A new NMR-based metabolomics approach for the diagnosis of biliary tract cancer

Metabolomics of Duodenal Juice for Biliary Tract Cancer Diagnosis

The poor prognosis of malignant biliary diseases is partially caused by their difficult early diagnosis. Therefore, many patients are only diagnosed at advanced stages. This study aimed to improve diagnosis by clarifying the differences in the duodenal juice metabolomes of benign and malignant biliary diseases. From October 2021 to January 2023, duodenal juice was obtained from 67 patients with suspected biliary diseases who required endoscopic ultrasonography and endoscopic retrograde cholangiography for diagnosis/treatment. The samples metabolomes were analyzed via nuclear magnet resonance spectroscopy using an 800-MHz spectrometer. Metabolomes of malignant and benign diseases were then compared, and multivariate analysis was performed to determine the relevant factors for malignancy/benignancy. For benignancy, no significant predictors were observed. For malignancy, acetone was a significant predictor, with higher concentrations in the malignant group than in the benign group. Regarding the receiver operating characteristic curve analysis for biliary tract carcinoma diagnosis, the predictive value of acetone in duodenal juice was comparable with serum CA19-9 levels (area under the curve: 0.7330 vs. 0.691, p = 0.697). In conclusion, duodenal juice metabolomics is a feasible method that is available for differential diagnosis in the biliary disease field.

Ex vivo NMR metabolomics approach using cerebrospinal fluid for the diagnosis of primary CNS lymphoma: Correlation with MR imaging characteristics

PURPOSE

Primary central nervous system lymphoma (PCNSL) is an uncommon extranodal non-Hodgkin's lymphoma. Here, the feasibility of nuclear magnetic resonance (NMR) metabolomics for the diagnosis and prognosis prediction of PCNSL, as well as its correlation with magnetic resonance imaging (MRI) characteristics, was assessed.

PATIENTS AND METHODS

Cerebrospinal fluid (CSF) samples from PCNSL and normal groups (n = 41 for each) were obtained along with MRI data including pre- and postcontrast as well as T1-, T2-, and diffusion-weighted imaging for the treatment-naïve PCNSL patients (n = 24). The CSF samples were analyzed using nuclear magnetic resonance (NMR).

RESULTS

The CSF NMR metabolomic exhibited clear differences with a diagnostic sensitivity of 100% and a specificity of 97.6%. The citrate level of the leptomeningeal enhancement (LE) (+) group was significantly lower than that of the LE (-) group (p = 0.018). In addition, the MRI apparent diffusion coefficient (ADC) value of the tumor was positively correlated with the glucose level (p = 0.025). However, none of the marker metabolites were significant prognosis predictors in univariate analysis.

CONCLUSIONS

In conclusion, the NMR metabolomics could be helpful to diagnose PCNSL, but not for the prognosis, and MRI features (LE or ADC) can reflect the metabolic profiles of PCNSL.

Pathological and molecular diagnoses of early cancer with bile and pancreatic juice

The dismal prognosis of pancreaticobiliary malignancies is mainly attributed to the extremely difficult detection of early-stage lesions, including intraepithelial neoplasia. To improve prognosis, several studies on the early detection of cancer have been conducted using bile and pancreatic juices for pathological or molecular analyses. One approach is liquid biopsy that includes information about the tumor, such as circulating tumor cells, circulating tumor DNA, microRNAs, and exosomes released by the tumor. Another approach is proteomics/metabolomics that reflects specific conditions in the tumor. These two approaches lead to artificial intelligence-based multiomics analyses that comprises genomics, proteomics/metabolomics, and transcriptomics. Based on the findings of molecular analysis, pathological analysis using immunohistochemical staining/fluorescence in situ hybridization has also been developed. Moreover, there have been reports of new methods/ingenuities for obtaining appropriate samples for the diagnosis of early-stage cancer. Here we review the knowledge on cutting-edge pathological and molecular analyses of bile and pancreatic juices, introduce some ingenuities in sampling and sample processing to promote effective clinical practice, and provide a basis for future studies.

Research progress of bile biomarkers and their immunoregulatory role in biliary tract cancers

Biliary tract cancers (BTCs), including cholangiocarcinoma and gallbladder carcinoma, originate from the biliary epithelium and have a poor prognosis. Surgery is the only choice for cure in the early stage of disease. However, most patients are diagnosed in the advanced stage and lose the chance for surgery. Early diagnosis could significantly improve the prognosis of patients. Bile has complex components and is in direct contact with biliary tract tumors. Bile components are closely related to the occurrence and development of biliary tract tumors and may be applied as biomarkers for BTCs. Meanwhile, arising evidence has confirmed the immunoregulatory role of bile components. In this review, we aim to summarize and discuss the relationship between bile components and biliary tract cancers and their ability as biomarkers for BTCs, highlighting the role of bile components in regulating immune response, and their promising application prospects.

Gas chromatography-mass spectrometry-based untargeted metabolomics reveals metabolic perturbations in medullary thyroid carcinoma

Medullary thyroid cancer (MTC) is a rare tumor that arises from parafollicular cells within the thyroid gland. The molecular mechanism underlying MTC has not yet been fully understood. Here, we aimed to perform plasma metabolomics profiling of MTC patients to explore the perturbation of metabolic pathways contributing to MTC tumorigenesis. Plasma samples from 20 MTC patients and 20 healthy subjects were obtained to carry out an untargeted metabolomics by gas chromatography–mass spectrometry. Multivariate and univariate analyses were employed as diagnostic tools via MetaboAnalyst and SIMCA software. A total of 76 features were structurally annotated; among them, 13 metabolites were selected to be differentially expressed in MTC patients compared to controls (P < 0.05). These metabolites were mainly associated with the biosynthesis of unsaturated fatty acids and amino acid metabolisms, mostly leucine, glutamine, and glutamate, tightly responsible for tumor cells' energy production. Moreover, according to the receiver operating characteristic curve analysis, metabolites with the area under the curve (AUC) value up to 0.90, including linoleic acid (AUC = 0.935), linolenic acid (AUC = 0.92), and leucine (AUC = 0.948) could discriminate MTC from healthy individuals. This preliminary work contributes to existing knowledge of MTC metabolism by providing evidence of a distinctive metabolic profile in MTC patients relying on the metabolomics approach.

Serum metabolomic profiling based on GC/MS helped to discriminate Diffuse Large B-cell Lymphoma patients with different prognosis

BACKGROUND

The varied clinical outcomes of patients with Diffuse Large B Cell Lymphoma (DLBCL) are attributed to the different genetic and phenotypic subtypes. The purpose of this study was to determine whether metabolic alterations were related to cell-of-origin subtypes of DLBCL and find some metabolites which are associated with the clinical outcomes.

METHODS

Pre-treatment serum samples from eighty (80) newly diagnosed DLBCL patients, including twenty-eight (28) patients with Germinal Center B cell-like (GCB) subtypes and fifty-two (52) patients with non-GCB subtypes, were tested by the Gas Chromatography-Mass Spectrometry (GC-MS) technique. Univariate and multivariate analysis methods, principal component analysis (PCA), and partial least square discriminant analysis (PLS-DA) were conducted to examine the potential differential metabolites. Overall survival (OS) was calculated.

RESULTS

Overall, 65 out of 1472 entities were identified for subsequent analysis. Unfortunately, the initial PLS-DA analysis failed to discriminate GCB from non-GCB samples. Intriguingly, further PLS-DA analysis identified two subgroups of DLBCL (named as group A and group B) and the metabolic subgroups were significantly associated with overall survival. Valine, hexadecenoic acid, and pyroglutamic acid were identified and verified as the most important altered metabolites and could be candidate biomarkers for the prognosis of DLBCL.

CONCLUSIONS

Our results demonstrated that metabolic alterations in serum could be helpful to predict different clinical outcomes of DLBCL patients. Further studies are warranted to understand whether the altered metabolites might serve as prognostic factors for DLBCL.

The Effect of Blood Contained in the Samples on the Metabolomic Profile of Mouse Brain Tissue: A Study by NMR Spectroscopy

(1) Recently, metabolic profiling of the tissue in the native state or extracts of its metabolites has become increasingly important in the field of metabolomics. An important factor, in this case, is the presence of blood in a tissue sample, which can potentially lead to a change in the concentration of tissue metabolites and, as a result, distortion of experimental data and their interpretation. (2) In this paper, the metabolomic profiling based on NMR spectroscopy was performed to determine the effect of blood contained in the studied samples of brain tissue on their metabolomic profile. We used 13 male laboratory CD-1^® IGS mice for this study. The animals were divided into two groups. The first group of animals (n = 7) was subjected to the perfusion procedure, and the second group of animals (n = 6) was not perfused. The brain tissues of the animals were homogenized, and the metabolite fraction was extracted with a water/methanol/chloroform solution. Samples were studied by high-frequency ¹H-NMR spectroscopy with subsequent statistical data analysis. The group comparison was performed with the use of the Student's test. We identified 36 metabolites in the brain tissue with the use of NMR spectroscopy. (3) For the major set of studied metabolites, no significant differences were found in the brain tissue metabolite concentrations in the native state and after the blood removal procedure. (4) Thus, it was shown that the presence of blood does not have a significant effect on the metabolomic profile of the brain in animals without pathologies.

Serum microRNAs as Biomarkers for the Noninvasive Early Diagnosis of Biliary Tract Cancer

Background

Biliary tract cancers (BTCs) are aggressive malignancies with difficult early diagnosis and poor prognosis. Studies have shown that microRNAs (miRNAs) are expected to be biomarkers of the disease, which indicates that we can diagnose cancers according to the miRNAs that have significant changes. The aim of this study was to explore miRNA biomarkers of BTCs.

Methods

A total of 163 samples were collected and divided into the control group, the benign group and the malignant group. High-throughput low-density chips were used to screen miRNAs with significant changes. Then, the preliminary screening test and the verification test were performed by quantitative real time PCR (qRT-PCR). Finally, the level of miRNAs in serum exosomes was measured.

Results

MiR-10a, miR-21, miR-135b, miR-221, and miR-214 were upregulated in the BTCs group compared to the control group. The change in the miR-221 level was statistically significant when the malignant group was compared with the benign group (P<0.01). Meanwhile, miR-135b and miR-214 were enriched in serum exosomes.

Conclusion

Five miRNAs in the serum were found to be significantly upregulated in patients with BTCs. Among them, miR-221 can serve as an early diagnostic marker for BTCs patients. MiR-10a, miR-21, miR-135b and miR-214 can be used as biomarkers for the diagnosis of biliary diseases.

Serum metabolite profiling of a 4-Nitroquinoline-1-oxide-induced experimental oral carcinogenesis model using gas chromatography-mass spectrometry

Background

Oral cancer progresses from hyperplastic epithelial lesions through dysplasia to invasive carcinoma. The critical needs in oral cancer treatment are expanding our knowledge of malignant tumour progression and the development of useful approaches to prevent dysplastic lesions. This study was designed to gain insights into the underlying metabolic transformations that occur during the process of oral carcinogenesis.

Methods

We used gas chromatography-mass spectrometry (GC-MS) in conjunction with multivariate statistical techniques to observe alterations in serum metabolites in a 4-Nitroquinoline 1-oxide (4NQO)-induced rat tongue carcinogenesis model. Thirty-eight male rats were randomly divided into two groups, including the 4NQO-induced model group of 30 rats and the healthy control group of five rats. Animals were sacrificed at weeks 9, 13, 20, 24, and 32, post-4NQO treatment. Tissue samples were collected for histopathological examinations and blood samples were collected for metabolomic analysis. Partial least squares discriminate analysis (PLS-DA) models generated from GC-MS metabolic profile data showed robust discrimination from rats with oral premalignant and malignant lesions induced by 4NQO, and normal controls.

Results

The results found 16 metabolites associated with 4NQO-induced rat tongue carcinogenesis. Dysregulated arachidonic acid, fatty acid, and glycine metabolism, as well as disturbed tricarboxylic acid (TCA) cycle and mitochondrial respiratory chains were observed in the animal model. The PLS-DA models of metabolomic results demonstrated good separations between the 4NQO-induced model group and the normal control group.

Conclusion

We found several metabolites modulated by 4NQO and provide a good reference for further study of early diagnosis in oral cancer.

Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach

Simple Summary

There are currently no effective specific biomarkers for the screening of early gastric cancer. Recently, metabolomics has been used to profile small endogenous metabolites, demonstrating significant potential in the diagnosis/screening of cancer, owing to its ability to conduct a noninvasive sample analysis. Here, we performed a urine metabolomics analysis in the context of an early diagnosis of gastric cancer. This approach showed very high diagnostic sensitivity and specificity and performed significantly better than the analysis of serum tumor markers modalities. An additional genomic data analysis revealed the up-regulation of several genes in gastric cancer. This metabolomics-based early diagnosis approach may have the potential for mass screening an average-risk population and may facilitate endoscopic examination through risk stratification.

Abstract

The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.

NMR-Based Metabolomics in Gallbladder Cancer Research

Gallbladder cancer (GBC) is a common form of malignancy, which has high incidence rates in the northern parts of India, South America, Japan, and in Native American populations. A few metabolomic studies have revealed changes in the composition of biofluids, tissues, and gallstones by nuclear magnetic resonance (NMR), which may prove useful in understanding GBC. In this chapter, we focus on the use of NMR in unveiling the metabolomics of GBC.

Identification of potential biomarkers for diagnosis of pancreatic and biliary tract cancers by sequencing of serum microRNAs

BACKGROUND

Pancreatic and biliary tract cancer (PC and BTC, respectively) are difficult to diagnose because of their clinical characteristics; however, recent studies suggest that serum microRNAs (miRNAs) might be the key to developing more efficient diagnostic methods for these cancers.

METHODS

We analysed the genome-wide expression of serum miRNAs in PC and BTC patients to identify novel biomarker candidates using high-throughput sequencing and experimentally validated miRNAs on clinical samples.

RESULTS

Statistical and classification analysis of the serum miRNA-expression profiles of 55 patient samples showed distinguishable patterns between cancer patients and healthy controls; however, we were unable to distinguish the two cancers. We found that three of the highest performing miRNAs were capable of distinguishing cancer patients from controls, with an accuracy of 92.7%. Additionally, dysregulation of these three cancer-specific miRNAs was demonstrated in an independent sample group by quantitative reverse transcription polymerase chain reaction.

CONCLUSIONS

These results suggested three candidate serum miRNAs (mir-744-5p, mir-409-3p, and mir-128-3p) as potential biomarkers for PC and BTC diagnosis.

Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints

Cell growth and survival depend on a delicate balance between energy production and synthesis of metabolites. Here, we provide evidence that an alternative mitochondrial complex II (CII) assembly, designated as CII_low, serves as a checkpoint for metabolite biosynthesis under bioenergetic stress, with cells suppressing their energy utilization by modulating DNA synthesis and cell cycle progression. Depletion of CII_low leads to an imbalance in energy utilization and metabolite synthesis, as evidenced by recovery of the de novo pyrimidine pathway and unlocking cell cycle arrest from the S-phase. In vitro experiments are further corroborated by analysis of paraganglioma tissues from patients with sporadic, SDHA and SDHB mutations. These findings suggest that CII_low is a core complex inside mitochondria that provides homeostatic control of cellular metabolism depending on the availability of energy.

Mitochondrial complex II is normally composed of four subunits. Here the authors show that bioenergetic stress conditions give rise to a partially assembled variant of complex II, which shifts the anabolic pathways to less energy demanding processes.

Emerging therapeutic biomarkers of autoimmune hepatitis and their impact on current and future management

Autoimmune hepatitis lacks a quantifiable biomarker that is close to its pathogenic mechanisms and that accurately reflects inflammatory activity, correlates with treatment response, and ensures inactive disease before treatment withdrawal. Areas covered: Micro-ribonucleic acids, programmed death-1 protein and its ligands, macrophage migration inhibitory factor, soluble CD163, B cell activating factor, and metabolite patterns in blood were considered the leading candidates as therapeutic biomarkers after search of PubMed from August 1981 to August 2017 using the search words 'biomarkers of autoimmune hepatitis'. Expert commentary: Each of the candidate biomarkers is close to the putative pathogenic mechanisms of autoimmune hepatitis, and each has attributes that support its potential role as a surrogate marker of inflammatory activity that can be monitored during treatment. Future studies must demonstrate the superiority of each biomarker to conventional indices of inflammatory activity and validate their correlation with treatment response and outcome. A reliable therapeutic biomarker would facilitate the individualization of current management algorithms, ensure that pathogenic mechanisms were disrupted or eliminated prior to treatment withdrawal, and reduce the frequency of relapse or unnecessary protracted therapy. The biomarker might also prove to be a target of next-generation therapies.

High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy for the Metabolic Assessment of Acute Rejection After Cardiac Transplantation in Rats

PURPOSE

To evaluate the potential of high-resolution magic angle spinning (HR-MAS) ¹H nuclear magnetic resonance (NMR) spectroscopy for metabolite characterization and the differentiation of acute rejection after heart transplantation in rat models.

METHODS

We transplanted syngeneic heart grafts from Lewis rats (n = 4) and allogeneic heart grafts from F344 rats (n = 4) heterotopically into Lewis recipients. On day 7 postoperatively, the transplanted hearts were harvested for ex vivo ¹H NMR spectroscopy and HR-MAS ¹H NMR spectroscopy. ¹H NMR spectroscopy and HR-MAS ¹H NMR spectroscopy were performed at 4.7 T and 11.7 T, respectively. Metabolomic profiles contributing to the differentiation of allogeneic and syngeneic graft groups were statistically assessed by orthogonal partial least squares discriminant analysis (OPLS/O2PLS-DA). Metabolite concentrations were normalized by total spectral intensities and were compared using Mann-Whitney U tests.

RESULTS

One allogeneic graft that showed extensive necrotic change suggesting graft failure was excluded from the statistical analysis of the NMR spectroscopy. In the 4.7-T ¹H NMR spectroscopy, the creatine peak was decreased in the allogeneic group. The PLS-DA and OPLS/O2PLS-DA score plot demonstrated good discrimination of the allogeneic graft group from syngeneic graft group. The concentrations of creatine, myo-inositol, glucose, niacinamide, hypoxanthine, inosine, and glutamine were significantly decreased in the allogeneic graft group, whereas the concentrations of glycine, phosphoethanolamine, xanthine, sn-glycero-3-phosphocholine, leucine, valine, and tyrosine were significantly increased (P < .05).

CONCLUSIONS

HR-MAS ¹H NMR spectroscopy can metabolically characterize the acute rejection of heart transplantation.

Comparison of the composition of bile acids in bile of patients with adenocarcinoma of the pancreas and benign disease

Bile acids have been implicated in the development of gastrointestinal malignancies. Both the specific nature of individual bile acids and their concentration appear key factors in the carcinogenic potency of bile. Using liquid chromatography mass spectrometry (LC-MS) we performed quantitative profiling of bile extracted directly from the common bile duct in 30 patients (15 patients with pancreatic cancer and 15 patients with benign disease). Separation and detection of bile acids was performed using a 1.7μm particle size reversed-phase C18 LC column at a flow rate of 200μL/min with negative electrospray ionization MS. A significant difference (p=0.018) was seen in the concentration of unconjugated cholic acid in the malignant group (0.643mmol/L) compared to the benign group (0.022mmol/L), with an overall significant difference (p=0.04) seen in the level of total unconjugated bile acids in the malignant group (1.816mmol/L) compared to the benign group (0.069mmol/L). This finding may offer the possibility of both understanding the biology of cancer development in the pancreas, as well as offering a potential diagnostic avenue to explore. However, a larger study is necessary to confirm the alterations in bile acid profiles reported here and explore factors such as diet and microbial populations on the bile acid profiles of these patient groups.

Magnetic resonance metabolic profiling of estrogen receptor-positive breast cancer: correlation with currently used molecular markers

Estrogen receptor (ER)-positive breast cancers overall have a good prognosis, however, some patients suffer relapses and do not respond to endocrine therapy. The purpose of this study was to determine whether there are any correlations between high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) metabolic profiles of core needle biopsy (CNB) specimens and the molecular markers currently used in patients with ER-positive breast cancers. The metabolic profiling of CNB samples from 62 ER-positive cancers was performed by HR-MAS MRS. Metabolic profiles were compared according to human epidermal growth factor receptor 2 (HER2) and Ki-67 status, and luminal type, using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). In univariate analysis, the HER2-positive group was shown to have higher levels of glycine and glutamate, compared to the HER2-negative group (P<0.01, and P <0.01, respectively). The high Ki-67 group showed higher levels of glutamate than the low Ki-67 group without statistical significance. Luminal B cancers showed higher levels of glycine (P=0.01) than luminal A cancers. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the subgroups according to HER2 and Ki-67 status, and luminal type. This study showed that the metabolic profiles of CNB samples assessed by HR-MAS MRS can be used to detect potential prognostic biomarkers as well as to understand the difference in metabolic mechanism among subtypes of ER-positive breast cancer.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis−mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Metabolomic analysis of percutaneous fine-needle aspiration specimens of thyroid nodules: Potential application for the preoperative diagnosis of thyroid cancer

Thyroid nodules are a very common problem. Since malignant thyroid nodules should be treated surgically, preoperative diagnosis of thyroid cancer is very crucial. Cytopathologic analysis of percutaneous fine-needle aspiration (FNA) specimens is the current gold standard for diagnosing thyroid nodules. However, this method has led to high rates of inconclusive results. Metabolomics has emerged as a useful tool in medical fields and shown great potential in diagnosing various cancers. Here, we evaluated the potential of nuclear magnetic resonance (NMR) analysis of percutaneous FNA specimens for preoperative diagnosis of thyroid cancer. We analyzed metabolome of FNA samples of papillary thyroid carcinoma (n = 35) and benign follicular nodule (n = 69) using a proton NMR spectrometer. The metabolomic profiles showed a considerable discrimination between benign and malignant nodules. Receiver operating characteristic (ROC) curve analysis indicated that seven metabolites could serve as discriminators (area under ROC curve value, 0.64–0.85). These findings demonstrated that NMR analysis of percutaneous FNA specimens of thyroid nodules can be potentially useful in the accurate and rapid preoperative diagnosis of thyroid cancer.

Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence

Metabolite profiling is being increasing employed in the study of prostate cancer as a means of identifying predictive, diagnostic, and prognostic biomarkers. This review provides a summary and critique of the current literature. Thirty-three human case-control studies of prostate cancer exploring disease prediction, diagnosis, progression, or treatment response were identified. All but one demonstrated the ability of metabolite profiling to distinguish cancer from benign, tumor aggressiveness, cases who recurred, and those who responded well to therapy. In the subset of studies where biomarker discriminatory ability was quantified, high AUCs were reported that would potentially outperform the current gold standards in diagnosis, prognosis, and disease recurrence, including PSA testing. There were substantial similarities between the metabolites and the associated pathways reported as significant by independent studies, and important roles for abnormal cell growth, intensive cell proliferation, and dysregulation of lipid metabolism were highlighted. The weight of the evidence therefore suggests metabolic alterations specific to prostate carcinogenesis and progression that may represent potential metabolic biomarkers. However, replication and validation of the most promising biomarkers is currently lacking and a number of outstanding methodologic issues remain to be addressed to maximize the utility of metabolomics in the study of prostate cancer. Cancer Epidemiol Biomarkers Prev; 25(6); 887-906. ©2016 AACR.

Serum nuclear magnetic resonance-based metabolomics and outcome in diffuse large B-cell lymphoma patients - a pilot study

The prognosis for diffuse large B-cell lymphoma (DLBCL) patients with early relapse or refractory disease is dismal. To determine if clinical outcome correlated to diverse serum metabolomic profiles, we used (1)H nuclear magnetic resonance (NMR) spectroscopy and compared two groups of DLBCL patients treated with immunochemotherapy: i) refractory/early relapse (REF/REL; n=27) and ii) long-term progression-free (CURED; n = 60). A supervised multivariate analysis showed a separation between the groups. Among discriminating metabolites higher in the REF/REL group were the amino acids lysine and arginine, the degradation product cadaverine and a compound in oxidative stress (2-hydroxybutyrate). In contrast, the amino acids aspartate, valine and ornithine, and a metabolite in the glutathione cycle, pyroglutamate, were higher in CURED patients. Together, our data indicate that NMR-based serum metabolomics can identify a signature for DLBCL patients with high-risk of failing immunochemotherapy, prompting for larger validating studies which could lead to more individualized treatment of this disease.

SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures

The method provided here can overcome the low S/N problem in ¹³C NMR by the integration of plural spectra to increase the resolution based on non-bucketing analysis without measurements.

A serum metabolomic analysis for diagnosis and biomarker discovery of primary biliary cirrhosis and autoimmune hepatitis

BACKGROUND

Because of the diversity of the clinical and laboratory manifestations, the diagnosis of autoimmune liver disease (AILD) remains a challenge in clinical practice. The value of metabolomics has been studied in the diagnosis of many diseases. The present study aimed to determine whether the metabolic profiles, based on ultraperformance liquid chromatography-mass spectrometry (UPLC-MS), differed between autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC), to identify specific metabolomic markers, and to establish a model for the diagnosis of AIH and PBC.

METHODS

Serum samples were collected from 20 patients with PBC, 19 patients with AIH, and 25 healthy individuals. UPLC-MS data of the samples were analyzed using principal component analysis, partial least squares discrimination analysis and orthogonal partial least squares discrimination analysis.

RESULTS

The partial least squares discrimination analysis model (R2Y=0.991, Q2=0.943) was established between the AIH and PBC groups and exhibited both sensitivity and specificity of 100%. Five groups of biomarkers were identified, including bile acids, free fatty acids, phosphatidylcholines, lysolecithins and sphingomyelin. Bile acids significantly increased in the AIH and PBC groups compared with the healthy control group. The other biomarkers decreased in the AIH and PBC groups compared with those in the healthy control group. In addition, the biomarkers were downregulated in the AIH group compared with the PBC group.

CONCLUSIONS

The biomarkers identified revealed the pathophysiological changes in AILD and helped to discriminate between AIH and PBC. The predictability of this method suggests its potential application in the diagnosis of AILD.

The roles of IP3 receptor in energy metabolic pathways and reactive oxygen species homeostasis revealed by metabolomic and biochemical studies

Inositol 1,4,5-trisphosphate receptors (IP(3)Rs) are calcium channels modulating important calcium-mediated processes. Recent studies implicate IP(3)R in cell metabolism, but specific evidence is missing regarding IP(3)R's effects on actual metabolic pathways and key energy metabolites. Here, we applied metabolomics and molecular biology to compare DT40 cell lines devoid of IP(3)R (KO) and its wild-type (WT) counterpart. NMR and LC-MS metabolomic data showed that the KO cell line has a very different basic energy metabolism from the WT cell line, showing enhanced Warburg effect. In particular, the KO cells exhibited significant perturbation in energy charge, reduced glutathione and NADPH ratios with slower cellular growth rate. Subsequent flow cytometry results showed that the KO cell line has a higher level of general reactive oxygen species (ROS) but has a lower level of peroxynitrites. This ROS disturbance could be explained by observing lower expression of superoxide dismutase 2 (SOD2) and unchanged expression of catalase. The higher ROS seems to be involved in the slower growth rate of the KO cells, with an ROS scavenger increasing their growth rate. However, the KO and WT cell lines did not show any noticeable differences in AMPK and phosphorylated AMPK levels, suggesting possible saturation of AMPK-mediated metabolic regulatory circuit in both cells. Overall, our study reveals IP3R's roles in ROS homeostasis and metabolic pathways as well as the effects of its KO on cellular phenotypes.

1H-NMR-based metabolomic analysis of cerebrospinal fluid from adult bilateral moyamoya disease: comparison with unilateral moyamoya disease and atherosclerotic stenosis

Although metabolomics has been increasingly used to observe metabolic pattern and disease-specific metabolic markers, metabolite profiling for moyamoya disease (MMD) has not yet been done in adults. This study investigated cerebrospinal fluid (CSF) metabolites specific to bilateral MMD (B-MMD) and compared them to those of unilateral MMD (U-MMD) or atherosclerotic stenosis with hydrogen-1 nuclear magnetic resonance spectroscopy to identify metabolic biomarkers associated with MMD in adults.CSF samples of B-MMD (n = 29), U-MMD (n = 11), and atherosclerotic cerebrovascular disease (ACVD) (n = 8) were recruited. Principal component analysis, partial least square discriminant analysis, and orthogonal projections to latent structure discriminant analysis (OPLS-DA) were done for the comparisons. Diagnostic performance was acquired by prediction of 1 left-out sample from the distinction model constructed with the rest of the samples.B-MMD showed an increase in glutamine (P < 0.001) and taurine (P = 0.004), and a decrease in glucose (P < 0.001), citrate (P = 0.002), and myo-inositol (P = 0.006) than those in ACVD. U-MMD showed a higher level of glutamine (P = 0.005) and taurine (P = 0.034), and a lower level of glutamate (P < 0.004) than those in ACVD. No difference at the metabolite level was observed between B-MMD and U-MMD. Cross-validation with the OPLS-DA model showed a high accuracy for the prediction of MMD.The results of the study suggest that a metabolomics approach may be helpful in confirming MMD and providing a better understanding of MMD pathogenesis. Elevated glutamine in the CSF may be associated with MMD pathogenesis, which was different from ACVD.

Distinct serum metabolomics profiles associated with malignant progression in the KrasG12D mouse model of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer deaths worldwide with less than a 6% 5-year survival rate. PDAC is associated with poor prognosis based on the late stage diagnosis of the disease. Current diagnostic tests lack the sensitivity and specificity to identify markers of early staging. Metabolomics has provided biomarkers for various diseases, stressors, and environmental exposures. In this study we utilized the p48-Cre/LSL-Kras^G12D mouse model with age-matched wild type mice. This model shows malignant progression to PDAC analogous to the human disease stages via early and late pancreatic intra-epithelial neoplasia (PanIN) lesions.

Results

Serum was collected from mice with early PanIN lesions (at 3-5 months) and with late PanIN or invasive PDAC lesions (13-16 months), as determined by histopathology. Metabolomics analysis of the serum samples was conducted through UPLC-TOFMS (Ultra Performance Liquid Chromatography coupled to Time-of-flight Mass Spectrometry). Multivariate data analysis revealed distinct metabolic patterns in serum samples collected during malignant progression towards invasive PDAC. Animals with early or late stage lesions were distinguished from their respective controls with 82.1% and 81.5% accuracy, respectively. This also held up for randomly selected subgroups in the late stage lesion group that showed less variability between animals. One of the metabolites, citrate, was validated through tandem mass spectrometry and showed increased levels in serum with disease progression. Furthermore, serum metabolite signatures from animals with early stage lesions identified controls and animals with late stage lesions with 81.5% accuracy (p<0.01) and vice-versa with 73.2% accuracy (p<0.01).

Conclusions

We conclude that metabolomics analysis of serum samples can identify the presence of early and late stage pancreatic cancer.

Gas chromatography-mass spectrometry of biofluids and extracts

Gas chromatography-mass spectrometry (GC-MS) has been widely used in metabonomics analyses of biofluid samples. Biofluids provide a wealth of information about the metabolism of the whole body and from multiple regions of the body that can be used to study general health status and organ function. Blood serum and blood plasma, for example, can provide a comprehensive picture of the whole body, while urine can be used to monitor the function of the kidneys, and cerebrospinal fluid (CSF) will provide information about the status of the brain and central nervous system (CNS). Different methods have been developed for the extraction of metabolites from biofluids, these ranging from solvent extracts, acids, heat denaturation, and filtration. These methods vary widely in terms of efficiency of protein removal and in the number of metabolites extracted. Consequently, for all biofluid-based metabonomics studies, it is vital to optimize and standardize all steps of sample preparation, including initial extraction of metabolites. In this chapter, recommendations are made of the optimum experimental conditions for biofluid samples for GC-MS, with a particular focus on blood serum and plasma samples.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

Identification of bile biomarkers of biliary tract cancer through a liquid chromatography/mass spectrometry-based metabolomic method

The incidence and mortality rate of biliary tract cancer have been increasing worldwide; however, its diagnosis and prognosis have not improved in recent years. A novel approach, termed 'metabolomics', may have the potential to be developed as an effective diagnostic tool. The present study prospectively obtained bile samples from 115 individuals, including 32 patients with biliary tract cancer, 61 patients with benign biliary tract diseases and 22 normal controls. A liquid chromatography/mass spectrometry (LC/MS)‑based approach was used to investigate the differences in bile samples between the three groups, followed by multivariate statistical analysis, which included partial least squares projection to latent structures with discriminant analysis (PLS‑DA) and orthogonal projection to latent structures with discriminant analysis (OPLS‑DA). The metabolomic 2D score plot and 3D plot revealed clear separation between the cancer, benign and normal control groups by PLS‑DA. To further address the significant difficulties in clinically differentiating between biliary tract cancer and benign biliary tract diseases, OPLS‑DA was performed to distinguish between the two disease groups and to select potential biomarkers. The cancer and benign groups were well differentiated. The metabolic analysis revealed significantly lower levels of lysophosphatidylcholine, phenylalanine, 2‑octenoylcarnitine, tryptophan and significantly higher levels of taurine‑ and glycine‑conjugated bile acids in the bile from patients with biliary tract cancer compared with those in the bile from patients with benign biliary tract disease. The present study suggested that an LC/MS‑based metabolomic investigation provides a potent and promising approach for discriminating biliary tract cancer from benign biliary tract diseases and the identified specific metabolites may offer potential as novel biomarkers for the early detection of biliary tract cancer.

Metabolomic Profiling of Autoimmune Hepatitis: The Diagnostic Utility of Nuclear Magnetic Resonance Spectroscopy

Autoimmune hepatitis (AIH) is often confused with other liver diseases because of their shared nonspecific symptoms and serological and histological overlap. This study compared the plasma metabolomic profiles of patients with AIH, primary biliary cirrhosis (PBC), PBC/AIH overlap syndrome (OS), and drug-induced liver injury (DILI) with those of healthy subjects to identify potential biomarkers of AIH. Metabolomic profiling and biomarker screening were performed using proton nuclear magnetic resonance spectroscopy (¹H NMR) coupled with a partial least-squares discriminant analysis. Compared with the levels in healthy volunteers and other liver disease patients, AIH patients exhibited relatively high levels of plasma pyruvate, lactate, acetate, acetoacetate, and glucose. Such metabolites are typically related to energy metabolism alterations and may be a sign of metabolic conversion to the aerobic glycolysis phenotype of excessive immune activation. Increased aromatic amino acids and decreased branched-chain amino acids were found in the plasma of AIH patients. The whole NMR profiles were stepwise-reduced, and nine metabolomic biomarkers having the greatest significance in the discriminant analysis were obtained. The diagnostic utility of the selected metabolites was assessed, and these biomarkers achieved good sensitivity, specificity, and accuracy (all above 93%) in distinguishing AIH from PBC, DILI, and OS. This report is the first to present the metabolic phenotype of AIH and the potential utility of ¹H NMR metabolomics in the diagnosis of AIH.

An NMR metabolomics approach for the diagnosis of leptomeningeal carcinomatosis in lung adenocarcinoma cancer patients

Leptomeningeal carcinomatosis (LC) is a metastatic cancer invading the central nervous system (CNS). We previously reported a metabolomic diagnostic approach as tested on an animal model and compared with current modalities. Here, we provide a proof of concept by applying it to human LC originating from lung cancer, the most common cause of CNS metastasis. Cerebrospinal fluid from LC (n = 26) and normal groups (n = 41) were obtained, and the diagnosis was established with clinical signs, cytology, MRI and biochemical tests. The cytology on the CSF, the current gold standard, exhibited 69% sensitivity (~100% specificity) from the first round of CSF tapping. In comparison, the nuclear magnetic resonance spectra on the CSF showed a clear difference in the metabolic profile between the LC and normal groups. Multivariate analysis and cross-validation yielded the diagnostic sensitivity of 92%, the specificity of 96% and the area under the curve (AUC) of 0.991. Further spectral and statistical analysis identified myo-inositol (p < 5 × 10(-14)), creatine (p < 7 × 10(-8)), lactate (p < 9 × 10(-4)), alanine (p < 7.9 × 10(-3)) and citrate (p < 3 × 10(-4)) as the most contributory metabolites, whose combination exhibited an receiver-operating characteristic diagnostic AUC of 0.996. In addition, the metabolic profile could be correlated with the grading of radiological leptomeningeal enhancement (R(2) = 0.3881 and p = 6.66 × 10(-4)), suggesting its potential utility in grading LC. Overall, we propose that the metabolomic approach might augment current diagnostic modalities for LC, the accurate diagnosis of which remains a challenge.

MRS-based Metabolomics in Cancer Research

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

GC-MS combined with chemometric techniques for the quality control and original discrimination of Curcumae longae rhizome: analysis of essential oils

Curcumae longae rhizome is a widely used traditional herb in many countries. Various geographical origins of this herb might lead to diversity or instability of the herbal quality. The objective of this work was to establish the chemical fingerprints for quality control and find the chemical markers for discriminating these herbs from different origins. First, chemical fingerprints of essential oil of 24 C. longae rhizome from four different geographical origins in China were determined by GC-MS. Then, pattern recognition techniques were introduced to analyze these abundant chemical data in depth; hierarchical cluster analysis was used to sort samples into groups by measuring their similarities, and principal component analysis and partial least-squares discriminate analysis were applied to find the main chemical markers for discriminating these samples. Curcumae longae rhizome from Guangxi province had the highest essential oil yield (4.32 ± 1.45%). A total of 46 volatile compounds were identified in total. Consistent results were obtained to show that C. longae rhizome samples could be successfully grouped according to their origins, and turmerone, ar-turmerone, and zingiberene were the characteristic components for discriminating these samples of various geographical origins and for quality control. This finding revealed that fingerprinting analysis based on GC-MS coupled with chemometric techniques could provide a reliable platform to discriminate herbs from different origins, which is a benefit for quality control.

Current state of nonsurgical therapies for cholangiocarcinoma

Cholangiocarcinomas (CCCs) are rare tumors that are derived from the epithelial cell lining of the bile ducts. They can be classified as intrahepatic, extrahepatic-perihilar and extrahepatic-distal tumors. The prognosis of CCCs is poor as, in many cases, they are diagnosed at advanced stages, at which point curative surgical resection is not possible. Furthermore, most patients will experience a tumor recurrence despite initial complete CCC resection. Therefore, alternative/additional therapeutic strategies are needed to improve tumor- and recurrence-free survival after surgery as well as tumor control in patients with advanced disease. In clinical practice, apart from systemic chemotherapies for the therapeutic management of CCCs, locoregional as well as multimodal strategies are available, including external and internal radiation therapies. This review focuses on the currently available nonsurgical therapies for patients with CCCs, alone or in combination with other modalities, and on evolving therapeutic concepts that are being explored in clinical studies.

Magnetic resonance metabolic profiling of breast cancer tissue obtained with core needle biopsy for predicting pathologic response to neoadjuvant chemotherapy

The purpose of this study was to determine whether metabolic profiling of core needle biopsy (CNB) samples using high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) could be used for predicting pathologic response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. After institutional review board approval and informed consent were obtained, CNB tissue samples were collected from 37 malignant lesions in 37 patients before NAC treatment. The metabolic profiling of CNB samples were performed by HR-MAS MRS. Metabolic profiles were compared according to pathologic response to NAC using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). Various metabolites including choline-containing compounds were identified and quantified by HR-MAS MRS in all 37 breast cancer tissue samples obtained by CNB. In univariate analysis, the metabolite concentrations and metabolic ratios of CNB samples obtained with HR-MAS MRS were not significantly different between different pathologic response groups. However, there was a trend of lower levels of phosphocholine/creatine ratio and choline-containing metabolite concentrations in the pathologic complete response group compared to the non-pathologic complete response group. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the pathologic response groups. This study showed OPLS-DA multivariate analysis using metabolic profiles of pretreatment CNB samples assessed by HR- MAS MRS may be used to predict pathologic response before NAC, although we did not identify the metabolite showing statistical significance in univariate analysis. Therefore, our preliminary results raise the necessity of further study on HR-MAS MR metabolic profiling of CNB samples for a large number of cancers.

Measurement of salivary metabolite biomarkers for early monitoring of oral cancer with ultra performance liquid chromatography-mass spectrometry

This study aimed to set-up an ultra performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) method for the determination of salivary L-phenylalanine and L-leucine for early diagnosis of oral squamous cell carcinoma (OSCC). In addition, the diagnostic accuracy for both biomarkers was established by using receiver operating characteristic (ROC) analysis. Mean recoveries of l-phenylalanine and L-leucine ranged from 88.9 to 108.6% were obtained. Intra- and inter-day precision for both amino acids was less than 7%, with acceptable accuracy. Linear regression coefficients of both biomarkers were greater than 0.99. The diagnostic accuracy for both biomarkers was established by analyzing 60 samples from apparently healthy individuals and 30 samples from OSCC patients. Both potential biomarkers demonstrated significant differences in concentrations in distinguishing OSCC from control (P<0.05). As a single biomarker, L-leucine might have better predictive power in OSCC with T1-2 (early stage of OSCC including stage I and II), and L-phenylalanine might be used for screening and diagnosis of OSCC with T3-4 (advanced stage of OSCC including stage III and IV). The combination of L-phenylalanine and L-leucine will improve the sensitivity (92.3%) and specificity (91.7%) for early diagnosis of OSCC. The possibility of salivary metabolite biomarkers for OSCC diagnosis is successfully demonstrated in this study. This developed method shows advantages with non-invasive, simple, reliable, and also provides lower detection limits and excellent precision and accuracy. These non-invasive salivary biomarkers may lead to a simple clinical tool for the early diagnosis of OSCC.

Investigation and identification of potential biomarkers in human saliva for the early diagnosis of oral squamous cell carcinoma

BACKGROUND

Oral cancer is 1 of the 6 most common human cancers, with an annual incidence of >300,000 cases worldwide. This study aimed to investigate potential biomarkers in human saliva to facilitate the early diagnosis of oral squamous cell carcinoma (OSCC).

METHODS

Unstimulated whole saliva obtained from OSCC patients (n=30) and apparently healthy individuals (n=30) were assayed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) in hydrophilic interaction chromatography mode. The data were analyzed using a nonparametric Mann-Whitney U test, logistic regression, and the receiver operating characteristic (ROC) to evaluate the predictive power of each of 4 biomarkers, or combinations of biomarkers, for OSCC screening.

RESULTS

Four potential salivary biomarkers demonstrated significant differences (P<0.05) in concentrations between patients at stages I-II and the healthy individuals. The area under the curve (AUC) values in control vs OSCC I-II mode based on choline, betaine, pipecolinic acid, and l-carnitine were 0.926, 0.759, 0.994, and 0.708, respectively. Four salivary biomarkers in combination yielded satisfactory accuracy (0.997), sensitivity (100%), and specificity (96.7%) in distinguishing OSCC I-II from control.

CONCLUSIONS

Salivary metabolite biomarkers for the early diagnosis of OSCC were verified in this study. The proposed approach is expected to be applied as a potential technique of preclinical screening of OSCC.

Urine metabolic profile changes of CCl4-liver fibrosis in rats and intervention effects of Yi Guan Jian Decoction using metabonomic approach

Background

Yi Guan Jian Decoction (YGJD), a famous Chinese prescription, has long been employed clinically to treat liver fibrosis. However, as of date, there is no report on the effects of YGJD from a metabonomic approach. In this study, a urine metabonomic method based on gas chromatography coupled with mass spectrometry (GC/MS) was employed to study the protective efficacy and metabolic profile changes caused by YGJD in carbon tetrachloride (CCl₄)-induced liver fibrosis.

Methods

Urine samples from Wistar rats of three randomly divided groups (control, model, and YGJD treated) were collected at various time-points, and the metabolic profile changes were analyzed by GC/MS with principal component analysis (PCA) and partial least squares-discriminate analysis (PLS-DA). Furthermore, histopathology and biochemical examination were also carried out to ensure the success of CCl₄-induced liver fibrosis model.

Results

Urine metabolic profile studies suggested distinct clustering of the three groups, and YGJD group was much closer to the control group by showing a tendency of recovering towards the control group. Fourteen significantly changed metabolites were found, and YGJD treatment could reverse the levels of these metabolites to normal levels or close to normal levels.

Conclusions

The current study indicates that the YGJD has significant anti-fibrotic effects on CCl₄-induced liver fibrosis in rats, which might be by regulating the dysfunction of energy metabolism, amino acid metabolism, tryptophan metabolism, cytochrome P450 metabolism, and gut microflora metabolism. The metabonomic approach can be recommended to study the pharmacological effect and mechanism of complex Chinese medicines.