Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer

Metabolomic Comparison of Patients With Colorectal Cancer at Different Anticancer Treatment Stages

BACKGROUND

The difficulties of early diagnosis of colorectal cancer (CRC) result in a high mortality rate. The ability to predict the response of a patient to surgical resection or chemotherapy may be of great value for clinicians when planning CRC treatments. Metabolomics is an emerging tool for biomarker discovery in cancer research. Previous reports have indicated that the metabolic profile of individuals can be significantly altered between CRC patients and healthy controls. However, metabolic changes in CRC patients at different treatment stages have not been explored.

METHODS

To this end, we performed nuclear magnetic resonance (NMR)-based metabolomic analysis to determine metabolite aberrations in CRC patients before and after surgical resection or chemotherapy. In general, a total of 106 urine samples from four clinical groups, namely, healthy volunteers (n = 31), presurgery CRC patients (n = 25), postsurgery CRC patients (n = 25), and postchemotherapy CRC patients (n = 25), were collected and subjected to further analysis.

RESULTS

In the present study, we identified five candidate metabolites, namely, N-phenylacetylglycine, succinate, 4-hydroxyphenylacetate, acetate, and arabinose, in CRC patients compared with healthy individuals, three of which were reported for the first time. Furthermore, approximately ten metabolites were uniquely identified at each stage of CRC treatment, serving as good candidates for biomarker panel selection.

CONCLUSION

In summary, these potential metabolite candidates may provide promising early diagnostic and monitoring approaches for CRC patients at different anticancer treatment stages.

Recent advancements in the exploitation of the gut microbiome in the diagnosis and treatment of colorectal cancer

Over the last few decades it has been established that the complex interaction between the host and the multitude of organisms that compose the intestinal microbiota plays an important role in human metabolic health and disease. Whilst there is no defined consensus on the composition of a healthy microbiome due to confounding factors such as ethnicity, geographical locations, age and sex, there are undoubtably populations of microbes that are consistently dysregulated in gut diseases including colorectal cancer (CRC). In this review, we discuss the most recent advances in the application of the gut microbiota, not just bacteria, and derived microbial compounds in the diagnosis of CRC and the potential to exploit microbes as novel agents in the management and treatment of CRC. We highlight examples of the microbiota, and their derivatives, that have the potential to become standalone diagnostic tools or be used in combination with current screening techniques to improve sensitivity and specificity for earlier CRC diagnoses and provide a perspective on their potential as biotherapeutics with translatability to clinical trials.

Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles.

Identification of novel neuroblastoma biomarkers in urine samples

Urine is a complex liquid containing numerous small molecular metabolites. The ability to non-invasively test for cancer biomarkers in urine is especially beneficial for screening child patients. This study attempted to identify neuroblastoma biomarkers by comprehensively analysing urinary metabolite samples from children. A total of 87 urine samples were collected from 54 participants (15 children with neuroblastoma and 39 without cancer) and used to perform a comprehensive analysis. Urine metabolites were extracted using liquid chromatography/mass spectrometry and analysed by Metabolon, Inc. Biomarker candidates were extracted using the Wilcoxon rank sum test, random forest method (RF), and orthogonal partial least squares discriminant analysis (OPLS-DA). RF identified three important metabolic pathways in 15 samples from children with neuroblastoma. One metabolite was selected from each of the three identified pathways and combined to create a biomarker candidate (3-MTS, CTN, and COR) that represented each of the three pathways; using this candidate, all 15 cases were accurately distinguishable from the control group. Two cases in which known biomarkers were negative tested positive using this new biomarker. Furthermore, the predictive value did not decrease in cases with a low therapeutic effect. This approach could be effectively applied to identify biomarkers for other cancer types.

Untargeted Metabolomics and Polyamine Profiling in Serum before and after Surgery in Colorectal Cancer Patients

Colorectal cancer is one of the most prevalent cancers in Korea and globally. In this study, we aimed to characterize the differential serum metabolomic profiles between pre-operative and post-operative patients with colorectal cancer. To investigate the significant metabolites and metabolic pathways associated with colorectal cancer, we analyzed serum samples from 68 patients (aged 20–71, mean 57.57 years). Untargeted and targeted metabolomics profiling in patients with colorectal cancer were performed using liquid chromatography-mass spectrometry. Untargeted analysis identified differences in sphingolipid metabolism, steroid biosynthesis, and arginine and proline metabolism in pre- and post-operative patients with colorectal cancer. We then performed quantitative target profiling of polyamines, synthesized from arginine and proline metabolism, to identify potential polyamines that may serve as effective biomarkers for colorectal cancer. Results indicate a significantly reduced serum concentration of putrescine in post-operative patients compared to pre-operative patients. Our metabolomics approach provided insights into the physiological alterations in patients with colorectal cancer after surgery.

Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives

Colorectal cancer (CRC) ranks third among the most commonly occurring cancers worldwide, and it causes half a million deaths annually. Alongside mechanistic study for CRC detection and treatment by conventional techniques, new technologies have been developed to study CRC. These technologies include genomics, transcriptomics, proteomics, and metabolomics which elucidate DNA markers, RNA transcripts, protein and, metabolites produced inside the colon and rectum part of the gut. All these approaches form the omics arena, which presents a remarkable opportunity for the discovery of novel prognostic, diagnostic and therapeutic biomarkers and also delineate the underlying mechanism of CRC causation, which may further help in devising treatment strategies. This review also mentions the latest developments in metagenomics and culturomics as emerging evidence suggests that metagenomics of gut microbiota has profound implications in the causation, prognosis, and treatment of CRC. A majority of bacteria cannot be studied as they remain unculturable, so culturomics has also been strengthened to develop culture conditions suitable for the growth of unculturable bacteria and identify unknown bacteria. The overall purpose of this review is to succinctly evaluate the application of omics technologies in colorectal cancer research for improving the diagnosis and treatment strategies.

Cancer metabolomic markers in urine: evidence, techniques and recommendations

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Urinary volatile organic compounds and faecal microbiome profiles in colorectal cancer

AIM

Volatile organic compounds (VOCs) are potential biomarkers for diagnosing colorectal cancer (CRC). We characterized urinary VOCs from CRC patients, their spouses/cohabitors (spouses) and first-degree relatives (relatives) to determine any differences. Correlation with stool-derived microbiomes was also undertaken.

METHODS

Urine from 56 CRC patients, 45 spouses and 37 relatives was assayed using liquid chromatography, field asymmetric ion mobility spectrometry (FAIMS), mass spectrometer technology. Analysis was performed using five-fold cross-validation and a random forest classifier. Faecal microbiome 16S rRNA was sequenced using Illumina MiSeq protocols and analysed using UPARSE and QIIME pipelines. VOC and microbiome profiles were also compared before and after cancer treatment.

RESULTS

Urinary VOC profiles of CRC patients were indistinguishable from either spouses or relatives. When spouses and relatives were grouped together to form a larger non-cancer control group (n = 82), their VOC profiles became distinguishable from those of CRC patients (n = 56) with 69% sensitivity and specificity, area under the curve 0.72 (P < 0.001). Microbiome analysis identified > 1300 operational taxonomic units across all groups. The analysis of similarity R value was 0.067 (P < 0.001), with significantly different bacterial abundances in 82 operational taxonomic units (6.2%) by Kruskal-Wallis testing. CRC patients' VOC or stool microbiome profiles were unchanged after treatment.

CONCLUSION

Although CRC patients' urinary VOC profiles cannot be differentiated from those of spouses or relatives they can be differentiated from a larger non-cancer control group. Comparison of the groups' microbiomes confirmed differences in bacterial species abundance. The current FAIMS-based assay can detect a unique, but modest, signal in CRC patients' urinary VOCs, which remains unaltered after treatment.

Urine-NMR metabolomics for screening of advanced colorectal adenoma and early stage colorectal cancer

Although colorectal cancer (CRC) is considered one of the most preventable cancers, no non-invasive, accurate diagnostic tool to screen CRC exists. We explored the potential of urine nuclear magnetic resonance (NMR) metabolomics as a diagnostic tool for early detection of CRC, focusing on advanced adenoma and stage 0 CRC. Urine metabolomics profiles from patients with colorectal neoplasia (CRN; 36 advanced adenomas and 56 CRCs at various stages, n = 92) and healthy controls (normal, n = 156) were analyzed by NMR spectroscopy. Healthy and CRN groups were statistically discriminated using orthogonal projections to latent structure discriminant analysis (OPLS-DA). The class prediction model was validated by three-fold cross-validation. The advanced adenoma and stage 0 CRC were grouped together as pre-invasive CRN. The OPLS-DA score plot showed statistically significant discrimination between pre-invasive CRN as well as advanced CRC and healthy controls with a Q2 value of 0.746. In the prediction validation study, the sensitivity and specificity for diagnosing pre-invasive CRN were 96.2% and 95%, respectively. The grades predicted by the OPLS-DA model showed that the areas under the curve were 0.823 for taurine, 0.783 for alanine, and 0.842 for 3-aminoisobutyrate. In multiple receiver operating characteristics curve analyses, taurine, alanine, and 3-aminoisobutyrate were good discriminators for CRC patients. NMR-based urine metabolomics profiles significantly and accurately discriminate patients with pre-invasive CRN as well as advanced CRC from healthy individuals. Urine-NMR metabolomics has potential as a screening tool for accurate diagnosis of pre-invasive CRN.

A Comprehensive Screening and Identification of Genistin Metabolites in Rats Based on Multiple Metabolite Templates Combined with UHPLC-HRMS Analysis

Genistin, an isoflavone belonging to the phytoestrogen family, has been reported to possess various therapeutic effects. In the present study, the genistin metabolites in rats were investigated by UHPLC-LTQ-Orbitrap mass spectrometer in both positive and negative ion modes. Firstly, the data sets were obtained based on data-dependent acquisition method and then 10 metabolite templates were established based on the previous reports. Then diagnostic product ions (DPIs) and neutral loss fragments (NLFs) were proposed to efficiently screen and ascertain the major-to-trace genistin metabolites. Meanwhile, the calculated Clog P values were used to identify the positional isomers with different retention times. Consequently, a total of 64 metabolites, including prototype drug, were positively or putatively characterized. Among them, 40 metabolites were found according to the templates of genistin and genistein, which was the same as the previous research method. After using other metabolite templates, 24 metabolites were added. The results demonstrated that genistin mainly underwent methylation, hydrogenation, hydroxylation, glucosylation, glucuronidation, sulfonation, acetylation, ring-cleavage and their composite reactions in vivo biotransformation. In conclusion, the research not only revealed the genistein metabolites and metabolic pathways in vivo comprehensively, but also proposed a method based on multiple metabolite templates to screen and identify metabolites of other natural compounds.

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to detect CRC, and an accurate pattern recognition to differentiate CRC with early stage cases from healthy controls are needed. Here, we utilized liquid chromatography triple quadrupole mass spectrometry to profile seven kinds of polyamines, such as spermine and spermidine with their acetylated forms. Urinary samples from 201 CRCs and 31 non-CRCs revealed the N₁,N₁₂-diacetylspermine showing the highest area under the receiver operating characteristic curve (AUC), 0.794 (the 95% confidence interval (CI): 0.704–0.885, p < 0.0001), to differentiate CRC from the benign and healthy controls. Overall, 59 samples were analyzed to evaluate the reproducibility of quantified concentrations, acquired by collecting three times on three days each from each healthy control. We confirmed the stability of the observed quantified values. A machine learning method using combinations of polyamines showed a higher AUC value of 0.961 (95% CI: 0.937–0.984, p < 0.0001). Computational validations confirmed the generalization ability of the models. Taken together, polyamines and a machine-learning method showed potential as a screening tool of CRC.

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis

Introduction

Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications.

Objectives

To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking.

Methods

A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls.

Results

The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively.

Conclusion

These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.

Ultra‑performance liquid chromatography coupled with quadrupole time‑of‑flight mass spectrometry‑based metabolic profiling of human serum prior to and following radical resection of colorectal carcinoma

Nearly one quarter of patients with colorectal carcinoma (CRC) were diagnosed at an advanced stage. Under these circumstances, radical resection of the tumor is the best strategy to enhance the five-year survival rate. However, up to 50% of post-operative patients experience cancer recurrence within the first few years. Therefore, post‑operative surveillance is important. However, currently performed post‑operative monitoring relies on relatively dated methods with insufficient sensitivity and specificity. The present study applied an advanced technology of ultra‑performance liquid chromatography coupled with quadrupole time‑of‑flight mass spectrometry in order to examine changes in metabolite patterns in serum with the aim of identifying reliable biomarkers in patients with CRC at various time-points. Serum samples were collected from and 20 CRC patients prior to radical resection (group 1) and one month following radical resection (group 2) as well as from 20 healthy volunteers (group 3). Multivariate pattern recognition was used to identify potential biomarkers of CRC. Compared with healthy volunteers, three groups of biomarkers were identified in patients with CRC (P<0.05), namely phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs) and diacylglycerols (DAGs). However, no statistical difference in the levels of these biomarkers between pre‑operative and post‑operative CRC patients was identified (P>0.05). PCs and LPCs, which contain polyunsaturated fatty acids, were decreased, whereas LPCs and DAGs, which contain saturated fatty acids, were increased in CRC patients. The present study demonstrated that obvious metabolic disturbances occur during the development of CRC and provided a novel analytic method, which is likely to be used as a diagnostic tool for CRC and may help to improve the patients' prognosis.

Changes in urinary metabolic profiles of colorectal cancer patients enrolled in a prospective cohort study (ColoCare)

INTRODUCTION

Metabolomics is a valuable tool for biomarker screening of colorectal cancer (CRC). In this study, we profiled the urinary metabolomes of patients enrolled in a prospective patient cohort (ColoCare). We aimed to describe changes in the metabolome in the longer clinical follow-up and describe initial predictors as candidate markers with possibly prognostic significance.

METHODS

In total, 199 urine samples from CRC patients pre-surgery (n=97), 1-8 days post-surgery (n=12) and then after 6 and 12 months (n=52 and 38, respectively) were analyzed using both GC-MS and ¹H-NMR. Both datasets were analyzed separately with built in uni- and multivariate analyses of Metaboanalyst 2.0. Furthermore, adjusted linear mixed effects regression models were constructed.

RESULTS

Many concentrations of the metabolites derived from the gut microbiome were affected by CRC surgery, presumably indicating a tumor-induced shift in bacterial species. Associations of the microbial metabolites with disease stage indicate an important role of the gut microbiome in CRC.We were able to differentiate the metabolite profiles of CRC patients prior to surgery from those at any post-surgery timepoint using a multivariate model containing 20 marker metabolites (AUCROC=0.89; 95% CI:0.84-0.95).

CONCLUSION

To the best of our knowledge, this is one of the first metabolomic studies to follow CRC patients in a prospective setting with repeated urine sampling over time. We were able to confirm markers initially identified in case-control studies and pin point metabolites which may serve as candidates for prognostic biomarkers of CRC.

Beneficial Regulation of Metabolic Profiles by Black Raspberries in Human Colorectal Cancer Patients

Dietary intervention of freeze-dried black raspberries (BRBs) in a group of human colorectal cancer patients has demonstrated beneficial effects, including proapoptosis, antiproliferation, and antiangiogenesis. The aim of this study was to investigate BRB-mediated metabolite changes from this same cohort of patients. Twenty-eight colorectal cancer patients were given 60 g BRB powder daily for 1 to 9 weeks. Urine and plasma specimens were collected before and after BRB intervention. A mass spectrometry-based nontargeted metabolomic analysis was conducted on each specimen. A total of more than 400 metabolites were annotated in each specimen. Of these 34 and 6 metabolites were significantly changed by BRBs in urine and plasma, respectively. Increased levels of 4-methylcatechol sulfate in both post-BRB urine and post-BRB plasma were significantly correlated with a higher level of apoptotic marker (TUNEL) in post-BRB tumors. One tricarboxylic acid (TCA) cycle metabolites, cis-aconitate, was increased in post-BRB urine. Furthermore, BRB-derived polyphenols were absorbed and metabolized to various benzoate species, which were significantly increased in post-BRB specimens. Increased benzoate levels were positively correlated with enhanced levels of amino acid metabolite. These results suggest that BRBs induce significant metabolic changes and affect energy generating pathways.This study supports the hypothesis that BRBs might be beneficial to colorectal cancer patients through the regulation of multiple metabolites.

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression

Despite the fact that colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world, the development of improved and robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC continues to be evasive. In particular, patients with colon polyps are at higher risk of developing colon cancer; however, noninvasive methods to identify these patients suffer from poor performance. In consideration of the challenges involved in identifying metabolite biomarkers in individuals with high risk for colon cancer, we have investigated NMR-based metabolite profiling in combination with numerous demographic parameters to investigate the ability of serum metabolites to differentiate polyp patients from healthy subjects. We also investigated the effect of disease risk on different groups of biologically related metabolites. A powerful statistical approach, seemingly unrelated regression (SUR), was used to model the correlated levels of metabolites in the same biological group. The metabolites were found to be significantly affected by demographic covariates such as gender, BMI, BMI(2), and smoking status. After accounting for the effects of the confounding factors, we then investigated potential of metabolites from serum to differentiate patients with polyps and age matched healthy controls. Our results showed that while only valine was slightly associated, individually, with polyp patients, a number of biologically related groups of metabolites were significantly associated with polyps. These results may explain some of the challenges and promise a novel avenue for future metabolite profiling methodologies.

A prospective study of serum metabolites and colorectal cancer risk

BACKGROUND

Colorectal cancer is highly prevalent, and the vast majority of cases are thought to be sporadic, although few risk factors have been identified. Using metabolomics technology, our aim was to identify biomarkers prospectively associated with colorectal cancer.

METHODS

This study included 254 incident colorectal cancers and 254 matched controls nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Serum samples were collected at baseline, and the mean length of follow-up was 8 years. Serum metabolites were analyzed by ultra-high performance liquid-phase chromatography with tandem mass spectrometry, and gas chromatography coupled with mass spectrometry. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) for metabolites above the limit of detection and present in at least 80% of participants.

RESULTS

A total of 676 serum metabolites were measured; of these, 447 were of known identity and 278 of these were present in >80% of individuals. Overall, there was no association between serum metabolites and colorectal cancer; however, some suggestive associations were observed between individual metabolites and colorectal cancer but none reached statistical significance after Bonferroni correction for multiple comparisons. For example, leucyl-leucine was inversely associated (OR comparing the 90th to the 10th percentile = 0.50; 95% CI = 0.32-0.80; P = .003). In sex-stratified analyses, serum glycochenodeoxycholate was positively associated with colorectal cancer among women (OR(90th vs.10th percentile)  = 5.34; 95% CI = 2.09-13.68; P = .0001).

CONCLUSIONS

No overall associations were observed between serum metabolites and colorectal cancer, but serum glycochenodeoxycholate, a bile acid metabolite, was positively associated with colorectal cancer among women.

Colorectal cancer detection using targeted serum metabolic profiling

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Metabolomics in diagnosis and biomarker discovery of colorectal cancer

Colorectal cancer (CRC), a major public health concern, is the second leading cause of cancer death in developed countries. There is a need for better preventive strategies to improve the patient outcome that is substantially influenced by cancer stage at the time of diagnosis. Patients with early stage colorectal have a significant higher 5-year survival rates compared to patients diagnosed at late stage. Although traditional colonoscopy remains the effective means to diagnose CRC, this approach generally suffers from poor patient compliance. Thus, it is important to develop more effective methods for early diagnosis of this disease process, also there is an urgent need for biomarkers to diagnose CRC, assess disease severity, and prognosticate course. Increasing availability of high-throughput methodologies open up new possibilities for screening new potential candidates for identifying biomarkers. Fortunately, metabolomics, the study of all metabolites produced in the body, considered most closely related to a patient's phenotype, can provide clinically useful biomarkers applied in CRC, and may now open new avenues for diagnostics. It has a largely untapped potential in the field of oncology, through the analysis of the cancer metabolome to identify marker metabolites defined here as surrogate indicators of physiological or pathophysiological states. In this review we take a closer look at the metabolomics used within the field of colorectal cancer. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.

Metabonomics identifies serum metabolite markers of colorectal cancer

Recent studies suggest that biofluid-based metabonomics may identify metabolite markers promising for colorectal cancer (CRC) diagnosis. We report here a follow-up replication study, after a previous CRC metabonomics study, aiming to identify a distinct serum metabolic signature of CRC with diagnostic potential. Serum metabolites from newly diagnosed CRC patients (N = 101) and healthy subjects (N = 102) were profiled using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS). Differential metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (VIP > 1) and the Mann-Whitney U test (p < 0.05). With a total of 249 annotated serum metabolites, we were able to differentiate CRC patients from the healthy controls using an orthogonal partial least-squares-discriminant analysis (OPLS-DA) in a learning sample set of 62 CRC patients and 62 matched healthy controls. This established model was able to correctly assign the rest of the samples to the CRC or control groups in a validation set of 39 CRC patients and 40 healthy controls. Consistent with our findings from the previous study, we observed a distinct metabolic signature in CRC patients including tricarboxylic acid (TCA) cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism. Our results demonstrated that a panel of serum metabolite markers is of great potential as a noninvasive diagnostic method for the detection of CRC.

Structure-oriented UHPLC-LTQ Orbitrap-based approach as a dereplication strategy for the identification of isoflavonoids from Amphimas pterocarpoides crude extract

Hyphenated techniques and especially ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) are nowadays widely employed in natural products research. However, the complex nature of plant extracts complicates considerably the analysis and the identification of their constituents. Nevertheless, new MS analyzers with increased resolving power and accuracy such as the orbital trap (Orbitrap) could facilitate drastically this process. The objective of this study is the development of a new structure-oriented approach based on fast UHPLC-high-resolution (HR)MS and HRMS/MS methodologies for the identification of isoflavonoids in crude extracts. In addition, aims to assist dereplication procedures, to decrease the laborious isolation steps and orient the focused isolation of compounds of interest. As a proof of concept, the methanol extract of the stem bark of Amphimas pterocarpoides (Leguminosae) was selected. Based on chromatographic (retention time, polarity) and spectrometric features (ultraviolet spectra, accurate m/z, proposed elemental composition, ring double bond equivalent, and relative isotopic abundance) as well as HRMS/MS spectra, several isoflavonoids were identified. In order to verify the proposed structures, 11 isoflavonoids were selectively isolated and unambiguously identified using 1&2D nuclear magnetic resonance techniques. Moreover, the isolated isoflavonoids were studied in HRMS/MS level, employing electrospray ionization and atmospheric pressure chemical ionization sources, in both modes. Useful information regarding their fragmentation patterns was obtained, and characteristic diagnostic ions were defined for the identification of methoxylated isoflavones, dihydroisoflavones and 5-hydroxylated isoflavonoids. Based on the current results, the proposed dereplication strategy was verified and could comprise a novel approach for the analysis of crude extracts in the future not only for isoflavonoids but also for other chemical classes of natural products.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Investigation of urinary volatile organic metabolites as potential cancer biomarkers by solid-phase microextraction in combination with gas chromatography-mass spectrometry

BACKGROUND

Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer.

METHODS

To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxen-polydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 50°C for 60 min using samples with high ionic strengths (17% sodium chloride, w v(-1)) and under agitation.

RESULTS

A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (P<0.05). A significant increase in the peak area of 2-methyl-3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients.

CONCLUSIONS

Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.

Metabolomics and surgical oncology: Potential role for small molecule biomarkers

Metabolomics, the newest of the "omics" sciences, has brought much excitement to the field of oncology as a potential new translational tool capable of bringing the molecular world of cancer care to the bedside. While still early in its development, metabolomics could alter the scope and role of surgery in the multidisciplinary treatment of cancer. This review examines potential roles of metabolomics in areas of early cancer detection, personalized therapeutics and tumorigenesis.

Urinary metabolic profiling of colorectal carcinoma based on online affinity solid phase extraction-high performance liquid chromatography and ultra performance liquid chromatography-mass spectrometry

Colorectal carcinoma (CRC) is the third most commonly encountered cancer and fourth cause of cancer-associated death worldwide. Abundant studies have demonstrated that one of the best effective therapies for enhancing the 5-year survival rate of patients is to diagnose the disease at an early stage. Urine metabonomics is widely being utilized as an efficient platform to investigate the metabolic changes and discover the potential biomarkers of malignant diseases. In this study both ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and online affinity solid phase extraction-high performance liquid chromatography (SPE-HPLC) were used to analyze the urinary metabolites from 34 healthy volunteers, 34 benign colorectal tumor and 50 colorectal carcinoma patients to produce comprehensive metabolic profiling data. A reliable separation between the control and disease groups as well as significantly changed metabolites were obtained from orthogonal signal correction partial least squares models which were built based on the two separate data sets from UPLC-MS and affinity SPE-HPLC, respectively. 15 metabolites, showing the metabolic disorders of CRC, were identified finally. These metabolites were found to be related to glutamine metabolism, fatty acid oxidation, nucleotide biosynthesis and protein metabolism.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.