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

INTRODUCTION: Oral squamous cell carcinoma (OSCC) represents the most prevalent form of oral cancer. Potentially malignant disorders of oral mucosa exhibit an elevated propensity for malignant progression. A substantial proportion of cases are discerned during advanced stages, significantly impacting overall survival. This investigation aims to ascertain salivary metabolites with potential utility in the early detection of OSCC. METHODS: A search encompassing PubMed, EMBASE, Scopus, Ovid, Science Direct, and Web of Science databases was conducted to identify eligible articles. The search strategy employed precise terms. The quality assessment of the included studies was executed using the QUADAS 2 ROB tool. This was registered with PROSPERO CRD42021278217. RESULTS: Upon removing duplicate articles and publications that didn't satisfy the inclusion criteria, seven articles were included in the current study. The Random Effects Maximum Likelihood (REML) model adopted for quantitative synthesis identified N-acetyl glucosamine as the sole metabolite in two studies included in this meta-analysis. The pathways significantly influenced by these identified metabolites were delineated. CONCLUSION: This study highlights N-acetyl glucosamine as a distinctive metabolite with the potential to serve as an early diagnostic marker for OSCC. Nevertheless, further research is warranted to validate its clinical utility.

Salivary metabolomic biomarkers for esophageal and gastric cancers by liquid chromatography-mass spectrometry.

Early detection of esophageal and gastric cancers is essential for patients' prognosis; however, optimal noninvasive screening tests are currently not available. Saliva is a biofluid that is readily available, allowing for frequent screening tests. Thus, we explored salivary diagnostic biomarkers for esophageal and gastric cancers using metabolomic analyses. Saliva samples were collected from patients with esophageal (n = 50) and gastric cancer (n = 63), and patients without cancer as controls (n = 20). Salivary metabolites were analyzed by liquid chromatography-mass spectrometry to identify salivary biomarkers. We also examined the metabolic profiles of gastric cancer tissues and compared them with the salivary biomarkers. The sensitivity of the diagnostic models based on salivary biomarkers was assessed by comparing it with that of serum tumor markers. Additionally, using postoperative saliva samples collected from patients with gastric cancer, we analyzed the changes in the biomarkers' concentrations before and after surgery. Cytosine was detected as a salivary biomarker for gastric cancer, and cytosine, 2-oxoglutarate, and arginine were detected as salivary biomarkers for esophageal cancer. Cytidine, a cytosine nucleotide, showed decreased concentrations in gastric cancer tissues. The sensitivity of the diagnostic models for esophageal and gastric cancers was 66.0% and 47.6%, respectively, while that of serum tumor markers was 40%. Salivary cytosine concentration increased significantly postoperatively relative to the preoperative value. In summary, we identified salivary biomarkers for esophageal and gastric cancers, which showed diagnostic sensitivity at least comparable to that of serum tumor markers. Salivary metabolomic tests could be promising screening tests for these types of cancer.

Prognostic Significance of 18F-FDG PET/CT and Tumor Metabolic Changes in Patients With Pancreatic Ductal Adenocarcinoma.

BACKGROUND/AIM: 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is reportedly associated with the malignant potential of cancer. This study aimed to evaluate the association between FDG accumulation and tumor metabolism in pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS: A prognostic analysis of data from 131 patients with PDAC who underwent FDG-PET/CT before curative-intent pancreatic surgery was performed. Capillary electrophoresis-mass spectrometry (CE-MS) was used to analyze the metabolome of tumor and non-neoplastic pancreas from 80 patients. These patients were divided into two groups: low SUVmax group (SUVmax <6.09) and high SUVmax group (SUVmax ≥6.09). RESULTS: Carbohydrate antigen 19-9 (CA19-9), maximum standardized uptake value (SUVmax) of PET, N stage, and postoperative chemotherapy were identified as significant prognostic factors by univariate analysis. SUVmax emerged as an independent prognostic factor for overall survival [hazard ratio (HR)=1.88, p<0.05] and disease-free survival (HR=2.01, p<0.05) in multivariate analysis. Metabolic analyses confirmed that 43 metabolites significantly differed depending on the accumulation of SUV in tumors. Metabolites involved in the removal of reactive oxygen species (e.g., hypotaurine, glutathione, Met), treatment resistance (UDP-N-acetylglucosamine), and proliferation (e.g., choline, leucine, isoleucine) were increased in the high SUVmax group. CONCLUSION: FDG accumulation is an important independent prognostic factor reflecting tumor activity associated with metabolic changes in cancer cells.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.

PURPOSE: Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach. METHODS: Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles. RESULTS: Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points. CONCLUSION: The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

Role of Organic Anion Transporter NPT4 in Renal Handling of Uremic Toxin 3-indoxyl Sulfate.

Sodium-phosphate transporter NPT4 (SLC17A3) is a membrane transporter for organic anionic compounds localized on the apical membranes of kidney proximal tubular epithelial cells and plays a role in the urinary excretion of organic anionic compounds. However, its physiological role has not been sufficiently elucidated because its substrate specificity is yet to be determined. The present study aimed to comprehensively explore the physiological substrates of NPT4 in newly developed Slc17a3-/- mice using a metabolomic approach. Metabolomic analysis showed that the plasma concentrations of 11 biological substances, including 3-indoxyl sulfate, were more than two-fold higher in Slc17a3-/- mice than in wild-type mice. Moreover, urinary excretion of 3-indoxyl sulfate was reduced in Slc17a3-/- mice compared to that in wild-type mice. The uptake of 3-indoxyl sulfate by NPT4-expressing Xenopus oocytes was significantly higher than that by water-injected oocytes. The calculated Km and Vmax values for NPT4-mediated 3-indoxyl sulfate uptake were 4.52 ± 1.18 mM and 1.45 ± 0.14 nmol/oocyte/90 min, respectively. In conclusion, the present study revealed that 3-indoxyl sulfate is a novel substrate of NPT4 based on the metabolomic analysis of Slc17a3-/- mice, suggesting that NPT4 regulates systemic exposure to 3-indoxyl sulfate by regulating its urinary excretion.

Metabolomic profiling of cancer-related fatigue involved in cachexia and chemotherapy.

Patients with advanced cancer are frequently burdened with a severe sensation of fatigue called cancer-related fatigue (CRF). CRF is induced at various stages and treatments, such as cachexia and chemotherapy, and reduces the overall survival of patients. Objective and quantitative assessment of CRF could contribute to the diagnosis and prediction of treatment efficacy. However, such studies have not been intensively performed, particularly regarding metabolic profiles. Here, we conducted plasma metabolomics of 15 patients with urological cancer. The patients with and without fatigue, including those with cachexia or chemotherapy-induced fatigue, were compared. Significantly lower concentrations of valine and tryptophan were observed in fatigued patients than in non-fatigued patients. In addition, significantly higher concentrations of polyamine pathway metabolites were observed in patients with fatigue and cachexia than in those without cachexia. Patients with exacerbated fatigue due to chemotherapy showed significantly decreased cysteine and methionine metabolism before chemotherapy compared with those without fatigue exacerbation. These findings suggest that plasma metabolic profiles could help improve the diagnosis and monitoring of CRF.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium.

BACKGROUND: The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies. METHOD: We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure. RESULT: Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle). CONCLUSION: These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

Salivary metabolites as potential predictive biomarkers for lung surgery complications: a retrospective cross-sectional study.

PURPOSE: Saliva is often used as a tool for identifying systemic diseases because of the noninvasive nature of its collection. Moreover, salivary metabolites can be potential predictive factors for postoperative survival. We conducted the present study to establish whether salivary metabolites can function as predictive biomarkers for lung surgery complications. METHODS: Unstimulated salivary samples were collected from 412 patients before lung surgery. Salivary metabolites were analyzed comprehensively by capillary electrophoresis mass spectrometry. Clinical data with the discriminatory ability of biomarkers were assessed to predict lung surgery complications using multivariate logistic regression analysis. The primary endpoint was the risk factors for postoperative complications of Clavien-Dindo grade ≥ III. RESULTS: Postoperative complications of Clavien-Dindo grade ≥ III developed in 36 patients (8.7%). There was no postoperative 30-day mortality. Male sex (odds ratio [OR], 3.852; 95% confidence interval CI 1.455-10.199; p = 0.007) and salivary gamma-butyrobetaine (OR, 0.809; 95% CI 0.694-0.943; p = 0.007) were identified as significant risk factors for postoperative complications of Clavien-Dindo grade ≥ III. CONCLUSION: Salivary metabolites are potential noninvasive biomarkers for predicting postoperative complications of lung surgery.

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.

Serum Carbohydrate Antigen 19-9 and Metabolite Hypotaurine Are Predictive Markers for Early Recurrence of Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: A significant number of patients experience early recurrence after surgical resection for pancreatic ductal adenocarcinoma (PDAC), negating the benefit of surgery. The present study conducted clinicopathologic and metabolomic analyses to explore the factors associated with the early recurrence of PDAC. MATERIALS AND METHODS: Patients who underwent pancreatectomy for PDAC at Kagawa University Hospital between 2011 and 2020 were enrolled. Tissue samples of PDAC and nonneoplastic pancreas were collected and frozen immediately after resection. Charged metabolites were quantified by capillary electrophoresis-mass spectrometry. Patients who relapsed within 1 year were defined as the early recurrence group. RESULTS: Frozen tumor tissue and nonneoplastic pancreas were collected from 79 patients. The clinicopathologic analysis identified 11 predictive factors, including preoperative carbohydrate antigen 19-9 levels. The metabolomic analysis revealed that only hypotaurine was a significant risk factor for early recurrence. A multivariate analysis, including clinical and metabolic factors, showed that carbohydrate antigen 19-9 and hypotaurine were independent risk factors for early recurrence ( P = 0.045 and P = 0.049, respectively). The recurrence-free survival rate 1 year after surgery with both risk factors was only 25%. CONCLUSIONS: Our results suggested that tumor hypotaurine is a potential metabolite associated with early recurrence. Carbohydrate antigen 19-9 and hypotaurine showed a vital utility for predicting early recurrence.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS).

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

Metabolomics profiles alterations in cigarette smokers and heated tobacco product users.

BACKGROUND: Heated tobacco products (HTPs) have gained global popularity, but their health risks remain unclear. Therefore, the current study aimed to identify plasma metabolites associated with smoking and HTP use in a large Japanese population to improve health risk assessment. METHODS: Metabolomics data from 9,922 baseline participants of the Tsuruoka Metabolomics Cohort Study (TMCS) were analyzed to determine the association between smoking habits and plasma metabolites. Moreover, alterations in smoking-related metabolites among HTP users were examined based on data obtained from 3,334 participants involved from April 2018 to June 2019 in a follow-up survey. RESULTS: Our study revealed that cigarette smokers had metabolomics profiles distinct from never smokers, with 22 polar metabolites identified as candidate biomarkers for smoking. These biomarker profiles of HTP users were closer to those of cigarette smokers than those of never smokers. The concentration of glutamate was higher in cigarette smokers, and biomarkers involved in glutamate metabolism were also associated with cigarette smoking and HTP use. Network pathway analysis showed that smoking was associated with the glutamate pathway, which could lead to endothelial dysfunction and atherosclerosis of the vessels. CONCLUSIONS: Our study showed that the glutamate pathway is affected by habitual smoking. These changes in the glutamate pathway may partly explain the mechanism by which cigarette smoking causes cardiovascular disease. HTP use was also associated with glutamate metabolism, indicating that HTP use may contribute to the development of cardiovascular disease through mechanisms similar to those in cigarette use.

Salivary metabolites as novel independent predictors of radiation pneumonitis.

PURPOSE: Radiotherapy is an integral treatment for non-small cell lung cancer (NSCLC); however, radiation-induced toxicities such as radiation pneumonitis (RP) present a considerable challenge. Herein, we aimed to evaluate the potential of salivary metabolomics as an independent risk factor for predicting RP. METHODS: This study included 62 consecutive patients with NSCLC who underwent thoracic radiotherapy at Tokyo Medical University between September 2016 and December 2018. The median age of the patients was 75 years (range: 41-89), comprising 47 (75.8%) males and 15 (24.2%) females. Patients with stage I NSCLC received 75 Gy in 30 fractions, whereas those with stage II and III NSCLC received 66 Gy in 33 fractions. Saliva samples were collected before treatment and at 2 weeks, 1 month, 3 months, and 1 year after initiating radiotherapy. Clinical RP was defined as grade 2 according to the Common Toxicity Criteria for Adverse Events. Salivary metabolomics were analyzed using capillary electrophoresis-mass spectrometry. Salivary metabolites were evaluated as potential predictors of RP. RESULTS: Clinical RP was observed in 11 patients (17.7%); no RP-related deaths were observed. Clinical RP developed at a median of 4 months (range: 2-6 months) after initiating radiotherapy. Three metabolites, butyrate, propionate, and hexanoate, collected before radiotherapy exhibited predictive ability for clinical RP. Multivariate logistic analysis indicated butyrate (P = 0.033) as a predictive factor, along with the previously known factor of lung volume irradiated with > 20 Gy (P = 0.045). CONCLUSION: Salivary metabolite butyrate was an independent risk factor for clinical RP.

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.

Discovery of lipid profiles in plasma-derived extracellular vesicles as biomarkers for breast cancer diagnosis.

Lipids are a major component of extracellular vesicles; however, their significance in tumorigenesis and progression has not been well elucidated. As we previously found that lipid profiles drastically changed in breast tumors upon progression, we hypothesized that lipid profiles of plasma-derived extracellular vesicles could be utilized as breast cancer biomarkers. Here, we adopted modified sucrose cushion ultracentrifugation to isolate plasma-derived extracellular vesicles from breast cancer (n = 105), benign (n = 11), and healthy individuals (n = 43) in two independent cohorts (n = 126 and n = 33) and conducted targeted lipidomic analysis. We established a breast cancer diagnostic model comprising three lipids that showed favorable performance with the area under the receiver operating characteristic curve of 0.759, 0.743, and 0.804 in the training, internal validation, and external test sets, respectively. Moreover, we identified several lipids that could effectively discriminate breast cancer progression and subtypes: phosphatidylethanolamines and phosphatidylserines were relatively higher in Stage III, whereas phosphatidylcholines and sphingomyelins were higher in Stage IV; phosphatidylcholines and ceramides were correspondingly concentrated in HER2-positive patients, while lysophosphatidylcholines and polyunsaturated triglycerides were concentrated in the triple-negative breast cancer subtype. Lipid profiling of plasma-derived extracellular vesicles is a non-invasive and promising approach for diagnosing, staging, and subtyping breast cancer.

Salivary Polyamines Help Detect High-Risk Patients with Pancreatic Cancer: A Prospective Validation Study.

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Targeted Metabolomic Profiling of Plasma Samples in Gastric Cancer by Liquid Chromatography-Mass Spectrometry.

INTRODUCTION: As the high mortality rate of gastric cancer (GC) is due to delayed diagnosis, early detection is vital for improved patient outcomes. Metabolic deregulation plays an important role in GC. Although various metabolite-level biomarkers for early detection have been assessed, there is still no unified early detection method. We conducted a plasma metabolome study to assess metabolites that may distinguish GC samples from non-GC samples. METHODS: Blood samples were collected from 72 GC patients and 29 control participants (non-GC group) at the Tokyo Medical University Hospital between March 2020 and November 2020. Hydrophilic metabolites were identified and quantified using liquid chromatography-time-of-flight mass spectrometry. Differences in metabolite concentrations between the GC and non-GC groups were evaluated using the Mann-Whitney test. The discrimination ability of each metabolite was evaluated by the area under the receiver operating characteristic curve. A radial basis function (RBF) kernel-based support vector machine (SVM) model was developed to assess the discrimination ability of multiple metabolites. The selection of variables used for the SVM utilized a step-wise regression method. RESULTS: Of the 96 quantified metabolites, 8 were significantly different between the GC and non-GC groups. Of these, N1-acetylspermine, succinate, and histidine were used in the RBF-SVM model to discriminate GC samples from non-GC samples. The area under the curve (AUC) of the RBF-SVM model was higher (0.915; 95% CI: 0.865-0.965, p < 0.0001), indicating good performance of the RBF-SVM model. The application of this RBF-SVM to the validation dataset resulted from the AUC of the RBF-SVM model was (0.885; 95% CI: 0.797-0.973, p < 0.0001), indicating the good performance of the RBF-SVM model. The sensitivity of the RBF-SVM model was better (69.0%) than those of the common tumor markers carcinoembryonic antigen (CEA) (10.5%) and carbohydrate antigen 19-9 (CA19-9) (2.86%). The RBF-SVM showed a low correlation with CEA and CA19-9, indicating its independence. CONCLUSION: We analyzed plasma metabolomics, and a combination of the quantified metabolites showed high sensitivity for the detection of GC. The independence of the RBF-SVM from tumor markers suggested that their complementary use would be helpful for GC screening.

Computational Simulation of Tumor-Induced Angiogenesis.

Cancer cells require higher oxygen levels and nutrition than normal cells. Cancer cells induce angiogenesis (the development of new blood vessels) from preexisting vessels. This biological process depends on the special, chemical, and physical properties of the microenvironment surrounding tumor tissues. The complexity of these properties hinders an understanding of their mechanisms. Various mathematical models have been developed to describe quantitative relationships related to angiogenesis. We developed a three-dimensional mathematical model that incorporates angiogenesis and tumor growth. We examined angiopoietin, which regulates the spouting and branching events in angiogenesis. The simulation successfully reproduced the transient decrease in new vessels during vascular network formation. This chapter describes the protocol used to perform the simulations.

Breast cancer detection using volatile compound profiles in exhaled breath via selected ion-flow tube mass spectrometry.

This study aimed to evaluate volatile compounds in exhaled breath as a non-invasive screening method to detect breast neoplasms. Exhaled breath samples were collected from patients with breast cancer (BC;n= 45) and non-breast cancer (NBC;n= 51) controls. Selected ion-flow tube mass spectrometry was used to quantify the volatile compounds. A multiple logistic regression (MLR) model was developed by combining multiple compounds to discriminate between BC and NBC samples. Amongst the 672 quantified peaks, 17 showed significant differences between BC and NBC samples (P< 0.05 corrected by false discovery rate). Pathway analysis revealed a significant difference in glycerophospholipid metabolism. The MLR model showed an area under the receiver operating characteristic curve (AUC) of 0.719 (95% confidence interval: 0.615-0.822,P< 0.0002). Cross-validation under various conditions resulted in a slight fluctuation in the AUC values, indicating the high generalizability of the MLR model. The model showed a higher BC probability for advanced-stage subjects and higher Ki67 (⩾30) for BC subjects. This study suggests the potential of volatile compounds in exhaled breath as a noninvasive screening method for BC.

Metabolomic Profiling of Plasma, Urine, and Saliva of Kidney Transplantation Recipients.

Kidney biopsy is commonly used to diagnose kidney transplant dysfunction after transplantation. Therefore, the development of minimally invasive and quantitative methods to evaluate kidney function in transplant recipients is necessary. Here, we used capillary electrophoresis-mass spectrometry to analyze the biofluids collected from transplant recipients with impaired (Group I, n = 31) and stable (Group S, n = 19) kidney function and from donors (Group D, n = 9). Metabolomics analyses identified and quantified 97 metabolites in plasma, 133 metabolites in urine, and 108 metabolites in saliva. Multivariate analyses revealed apparent differences in the metabolomic profiles of the three groups. In plasma samples, arginine biosynthesis and purine metabolism between the I and S Groups differed. In addition, considerable differences in metabolomic profiles were observed between samples collected from participants with T cell-mediated rejection (TCR), antibody-mediated rejection, and other kidney disorders (KD). The metabolomic profiles in the three types of biofluids showed different patterns between TCR and KD, wherein 3-indoxyl sulfate showed a significant increase in TCR consistently in both plasma and urine samples. These results suggest that each biofluid has different metabolite features to evaluate kidney function after transplantation and that 3-indoxyl sulfate could predict acute rejection.