Metabolomics: a revolution for novel cancer marker identification

The repertoire of small-molecular-weight substances present in cells, tissue and body fluids are known as the metabolites. The global analysis of metabolites, such as by high-resolution 1H nuclear magnetic resonance spectroscopy and mass spectrometry, is integral to the rapidly expanding field of metabolomics, which is making progress in various diseases. In the area of cancer and metabolic phenotype, the integrated analysis of metabolites may provide a powerful platform for detecting changes related to cancer diagnosis and discovering novel biomarkers. In this review, metabolomics including the technologies in metabolomics research and extracting information from metabolomics datasets are described. Then we discuss the challenges and opportunities in metabolomics for finding metabolic processes in cancer and discovering novel cancer biomarkers. Finally, we assess the clinical applicability of metabolomics.     

Recent advances in metabolomics in neurological disease, and future perspectives

Discovery of clinically relevant biomarkers for diseases has revealed metabolomics has potential advantages that classical diagnostic approaches do not. The great asset of metabolomics is that it enables assessment of global metabolic profiles of biofluids and discovery of biomarkers distinguishing disease status, with the possibility of enhancing clinical diagnostics. Most current clinical chemistry tests rely on old technology, and are neither sensitive nor specific for a particular disease. Clinical diagnosis of major neurological disorders, for example Alzheimer’s disease and Parkinson’s disease, on the basis of current clinical criteria is unsatisfactory. Emerging metabolomics is a powerful technique for discovering novel biomarkers and biochemical pathways to improve diagnosis, and for determination of prognosis and therapy. Identifying multiple novel biomarkers for neurological diseases has been greatly enhanced with recent advances in metabolomics that are more accurate than routine clinical practice. Cerebrospinal fluid (CSF), which is known to be a rich source of small-molecule biomarkers for neurological and neurodegenerative diseases, and is in close contact with diseased areas in neurological disorders, could potentially be used for disease diagnosis. Metabolomics will drive CSF analysis, facilitate and improve the development of disease treatment, and result in great benefits to public health in the long-term. This review covers different aspects of CSF metabolomics and discusses their significance in the postgenomic era, emphasizing the potential importance of endogenous small-molecule metabolites in this emerging field.     

Modern analytical techniques in metabolomics analysis

Metabolomics is the comprehensive assessment of endogenous metabolites and attempts to systematically identify and quantify metabolites from a biological sample. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand disease phenotypes. Metabolites represent a diverse group of low-molecular-weight structures including lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols and carbohydrates, which makes global analysis a difficult challenge. The recent rapid development of a range of analytical platforms, including GC, HPLC, UPLC, CE coupled to MS and NMR spectroscopy, could enable separation, detection, characterization and quantification of such metabolites and related metabolic pathways. Owing to the complexity of the metabolome and the diverse properties of metabolites, no single analytical platform can be applied to detect all metabolites in a biological sample. The combined use of modern instrumental analytical approaches has unravelled the ideal outcomes in metabolomics, and is beneficial to increase the coverage of detected metabolites that can not be achieved by single-analysis techniques. Integrated platforms have been frequently used to provide sensitive and reliable detection of thousands of metabolites in a biofluid sample. Continued development of these analytical platforms will accelerate widespread use and integration of metabolomics into systems biology. Here, the application of each hyphenated technique is discussed and its strengths and limitations are discussed with selected illustrative examples; furthermore, this review comprehensively highlights the role of integrated tools in metabolomic research.     

Bioanalytical methods for metabolomic profiling: Detection of head and neck cancer,including oral cancer

Metabolomics is an emerging field dealing with the measurement and interpretation of small molecular byproducts of biochemical processes, or metabolites, which can be used to generate profiles from biological samples. Promising for use in pathophysiology, metabolomic profiles give the immediate biological state of a sample. These profiles are altered in diseases and are detectable in biological samples, such as tissue, blood, urine, saliva, and others. Most remarkably, metabolic profiles usually are altered before symptoms appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through disease biomarker identification. This application is most prevalent in cancer, such as head and neck cancer (HNC). Metabolomic studies offer avenues to improve on current medical techniques through the application of mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and statistical analysis to determine better biomarkers than those currently known. In this review, we discuss the use of MS and NMR tools for detecting biomarkers in tissue and fluid samples, and the appropriateness of metabolomics in analyzing cancer. Advantages, disadvantages, and recent studies on metabolomic profiling techniques in HNC analysis are also discussed herein.     

NMR‐based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so‐called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements. Copyright © 2013 John Wiley & Sons, Ltd.     

Metabolomics Approaches and Applications in Prostate Cancer Research

Prostate cancer is a leading cause of cancer deaths in men worldwide. Although prostate-specific antigen (PSA) has been extensively used as a serum biomarker to detect prostate cancer, this screening method has suffered from a lack of specificities and sensitivities. The successful prevention and treatment of prostate cancer relies on the early and accurate detection of the disease; therefore, more sensitive biomarkers are urgently needed. Prostate has long been known to exhibit unique metabolite profiles, fortunately, metabolomics, the study of all metabolites produced in the body, can be considered most closely related to a patient’s phenotype. It may provide clinically useful biomarkers applied toward identifying metabolic alterations in prostate cancer and has introduced new insights into the pathology of prostate cancer. This advanced bioanalytic method may now open door for diagnostics. Metabolomics has a great and largely potential in the field of disease, and the analysis of the cancer metabolome to identify novel biomarkers and targets can now be undertaken in many research laboratories. In this review, we take a closer look at the metabolomics in the field of prostate cancer and highlight the interesting publications as references for the interested reader.     

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.     

Using direct infusion mass spectrometry for serum metabolomics in Alzheimer’s disease

Currently, there is no cure for Alzheimer’s disease and early diagnosis is very difficult, since no biomarkers have been established with the necessary reliability and specificity. For the discovery of new biomarkers, the application of omics is emerging, especially metabolomics based on the use of mass spectrometry. In this work, an analytical approach based on direct infusion electrospray mass spectrometry was applied for the first time to blood serum samples in order to elucidate discriminant metabolites. Complementary methodologies of extraction and mass spectrometry analysis were employed for comprehensive metabolic fingerprinting. Finally, the application of multivariate statistical tools allowed us to discriminate Alzheimer patients and healthy controls, and identify some compounds as potential markers of disease. This approach provided a global vision of disease, given that some important metabolic pathways could be studied, such as membrane destabilization processes, oxidative stress, hypometabolism, or neurotransmission alterations. Most remarkable results are the high levels of phospholipids containing saturated fatty acids, respectively, polyunsaturated ones and the high concentration of whole free fatty acids in Alzheimer’s serum samples. Thus, these results represent an interesting approximation to understand the pathogenesis of disease and the identification of potential biomarkers. Graphical Abstract

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Analysis of Serum Metabolomics in Rats with Osteoarthritis by Mass Spectrometry

Osteoarthritis is a common multifactorial chronic disease that occurs in articular cartilage, subchondral bone, and periarticular tissue. The pathogenesis of OA is still unclear. To investigate the differences in serum metabolites between OA and the control group, liquid chromatography/mass spectrometry (LC/MS)-based metabolomics was used. To reveal the pathogenesis of OA, 12 SD male rats were randomly divided into control and OA groups using collagenase to induce OA for modeling, and serum was collected 7 days after modeling for testing. The OA group was distinguished from the control group by principal component analysis and orthogonal partial least squares-discriminant analysis, and six biomarkers were finally identified. These biomarkers were metabolized through tryptophan metabolism, glutamate metabolism, nitrogen metabolism, spermidine metabolism, and fatty acid metabolism pathways. The study identified metabolites that may be altered in OA, suggesting a role in OA through relevant metabolic pathways. Metabolomics, as an important tool for studying disease mechanisms, provides useful information for studying the metabolic mechanisms of OA.     

Ultra-performance liquid chromatography coupled to mass spectrometry as a sensitive and powerful technology for metabolomic studies

Metabolomics is the comprehensive assessment of endogenous metabolites of a biological system. These large-scale analyses of metabolites are intimately bound to advancements in ultra-performance liquid chromatography-electrospray (UPLC) technologies and have emerged in parallel with the development of novel mass analyzers and hyphenated techniques. Recently, the combination of UPLC with MS covers a number of polar metabolites, thus enlarging the number of detected analytes in the widely used separation sciences. This technology has rapidly been accepted by the analytical community and is being gradually applied to various fields such as metabolomics and traditional Chinese medicine (TCM). Given the power of the technology, metabolomics has become increasingly popular in drug development, molecular medicine, traditional medicine and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. Hyphenated UPLC/MS technique is becoming a useful tool in the study of body fluids, represents a promising hyphenated microseparation platform in metabolomics and has a strong potential to contribute to disease diagnosis. This review describes the applications of UPLC/MS in metabolomic research, and comparison role of HPLC/MS, NMR and GC/MS, highlights its advantages and limitations with certain characteristic examples in the life and TCM sciences.     

Serum metabolomics study of patients with allergic rhinitis

Allergic rhinitis (AR) negatively affects the healthy lives of many individuals. Most previous studies on AR focused on the expression of cytokines, with only a few analyzing cytokine expression from a metabolomics viewpoint. Therefore, it is worthwhile to study AR at the metabolic level. Consequently, we aimed to identify differential serum biomarkers by metabolomics. In this study, the orthogonal partial least squares discriminant analysis (OPLS-DA) model was applied to characterize the differences in serum samples collected from patients with AR and healthy volunteers. Ten metabolites (except hexadecanoic acid) were found to be altered significantly (p < .05) in the former group, according to results of principal component analysis and OPLS-DA, indicating that these metabolites could be potential biomarkers. MetaboAnalyst 4.0 and pathway enrichment analysis showed that these changes in metabolites mainly involved three pathways, namely, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, and purine metabolism. Our findings may contribute to a better understanding of the potential pathogenesis mechanisms and provide a metabolic evidence for in-depth studies of AR.     

Measurement of gut microbial metabolites in cardiometabolic health and translational research

The human gut microbiota is a functioning endocrine organ and stands at the intersection between dietary components and health or disease. There are very many microbial metabolites with numerous structures and functions arising from the gut microbial fermentation of foods and become signals for biological communication in the human body. These small molecules can be absorbed and delivered to distant organs through the circulatory system to build the gut–systemic axis. The gut microbial metabolomes are thus believed to play important roles in regulating cardiometabolic health and provide opportunities in mechanistic research and new drug discovery. Measurement of these novel microbial metabolites in clinical samples may serve as a tool for investigating disease biomarkers. In the past decade, the development of untargeted and targeted metabolomics approaches using NMR, LC/MS, and GC/MS has contributed to the exploration of gut microbial metabolomes in cardiometabolic health and disease. Some important targets are currently being translated into clinical applications. In this review article, we introduce an oral carnitine challenge test developed as an example to demonstrate the potential applications in personalized nutrition based on the function of gut microbiota. It is a method taking the gut microbiota as a bioreactor and provides fermentable materials as inputs and measures the outputs of targeted microbial byproducts in the blood or urine. This challenge test may be extended to measure metabolites from microbial fermentation related to other endocrinological or inflammatory diseases. We review current gut metabolome research approaches and propose a gut microbial functional measurement using a challenge test. We suggest that the maturation in measuring gut microbial metabolites may provide an important piece to complete the puzzle of precision medicine.     

Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach

Metabolomics represents an emerging and powerful discipline concerned with the comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Recent development of biomarkers for diagnosis and therapeutic monitoring of liver-stagnation and spleen-deficiency syndrome (LSS)-type disease remains challenging. This study was undertaken to discover novel potential biomarkers for the non-invasive early diagnosis of human LSS. Urine samples which are potentially a rich source of metabolites were collected from patients with LSS, together with healthy control samples. Metabolite profiling was performed by ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry (UPLC-Q-TOF-HDMS) in conjunction with multivariate data analysis and ingenuity pathway analysis that were used to select the metabolites to be used for the non-invasive diagnosis of LSS. Twelve urinary differential metabolites contributing to the complete separation of LSS patients from matched healthy controls were identified involving several key metabolic pathways such as pentose and glucuronate interconversions, ascorbate, aldarate, cysteine, methionine, tyrosine, tryptophan, amino sugar and nucleotide sugar metabolism. More importantly, of the 12 differential metabolites, 4 metabolite markers, prolylhydroxyproline, l-homocystine, 2-octenoylcarnitine and α-N-phenylacetyl-l-glutamine, were effective for the diagnosis of human LSS, with an achieved sensitivity of 93.0%. These results demonstrate that robust metabolomics has the potential as a non-invasive strategy and promising screening tool to evaluate the potential of these metabolites in the early diagnosis of LSS patients and provides new insight into pathophysiological mechanisms.     

Untargeted metabolomic study on the insomnia effect of Suan‐Zao‐Ren decoction in the rat serum and brain using ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry combined with data processing analysis

Insomnia is a common clinical disease that can seriously damage the normal lives of sufferers. Suan‐Zao‐Ren decoction has been used to treat insomnia for a long time. However, the underlying molecular mechanism of Suan‐Zao‐Ren decoction is still not clear. In this study, the nontargeted metabolomics based on high‐resolution mass spectrometry and multiple statistical approaches were initially used to investigate the changes of potential serum and brain biomarkers and metabolic pathways in the insomnia model rat. Principal component analysis‐discriminate analysis indicated that the Suan‐Zao‐Ren decoction treatment improved the metabolic phenotype insomnia. Moreover, the heatmap analysis identified the most important biomarkers involved in insomnia. According to the pathway analysis, phenylalanine metabolism, tryptophan metabolism, and so on were recognized as the most affected metabolic pathways associated with insomnia disease. These findings provided a comprehensive understanding of the regulative effects of Suan‐Zao‐Ren decoction on the host metabolic phenotype of the insomnia rats. Our work demonstrated that the metabolomics approach is a promising tool that could help us to conduct the exploration of the therapeutic effects and mechanism of traditional Chinese medicines.     

Metabolic profiling of serum in patients with cartilage tumours using 1H-NMR spectroscopy: A pilot study

Cartilage-forming lesions include tumours that can vary in severity from benign enchondromas to high-grade malignant chondrosarcomas. Chondrosarcoma is the second most frequent malignant bone tumour, accounting for 20–30% of all malignant bone neoplasms. Surgery is the standard treatment for cartilage tumours (CTs); however, their incidental diagnosis and the difficult differentiation of low-grade lesions like chondrosarcoma grade I from benign entities like enchondroma are challenges for clinical management. In this sense, the search for circulating biomarkers for early detection and prognosis is an ongoing interest. Targeted metabolomics is a powerful tool that can propose potential biomarkers in biological fluids as well as help to discover disturbed metabolic pathways to reveal tumour pathogenesis. In this context, the aim of this study was to investigate the ¹H nuclear magnetic resonance metabolomic serum profile of patients with CTs contrasted with healthy controls. Forty-one metabolites were identified and quantified; the multivariate statistical methods principal component analysis and partial least squares discriminant analysis reveal a clear separation of the CT group, that is, the differential metabolites that were involved in two main metabolic pathways: the taurine and hypotaurine metabolism and synthesis and degradation of ketone bodies. Our results represent preliminary work for emergent serum-based diagnostics or prognostic methods for patients with chondrogenic tumours.     

Metabolic analysis of the antidepressive effects of Yangxinshi Tablet in a vascular depression model in mice

In recent years, vascular depression has become the focus of international attention. Yangxinshi Tablet (YXST) is usually used in cthe linic for the treatment of arrhythmia and heart failure, but we found that it also has antidepressive effects. The objective of the study was to identify biomarkers related to vascular depression in hippocampus and explore the antidepressive effects of YXST on the mouse model. Untargeted metabolomics based on UHPLC‐Q‐TOF/MS was applied to identify significantly differential biomarkers between the model group and control group. Unsupervised principal component analysis (PCA) was used to scan the tendency of groups and partial least squares‐discriminant analysis (PLS‐DA) to distinguish between the vascular depressive mice and the sham. PCA stores showed clear differences in metabolism between the vascular depressive mice and sham groups. The PLS‐DA model exhibited 38 metabolites as the biomarkers to distinguish the vascular depressive mice and the sham. Further, YXST significantly regulated 22 metabolites to normal levels. The results suggested that YXST has a comprehensive antidepressive effect on vascular depression via regulation of multiple metabolic pathways including amino acid, the tricarboxylic acid cycle and phosphoglyceride metabolisms. These findings provide insight into the pathophysiological mechanism underlying vascular depression and the mechanism of YXST.     

基于超高效液相色谱-四极杆飞行时间质谱联用技术的血瘀模型大鼠血浆代谢组学分析

采用盐酸肾上腺素加冰水浴建立急性血瘀大鼠模型，使用超高效液相色谱-四极杆飞行时间质谱（UPLC-Q-TOF/MS）检测空白对照组与血瘀模型组中血浆代谢物，用主成分分析（PCA）、有监督偏最小二乘法判别分析（PLS-DA）及正交偏最小二乘法判别分析（OPLS-DA）对代谢组学数据进行多维统计分析，筛选潜在生物标志物。与对照组相比，在血瘀模型组大鼠血浆中检测出46个差异代谢物，血瘀模型组中乙酰胆碱、N6，N6，N6-三甲基-L-赖氨酸、胞嘧啶、乙酰肉碱等21个代谢物显著上调，吲哚丙酸、LysoPC（14：0）等25个代谢物显著下调，可能与脂质代谢、半乳糖代谢、亚油酸代谢、不饱和脂肪酸生物合成、糖酵解、花生四烯酸代谢等通路有关。代谢产物可作为血瘀证研究中的重要标记物，该研究结果有助于揭示血瘀证的发病机制，可为临床血瘀疾病的诊断及选用药物治疗提供思路，为后续治疗手段提供参考依据。     

基于液相色谱-质谱联用的代谢组学研究中代谢物的结构鉴定进展

基于液相色谱-质谱联用的代谢组学技术因其高效分离能力和高灵敏检测能力已成为生命科学研究的重要手段,但由于缺乏有效的通用标准谱图库,检测到的大量代谢物的结构难以鉴定。这制约了代谢组学覆盖度的提高和生物标志物的发现,造成化学和生物信息的严重丢失,成为代谢组学发展的主要技术瓶颈。随着质谱仪器及计算机技术的进步,基于大气压电离质谱（API-MS）的代谢物结构鉴定技术飞速发展,本文从质谱仪器、代谢物分子结构式判别、数据库及谱图检索以及计算机辅助谱图解析等方面,对代谢物结构鉴定的最新进展进行了综述。     

Metabolomics of lung cancer: Analytical platforms and their applications

Lung cancer is the leading type of cancer worldwide in terms of the number of new cases and is responsible for the largest number of deaths due to poor prognosis and difficult early detection. Due to its ability to detect numerous small molecular metabolites simultaneously, metabolomics has been widely used for the assessment of global metabolic changes in a living organism to discover candidate biomarkers for cancer diagnosis, investigate the development of cancer, and provide insights into the underlying pathophysiology. This review will mainly describe recent developments in lung cancer metabolomics in terms of early‐stage detection, biomarker discovery and mechanism exploration by using nuclear magnetic resonance, liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry, and capillary electrophoresis–mass spectrometry in the last 10 years. The sample collection and metabolite extraction methods are also summarized.