(1)H-NMR urinary metabolomic profiling for diagnosis of gastric cancer

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Metabolomic Study on Nude Mice Models of Gastric Cancer Treated with Modified Si Jun Zi Tang via HILIC UHPLC-Q-TOF/MS Analysis

Recently, metabolomic methods have been used to explore the complex pathogenesis of cancer and the mechanism of action of traditional Chinese medicine (TCM) formulae. In this study, first, modified Si Jun Zi Tang (MSJZT) was prepared with strict quality control using the instrument method of ultra performance liquid chromatography and photodiode array detector (UPLC-PDA). Subsequently, in vivo experiments with tumour-bearing nude mice demonstrated that MSJZT exerted good antitumour effects. MSJZT not only significantly increased mouse body weight but also shrank the tumour volume. Then, the HILIC UHPLC-Q-TOF/MS-based metabolomics approach was used for exploring the pathogenesis of gastric cancer and the molecular mechanism of MSJZT. A total of 59 potential biomarkers in plasma were identified, and 6 pathways were found to be disturbed in gastric cancer. In contrast, after 3 weeks of MSJZT intervention, 32 potential biomarkers were identified, and 4 altered pathways were detected. The changes in glycolytic, amino acid, and lipid metabolisms could be partially regulated by MSJZT through decreasing the content of lactic dehydrogenase (LDH), glutamine synthetase (GS), phosphocholine cytidylyltransferase (PCYT2) mRNA, and protein level. In conclusion, we established a HILIC UHPLC-Q-TOF/MS metabolomic analysis method to demonstrate a complex metabolic profile of gastric cancer. The disordered metabolism could be partially regulated by MSJZT. These findings not only establish a solid foundation for TCM to treat gastric cancer but also provide a basis for further exploration of the precise mechanism of MSJZT activity.

Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine

This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early-stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non-atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100-pixel points were randomly extracted after binarization. Diagnostic models of non-atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial-least-square discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms. The prediction effects of PLS-DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS-DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early-stage gastric cancer.

The diagnostic performance of serum LIM homeobox transcription factor 1 alpha in patients with gastric cancer

The study was conducted to investigate the diagnostic performance of serum LIM homeobox transcription factor 1 alpha (LMX1A) in patients with gastric cancer (GC).The serum level of LMX1A in GC, benign, and healthy groups was measured using quantitative real time PCR (qRT-PCR) and compared with the student t test. The associations of serum LMX1A levels with clinical parameters were analyzed with chi-square test. The diagnostic value of serum LMX1A in GC was evaluated by receiver operating characteristic (ROC) curve.The level of serum LMX1A in GC group (1.309 ± 0.553) was significantly lower than that in the benign group (2.174 ± 0.676) and healthy group (2.598 ± 0.826) (P < .01 for both). The decreased level of LMX1A was associated with large tumor size (P = .009), positive lymph node metastasis (P = .027), and advanced TNM stages (P = .002). Receiver operating characteristic (ROC) analysis demonstrated that serum LMX1A could discriminate GC patients from the healthy individuals, with the area under the curve (AUC) of 0.889 (95% confidence interval [CI] = 0.838-0.938) combining with the sensitivity and specificity of 82.68% and 82.61%. Additionally, serum LMX1A also exhibited high accuracy in discriminating between GC patients and benign gastric disease cases (AUC = 0.842, 95% CI = 0.782-0.901), with the sensitivity of 81.89% and specificity of 72.41%.Serum LMX1A may be an effective biomarker for early detection of GC.

Standardization of complex biologically derived spectrochemical datasets

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO₂ levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

Glycerophospholipids pathways and chromosomal instability in gastric cancer: Global lipidomics analysis

BACKGROUND

Based on the breakthrough of genomics analysis, The Cancer Genome Atlas Research Group recently proposed an integrative genomic analysis, dividing gastric cancer (GC) into four subtypes, characterized by the chromosomal instability (CIN) status. However, the CIN status of GC is still vaguely characterized and lacking the valuable easy-to-use CIN markers to diagnosis in molecular and histological detection.

AIM

To explore the associations of CIN with downstream lipidomics profiles.

METHODS

We collected cancerous and noncancerous tissue samples from 18 patients with GC; the samples were divided into CIN and non-CIN types based on the system of The Cancer Genome Atlas Research Group and 409 sequenced oncogenes and tumor suppressor genes. We identified the lipidomics profiles of the GC samples and samples of their adjacent noncancerous tissues by using liquid chromatography–mass spectrometry. Furthermore, we selected leading metabolites based on variable importance in projection scores of > 1.0 and P < 0.05.

RESULTS

Twelve men and six women participated in this study; the participants had a median age of 67.5 years (range, 52–87 years) and were divided into CIN (n = 9) and non-CIN (n = 9) groups. The GC samples exhibited distinct profiles of lysophosphocholine, phosphocholine, phosphatidylethanolamine, phosphatidylinositol, phosphoserine, sphingomyelin, ceramide, and triglycerides compared with their adjacent noncancerous tissues. The glycerophospholipid levels (phosphocholine, phosphatidylethanolamine, and phosphatidylinositol) were 1.4- to 2.3-times higher in the CIN group compared with the non-CIN group (P < 0.05). Alterations in the glycerolipid and glycerophospholipid pathways indicated progression of GC toward CIN.

CONCLUSION

The lipidomics profiles of GC samples were distinct from those of their adjacent noncancerous tissues. CIN status of GC is primarily associated with downstream lipidomics in the glycerophospholipid pathway.

A Metabolomic Study to Identify Potential Tissue Biomarkers for Indomethacin-Induced Gastric Ulcer in Rats

BACKGROUND

Gastric Ulcer (GU) is the most prevalent gastrointestinal disorder induced by various factors and Non-Steroid Anti-Inflammatory Drugs (NSAIDs) as one of the most common reasons. Due to the absence of appropriate molecular markers for GU, the aim of this study was to utilize a metabolomics approach in order to find potential metabolite markers for the disease.

METHODS

Stomach tissue samples from indomethacin-treated rats and normal controls were used to perform a 1H-NMR metabolomics study. The altered metabolites were identified using random forest multivariate analysis.

RESULTS

ROC curves showed that the random forest model had a good predictive performance with AUC of 1 for the test and 0.708 for the training sets. Seventeen differentially expressed metabolites were found between GU and normal tissue sample. These metabolites included trimethylamine, betaine, carnitine, methionine, acetylcho line, choline, N,N-Dimethylglycine, cis-aconitate, tryptophan, spermidine, acetylcarnitine, creatinine, pantothenate, taurine, isoleucine, glucose and kynurenine.

CONCLUSION

The results of the study demonstrated that metabolomics approach could serve as a viable method to find potential markers for GU. Surely, further studies are needed for the validation of the results.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Free amino acid profiling of gastric juice as a method for discovering potential biomarkers of early gastric cancer

Background: Prior studies showed that aromatic amino acids (AAAs) could be used as potential gastric juice biomarkers in screening gastric cancer (GC). To identify new biomarkers for early diagnosis of GC, the characteristics of gastric juice free amino acid (GJFAA) profiling was determined. Method: First, gastric juice was collected from 130 consecutive patients who underwent gastroscopy. They were divided into GC group (n = 47) and non-neoplastic gastric disease (NGD) group (n = 83) according to the pathological diagnosis. The concentrations of 34 GJFAAs were examined by amino acid analyzer. Multivariate and univariate analyses were used for comparing the alterations of GJFAA profiles between the two groups. Then candidate differential GJFAAs were verified by LC-MS/MS in another set of patients, which included 32 GC patients and 38 NGD patients. The diagnostic performance of GJFAAs was evaluated by ROC curve. Results: Significant alterations in GJFAA profiles were observed in GC patients compared to NGD patients in the training set. A total of 14 amino acids were screened as differential GJFAAs. Leucine, threonine and serine were the most frequently altered. Combined AUC of the three non-AAAs [0.869 (95% CI, 0.805-0.934)] was superior to the combined three AAAs [0.841 (95% CI, 0.773-0.908)]. In addition, a combined AUC comprisingthe six ones was further improved to 0.871 (95% CI, 0.809-0.933) in the diagnosis of GC. A similar variation trend and diagnostic value were observed in the validation set. Conclusion: This study indicates the potential of GJFAA profiling as a promising approach for the early detection and screening of GC.

A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis

Early diagnosis of gastric cancer is crucial to improve patient′ outcome. A good biomarker will function in early diagnosis for gastric cancer. In order to find practical and cost-effective biomarkers, we used gas chromatography combined mass spectrometer (GC-MS) to profile urinary metabolites on 293 urine samples. Ninety-four samples are taken as training set, others for validating study. Orthogonal partial least squares discriminant analysis (OPLS-DA), significance analysis of microarray (SAM) and Mann-Whitney U test are used for data analysis. The diagnostic value of urinary metabolites was evaluated by ROC curve. As results, Seventeen metabolites are significantly different between patients and healthy controls in training set. Among them, 14 metabolites show diagnostic value better than classic blood biomarkers by quantitative assay on validation set. Ten of them are amino acids and four are organic metabolites. Importantly, proline, p-cresol and 4-hydroxybenzoic acid disclose outcome-prediction value by means of survival analysis. Therefore, the examination of urinary metabolites is a promising noninvasive strategy for gastric cancer screening.

Plasma sample based analysis of gastric cancer progression using targeted metabolomics

Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We analyzed plasma samples collected from 80 patients diagnosed with NAG without H. pylori infection (NAG−), CAG with H. pylori infection (CAG+), PLGC and GC. A panel of 208 metabolites including acylcarnitines, amino acids and biogenic amines, sphingolipids, glycerophospholipids, hexoses, and tryptophan and phenylalanine metabolites were quantified using two complementary quantitative approaches: Biocrates AbsoluteIDQ®p180 kit and a LC-MS method designed for the analysis of 29 tryptophan pathway and phenylalanine metabolites. Significantly altered metabolic profiles were found in GC patients that allowing discrimination from NAG−, CAG+ and PLGC patients. Pathway analysis showed significantly altered tryptophan and nitrogen metabolic pathways (FDR P < 0.01). Three metabolites (histidine, tryprophan and phenylacetylglutamine) discriminated between non-GC and GC groups. These metabolic signatures open new possibilities to improve surveillance of PLGC patients using a minimally invasive blood analysis.

NMR-based metabolomics for the environmental assessment of Kaohsiung Harbor sediments exemplified by a marine amphipod (Hyalella azteca)

Inflow of wastewater from upstream causes a large flux of pollutants to enter Kaohsiung Harbor in Taiwan daily. To reveal the ecological risk posed by Kaohsiung Harbor sediments, an ecological metabolomic approach was employed to investigate environmental factors pertinent to the physiological regulation of the marine amphipod Hyalella azteca. The amphipods were exposed to sediments collected from different stream inlets of the Love River (LR), Canon River (CR), Jen-Gen River (JR), and Salt River (SR). Harbor entrance 1 (E1) was selected as a reference site. After 10-day exposure, metabolomic analysis of the Hyalella azteca revealed differences between two groups: {E1, LR, CR} and {JR, SR}. The metabolic pathways identified in the two groups of amphipods were significantly different. The results demonstrated that NMR-based metabolomics can be effectively used to characterize metabolic response related to sediment from polluted areas.

A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks

Analyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PB-DSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks. A differential sub-network is extracted to identify crucial information for discriminating different groups and indicating the emergence of complex diseases. Subsequently, PB-DSN defines potential biomarkers based on the topological analysis of these differential sub-networks. In this study, PB-DSN is applied to handle a static genomics dataset of small, round blue cell tumors and a time-series metabolomics dataset of hepatocellular carcinoma. PB-DSN is compared with support vector machine-recursive feature elimination, multivariate empirical Bayes statistics, analyzing time-series data based on dynamic networks, molecular networks based on PCC, PinnacleZ, graph-based iterative group analysis, KeyPathwayMiner and BioNet. The better performance of PB-DSN not only demonstrates its effectiveness for the identification of discriminative features that facilitate disease classification, but also shows its potential for the identification of warning signals.

Metabolomic profiling of CSF in multiple sclerosis and neuromyelitis optica spectrum disorder by nuclear magnetic resonance

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are inflammatory diseases of the central nervous system. Although several studies have characterized the metabolome in the cerebrospinal fluid (CSF) from MS and NMOSD patients, comparative analyses between them and between the relapse and the remission of each disease have not been performed. Both univariate and multivariate analyses were used to compare 1H-NMR spectra of CSF from MS, NMOSD, and healthy controls (HCs). The statistical analysis showed alterations of eight metabolites that were dependent on the disease. Levels of 2-hydroxybutyrate, acetone, formate, and pyroglutamate were higher and levels of acetate and glucose were lower in both MS and NMOSD. Citrate was lower in MS patients, whereas lactate was higher in only NMOSD specifically. The shared feature of metabolic changes between MS and NMOSD may be related to altered energy metabolism and fatty acid biosynthesis in the brain. Another analysis to characterize relapse and remission status showed that isoleucine and valine were down-regulated in MS relapse compared to MS remission. The other metabolites identified in the disease comparison showed the same alterations regardless of disease activity. These findings would be helpful in understanding the biological background of these diseases, and distinguishing between MS and NMOSD, as well as determining the disease activity.

Gastric cancer: Metabolic and metabolomics perspectives (Review)

Gastric cancer is one of the most malignant tumors worldwide and remains a major health threat in Asia-Pacific regions, while its pathological mechanism is generally unknown. Recent research has advanced the understanding of the relationship between metabolic reprogramming and carcinogenesis. In particular, metabolic regulation and cancer research are being further brought into sharp focus with the emergence of metabolomics. Not only can metabolomics provide global information on metabolic profiles of specific tumors, but it can also act as a promising tool to discover biomarkers regarding diagnosis, metastatic surveillance and chemotherapeutic sensitivity prediction. Meanwhile, metabolism-based anticancer therapies will be further discovered. Up to now, accumulative studies have highlighted the application of metabolomics in gastric cancer research regarding different aspects; therefore we summarized the current available results of how metabolic changes are linked to gastric carcinogenesis, and how metabolomics holds promise for the diagnosis, metastatic surveillance, treatment and prognosis prediction of gastric cancer.

A metabolite profiling method for diagnosis of precancerous cervical lesions and HPV persistence

Aim: With the advent of rapid metabolic profiling techniques and of portable mass spectrometers we examined whether cells distinguished by their cytology and persistence of human papillomavirus infection, could be easily differentiated by their metabolite profile. Materials & methods: Direct injection electrospray mass spectrometry was used in a nontargeted double-blind experiment. Samples were collected from women diagnosed with one of two grades of cervical cytology and exhibiting either human papilloma virus persistence or clearance. Cell extracts were prepared using a DNA extraction procedure and the resulting supernatant, normally discarded, was analyzed. Data were interpreted using principal component analysis. Results: The results indicate strongly that a simple metabolite profiling method could be used to rapidly identify women at increased risk of cervical cancer.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

1H NMR metabolomic signatures related to giant cell tumor of the bone

¹H NMR metabolomic profiling for giant cell tumor of the bone.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

INTRODUCTION

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

OBJECTIVES

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs.

METHODS

A literature search of the current relevant primary research was performed.

RESULTS

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

CONCLUSION

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

Metabolomic Analysis of Gastric Cancer Progression within the Correa's Cascade Using Ultraperformance Liquid Chromatography-Mass Spectrometry

Gastric cancer (GC) is among the most common cancers worldwide. Gastric carcinogenesis is a multistep and multifactorial process beginning with chronic gastritis induced by Helicobacter pylori (H. pylori) infection. This process is often described via a sequence of events known as Correas's cascade, a stepwise progression from nonactive gastritis, chronic active gastritis, precursor lesions of gastric cancer (atrophy, intestinal metaplasia, and dysplasia), and finally adenocarcinoma. Our aim was to identify a plasma metabolic pattern characteristic of GC through disease progression within the Correa's cascade. This study involved the analysis of plasma samples collected from 143 patients classified in four groups: patients with nonactive gastritis and no H. pylori infection, H. pylori infected patients with chronic active gastritis, infected or noninfected patients with precursor lesions of gastric cancer, and GC. Independent partial least-squares-discriminant binary models of UPLC-ESI(+)-TOFMS metabolic profiles, implemented in a decision-directed acyclic graph, allowed the identification of tryptophan and kynurenine as discriminant metabolites that could be attributed to indoleamine-2,3-dioxygenase upregulation in cancer patients leading to tryptophan depletion and kynurenine metabolites generation. Furthermore, phenylacetylglutamine was also classified as a discriminant metabolite. Our data suggest the use of tryptophan, kynurenine, and phenylacetylglutamine as potential GC biomarkers.