chromen-4-one (compound 11), (E)-6-methoxy-3-(4-methoxystyryl)-4H-chromen-4-one (compound 4), (E)-6-methoxy-3-(3,4,5-trimethoxystyryl)-4H-chromen-4-one (compound 6) showed much higher cytotoxicities against four epithelial human oral squamous cell carcinoma (OSCC) lines than human normal oral mesenchymal cells. In order to further confirm the tumor specificities of these compounds, we compared their cytotoxicities against both human epithelial malignant and non-malignant cells, and then investigated their effects on fine cell structures and metabolic profiles and cell death in human OSCC cell line HSC-2. Cytotoxicities of compounds 4, 6, 11 were assayed with MTT method. Fine cell structures were observed under transmission electron microscope. Cellular metabolites were extracted with methanol and subjected to CE-TOFMS analysis. Compounds 4, 6, 11 showed much weaker cytotoxicity against human oral keratinocyte and primary human gingival epithelial cells, as compared with HSC-2, confirming their tumor-specificity, whereas doxorubicin and 5-FU were highly cytotoxic to these normal epithelial cells, giving unexpectedly lower tumor-specificity. The most cytotoxic compound 11, induced the mitochondrial vacuolization, autophagy suppression followed by apoptosis induction, and changes in the metabolites involved in amino acid and glycerophospholipid metabolisms. Chemical modification of lead compound 11 may be a potential choice for designing new type of anticancer drugs.

Metabolic profiling to identify potential serum biomarkers for gastric ulceration induced by nonsteroid anti-inflammatory drugs.

Nonsteroid anti-inflammatory drugs (NSAIDs) are among the most frequently prescribed drugs currently available. The most frequently reported serious side effects associated with NSAIDs are gastric mucosal ulceration and gastric hemorrhage. Presently, these side effects are only detectable by endoscopy, however, and no biomarkers have yet been identified. The ability to identify serum biomarkers would likely improve the safety of NSAID use. In this study we performed capillary electrophoresis-mass spectrometry (CE-MS)-based metabolomic profiling in stomach extract and serum from rats administered NSAIDs. Results showed drug-induced decreases in levels of citrate, cis-aconitate, succinate, 3-hydroxy butanoic acid, o-acetyl carnitine, proline, and hydroxyproline. We consider that these changes are due to NSAID-induced depression of mitochondrial function and activation of collagenase by lesions in the stomach. In addition, four of these changes in metabolite levels in the stomach were significantly correlated with changes in the serum. While further study is needed to clarify the mechanism of change in the level of these biomarkers, limitation of indications, and extrapolation to humans, these new serum biomarker candidates of gastric injury may be useful in the monitoring of NSAID-induced tissue damage.