Urinary hydrophilic and hydrophobic metabolic profiling based on liquid chromatography-mass spectrometry methods: Differential metabolite discovery specific to ovarian cancer

Urine biomarkers in cancer detection: A systematic review of preanalytical parameters and applied methods

The aim of this review was to explore the status of urine sampling as a liquid biopsy for noninvasive cancer research by reviewing used preanalytical parameters and protocols. We searched two main health sciences databases, PubMed and Web of Science. From all eligible publications (2010-2022), information was extracted regarding: (a) study population characteristics, (b) cancer type, (c) urine preanalytics, (d) analyte class, (e) isolation method, (f) detection method, (g) comparator used, (h) biomarker type, (i) conclusion and (j) sensitivity and specificity. The search query identified 7835 records, of which 924 unique publications remained after screening the title, abstract and full text. Our analysis demonstrated that many publications did not report information about the preanalytical parameters of their urine samples, even though several other studies have shown the importance of standardization of sample handling. Interestingly, it was noted that urine is used for many cancer types and not just cancers originating from the urogenital tract. Many different types of relevant analytes have been shown to be found in urine. Additionally, future considerations and recommendations are discussed: (a) the heterogeneous nature of urine, (b) the need for standardized practice protocols and (c) the road toward the clinic. Urine is an emerging liquid biopsy with broad applicability in different analytes and several cancer types. However, standard practice protocols for sample handling and processing would help to elaborate the clinical utility of urine in cancer research, detection and disease monitoring.

The importance of pseudouridylation: human disorders related to the fifth nucleoside

Pseudouridylation is one of the most abundant RNA modifications in eukaryotes, making pseudouridine known as the "fifth nucleoside." This highly conserved alteration affects all non-coding and coding RNA types. Its role and importance have been increasingly widely researched, especially considering that its absence or damage leads to serious hereditary diseases. Here, we summarize the human genetic disorders described to date that are related to the participants of the pseudouridylation process.

Overall metabolic network analysis of urine in hyperlipidemic rats treated with Bidens bipinnata L

Hyperlipidemia has been highlighted as one of the most prominent and global chronic conditions nowadays. Bidens bipinnata L. (BBL), a folk medicine in contemporary China, has efficacy in the treatment of hyperlipidemia (HLP) in China. Although some physiological and pathological function parameters of hyperlipidemia have been investigated, little information about the changes in small metabolites in biofluids has been reported. In the present study, global metabolic profiling with high-performance liquid chromatography-linear ion trap/Orbitrap high-resolution mass spectrometry (HPLC-LTQ/Orbitrap MS) combined with a pattern recognition method was performed to discover the underlying lipid-regulating mechanisms of BBL on hyperlipidemic rats induced by high-fat diet (HFD). The total of four metabolites, up- or down-regulated (p < 0.05 or 0.01), were identified and contributed to the progression of hyperlipidemia. These promising identified biomarkers underpin the metabolic pathway, including glyoxylate and dicarboxylate metabolism, the TCA cycle, sphingolipid metabolism and purine metabolism. They are disturbed in hyperlipidemic rats, and are identified using pathway analysis with MetPA. The altered metabolite indices could be regulated closer to normal levels after BBL intervention. The results demonstrated that urinary metabolomics is a powerful tool in the clinical diagnosis and treatment of hyperlipidemia to provide information on changes in metabolite pathways.

Metabolomics combined with proteomics provides a novel interpretation of the compound differences among Chinese tea cultivars (Camellia sinensis var. sinensis) with different manufacturing suitabilities

Different tea cultivars differ in their manufacturing suitability. In this study, metabolomics and proteomics were applied to investigate the metabolite and protein differences in fresh leaves from 23 Chinese tea cultivars suitable for manufacturing green, white, oolong, and black teas. The combined analysis revealed 115 differential metabolites and significant differences in the biosynthesis pathways for amino acids, phenylpropanoids, flavonoids, and terpenoids, and in the peroxidases abundances among these four groups. Green tea cultivars had higher abundances of amino acids and amino acids biosynthesis-related enzymes but lower abundances of flavanols and flavonoids biosynthesis-related enzymes. Black tea cultivars presented higher abundances of flavanols, flavanol-O-glycosides, flavonoids biosynthesis-related enzymes, and peroxidases. Oolong tea cultivars showed higher abundances of enzymes involved in terpenoids biosynthesis. Our study provides a novel interpretation of the manufacturing suitability of tea cultivars from the perspective of both metabolites and proteins and will be helpful for cultivar breeding.

OUP accepted manuscript

Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.

Pseudouridine as a novel biomarker in prostate cancer

Epitranscriptomic analysis has recently led to the profiling of modified nucleosides in cancer cell biological matrices, helping to elucidate their functional roles in cancer and reigniting interest in exploring their use as potential markers of cancer development and progression. Pseudouridine, one of the most well-known and the most abundant of the RNA nucleotide modifications, is the C5-glycoside isomer of uridine and its distinctive physiochemical properties allows it to perform many essential functions. Pseudouridine functionally (a) confers rigidity to local RNA structure by enhancing RNA stacking, engaging in a cooperative effect on neighboring nucleosides that overall contributes to RNA stabilization (b) refines the structure of tRNAs, which influences their decoding activity (c) facilitates the accuracy of decoding and proofreading during translation and efficiency of peptide bond formation, thus collectively improving the fidelity of protein biosynthesis and (e) dynamically regulates mRNA coding and translation. Biochemical synthesis of pseudouridine is carried out by pseudouridine synthases. In this review we discuss the evidence supporting an association between elevated pseudouridine levels with the incidence and progression of human prostate cancer and the translational significance of the value of this modified nucleotide as a novel biomarker in prostate cancer progression to advanced disease.

Serum metabonomics coupled with HPLC-LTQ/orbitrap MS and multivariate data analysis on the ameliorative effects of Bidens bipinnata L. in hyperlipidemic rats

ETHNOPHARMACOLOGICAL RELEVANCE

Hyperlipidemia (HLP) is a prevalence chronic cardiovascular disease, which is treated by traditional Chinese medicine (TCM) in China. More and more attention has been paid to the application of metabolomics in the study of TCM. Bidens bipinnata L. (BBL), a folk medicine in contemporary China, has the efficacy in the treatment of hyperlipidemia (HLP) in China. However, little is known of the underlying mechanism of BBL. This research aimed to investigate ameliorative effects of BBL on hyperlipidemic rats and explore the mechanism by metabolomics method.

MATERIALS AND METHODS

Hyperlipidemic rats were established by high fat diet (HFD). Biochemical assay was used to evaluate the efficacy of BBL. A metabolomics approach based on high performance liquid chromatography-linear ion trap/orbitrap high-resolution mass spectrometry (HPLC-LTQ/orbitrap MS) was performed to analyze the serum biomarkers from model group, control group and BBL group. Principle component analysis (PCA) and partial least-squares discriminate analysis (PLS-DA) were utilized to identify differences of metabolic profiles in rats among the three groups. In order to identify possible pathways that were affected by HLP, the identified endogenous metabolites were analyzed by using MetaboAnalyst. In the network pharmacology study, our research group found that PPAR signaling pathway was the most important pathway of BBL in the treatment of HLP. Then, it was found that changes in the major metabolic pathways would affect the PPAR signaling pathway through comprehensive analysis based on KEGG database. Therefore, the expression of key genes in the PPAR signaling pathway was detected by real-time quantitative fluorescence PCR (RT-qPCR).

RESULTS

Six metabolites, which showed a significantly restoring trend from HLP to normal condition, were regarded as potential biomarkers of BBL treatment. The levels of phosphorylcholine, mevalonic acid and leukotriene B₄ (LTB₄) increased significantly (P < 0.01) in hyperlipidemic rats, while the levels of linoleic acid, arachidonic acid (AA) and lysophosphatidylcholine (18:0) (Lyso PC (18:0)) decreased significantly (P < 0.01) in comparison with control rats. Those endogenous metabolites were chiefly involved in linoleic acid metabolism, AA metabolism and terpenoid backbone biosynthesis. According to the results of RT-qPCR analysis, the mRNA expressions of PPAR α, PPAR β and PPARγ in model group were difference compared with control group. And the expression difference could be regulated closer to normal level after BBL intervention.

CONCLUSIONS

The results of biochemical assay, serum metabolic pattern and RT-qPCR analysis showed that BBL could exert a significant improvement on lipid levels, liver function, renal function, as well as the mRNA expression level of PPAR signaling pathway.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model

High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70-80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.

Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art

Liquid chromatography (LC) hyphenated to mass spectrometry is currently the most widely used means of determining metabolic phenotypes via both untargeted and targeted analysis. At present a range of analytical separations, including reversed-phase, hydrophilic interaction and ion-pair LC are employed to maximise metabolome coverage with ultra (high) performance liquid chromatography (UHPLC) increasingly displacing conventional high performance liquid chromatography because of the need for short analysis times and high peak capacity in such applications. However, it is widely recognized that these methodologies do not entirely solve the problems facing researchers trying to perform comprehensive metabolic phenotyping and in addition to these "routine" approaches there are continuing investigations of alternative separation methods including 2-dimensional/multi column approaches. These involve either new stationary phases or multidimensional combinations of the more conventional materials currently used, as well as application of miniaturization or "new" approaches such as supercritical HP and UHP- chromatographic separations. There is also a considerable amount of interest in the combination of chromatographic and ion mobility separations, with the latter providing both an increase in resolution and the potential to provide additional structural information via the determination of molecular collision cross section data. However, key problems remain to be solved including ensuring quality, comparability across different laboratories and the ever present difficulty of identifying unknowns.

Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges

BACKGROUND

The development of effective screening methods for early cancer detection is one of the foremost challenges facing modern cancer research. Urinary metabolomics has recently emerged as a potentially transformative approach to cancer biomarker discovery owing to its noninvasive sampling characteristics and robust analytical feasibility.

OBJECTIVE

To provide an overview of new developments in urinary metabolomics, cover the most promising aspects of hyphenated techniques in untargeted and targeted metabolomics, and to discuss technical and clinical limitations in addition to the emerging challenges in the field of urinary metabolomics and its application to cancer biomarker discovery.

METHODS

A systematic review of research conducted in the past five years on the application of urinary metabolomics to cancer biomarker discovery was performed. Given the breadth of this topic, our review focused on the five most widely studied cancers employing urinary metabolomics approaches, including lung, breast, bladder, prostate, and ovarian cancers.

RESULTS

As an extension of conventional metabolomics, urinary metabolomics has benefitted from recent technological developments in nuclear magnetic resonance, mass spectrometry, gas and liquid chromatography, and capillary electrophoresis that have improved urine metabolome coverage and analytical reproducibility. Extensive metabolic profiling in urine has revealed a significant number of altered metabolic pathways and putative biomarkers, including pteridines, modified nucleosides, and acylcarnitines, that have been associated with cancer development and progression.

CONCLUSION

Urinary metabolomics presents a transformative new approach toward cancer biomarker discovery with high translational capacity to early cancer screening.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography-mass spectrometry (LC-MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online ( XCMSOnline.scripps.edu ).

Metabolomics Analysis Reveals that Ethylene and Methyl Jasmonate Regulate Different Branch Pathways to Promote the Accumulation of Terpenoid Indole Alkaloids in Catharanthus roseus

The medicinal plant Catharanthus roseus accumulates large numbers of terpenoid indole alkaloids (TIAs), including the pharmaceutically important vinblastine, vincristine, ajmalicine, and serpentine. The phytohormone ethylene or methyl jasmonate (MeJA) can markedly enhance alkaloid accumulation. The interaction between ethylene or MeJA in the regulation of TIA biosynthesis in C. roseus is unknown. Here, a metabolomics platform is reported that is based on liquid chromatography (LC) coupled with time-of-flight mass spectrometry to study candidate components for TIA biosynthesis, which is controlled by ethylene or MeJA in C. roseus. Multivariate analysis identified 16 potential metabolites mostly associated with TIA metabolic pathways and seven targeted metabolites, outlining the TIA biosynthesis metabolic networks controlled by ethylene or MeJA. Interestingly, ethylene and MeJA regulate the 2-C-methyl-d-erythritol 4-phosphate (MEP) and acetate-mevalonate (MVA) pathways through AACT and HMGS and through DXS, respectively, to induce TIA biosynthesis in C. roseus. Overall, both nontargeted and targeted metabolomics, as well as transcript analysis, were used to reveal that MeJA and ethylene control different metabolic networks to induce TIA biosynthesis.

Metabolomics-A Promising Approach to Pituitary Adenomas

Background: Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. Objectives: To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA). Methods: A systematic review was conducted via PubMed, Web of Science Core Collection and Scopus databases, summarizing studies that have described metabolomic aspects of PA. Results: Liquid chromatography tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectrometry, which are traditional techniques employed in metabolomics, suggest amino acids metabolism appears to be primarily altered in PA. N-acetyl aspartate, choline-containing compounds and creatine appear as highly effective in differentiating PA from healthy tissue. Deoxycholic and 4-pyridoxic acids, 3-methyladipate, short chain fatty acids and glucose-6-phosphate unveil metabolite biomarkers in patients with Cushing's disease. Phosphoethanolamine, N-acetyl aspartate and myo-inositol are down regulated in prolactinoma, whereas aspartate, glutamate and glutamine are up regulated. Phosphoethanolamine, taurine, alanine, choline-containing compounds, homocysteine, and methionine were up regulated in unclassified PA across studies. Intraoperative use of ultra high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which allows localization and delineation between functional PA and healthy pituitary tissue, may contribute to achievement of complete tumor resection in addition to preservation of pituitary cell lines and vasopressin secretory cells, thus avoiding postoperative diabetes insipidus. Conclusion: Implementation of ultra high performance metabolomics analysis techniques in the study of PA will significantly improve diagnosis and, potentially, the therapeutic approach, by identifying highly specific disease biomarkers in addition to novel molecular pathogenic mechanisms. Ultra high mass resolution MALDI-MSI emerges as a helpful clinical tool in the neurosurgical treatment of pituitary tumors. Therefore, metabolomics appears to be a science with a promising prospect in the sphere of PA, and a starting point in pituitary care.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Aptamer-based trapping of phytosphingosine in urine samples

Usually, small molecules like single metabolites used in clinical diagnostic can be quantified by instrumental approaches like LC-MS or bioanalytical techniques using antibodies or aptamers as selective receptors. The present work comprises the generation of aptamers with an affinity towards the medically relevant metabolite phytosphingosine via the previously reported just in time-Selection approach (Hünniger et al., 2014). The whole approach could be seen as a proof of concept to extend the existing just in time-Selection protocol for selection towards small molecules with dissociation constants in the low nanomolar range. Moreover it is conceivable that the shown methods could be quickly adapted to further scopes. Aptamers could be applied for clean-up or concentration processes prior to further analysis. As an example, we used the selected aptamers towards phytosphingosine bound to magnetic particles for affinity enrichment in both selection buffer and urine samples. As an outcome, enrichment factors of up to 9-fold (selection buffer)/4-fold (urine samples) were achieved by this approach.

HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS

Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

INTRODUCTION

Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

OBJECTIVES

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

METHODS

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

RESULTS

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

CONCLUSION

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.

UHPLC-MS-based metabolomics analysis on mice bearing neoplasm (H22) for hispidulin

Although some physiological and pathological function parameters of hepatitis and liver cancer have been investigated in relation to hispidulin (5,7,4'-trihydroxy-6-methoxyflavone), the changes of small metabolites in biofluids have been reported rarely. Recent research has shown that metabolic profiling with ultra-high-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry (UHPLC-QTOF/MS) coupled with multivariate statistical analysis provides a good understanding of hispidulin effects on mice vaccinated intraperitoneally with H22 tumor cells. Twenty-five potential biomarkers, up- or down-regulated (P<0.05 or 0.01), were identified, and 17 metabolic pathways were constructed. These potential biomarkers underpin the metabolic pathways, which are disturbed in the mice bearing neoplasm (H22). These pathways include pantothenate and CoA biosynthesis; glycine, serine and threonine metabolism; nicotinate and nicotinamide metabolism; steroid hormone biosynthesis; pyrimidine metabolism; and glyoxylate and dicarboxylate metabolism. Furthermore, 4-phosphopantothenoylcysteine, glycine, niacinamide, cortisol, uracil and 5-thymidylic acid are potential biomarkers that may explain the link between hispidulin and the metabolism of mice bearing neoplasm (H22). Most of the potential biomarkers related to the function of TCA (tricarboxylic acid cycle). The rise of potential biomarkers in the drug groups promoted the up-regulation of TCA cycle compared with the model group.

The identification of metabolic disturbances in the prefrontal cortex of the chronic restraint stress rat model of depression

Major depressive disorder, with serious impairment in cognitive and social functioning, is a complex psychiatric disorder characterized by pervasive and persistent low mood and a loss of interest or pleasure. However, the underlying molecular mechanisms of depression remain largely unknown. In this study, we used a non-targeted metabolomics approach based on gas chromatography-mass spectrometry of the prefrontal cortex in chronic restraint stress (CRS)-treated rats. CRS was induced in the stress group by restraining rats in a plastic restrainer for 6h every day. This stress paradigm continued for 21 days. Body weight measurement and behavior tests were applied, including the sucrose preference test for anhedonia, the forced swimming test for despair-like behavior, and open field test and the elevated plus-maze to test for anxiety-like behaviors in rats after CRS. Differentially expressed metabolites associated with CRS-treated rats were identified by combining multivariate and univariate statistical analysis and corrected for multiple testing using the Benjamini-Hochberg procedure. A heat map of differential metabolites was constructed using Matlab. Ingenuity Pathways Analysis was applied to identify the predicted pathways and biological functions relevant to the bio-molecules of interest. Our findings showed that CRS induces depression-like behaviors and not anxiety-like behaviors. Thirty-six metabolites were identified as potential depression biomarkers involved in amino acid metabolism, energy metabolism and lipid metabolism, as well as a disturbance in neurotransmitters. Consequently, this study provides useful insights into the molecular mechanisms of depression.

Biomarkers in bladder cancer: A metabolomic approach using in vitro and ex vivo model systems

Metabolomics has recently proved to be useful in the area of biomarker discovery for cancers in which early diagnostic and prognostic biomarkers are urgently needed, as is the case of bladder cancer (BC). This article presents a comprehensive review of the literature on the metabolomic studies on BC, highlighting metabolic pathways perturbed in this disease and the altered metabolites as potential biomarkers for BC detection. Current disease model systems used in the study of BC metabolome include in vitro-cultured cancer cells, ex vivo neoplastic bladder tissues and biological fluids, mainly urine but also blood serum/plasma, from BC patients. The major advantages and drawbacks of each model system are discussed. Based on available data, it seems that BC metabolic signature is mainly characterized by alterations in metabolites related to energy metabolic pathways, particularly glycolysis, amino acid and fatty acid metabolism, known to be crucial for cell proliferation, as well as glutathione metabolism, known to be determinant in maintaining cellular redox balance. In addition, purine and pyrimidine metabolism as well as carnitine species were found to be altered in BC. Finally, it is emphasized that, despite the progress made in respect to novel biomarkers for BC diagnosis, there are still some challenges and limitations that should be addressed in future metabolomic studies to ensure their translatability to clinical practice.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Proteomics in cancer biomarkers discovery: challenges and applications

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.

A metabolomic approach to identifying platinum resistance in ovarian cancer

BACKGROUND

Acquisition of metabolic alterations has been shown to be essential for the unremitting growth of cancer, yet the relation of such alterations to chemosensitivity has not been investigated. In the present study our aim was to identify the metabolic alterations that are specifically associated with platinum resistance in ovarian cancer. A global metabolic analysis of the A2780 platinum-sensitive and its platinum-resistant derivative C200 ovarian cancer cell line was performed utilizing ultra-high performance liquid chromatography/mass spectroscopy and gas chromatography/mass spectroscopy. Per-metabolite comparisons were made between cell lines and an interpretive analysis was carried out using the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic library and the Ingenuity exogenous molecule library.

RESULTS

We observed 288 identified metabolites, of which 179 were found to be significantly different (t-test p < 0.05) between A2780 and C200 cells. Of these, 70 had increased and 109 had decreased levels in platinum resistant C200 cells. The top altered KEGG pathways based on number or impact of alterations involved the cysteine and methionine metabolism. An Ingenuity Pathway Analysis also revealed that the methionine degradation super-pathway and cysteine biosynthesis are the top two canonical pathways affected. The highest scoring network of altered metabolites was related to carbohydrate metabolism, energy production, and small molecule biochemistry. Compilation of KEGG analysis and the common network molecules revealed methionine and associated pathways of glutathione synthesis and polyamine biosynthesis to be most significantly altered.

CONCLUSION

Our findings disclose that the chemoresistant C200 ovarian cancer cells have distinct metabolic alterations that may contribute to its platinum resistance. This distinct metabolic profile of platinum resistance is a first step towards biomarker development for the detection of chemoresistant disease and metabolism-based drug targets specific for chemoresistant tumors.

A short review of applications of liquid chromatography mass spectrometry based metabolomics techniques to the analysis of human urine

Mass spectrometry based metabolomics profiling.

Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery

Hepatocellular carcinoma (HCC) is one of the pestilent malignancies leading to cancer-related death. Discovering effective biomarkers for HCC diagnosis is an urgent demand. To identify potential metabolite biomarkers, we developed a urinary pseudotargeted method based on liquid chromatography-hybrid triple quadrupole linear ion trap mass spectrometry (LC-QTRAP MS). Compared with nontargeted method, the pseudotargeted method can achieve better data quality, which benefits differential metabolites discovery. The established method was applied to cirrhosis (CIR) and HCC investigation. It was found that urinary nucleosides, bile acids, citric acid, and several amino acids were significantly changed in liver disease groups compared with the controls, featuring the dysregulation of purine metabolism, energy metabolism, and amino metabolism in liver diseases. Furthermore, some metabolites such as cyclic adenosine monophosphate, glutamine, and short- and medium-chain acylcarnitines were the differential metabolites of HCC and CIR. On the basis of binary logistic regression, butyrylcarnitine (carnitine C4:0) and hydantoin-5-propionic acid were defined as combinational markers to distinguish HCC from CIR. The area under curve was 0.786 and 0.773 for discovery stage and validation stage samples, respectively. These data show that the established pseudotargeted method is a complementary one of targeted and nontargeted methods for metabolomics study.

Metabolomic investigation of human diseases biomarkers by CE and LC coupled to MS

Metabolomics is one of the most recent trends in the "omics" era that investigates the end products of an organism activity, that is, all metabolites in a biological system, which are small molecules (less than 1000 Da) from different chemical classes. Metabolomics represents a tool to assess the biochemical activity of a living system through the analysis of substrates and products processed during the metabolism. The analysis of the metabolic profile (nontargeted analysis, i.e. a comparison between samples profiles of individuals) and of specific metabolites (targeted analysis, which quantifies a selected group of metabolites) in biological samples provides an insight into the metabolic state and the biochemical processes of the organism and, therefore, may indicate the onset and the stage of different diseases. An early and accurate diagnosis is essential for successful treatment and probable cure of most illnesses; hence, the investigation of metabolites as disease biomarkers has increased considerably in recent years. This review aims to present the most relevant works that address the nontargeted and targeted analysis of metabolites in different diseases for the past 10 years, including kidney and neurological disorders, cardiovascular diseases, diabetes, and cancer, using CE and LC coupled with the accurate detection of mass spectroscopy.

Metabolomics in cancer biomarker discovery: current trends and future perspectives

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.

Sarcosine as a potential prostate cancer biomarker--a review

Prostate cancer (CaP) is the most common type of tumour disease in men. Early diagnosis of cancer of the prostate is very important, because the sooner the cancer is detected, the better it is treated. According to that fact, there is great interest in the finding of new markers including amino acids, proteins or nucleic acids. Prostate specific antigen (PSA) is commonly used and is the most important biomarker of CaP. This marker can only be detected in blood and its sensitivity is approximately 80%. Moreover, early stages cannot be diagnosed using this protein. Currently, there does not exist a test for diagnosis of early stages of prostate cancer. This fact motivates us to find markers sensitive to the early stages of CaP, which are easily detected in body fluids including urine. A potential is therefore attributed to the non-protein amino acid sarcosine, which is generated by glycine-N-methyltransferase in its biochemical cycle. In this review, we summarize analytical methods for quantification of sarcosine as a CaP marker. Moreover, pathways of the connection of synthesis of sarcosine and CaP development are discussed.