A distinct metabolic signature of human colorectal cancer with prognostic potential

Preoperative metabolic syndrome and prognosis after radical resection for colorectal cancer: The Fujian prospective investigation of cancer (FIESTA) study

This prospective study sought to investigate the prediction of preoperative metabolic syndrome and its components for the risk of colorectal cancer (CRC) mortality by analyzing a subset of data from the ongoing Fujian prospective investigation of cancer (FIESTA) study. In total, 1,318 CRC patients who received radical resection were consecutively enrolled between January 2000 and December 2008. The median follow-up time was 58.6 months, with 412 deaths from CRC. The CRC patients with metabolic syndrome had significantly shorter median survival time (MST) than those without (50.9 vs. 170.3 months, p < 0.001). Among four components of metabolic syndrome, hyperglycemia was the strongest predictor and its presence was associated with shorter MST than its absence (44.4 vs. 170.3 months, p < 0.001). Moreover, the complication of metabolic syndrome in CRC patients was associated with a 2.98-fold increased risk of CRC mortality (hazard ratio [HR] = 2.98, 95% confidence interval [CI]: 2.40-3.69, p < 0.001) after adjusting for confounding factors. The magnitude of this association was especially potentiated in CRC patients with tumor-node-metastasis stage I/II (HR = 3.94, 95% CI: 2.65-5.85, p < 0.001), invasion depth T1/T2 (HR = 5.41, 95% CI: 2.54-11.50, p < 0.001), regional lymph node metastasis N0 (HR = 4.06, 95% CI: 2.85-5.80, p < 0.001) and negative distant metastasis (HR = 3.23, 95% CI: 2.53-4.12, p < 0.001). Further survival tree analysis reinforced the prognostic capability of fasting blood glucose in CRC survival. Our findings convincingly demonstrated that preoperative metabolic syndrome, especially hyperglycemia, was a robust predictor for CRC mortality, and the protection was more obvious in patients with Stage I/II.

Prediction of biomarkers of therapeutic effects of patients with lung adenocarcinoma treated with gefitinib based on progression-free-survival by metabolomic fingerprinting

Lung carcinoma is one of the most frequently diagnosed malignancy and threats human life and health. In clinical practice, gefitinib, one of the most well-known epidermal growth factor receptor tyrosine kinase inhibitors, was frequently used in the treatment of non-small cell lung carcinoma. However, this drug is not useful for all non-small cell patients. In this study, the biomarkers were found out to predict the therapeutic effects of gefitinib for lung carcinoma patients. Serum samples were collected from patients with advanced lung adenocarcinoma. The ultra-high performance liquid chromatography (UHPLC)-quadrupole-time of flight mass spectrometry (Q-TOF MS) was conducted to obtain the metabolic data for each patient. Partial least squares-discriminate analysis (PLS-DA) was performed to indicate the differences between metabolites of patients, and Cox proportional hazards regression analysis was used to eliminate the interference of the patient's gender, age, smoking history and disease stage. Thus, differential biomarkers were found. The combination of these biomarkers was statistically significant predictors based on progression-free survival. If these biomarkers can be further confirmed by the clinic, it could suggest the proper therapeutic schedule, and help to reduce patients' economic burden and medication side effects.

Osteocalcin Signaling in Myofibers Is Necessary and Sufficient for Optimum Adaptation to Exercise

Circulating levels of undercarboxylated and bioactive osteocalcin double during aerobic exercise at the time levels of insulin decrease. In contrast, circulating levels of osteocalcin plummet early during adulthood in mice, monkeys, and humans of both genders. Exploring these observations revealed that osteocalcin signaling in myofibers is necessary for adaptation to exercise by favoring uptake and catabolism of glucose and fatty acids, the main nutrients of myofibers. Osteocalcin signaling in myofibers also accounts for most of the exercise-induced release of interleukin-6, a myokine that promotes adaptation to exercise in part by driving the generation of bioactive osteocalcin. We further show that exogenous osteocalcin is sufficient to enhance the exercise capacity of young mice and to restore to 15-month-old mice the exercise capacity of 3-month-old mice. This study uncovers a bone-to-muscle feedforward endocrine axis that favors adaptation to exercise and can reverse the age-induced decline in exercise capacity.

Chitooligosaccharides promote radiosensitivity in colon cancer line SW480

AIM: To investigate the anti-proliferation and radiosensitization effect of chitooligosaccharides (COS) on human colon cancer cell line SW480.

METHODS: SW480 cells were treated with 0, 1.0, 2.0, 3.0, 4.0 and 5.0 mg/mL of COS for 48 h. CCK-8 assay was employed to obtain the cell survival ratio of SW480 cells, and the anti-proliferation curve was observed with the inhibition ratio of COS on SW480 cells. The RAY + COS group was treated with 1.0 mg/mL of COS for 48 h, while both the RAY and RAY+COS groups were exposed to X-ray at 0, 1, 2, 4, 6 and 8 Gy, respectively. Clonogenic assay was used to analyze cell viability in the two groups at 10 d after treatment, and a cell survival curve was used to analyze the sensitization ratio of COS. The RAY group was exposed to X-ray at 6 Gy, while the RAY+COS group was treated with 1.0 mg/mL of COS for 48 h in advance and exposed to X-ray at 6 Gy. Flow cytometry was employed to detect cell cycle and apoptosis rate in the non-treatment group, as well as in the RAY and RAY + COS groups after 24 h of treatment.

RESULTS: COS inhibited the proliferation of SW480 cells, and the inhibition rate positively correlated with the concentration of COS (P < 0.01). Cell viability decreased as radiation dose increased in the RAY and RAY+COS groups (P < 0.01). Cell viabilities in the RAY+COS group were lower than in the RAY group at all doses of X-ray exposure (P < 0.01), and the sensitization ratio of COS on SW480 cells was 1.39. Compared with the non-treatment group, there was a significant increase in apoptosis rate in both the RAY and RAY + COS groups; while the apoptosis rate in the RAY+COS group was significantly higher than in the RAY group (P < 0.01). In comparing these three groups, the percentage of G₂/M phase in both the RAY and RAY + COS groups significantly increased, and the percentage of the S phase and G₀/G₁ phase was downregulated. Furthermore, the percentage in the G₂/M phase was higher, and the percentage in the S phase and G₀/G₁ phase was lower in the RAY + COS group vs RAY group (P < 0.01).

CONCLUSION: COS can inhibit the proliferation of SW480 cells and enhance the radiosensitization of SW480 cells, inducing apoptosis and G₂/M phase arrest.

Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool

Background

Colorectal cancers (CRC) are associated with perturbations in cellular amino acids, nucleotides, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates. A non-targeted global metabolome approach was utilized for exploring human CRC, adjacent mucosa, and stool. In this pilot study, we identified metabolite profile differences between CRC and adjacent mucosa from patients undergoing colonic resection. Metabolic pathway analyses further revealed relationships between complex networks of metabolites.

Methods

Seventeen CRC patients participated in this pilot study and provided CRC, adjacent mucosa ~10 cm proximal to the tumor, and stool. Metabolomes were analyzed by gas chromatography-mass spectrometry (GC/MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). All of the library standard identifications were confirmed and further analyzed via MetaboLync^TM for metabolic network interactions.

Results

There were a total of 728 distinct metabolites identified from colonic tissue and stool matrices. Nineteen metabolites significantly distinguished CRC from adjacent mucosa in our patient-matched cohort. Glucose-6-phosphate and fructose-6-phosphate demonstrated 0.64-fold and 0.75-fold lower expression in CRC compared to mucosa, respectively, whereas isobar: betaine aldehyde, N-methyldiethanolamine, and adenylosuccinate had 2.68-fold and 1.88-fold higher relative abundance in CRC. Eleven of the 19 metabolites had not previously been reported for CRC relevance. Metabolic pathway analysis revealed significant perturbations of short-chain fatty acid metabolism, fructose, mannose, and galactose metabolism, and glycolytic, gluconeogenic, and pyruvate metabolism. In comparison to the 500 stool metabolites identified from human CRC patients, only 215 of those stool metabolites were also detected in tissue. This CRC and stool metabolome investigation identified novel metabolites that may serve as key small molecules in CRC pathogenesis, confirmed the results from previously reported CRC metabolome studies, and showed networks for metabolic pathway aberrations. In addition, we found differences between the CRC and stool metabolomes.

Conclusions

Stool metabolite profiles were limited for direct associations with CRC and adjacent mucosa, yet metabolic pathways were conserved across both matrices. Larger patient-matched CRC, adjacent non-cancerous colonic mucosa, and stool cohort studies for metabolite profiling are needed to validate these small molecule differences and metabolic pathway aberrations for clinical application to CRC control, treatment, and prevention.

Electronic supplementary material

The online version of this article (doi:10.1186/s40170-016-0151-y) contains supplementary material, which is available to authorized users.

Distinct Metabolic Signature of Human Bladder Cancer Cells Carrying an Impaired Fanconi Anemia Tumor-Suppressor Signaling Pathway

Metabolic profiling has great potential to help the diagnosis and prognosis of cancer patients. Fanconi Anemia (FA) tumor-suppressor signaling has been instrumental in understanding human tumorigenesis. However, this instrumental understanding has never been demonstrated at the metabolic level. Here, we show that impaired FA signaling can lead cells to exhibit metabolic signatures of tumorigenesis. This is consistent with our original studies of the roles of FA signaling in suppressing non-FA tumorigenesis at functional and genetic levels. Using ultraperformance liquid chromatography-mass spectroscopy and gas chromatography-mass spectrometry, we characterized metabolic alterations in bladder cancer cells carrying an intact or impaired FA pathway. The latter was obtained by ectopically expressing FAVL (FAVL-high), which we previously found to be capable of inactivating FA signaling. A total of 18 metabolites, end products of cell proliferation or apoptosis, were significantly different between FAVL-high and -low cells. Methionine, phenylalanine, and threonine, resulting from a tumorigenic process, were substantially increased in FAVL-high cells. With this study, we achieved genomic, functional, and metabolomic characterization of the roles of FA signaling in the development of human cancer. Furthermore, this study provides novel insights into how to translate FA basic research into strategies for producing effective biomarkers in human cancer diagnosis and prognosis.

Isotopic Ratio Outlier Analysis of the S. cerevisiae Metabolome Using Accurate Mass Gas Chromatography/Time-of-Flight Mass Spectrometry: A New Method for Discovery

Isotopic ratio outlier analysis (IROA) is a (13)C metabolomics profiling method that eliminates sample to sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass liquid chromatography/mass spectrometry (LC/MS). This is the first report using IROA technology in combination with accurate mass gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS), here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% (13)C, or 5%(13)C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%(13)C extracts, or light isotopologues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the (12)C monoisotopic and the (13)C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both chemical and electron ionization, extends the information acquired from the isotopic peak patterns for formulas generation. The process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations are used as search constraints. In electron impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of chemical ionization (CI) IROA and EI/IROA affords a metabolite identification procedure that enables the identification of coeluting metabolites, and allowed us to characterize 126 metabolites in the current study.

Metabolomic profiles of arsenic (+3 oxidation state) methyltransferase knockout mice: effect of sex and arsenic exposure

Arsenic (+3 oxidation state) methyltransferase (As3mt) is the key enzyme in the pathway for methylation of inorganic arsenic (iAs). Altered As3mt expression and AS3MT polymorphism have been linked to changes in iAs metabolism and in susceptibility to iAs toxicity in laboratory models and in humans. As3mt-knockout mice have been used to study the association between iAs metabolism and adverse effects of iAs exposure. However, little is known about systemic changes in metabolism of these mice and how these changes lead to their increased susceptibility to iAs toxicity. Here, we compared plasma and urinary metabolomes of male and female wild-type (WT) and As3mt-KO (KO) C57BL/6 mice and examined metabolomic shifts associated with iAs exposure in drinking water. Surprisingly, exposure to 1 ppm As elicited only small changes in the metabolite profiles of either WT or KO mice. In contrast, comparisons of KO mice with WT mice revealed significant differences in plasma and urinary metabolites associated with lipid (phosphatidylcholines, cytidine, acyl-carnitine), amino acid (hippuric acid, acetylglycine, urea), and carbohydrate (L-sorbose, galactonic acid, gluconic acid) metabolism. Notably, most of these differences were sex specific. Sex-specific differences were also found between WT and KO mice in plasma triglyceride and lipoprotein cholesterol levels. Some of the differentially changed metabolites (phosphatidylcholines, carnosine, and sarcosine) are substrates or products of reactions catalyzed by other methyltransferases. These results suggest that As3mt KO alters major metabolic pathways in a sex-specific manner, independent of iAs treatment, and that As3mt may be involved in other cellular processes beyond iAs methylation.

Comprehensive molecular tumor profiling in radiation oncology: How it could be used for precision medicine

New technologies enabling the analysis of various molecules, including DNA, RNA, proteins and small metabolites, can aid in understanding the complex molecular processes in cancer cells. In particular, for the use of novel targeted therapeutics, elucidation of the mechanisms leading to cell death or survival is crucial to eliminate tumor resistance and optimize therapeutic efficacy. While some techniques, such as genomic analysis for identifying specific gene mutations or epigenetic testing of promoter methylation, are already in clinical use, other "omics-based" assays are still evolving. Here, we provide an overview of the current status of molecular profiling methods, including promising research strategies, as well as possible challenges, and their emerging role in radiation oncology.

Serum Metabolomics to Identify the Liver Disease-Specific Biomarkers for the Progression of Hepatitis to Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy that has region specific etiologies. Unfortunately, 85% of cases of HCC are diagnosed at an advanced stage. Reliable biomarkers for the early diagnosis of HCC are urgently required to reduced mortality and therapeutic expenditure. We established a non-targeted gas chromatography–time of flight–mass spectrometry (GC-TOFMS) metabolomics method in conjunction with Random Forests (RF) analysis based on 201 serum samples from healthy controls (NC), hepatitis B virus (HBV), liver cirrhosis (LC) and HCC patients to explore the metabolic characteristics in the progression of hepatocellular carcinogenesis. Ultimately, 15 metabolites were identified intimately associated with the process. Phenylalanine, malic acid and 5-methoxytryptamine for HBV vs. NC, palmitic acid for LC vs. HBV, and asparagine and β-glutamate for HCC vs. LC were screened as the liver disease-specific potential biomarkers with an excellent discriminant performance. All the metabolic perturbations in these liver diseases are associated with pathways for energy metabolism, macromolecular synthesis, and maintaining the redox balance to protect tumor cells from oxidative stress.

Transcriptomics in cancer diagnostics: developments in technology, clinical research and commercialization

Transcriptomic technologies are evolving to diagnose cancer earlier and more accurately to provide greater predictive and prognostic utility to oncologists and patients. Digital techniques such as RNA sequencing are replacing still-imaging techniques to provide more detailed analysis of the transcriptome and aberrant expression that causes oncogenesis, while companion diagnostics are developing to determine the likely effectiveness of targeted treatments. This article examines recent advancements in molecular profiling research and technology as applied to cancer diagnosis, clinical applications and predictions for the future of personalized medicine in oncology.

GC-MS-based metabolomic analysis of human papillary thyroid carcinoma tissue

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Elucidating the molecular network that is altered in PTC may lead to the identification of the critical insight into the pathogenesis of PTC. Thus far, little is known regarding the global metabolomic alterations of PTC. Gas chromatography coupled with mass spectrometry-based metabolomics was used to analyze metabolomic alterations in matched PTC and normal thyroid tissues obtained from the patients. Multivariate statistical analyses were employed to determine the significant metabolomic differences. The mRNA levels of the associated metabolic enzyme genes were further assayed with reverse transcription-quantitative polymerase chain reaction analysis. Principal component analysis, partial least-squares discriminant analysis (PLS-DA) and orthogonal PLS-DA models were established, which could clearly separate human normal thyroid and PTC samples, and identified that metabolites in carbohydrate metabolism, including glucose, fructose, galactose, mannose, 2-keto-D-gluconic acid and rhamnose, consistently decreased, while metabolites in nucleotide metabolism, including malonic acid and inosine, and lipid metabolism, including cholesterol and arachidonic acid, significantly altered in PTC. Furthermore, the mRNA levels of metabolic enzyme genes, including glucose-6-phosphate dehydrogenase, phosphoglycerate kinase 1, lactate dehydrogenase A, phosphoglycerate dehydrogenase and prostaglandin-endoperoxide synthase 2, significantly increased in PTC. Based on the metabolomic and mRNA data, various metabolites may be used for increased synthesis of nucleotides and oncogenic lipids in PTC, which may contribute to the pathogenesis of PTC. The present study provides a new understanding of the dysregulated metabolism in PTC and identifies potential avenues for the therapeutic intervention for this disease.

Metabonomic Profiling Reveals Cancer Chemopreventive Effects of American Ginseng on Colon Carcinogenesis in Apc(Min/+) Mice

American ginseng (Panax quinquefolius L.) is one of the most commonly used herbal medicines in the West. It has been reported to possess significant antitumor effects that inhibit the process of carcinogenesis. However, the mechanisms underlying its anticancer effects remain largely unresolved. In this study, we investigated the cancer chemopreventive effects of American ginseng on the progression of high fat (HF) diet-enhanced colorectal carcinogenesis with a genetically engineered Apc(Min/+) mouse model. The metabolic alterations in sera of experimental mice perturbed by HF diet intervention as well as the American ginseng treatment were measured by gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and liquid chromatography time-of-flight mass spectrometry (LC-TOFMS) analysis. American ginseng treatment significantly extended the life span of the Apc(Min/+) mouse. Significant alterations of metabolites involving amino acids, organic acids, fatty acids, and carbohydrates were observed in Apc(Min/+) mouse in sera, which were attenuated by American ginseng treatment and concurrent with the histopathological improvement with significantly reduced tumor initiation, progression and gut inflammation. These metabolic changes suggest that the preventive effect of American ginseng is associated with attenuation of impaired amino acid, carbohydrates, and lipid metabolism. It also appears that American ginseng induced significant metabolic alterations independent of the Apc(Min/+) induced metabolic changes. The significantly altered metabolites induced by American ginseng intervention include arachidonic acid, linolelaidic acid, glutamate, docosahexaenoate, tryptophan, and fructose, all of which are associated with inflammation and oxidation. This suggests that American ginseng exerts the chemopreventive effects by anti-inflammatory and antioxidant mechanisms.

Metabolic signatures differentiate ovarian from colon cancer cell lines

Background

In this era of precision medicine, the deep and comprehensive characterization of tumor phenotypes will lead to therapeutic strategies beyond classical factors such as primary sites or anatomical staging. Recently, “-omics” approached have enlightened our knowledge of tumor biology. Such approaches have been extensively implemented in order to provide biomarkers for monitoring of the disease as well as to improve readouts of therapeutic impact. The application of metabolomics to the study of cancer is especially beneficial, since it reflects the biochemical consequences of many cancer type-specific pathophysiological processes. Here, we characterize metabolic profiles of colon and ovarian cancer cell lines to provide broader insight into differentiating metabolic processes for prospective drug development and clinical screening.

Methods

We applied non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography and gas chromatography for the metabolic phenotyping of four cancer cell lines: two from colon cancer (HCT15, HCT116) and two from ovarian cancer (OVCAR3, SKOV3). We used the MetaP server for statistical data analysis.

Results

A total of 225 metabolites were detected in all four cell lines; 67 of these molecules significantly discriminated colon cancer from ovarian cancer cells. Metabolic signatures revealed in our study suggest elevated tricarboxylic acid cycle and lipid metabolism in ovarian cancer cell lines, as well as increased β-oxidation and urea cycle metabolism in colon cancer cell lines.

Conclusions

Our study provides a panel of distinct metabolic fingerprints between colon and ovarian cancer cell lines. These may serve as potential drug targets, and now can be evaluated further in primary cells, biofluids, and tissue samples for biomarker purposes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0576-z) contains supplementary material, which is available to authorized users.

Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and Benign Thyroid Adenoma

Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic differences between PTC and BTA have not been characterized. The aim of this study was to identify the metabolic profiles of these two types of tumors using a metabolomics approach. Tumors and adjacent nontumor specimens collected from 57 patients with PTC and 48 patients with BTA were profiled using gas chromatography-time-of-flight mass spectrometry and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. A panel of 46 and 44 differentially expressed metabolites were identified in the PTC and BTA specimens, respetively, and compared with nontumor tissues. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one carbon metabolism and tryptophan metabolism, were found in both types of tumors. Purine and pyrimidine metabolism was significantly elevated in the PTC specimens, and taurine and hypotaurine levels were also higher in the PTC tissues. Increased fatty acid and bile acid levels were found, especially in the BTA tissues. The metabolic profiles of the PTC and BTA tissues include both similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis.

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.

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

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

Prediction of advanced ovarian cancer recurrence by plasma metabolic profiling

Epithelial ovarian cancer (EOC) is the most lethal of gynecologic malignancies due to the high rate of recurrence and poor prognosis. Predicting the prognosis in patients with EOC is clinically challenging, partly because appropriate biomarkers of recurrence have yet to be explored. In this prospective study, pre-treatment plasma samples were collected from 38 patients with stage III or IV EOC who were subsequently followed up. Ultra-performance liquid chromatography mass spectrometry was used to perform metabolic profiling, which yielded five metabolites that were potential biomarkers for EOC recurrence: l-tryptophan, kynurenine, bilirubin, LysoPC (14 : 0) and LysoPE (18 : 2). A combination of these five potential biomarkers strongly predicted recurrence, the area under the curve being 0.91. In summary, the candidate biomarkers identified in this study may both facilitate clinical prediction of EOC recurrence and prognosis and serve as potential therapeutic targets in patients with EOC.

Colorectal cancer: the potential of metabolic fingerprinting

Metabolomics is a relatively new omics science that can provide a strong individual small-molecule fingerprint. Disease onset can be monitored as a deviation from the normal healthy fingerprint at the systemic level or in tissues from the diseased organ(s). By applying mass spectrometry and nuclear magnetic resonance as analytical platforms, metabolomics has been used for colorectal cancer phenotyping at different levels. The metabolic profile as a whole is a complex biomarker of diagnostic and prognostic value. Results are promising for the implementation of the method at the clinical level, but larger scale studies and extensive standardization of the pre-analytical phase are needed for a validated definition of the colorectal cancer signature.

Metabolomics in cell culture--a strategy to study crucial metabolic pathways in cancer development and the response to treatment

Metabolomics is a comprehensive tool for monitoring processes within biological systems. Thus, metabolomics may be widely applied to the determination of diagnostic biomarkers for certain diseases or treatment outcomes. There is significant potential for metabolomics to be implemented in cancer research because cancer may modify metabolic pathways in the whole organism. However, not all biological questions can be answered solely by the examination of small molecule composition in biofluids; in particular, the study of cellular processes or preclinical drug testing requires ex vivo models. The major objective of this review was to summarise the current achievement in the field of metabolomics in cancer cell culture-focusing on the metabolic pathways regulated in different cancer cell lines-and progress that has been made in the area of drug screening and development by the implementation of metabolomics in cell lines.

Serum metabolomic signatures of lymph node metastasis of esophageal squamous cell carcinoma

Lymph node metastasis was recently proven to be the single most important prognostic factor for esophageal cancer, an important malignant tumor with poor prognosis. A global metabolomics approach was applied to study lymph node metastasis of esophageal squamous cell carcinoma (ESCC). Metabolomics analyses were performed using gas chromatography/mass spectrometry together with univariate and multivariate statistical analyses. There were clear metabolic distinctions between ESCC patients and healthy subjects. ESCC patients could be well-classified according to lymph node metastasis. We further identified a series of differential serum metabolites for ESCC and lymph node metastatic ESCC patients, suggesting metabolic dysfunction in proliferation (aerobic glycolysis, glutaminolysis, fatty acid metabolism, and branched-chain amino acid consumption), apoptosis, migration, immune escape, and oxidative stress of cancer cells in metastatic ESCC patients. In total, three serum metabolites (valine, γ-aminobutyric acid, and pyrrole-2-carboxylic acid) were selected by binary logistic regression analysis, and their combined use resulted in high diagnostic capacity for ESCC metastasis by receiver operating characteristic analysis. The present metabolomics study staged ESCC patients by lymph node metastasis, and the results suggest promising applications of this approach in prognostic prediction, tailored therapeutics, and understanding the pathological mechanisms of poor prognosis of ESCC patients.

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.

Novel methodologies in analysis of small molecule biomarkers and living cells

Enzyme-linked immuno-sorbent assay (ELISA) is widely used for biomarker detection. A good biomarker can distinguish patients from healthy or benign diseases. However, the ELISA method is not suitable for small molecule or trace substance detection. Along with the development of new technologies, an increasing level of biomaterials, especially small molecules, will be identified as novel biomarkers. Quantitative immuno-PCR, chromatography-mass spectrometry, and nucleic acid aptamer are emerging methodologies for detection of small molecule biomarkers, even in living cells. In this review, we focus on these novel technologies and their potential for small molecule biomarkers and living cell analysis.

From genotype to functional phenotype: unraveling the metabolomic features of colorectal cancer

Much effort in recent years has been expended in defining the genomic and epigenetic alterations that characterize colorectal adenocarcinoma and its subtypes. However, little is known about the functional ramifications related to various subtypes. Metabolomics, the study of small molecule intermediates in disease, provides a snapshot of the functional phenotype of colorectal cancer. Data, thus far, have characterized some of the metabolic perturbations that accompany colorectal cancer. However, further studies will be required to identify biologically meaningful metabolic subsets, including those corresponding to specific genetic aberrations. Moreover, further studies are necessary to distinguish changes due to tumor and the host response to tumor.

Fecal metabolomics: assay performance and association with colorectal cancer

Metabolomic analysis of feces may provide insights on colorectal cancer (CRC) if assay performance is satisfactory. In lyophilized feces from 48 CRC cases, 102 matched controls, and 48 masked quality control specimens, 1043 small molecules were detected with a commercial platform. Assay reproducibility was good for 527 metabolites [technical intraclass correlation coefficient (ICC) >0.7 in quality control specimens], but reproducibility in 6-month paired specimens was lower for the majority of metabolites (within-subject ICC ≤0.5). In the CRC cases and controls, significant differences (false discovery rate ≤0.10) were found for 41 of 1043 fecal metabolites. Direct cancer association was found with three fecal heme-related molecules [covariate-adjusted 90th versus 10th percentile odds ratio (OR) = 17-345], 18 peptides/amino acids (OR = 3-14), palmitoyl-sphingomyelin (OR = 14), mandelate (OR = 3) and p-hydroxy-benzaldehyde (OR = 4). Conversely, cancer association was inverse with acetaminophen metabolites (OR <0.1), tocopherols (OR = 0.3), sitostanol (OR = 0.2), 3-dehydrocarnitine (OR = 0.4), pterin (OR = 0.3), conjugated-linoleate-18-2N7 (OR = 0.2), N-2-furoyl-glycine (OR = 0.3) and p-aminobenzoate (PABA, OR = 0.2). Correlations suggested an independent role for palmitoyl-sphingomyelin and a central role for PABA (which was stable over 6 months, within-subject ICC 0.67) modulated by p-hydroxy-benzaldehyde. Power calculations based on ICCs indicate that only 45% of metabolites with a true relative risk 5.0 would be found in prospectively collected, prediagnostic specimens from 500 cases and 500 controls. Thus, because fecal metabolites vary over time, very large studies will be needed to reliably detect associations of many metabolites that potentially contribute to CRC.