Serum metabolomics as a novel diagnostic approach for gastrointestinal cancer

Conventional tumor markers are unsuitable for detecting carcinoma at an early stage and lack clinical efficacy and utility. In this study, we attempted to investigate the differences in serum metabolite profiles of gastrointestinal cancers and healthy volunteers using a metabolomic approach and searched for sensitive and specific metabolomic biomarker candidates. Human serum samples were obtained esophageal (n = 15), gastric (n = 11), and colorectal (n = 12) cancer patients and healthy volunteers (n = 12). A model for evaluating metabolomic biomarker candidates was constructed using multiple classification analysis, and the results were assessed with receiver operating characteristic curves. Among the 58 metabolites, the levels of nine, five and 12 metabolites were significantly changed in the esophageal, gastric and colorectal cancer patients, respectively, compared with the healthy volunteers. Multiple classification analysis revealed that the variations in the levels of malonic acid and l ‐serine largely contributed to the separation of esophageal cancer; gastric cancer was characterized by changes in the levels of 3‐hydroxypropionic acid and pyruvic acid; and l ‐alanine, glucuronoic lactone and l ‐glutamine contributed to the separation of colorectal cancer. Our approach revealed that some metabolites are more sensitive for detecting gastrointestinal cancer than conventional biomarkers. Our study supports the potential of metabolomics as an early diagnostic tool for cancer. Copyright © 2011 John Wiley & Sons, Ltd.     

Uncovering the anticancer mechanism of petroleum extracts of Farfarae Flos against Lewis lung cancer by metabolomics and network pharmacology analysis

Lung cancer shows the highest incidence rate in the world. Thus, it has become increasingly important to find therapeutic drugs to treat lung cancer. Farfarae Flos (FF) has been used in traditional Chinese medicine to treat pulmonary diseases such as cough, bronchitis and asthmatic disorders. In this study, the anti-proliferation effects of petroleum extracts of FF (PEFF) on Lewis lung cancer cells and the corresponding mechanisms were studied using cell metabolomics. Fifteen differential metabolites in the cell extracts and the corresponding medium related to the anti-proliferation effect of PEFF were identified, which were probably involved in pyruvate metabolism and glycine, serine and threonine metabolism. For the cellular uptake compounds in PEFF, six metabolites derived from two prototype compounds were also tentatively identified by UHPLC-Q-Orbitrap high-resolution MS. Network pharmacology analysis demonstrated that the anti-proliferation mechanism of PEFF was also probably related to the target genes, including, Aurora-A, glutathione S-transferase Mu 1 (GSTM1), glutathione S-transferase P 1 (GSTP1), progesterone receptor and heme oxygenase-1 (HO-1), and further associated with the proteoglycans and PI3K/Akt signaling pathway. Cell metabolomics and network pharmacology analysis provided a holistic method to investigate the anti-proliferation mechanisms of PEFF. However, further studies were still needed to validate the potential target genes, pathways and active compounds in PEFF.     

Separation strategies for untargeted metabolomics

Metabolomics has rapidly become a profiling technique of choice for biomarker elucidation and molecular diagnostics in addition to studies focused on understanding disease pathogenesis. Key to the success of metabolomics in these areas has been the techniques to separate and analyze the chemically diverse group of compounds comprising the metabolome by using global and untargeted approaches. Untargeted metabolomic efforts have the goal of examining as many metabolites as possible simultaneously and most frequently use an LC/MS-based approach. Here, the importance of LC in an untargeted metabolomic workflow is outlined and separation strategies for optimization are reviewed within the context of these criteria.     

Data-processing strategies for metabolomics studies

Metabolomics studies aim at a better understanding of biochemical processes by studying relations between metabolites and between metabolites and other types of information (e.g., sensory and phenotypic features). The objectives of these studies are diverse, but the types of data generated and the methods for extracting information from the data and analysing the data are similar. Besides instrumental analysis tools, various data-analysis tools are needed to extract this relevant information. The entire data-processing workflow is complex and has many steps. For a comprehensive overview, we cover the entire workflow of metabolomics studies, starting from experimental design and sample-size determination to tools that can aid in biological interpretation. We include illustrative examples and discuss the problems that have to be dealt with in data analysis in metabolomics. We also discuss where the challenges are for developing new methods and tailor-made quantitative strategies.     

Data processing for mass spectrometry-based metabolomics

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.     

Quantitative NMR for bioanalysis and metabolomics

Over the last several decades, significant technical and experimental advances have made quantitative nuclear magnetic resonance (qNMR) a valuable analytical tool for quantitative measurements on a wide variety of samples. In particular, qNMR has emerged as an important method for metabolomics studies where it is used for interrogation of large sets of biological samples and the resulting spectra are treated with multivariate statistical analysis methods. In this review, recent developments in instrumentation and pulse sequences will be discussed as well as the practical considerations necessary for acquisition of quantitative NMR experiments with an emphasis on their use for bioanalysis. Recent examples of the application of qNMR for metabolomics/metabonomics studies, the characterization of biologicals such as heparin, antibodies, and vaccines, and the analysis of botanical natural products will be presented and the future directions of qNMR discussed.     

A handheld device for potential point-of-care screening of cancer

A simple handheld device based on the fluorescence analysis of 3,6-bis(1-methyl-4-vinylpyridinium)carbazole diiodide (BMVC) stained cells was established for routine screening and potentially for early detection of cancer cells at extremely low cost. Flow cytometry assay further supported the utility of this simple device, where a preliminary study of tissue biopsy showed highly encouraging results.     

Missing value imputation strategies for metabolomics data

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros.     

Analysis of breath samples for lung cancer survival

Analyses of exhaled air by means of electronic noses offer a large diagnostic potential. Such analyses are non-invasive; samples can also be easily obtained from severely ill patients and repeated within short intervals. Lung cancer is the most deadly malignant tumor worldwide, and monitoring of lung cancer progression is of great importance and may help to decide best therapy. In this report, twenty-two patients with diagnosed lung cancer and ten healthy volunteers were studied using breath samples collected several times at certain intervals and analysed by an electronic nose. The samples were divided into three sub-groups; group d for survivor less than one year, group s for survivor more than a year and group h for the healthy volunteers. Prediction models based on partial least square and artificial neural nets could not classify the collected groups d, s and h, but separated well group d from group h. Using artificial neural net, group d could be separated from group s. Excellent predictions and stable models of survival day for group d were obtained, both based on partial least square and artificial neural nets, with correlation coefficients 0.981 and 0.985, respectively. Finally, the importance of consecutive measurements was shown.     

Targeted therapies for advanced non-small cell lung cancer

The incorporation of targeted agents has considerably improved the management of patients with advanced non-small cell lung cancer (NSCLC) over the last years. The main targets include the epidermal growth factor receptor (EGFR) and the vascular endothelial growth factor (VEGF). Currently available agents with established role in NSCLC include the anti-EGFR tyrosine-kinase inhibitors (TKIs) erlotinib/gefitinib and the anti-VEGF monoclonal antibody bevacizumab. Moreover, several other agents targeting critical pathways in lung carcinogenesis are currently under preclinical or clinical evaluation. This review presents an update on the role of targeted agents in advanced NSCLC. In addition, we present the main clinical studies investigating the activity of these agents in NSCLC and we provide recent data with respect to future therapeutic strategies.     

基于代谢组学的食品真实属性鉴别研究进展

随着食品工业的快速发展以及生活水平的提高,人们对食品的质量安全提出了更高的要求,市场上的食品掺假造假现象也日益受到社会的关注。目前,主要的食品掺假手段包括假冒物种及品种、冒充或虚标原产地、原料品质以次充好、掺入杂劣质及违禁原料等。因此亟须建立切实有效的食品真实属性鉴别方法。近几年来,国内外一些学者开始将代谢组学研究平台应用于解决食品安全问题的研究,对食品中尽可能多的代谢产物从整体角度进行定性定量分析,为食品真实属性鉴别研究提供了一种新的研究工具。该文综述了基于代谢组学的食品物种及品种鉴别、产地溯源、品质分级和掺假掺杂识别等真实属性鉴别研究,为进一步保证食品质量安全、保障消费者利益提供了技术支撑。     

Expanded metabolomics approach to profiling endogenous carbohydrates in the serum of ovarian cancer patients

We applied hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry to the quantitative analysis of serum from 58 women, including ovarian cancer patients, ovarian benign tumor patients, and healthy controls. All of these ovarian cancer and ovarian benign tumor patients have elevated cancer antigen 125, which makes them clinically difficult to differentiate the malignant from the benign. All of the 16 endogenous carbohydrates were quantitatively detected in the human sera, of which, eight endogenous carbohydrates were significantly different (P‐value < 0.05) between the ovarian cancer and healthy control. According to the receiver operating characteristic curve analysis, arabitol was the most potentially specific biomarker for discriminating ovarian cancer from healthy control, having an area under the curve of 0.911. A panel of metabolite markers composed of maltose, maltotriose, raffinose, and mannitol was selected, which was able to discriminate the ovarian cancer from the benign ovarian tumor counterparts, with an area under concentration‐time curve value of 0.832. Endogenous carbohydrates in the expanded metabolomics approach after the global metabolic profiling are characterized and are potential biomarkers for the early diagnosis of ovarian cancer.     

基于拟靶向液相色谱-质谱联用的胃癌患者血清代谢组分析

胃癌是一种高发的恶性肿瘤,是癌症相关死亡的第二大病因。早期筛查是提高患者生存率的有效手段,但目前临床上尚缺乏实现胃癌无创筛检的可靠标志物。本研究采用了基于液相色谱-质谱联用的拟靶向代谢组学方法分析了20例胃癌患者及40例正常人血清代谢组,以期发现新的潜在代谢标志物。代谢组数据的主成分分析和偏最小二乘法数据分析结果显示,胃癌患者与健康人群的血清代谢组存在明显的差异,结合非参数检验进一步筛选并定性出57个差异代谢物。其中二氢胆固醇经验证组样本验证,具有成为胃癌代谢标志物的潜力。本研究在发现胃癌的潜在代谢标志物的同时,也为胃癌患者代谢分型提供了重要的科学依据。     

Assessment of two complementary liquid chromatography coupled to high resolution mass spectrometry metabolomics strategies for the screening of anabolic steroid treatment in calves

Anabolic steroids are banned in food producing livestock in Europe. Efficient methods based on mass spectrometry detection have been developed to ensure the control of such veterinary drug residues. Nevertheless, the use of "cocktails" composed of mixtures of low amounts of several substances as well as the synthesis of new compounds of unknown structure prevent efficient prevention. New analytical tools able to detect such abuse are today mandatory. In this context, metabolomics may represent new emerging strategies for investigating the global physiological effects associated to a family of substances and therefore, to suspect the administration of steroids. The purpose of the present study was to set up, assess and compare two complementary mass spectrometry-based metabolomic strategies as new tools to screen for steroid abuse in cattle and demonstrate the feasibility of such approaches. The protocols were developed in two European laboratories in charge of residues analysis in the field of food safety. Apart from sample preparation, the global process was different in both laboratories from LC-HRMS fingerprinting to multivariate data analysis through data processing and involved both LC-Orbitrap-XCMS and UPLC-ToF-MS-MetAlign strategies. The reproducibility of both sample preparation and MS measurements were assessed in order to guarantee that any differences in the acquired fingerprints were not caused by analytical variability but reflect metabolome modifications upon steroids administration. The protocols were then applied to urine samples collected on a large group of animals consisting of 12 control calves and 12 calves administrated with a mixture of 17β-estradiol 3-benzoate and 17β-nandrolone laureate esters according to a protocol reflecting likely illegal practices. The modifications in urine profiles as indicators of steroid administration have been evaluated in this context and proved the suitability of the approach for discriminating anabolic treated animals from control ones. Such an approach may therefore open a new way for the screening of anabolic steroid administration through targeted monitoring of relevant biomarkers highlighted as a result of the metabolomics study.     

Chromatographic fingerprinting and metabolomics for quality control of TCM

Chromatographic fingerprinting technique of traditional Chinese medicine (TCM) has proved to be a comprehensive strategy for assessing the intact quality of herbal medicine. In general, one could use the chromatographic techniques to obtain a relatively complete picture of herbal medicines, which are in common called chromatographic fingerprints of herbal medicines to represent the so-called phytoequivalence. Based on this, the features of chromatographic fingerprints of herbal medicines have been discussed in some detail. The technique based on chromatographic fingerprinting is essentially a kind of high-throughput and integral tools to explore the complexity of herbal medicines. In order to further control the comprehensive quality of TCMs, some new strategies are proposed to trace the chemical changes of chromatographic fingerprints both in product processing and/or after their administration by modern chromatographic techniques and chemometrics. Combined with metabolomics, it seems possible for one to reveal the working mechanism of TCMs and to further control their intrinsic quality. Finally, the intensive study of chromatographic fingerprinting coupled with multivariate analysis tools developed in bioinformatics and chemometrics are emphasized in order to achieve the aim to reveal the working mechanisms of TCMs and to further control and strengthen TCMs' intrinsic quality in a comprehensive manner.     

Intact living-cell electrolaunching ionization mass spectrometry for single-cell metabolomics

While single-cell mass spectrometry can reveal cellular heterogeneity and the molecular mechanisms of intracellular biochemical reactions, its application is limited by the insufficient detection sensitivity resulting from matrix interference and sample dilution. Herein, we propose an intact living-cell electrolaunching ionization mass spectrometry (ILCEI-MS) method. A capillary emitter with a narrow-bore, constant-inner-diameter ensures that the entire living cell enters the MS ion-transfer tube. Inlet ionization improves sample utilization, and no solvent is required, preventing sample dilution and matrix interference. Based on these features, the detection sensitivity is greatly improved, and the average signal-to-noise (S/N) ratio is about 20 : 1 of single-cell peaks in the TIC of ILCEI-MS. A high detection throughput of 51 cells per min was achieved by ILCEI-MS for the single-cell metabolic profiling of multiple cell lines, and 368 cellular metabolites were identified. Further, more than 4000 primary single cells digested from the fresh multi-organ tissues of mice were detected by ILCEI-MS, demonstrating its applicability and reliability.

A novel living-cell mass spectrometry method allows a whole cell to enter entirely into the MS inlet and ionize with almost no sample dilution and matrix interference, which greatly improves the sensitivity of single-cell metabolite detection.