Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods

Causal effects of genetically determined metabolites and metabolite ratios on esophageal diseases: a two-sample Mendelian randomization study

BACKGROUND

Esophageal diseases (ED) are a kind of common diseases of upper digestive tract. Previous studies have proved that metabolic disorders are closely related to the occurrence and development of ED. However, there is a lack of evidence for causal relationships between metabolites and ED, as well as between metabolite ratios representing enzyme activities and ED. Herein, we explored the causality of genetically determined metabolites (GDMs) on ED through Mendelian Randomization (MR) study.

METHODS

Two-sample Mendelian randomization analysis was used to assess the causal effects of genetically determined metabolites and metabolite ratios on ED. A genome-wide association analysis (GWAS) encompassing 850 individual metabolites along with 309 metabolite ratios served as the exposures. Meanwhile, the outcomes were defined by 10 types of ED phenotypes, including Congenital Malformations of Esophagus (CME), Esophageal Varices (EV), Esophageal Obstructions (EO), Esophageal Ulcers (EU), Esophageal Perforations (EP), Gastroesophageal Reflux Disease (GERD), Esophagitis, Barrett's Esophagus (BE), Benign Esophageal Tumors (BETs), and Malignant Esophageal Neoplasms (MENs). The standard inverse variance weighted (IVW) method was applied to estimate the causal relationship between exposure and outcome. Sensitivity analyses were carried out using multiple methods, including MR-Egger, Weighted Median, MR-PRESSO, Cochran's Q test, and leave-one-out analysis. P < 0.05 was conventionally considered statistically significant. After applying the Bonferroni correction for multiple testing, a threshold of P < 4.3E-05 (0.05/1159) was regarded as indicative of a statistically significant causal relationship. Furthermore, metabolic pathway analysis was performed using the web-based MetaboAnalyst 6.0 software.

RESULTS

The findings revealed that initially, a total of 869 candidate causal association pairs ( P ivw < 0.05) were identified, involving 442 metabolites, 145 metabolite ratios and 10 types of ED. However, upon applying the Bonferroni correction for multiple testing, only 36 pairs remained significant, involving 28 metabolites (predominantly lipids and amino acids), 5 metabolite ratios and 6 types of ED. Sensitivity analyses and reverse MR were performed for these 36 causal association pairs, where the results showed that the pair of EV and 1-(1-enyl-palmitoyl)-2-linoleoyl-GPE (p-16:0/18:2) did not withstand the sensitivity tests, and Hexadecenedioate (C16:1-DC) was found to have a reverse causality with GERD. The final 34 robust causal pairs included 26 metabolites, 5 metabolite ratios and 5 types of ED. The involved 26 metabolites predominantly consisted of methylated nucleotides, glycine derivatives, sex hormones, phospholipids, bile acids, fatty acid dicarboxylic acid derivatives, and N-acetylated amino acids. Furthermore, through metabolic pathway analysis, we uncovered 8 significant pathways that played pivotal roles in five types of ED conditions.

CONCLUSIONS

This study integrated genomics with metabolomics to assess causal relationships between ED and both metabolites and metabolite ratios, uncovering several key metabolic features in ED pathogenesis. These findings have potential as novel biomarkers for ED and provide insights into the disease's etiology and progression. However, further clinical and experimental validations are necessary.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis

OBJECTIVE

To conduct a systematic review and meta-analysis of case-control and cohort human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on esophageal cancer (EC), cancer of the gastroesophageal junction (GEJ), and gastric cancer (GC) in blood and tissue.

BACKGROUND

Upper gastrointestinal cancers (UGC), predominantly EC, GEJ, and GC, are malignant tumour types with high morbidity and mortality rates. Numerous studies have focused on metabolomic profiling of UGC in recent years. In this systematic review and meta-analysis, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with EC, GEJ and GC.

METHODS

Following the PRISMA procedure, a systematic search of four databases (Embase, PubMed, MEDLINE, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of EC, GEJ and GC was conducted and registered at PROSPERO (CRD42023486631). The Newcastle-Ottawa Scale (NOS) was used to benchmark the risk of bias for case-controlled and cohort studies. QUADOMICS, an adaptation of the QUADAS-2 (Quality Assessment of Diagnostic Accuracy) tool, was used to rate diagnostic accuracy studies. Original articles comparing metabolite patterns between patients with and without UGC were included. Two investigators independently completed title and abstract screening, data extraction, and quality evaluation. Meta-analysis was conducted whenever possible. We used a random effects model to investigate the association between metabolite levels and UGC.

RESULTS

A total of 66 original studies involving 7267 patients that met the required criteria were included for review. 169 metabolites were differentially distributed in patients with UGC compared to healthy patients among 44 GC, 9 GEJ, and 25 EC studies including metabolites involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and lipid metabolism. Phosphatidylcholines, eicosanoids, and adenosine triphosphate were among the most frequently reported lipids and metabolites of cellular respiration, while BCAA, lysine, and asparagine were among the most commonly reported amino acids. Previously identified lipid metabolites included saturated and unsaturated free fatty acids and ketones. However, the key findings across studies have been inconsistent, possibly due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group.

CONCLUSION

Thus far, metabolomic studies have provided new opportunities for screening, etiological factors, and biomarkers for UGC, supporting the potential of applying metabolomic profiling in early cancer diagnosis. According to the results of our meta-analysis especially BCAA and TMAO as well as certain phosphatidylcholines should be implicated into the diagnostic procedure of patients with UGC. We envision that metabolomics will significantly enhance our understanding of the carcinogenesis and progression process of UGC and may eventually facilitate precise oncological and patient-tailored management of UGC.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

A critical review of advances in tumor metabolism abnormalities induced by nitrosamine disinfection by-products in drinking water

Intensified sanitation practices amid the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak might result in the increased release of chloramine disinfectants into surface water, significantly promoting the formation of nitrosamine disinfection by-products (DBPs) in drinking water. Unfortunately, these nitrosamine DBPs exhibit significant genotoxic, carcinogenic, and mutagenic properties, whereas chlorinating disinfectants remain in global practice. The current review provides valuable insights into the occurrence, identification, contamination status, exposure limits, and toxicity of the new unregulated disinfection by-products (nitrosamine DBPs) in drinking water. As a result, concentrations of nitrosamine DBPs far exceed allowable limits in drinking water, and prolonged exposure has the potential to cause metabolic disorders, a critical step in tumor initiation and progression. Importantly, based on recent research, we have concluded the role of nitrosamines DBPs in different metabolic pathways. Remarkably, nitrosamine DBPs can induce chronic inflammation and initiate tumors by activating sphingolipid and polyunsaturated fatty acid metabolism. Regarding amino acid and nucleotide metabolism, nitrosamine DBPs can inhibit tryptophan metabolism and de novo nucleotide synthesis. Moreover, inhibition of de novo nucleotide synthesis fails to repair DNA damage induced by nitrosamines. Additionally, the accumulation of lactate induced by nitrosamine DBPs may act as a pivotal signaling molecule in communication within the tumor microenvironment. However, with the advancement of tumor metabolomics, understanding the role of nitrosamine DBPs in causing cancer by inducing metabolic abnormalities significantly lags behind, and specific mechanisms of toxic effects are not clearly defined. Urgently, further studies exploring this promising area are needed.

The Role of Amino Acids in Non-Enzymatic Antioxidant Mechanisms in Cancer: A Review

Currently, the antioxidant properties of amino acids and their role in the physicochemical processes accompanying oxidative stress in cancer remain unclear. Cancer cells are known to extensively uptake amino acids, which are used as an energy source, antioxidant precursors that reduce oxidative stress in cancer, and as regulators of inhibiting or inducing tumor cell-associated gene expression. This review examines nine amino acids (Cys, His, Phe, Met, Trp, Tyr, Pro, Arg, Lys), which play a key role in the non-enzymatic oxidative process in various cancers. Conventionally, these amino acids can be divided into two groups, in one of which the activity increases (Cys, Phe, Met, Pro, Arg, Lys) in cancer, and in the other, it decreases (His, Trp, Tyr). The review examines changes in the metabolism of nine amino acids in eleven types of oncology. We have identified the main nonspecific mechanisms of changes in the metabolic activity of amino acids, and described direct and indirect effects on the redox homeostasis of cells. In the future, this will help to understand better the nature of life of a cancer cell and identify therapeutic targets more effectively.

Metabolic signatures and potential biomarkers for the diagnosis and treatment of colon cancer cachexia

Cancer cachexia (CAC) is a debilitating condition that often arises from noncachexia cancer (NCAC), with distinct metabolic characteristics and medical treatments. However, the metabolic changes and underlying molecular mechanisms during cachexia progression remain poorly understood. Understanding the progression of CAC is crucial for developing diagnostic approaches to distinguish between CAC and NCAC stages, facilitating appropriate treatment for cancer patients. In this study, we establish a mouse model of colon CAC and categorize the mice into three groups: CAC, NCAC and normal control (NOR). By performing nuclear magnetic resonance (NMR)-based metabolomic profiling on mouse sera, we elucidate the metabolic properties of these groups. Our findings unveil significant differences in the metabolic profiles among the CAC, NCAC and NOR groups, highlighting significant impairments in energy metabolism and amino acid metabolism during cachexia progression. Additionally, we observe the elevated serum levels of lysine and acetate during the transition from the NCAC to CAC stages. Using multivariate ROC analysis, we identify lysine and acetate as potential biomarkers for distinguishing between CAC and NCAC stages. These biomarkers hold promise for the diagnosis of CAC from noncachexia cancer. Our study provides novel insights into the metabolic mechanisms underlying cachexia progression and offers valuable avenues for the diagnosis and treatment of CAC in clinical settings.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Cancer insights from magnetic resonance spectroscopy of cells and excised tumors

Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding field that is providing unique insights into cancer. Starting from the 1970s, the field has continued to evolve as a stand-alone technology or as a complement to in vivo MRS to characterize the metabolome of cancer cells, cancer-associated stromal cells, immune cells, tumors, biofluids and, more recently, changes in the metabolome of organs induced by cancers. Here, we review some of the insights into cancer obtained with ex vivo MRS and provide a perspective of future directions. Ex vivo MRS of cells and tumors provides opportunities to understand the role of metabolism in cancer immune surveillance and immunotherapy. With advances in computational capabilities, the integration of artificial intelligence to identify differences in multinuclear spectral patterns, especially in easily accessible biofluids, is providing exciting advances in detection and monitoring response to treatment. Metabolotheranostics to target cancers and to normalize metabolic changes in organs induced by cancers to prevent cancer-induced morbidity are other areas of future development.

Untargeted, High-Resolution Metabolomics in Pediatric Eosinophilic Esophagitis

BACKGROUND/OBJECTIVES

Eosinophilic esophagitis (EoE) is an inflammatory disease of unclear etiology. The aim of this study was to use untargeted plasma metabolomics to identify metabolic pathway alterations associated with EoE to better understand the pathophysiology.

METHODS

This prospective, case-control study included 72 children, aged 1-17 years, undergoing clinically indicated upper endoscopy (14 diagnosed with EoE and 58 controls). Fasting plasma samples were analyzed for metabolomics by high-resolution dual-chromatography mass spectrometry. Analysis was performed on sex-matched groups at a 2:1 ratio. Significant differences among the plasma metabolite features between children with and without EoE were determined using multivariate regression analysis and were annotated with a network-based algorithm. Subsequent pathway enrichment analysis was performed.

RESULTS

Patients with EoE had a higher proportion of atopic disease (85.7% vs 50%, P = 0.019) and any allergies (100% vs 57.1%, P = 0.0005). Analysis of the dual chromatography features resulted in a total of 918 metabolites that differentiated EoE and controls. Glycerophospholipid metabolism was significantly enriched with the greatest number of differentiating metabolites and overall pathway enrichment ( P < 0.01). Multiple amino and fatty acid pathways including linoleic acid were also enriched, as well as pyridoxine metabolism ( P < 0.01).

CONCLUSIONS

In this pilot study, we found differences in metabolites involved in glycerophospholipid and inflammation pathways in pediatric patients with EoE using untargeted metabolomics, as well as overlap with amino acid metabolome alterations found in atopic disease.

1H NMR-based metabolomics of paired tissue, serum and urine samples reveals an optimized panel of biofluids metabolic biomarkers for esophageal cancer

INTRODUCTION

The goal of this study was to establish an optimized metabolic panel by combining serum and urine biomarkers that could reflect the malignancy of cancer tissues to improve the non-invasive diagnosis of esophageal squamous cell cancer (ESCC).

METHODS

Urine and serum specimens representing the healthy and ESCC individuals, together with the paralleled ESCC cancer tissues and corresponding distant non-cancerous tissues were investigated in this study using the high-resolution 600 MHz 1H-NMR technique.

RESULTS

We identified distinct 1H NMR-based serum and urine metabolic signatures respectively, which were linked to the metabolic profiles of esophageal-cancerous tissues. Creatine and glycine in both serum and urine were selected as the optimal biofluids biomarker panel for ESCC detection, as they were the overlapping discriminative metabolites across serum, urine and cancer tissues in ESCC patients. Also, the were the major metabolites involved in the perturbation of "glycine, serine, and threonine metabolism", the significant pathway alteration associated with ESCC progression. Then a visual predictive nomogram was constructed by combining creatine and glycine in both serum and urine, which exhibited superior diagnostic efficiency (with an AUC of 0.930) than any diagnostic model constructed by a single urine or serum metabolic biomarkers.

DISCUSSION

Overall, this study highlighted that NMR-based biofluids metabolomics fingerprinting, as a non-invasive predictor, has the potential utility for ESCC detection. Further studies based on a lager number size and in combination with other omics or molecular biological approaches are needed to validate the metabolic pathway disturbances in ESCC patients.

Integrative metabolic analysis of orbital adipose/connective tissue in patients with thyroid-associated ophthalmopathy

Objective

Thyroid-associated ophthalmopathy (TAO) is a disfiguring autoimmune disease, which destroys the structure of orbital tissues and even threatens vision. Metabolic reprograming is critical in autoimmune diseases; however, the metabolic basis of TAO remains to be clarified. Our study aimed to reveal the metabolic profile of TAO.

Methods

Orbital adipose/connective tissues from eleven TAO patients and twelve control subjects were collected during surgeries and analyzed with liquid chromatograph-mass spectrometer. Orthogonal partial least-squares discrimination analysis (OPLS-DA), variable importance in projection (VIP), heat map, and volcano plot were used to reveal metabolic profile in TAO. Pathway analysis and metabolites-gene analysis were utilized to explore potential metabolic metabolism in TAO.

Results

3038 metabolites were detected in samples from the TAO patients and the controls. OPLS-DA analysis of the metabolomics results showed two distinguished groups, demonstrating that TAO has a unique metabolome. Univariate tests identified 593 dysregulated metabolites (P < 0.05), including 367 increased metabolites and 226 decreased metabolites. Pathway analysis showed that changed metabolites were enriched in cholesterol metabolism, choline metabolism in cancer, fat digestion and absorption, regulation of lipolysis in adipocytes, and insulin resistance. In addition, metabolites-gene analysis illustrated that cholesterol metabolism was involved in the pathogenesis of TAO. Endoplasmic reticulum stress-related genes (ATF6, PERK, and IRE1α) expressions were higher in TAO orbital tissues than in control orbital tissues verified by western blot. Additionally, the expression level of diacylglycerol acyltransferase 1 (DGAT1), a key metabolic protein for triacylglycerol synthesis, was increased in orbital tissues of TAO detected by qRT-PCR, indicating disrupted cholesterol metabolism in TAO.

Conclusion

The present study demonstrated different metabolite profiles and potential metabolic mechanisms in TAO.

Sleep Disorder and Cocaine Abuse Impact Purine and Pyrimidine Nucleotide Metabolic Signatures

Disturbances in the circadian rhythm alter the normal sleep-wake cycle, which increases vulnerability to drug abuse. Drug abuse can disrupt several homeostatic processes regulated by the circadian rhythm and influence addiction paradigms, including cravings for cocaine. The relationship between circadian rhythm and cocaine abuse is complex and bidirectional, and disruption impacts both brain function and metabolic profiles. Therefore, elucidating the impact of circadian rhythm changes and cocaine abuse on the human metabolome may provide new insights into identifying potential biomarkers. We examine the effect of cocaine administration with and without circadian rhythm sleep disruption (CRSD) on metabolite levels and compare these to healthy controls in an in vivo study. A metabolomics analysis is performed on the control, CRSD, cocaine, and CRSD with cocaine groups. Plasma metabolite concentrations are analyzed using a liquid chromatography electrochemical array platform. We identify 242 known metabolites compared to the control; 26 in the CRSD with cocaine group, 4 in the CRSD group, and 22 in the cocaine group are significantly differentially expressed. Intriguingly, in the CRSD with cocaine treatment group, the expression levels of uridine monophosphate (p < 0.008), adenosine 5′-diphosphate (p < 0.044), and inosine (p < 0.019) are significantly altered compared with those in the cocaine group. In summary, alterations in purine and pyrimidine metabolism provide clues regarding changes in the energy profile and metabolic pathways associated with chronic exposure to cocaine and CRSD.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with esophageal cancer

Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0–II) and the late stage (III and IV) among the 0–IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Background

Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims

To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results

Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions

Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Highly Efficient Separation of Ultratrace Radioactive Copper Using a Flow Electrolysis Cell

Preparing compounds containing the radioisotope ⁶⁴Cu for use in positron emission tomography cancer diagnostics is an ongoing area of research. In this study, a highly efficient separation method to recover ⁶⁴Cu generated by irradiating the target ⁶⁴Ni with a proton beam was developed by employing a flow electrolysis cell (FE). This system consists of (1) applying a reduction potential for the selective adsorption of ⁶⁴Cu from the target solution when dissolved in HCl and (2) recovering the ⁶⁴Cu deposited onto the carbon working electrode by desorbing it from the FE during elution with 10 mmol/L HNO₃, which applies an oxidation potential. The ⁶⁴Cu was selectively eluted at approximately 30 min under a flow rate of 0.5 mL/min from the injection to recovery. The newly developed flow electrolysis system can separate the femtomolar level of ultratrace radioisotopes from the larger amount of target metals as an alternative to conventional column chromatography.

Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

An integrative chemometric approach and correlative metabolite networking of LC-MS and 1H NMR based urine metabolomics for radiation signatures

The increasing threat of nuclear terrorism or radiological accident has made high throughput radiation biodosimetry a requisite for the immediate response for triage. Owing to detection of subtle alterations in biological pathways before the onset of clinical conditions, metabolomics has become an important tool for studying biomarkers and the related mechanisms for radiation induced damage. Here, we have attempted to combine two detection techniques, LC-MS and ¹H NMR spectroscopy, to obtain a comprehensive metabolite profile of urine at 24 h following lethal (7.5 Gy) and sub-lethal (5 Gy) irradiation in mice. Integrated data analytics using multiblock-OPLSDA (MB-OPLSDA), correlation networking and pathway analysis was used to identify metabolic disturbances associated with radiation exposure. MB-OPLSDA revealed better clustering and separation of irradiated groups compared with controls without overfitting (p-value of CV-ANOVA: 1.5 × 10^-3). Metabolites identified through MB-OPLSDA, namely, taurine, creatine, citrate and 2-oxoglutarate, were found to be dose independent markers and further support and validate our earlier findings as potential radiation injury biomarkers. Integrated analysis resulted in the enhanced coverage of metabolites and better correlation networking in energy, taurine, gut flora, L-carnitine and nucleotide metabolism observed post irradiation in urine. Our study thus emphasizes the major advantage of using the two detection techniques along with integrated analysis for better detection and comprehensive understanding of disturbed metabolites in biological pathways.

NMR-Based Metabolomics Revealed the Underlying Inflammatory Pathology in Reactive Arthritis Synovial Joints

Reactive arthritis (ReA) is an aseptic synovitis condition that often develops 2-4 weeks after a distant (extra-articular) infection with Chlamydia, Salmonella, Shigella, Campylobacter, and Yersinia species. The metabolic changes in the synovial fluid (SF) may serve as indicative markers to both improve the diagnostic accuracy and understand the underlying inflammatory pathology of ReA. With this aim, the metabolic profiles of SF collected from ReA (n = 58) and non-ReA, i.e., rheumatoid arthritis (RA, n = 21) and osteoarthritis (OA, n = 20) patients, respectively, were measured using NMR spectroscopy and compared using orthogonal partial least-squares discriminant analysis (OPLS-DA). The discriminatory metabolic features were further evaluated for their diagnostic potential using the receiver operating characteristic (ROC) curve analysis. Compared to RA, two (alanine and carnitine), and compared to OA, six (NAG, glutamate, glycerol, isoleucine, alanine, and glucose) metabolic features were identified as diagnostic biomarkers. We further demonstrated the impact of ReA synovitis condition on the serum metabolic profiles through performing a correlation analysis. The Pearson rank coefficient (r) was estimated for 38 metabolites (profiled in both SF and serum samples obtained in pair from ReA patients) and was found significantly positive for 71% of the metabolites (r ranging from 0.17 to 0.87).

A systematic review of metabolomic profiling of gastric cancer and esophageal cancer

Objective: Upper gastrointestinal (UGI) cancers, predominantly gastric cancer (GC) and esophageal cancer (EC), are malignant tumor types with high morbidity and mortality rates. Accumulating studies have focused on metabolomic profiling of UGI cancers in recent years. In this systematic review, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with GC and EC.

Methods: A systematic search of three databases (Embase, PubMed, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of GC and EC was conducted. The Newcastle–Ottawa Scale (NOS) was used to assess the quality of the included articles.

Results: A total of 52 original studies were included for review. A number of metabolites were differentially distributed between GC and EC cases and non-cases, including those involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and protein and lipid metabolism. Lactic acid, glucose, citrate, and fumaric acid were among the most frequently reported metabolites of cellular respiration while glutamine, glutamate, and valine were among the most commonly reported amino acids. The lipid metabolites identified previously included saturated and unsaturated free fatty acids, aldehydes, and ketones. However, the key findings across studies to date have been inconsistent, potentially due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group.

Conclusions: Studies on metabolomics have thus far provided insights into etiological factors and biomarkers for UGI cancers, supporting the potential of applying metabolomic profiling in cancer prevention and management efforts.

The Role of Microbiota in the Pathogenesis of Esophageal Adenocarcinoma

Simple Summary

Esophageal adenocarcinoma has a poor 5-year survival rate and is among the highest mortality cancers. Changes in the esophageal microbiome have been associated with cancer pathogenesis; however, the molecular mechanism remains obscure. This review article critically analyzes the molecular mechanisms through which microbiota may mediate the development and progression of esophageal adenocarcinoma and its precursors-gastroesophageal reflux disease and Barrett’s esophagus. It summarizes changes in esophageal microbiome composition in normal and pathologic states and subsequently discusses the role of altered microbiota in disease progression. The potential role of esophageal microbiota in protecting against the development of esophageal adenocarcinoma is also discussed. By doing so, this article highlights specific directions for future research developing microbiome-mediated therapeutics for esophageal adenocarcinoma.

Abstract

Esophageal adenocarcinoma (EAC) is associated with poor overall five-year survival. The incidence of esophageal cancer is on the rise, especially in Western societies, and the pathophysiologic mechanisms by which EAC develops are of extreme interest. Several studies have proposed that the esophageal microbiome may play an important role in the pathophysiology of EAC, as well as its precursors—gastroesophageal reflux disease (GERD) and Barrett’s esophagus (BE). Gastrointestinal microbiomes altered by inflammatory states have been shown to mediate tumorigenesis directly and are now being considered as novel targets for both cancer treatment and prevention. Elucidating molecular mechanisms through which the esophageal microbiome potentiates the development of GERD, BE, and EAC will provide a foundation on which new therapeutic targets can be developed. This review summarizes current findings that elucidate the molecular mechanisms by which microbiota promote the pathogenesis of GERD, BE, and EAC, revealing potential directions for additional research on the microbiome-mediated pathophysiology of EAC.

Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities

BACKGROUND

Esophageal cancer has the sixth highest rate of cancer-associated deaths worldwide, with many patients displaying metastases and chemotherapy resistance. We sought to find subtypes to see if precision medicine could play a role in finding new potential targets and predicting responses to therapy. Since metabolism not only drives cancers but also serves as a readout, metabolism was examined as a key reporter for differences.

METHODS

Unsupervised and supervised classification methods, including hierarchical clustering, partial least squares discriminant analysis, k-nearest neighbors, and machine learning techniques, were used to discover and display two major subgroups. Genes, pathways, gene ontologies, survival, and immune differences between the groups were further examined, along with biomarkers between the groups and against normal tissue.

RESULTS

Esophageal cancer had two major unique metabolic profiles observed between the histological subtypes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). The metabolic differences suggest that ESCC depends on glycolysis, whereas EAC relies more on oxidative metabolism, catabolism of glycolipids, the tricarboxylic acid (TCA) cycle, and the electron transport chain. We also noted a robust prognostic risk associated with COQ3 expression. In addition to the metabolic alterations, we noted significant alterations in key pathways regulating immunity, including alterations in cytokines and predicted immune infiltration. ESCC appears to have increased signature associated with dendritic cells, Th17, and CD8 T cells, the latter of which correlate with survival in ESCC. We bioinformatically observed that ESCC may be more responsive to checkpoint inhibitor therapy than EAC and postulate targets to enhance therapy further. Lastly, we highlight correlations between differentially expressed enzymes and the potential immune status.

CONCLUSION

Overall, these results highlight the extreme differences observed between the histological subtypes and may lead to novel biomarkers, therapeutic strategies, and differences in therapeutic response for targeting each esophageal cancer subtype.

1 H NMR-based metabolomics of paired esophageal tumor tissues and serum samples identifies specific serum biomarkers for esophageal cancer

Serum metabolites of healthy controls and esophageal cancer (EC) patients have previously been compared to predict cancer-specific profiles. However, the association between metabolic alterations in serum samples and esophageal tissues in EC patients remains unclear. Here, we analyzed 50 pairs of EC tissues and distant noncancerous tissues, together with patient-matched serum samples, using ¹ H NMR spectroscopy and pattern recognition algorithms. EC patients could be differentiated from the controls based on the metabolic profiles at tissue and serum levels. Some overlapping discriminatory metabolites, including valine, alanine, glucose, acetate, citrate, succinate and glutamate, were identified in both matrices. These results suggested deregulation of metabolic pathways, and potentially revealed the links between EC and several metabolic pathways, such as the tricarboxylic acid cycle, glutaminolysis, short-chain fatty acid metabolism, lipometabolism and pyruvate metabolism. Perturbation of the pyruvate metabolism was most strongly associated with EC progression. Consequently, an optimal serum metabolite biomarker panel comprising acetate and pyruvate was developed, as these two metabolites are involved in pyruvate metabolism, and changes in their serum levels were significantly correlated with alterations in the levels of some other esophageal tissue metabolites. In comparison with individual biomarkers, this panel exhibited better diagnostic efficiency for EC, with an AUC of 0.948 in the test set, and a good predictive ability of 82.5% in the validation set. Analysis of key genes related to pyruvate metabolism in EC patients revealed patterns corresponding to the changes in serum pyruvate and acetate levels. These correlation analyses demonstrate that there were distinct metabolic characteristics and pathway aberrations in the esophageal tumor tissue and in the serum. Changes in the serum metabolic signatures could reflect the alterations in the esophageal tumor profile, thereby emphasizing the importance of distinct serum metabolic profiles as potential noninvasive biomarkers for EC.

Serum metabolomics analysis for the progression of esophageal squamous cell carcinoma

BACKGROUND: Previous metabolomics studies have found differences in metabolic characteristics between the healthy and ESCC patients. However, few of these studies concerned the whole process of the progression of ESCC. This study aims to explore serum metabolites associated with the progression of ESCC.

METHODS: Serum samples from 653 participants (305 normal, 77 esophagitis, 228 LGD, and 43 HGD/ESCC) were examined by ultra-high performance liquid chromatography quadruple time-of-flight mass spectrometry (UHPLC-QTOF/MS). Principal component analysis (PCA) was first applied to obtain an overview of the clustering trend for the multidimensional data. Fuzzy c-means (FCM) clustering was then used to screen metabolites with a changing tendency in the progression of ESCC. Univariate ordinal logistic regression analysis and multiple ordinal logistic regression analysis were applied to evaluate the association of metabolites with the risk of ESCC progression, and adjusted for age, gender, BMI, tobacco smoking, and alcohol drinking status.

RESULTS: After FCM clustering analysis, a total of 38 metabolites exhibiting changing tendency among normal, esophagitis, LGD, and HGD/ESCC patients. Final results showed 15 metabolites associated with the progression of ESCC. Ten metabolites (dopamine, L-histidine, 5-hydroxyindoleacetate, L-tryptophan, 2'-O-methylcytidine, PC (14:0/0:0), PC (O-16:1/0:0), PE (18:0/0:0), PC (16:1/0:0), PC (18:2/0:0)) were associated with decreased risk of developing ESCC. Five metabolites (hypoxanthine, inosine, carnitine (14:1), glycochenodeoxycholate, PC (P-18:0/18:3)) were associated with increased risk of developing ESCC.

CONCLUSIONS: These results demonstrated that serum metabolites are associated with the progression of ESCC. These metabolites are capable of potential biomarkers for the risk prediction and early detection of ESCC.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

UPLC-Q-TOF/MS-based plasma metabolome to identify biomarkers and time of injury in traumatic brain injured rats

BACKGROUND

To identify the potent metabolic biomarkers and time of injury of traumatic brain injured (TBI).

METHODS

A total of 70 Sprague-Dawley rats were used to establish the TBI model in this study. The serum was collected at 3 h, 6 h, 12 h, 24 h, 3 days and 7 days after surgery. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was performed to analyze metabolic changes in the serum of the TBI rats from different groups. The differences between the metabolic profiles of the rats in seven groups were analyzed using partial least squares discriminant analysis.

RESULTS

Metabolic profiling revealed significant differences between the sham-operated and other groups. A total of 49 potential TBI metabolite biomarkers were identified between the sham-operated group and the model groups at different time points. Among them, six metabolites (methionine sulfone, kynurenine, 3-hydroxyanthranilic acid, 3-Indolepropionic acid, citric acid and glycocholic acid) were identified as biomarkers of TBI to estimate the injury time.

CONCLUSION

Using metabolomic analysis, we identified new TBI serum biomarkers for accurate detection and determination of the timing of TBI injury.

NMR and LCMS analytical platforms exhibited the nephroprotective effect of Clinacanthus nutans in cisplatin-induced nephrotoxicity in the in vitro condition

Background

Clinacanthus nutans (C. nutans) Lind. locally known as Belalai Gajah or Sabah snake grass is a medicinal plant belonging to Acanthaceae family. In Asia, this plant is traditionally used for treating skin rashes, insects and snake bites, diabetes mellitus, fever and for diuretic effect. C. nutans has been reported to possess biological activities including anti-oxidant, anti-inflammation, anti-cancer, anti-diabetic and anti-viral activities.

Methods

Proton Nuclear Magnetic Resonance (¹H NMR) and Liquid Chromatography Mass Spectroscopy (LCMS) coupled with multivariate data analysis were employed to characterize the metabolic variations of intracellular metabolites and the compositional changes of the corresponding culture media in rat renal proximal tubular cells (NRK-52E).

Results

NMR and LCMS analysis highlighted choline, creatine, phosphocholine, valine, acetic acid, phenylalanine, leucine, glutamic acid, threonine, uridine and proline as the main metabolites which differentiated the cisplatin-induced group of NRK-52E from control cells extract. The corresponding media exhibited lactic acid, glutamine, glutamic acid and glucose-1-phosphate as the varied metabolites. The altered pathways perturbed by cisplatin nephrotoxic on NRK-52E cells included changes in amino acid metabolism, lipid metabolism and glycolysis.

Conclusion

The C. nutans aqueous extract (1000 μg/mL) exhibited the most potential nephroprotective effect against cisplatin toxicity on NRK-52E cell lines at 89% of viability. The protective effect could be seen through the changes of the metabolites such as choline, alanine and valine in the C. nutans pre-treated samples with those of the cisplatin-induced group.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12906-020-03067-3.

The Function and Mechanism of Lipid Molecules and Their Roles in The Diagnosis and Prognosis of Breast Cancer

Lipids are essential components of cell structure and play important roles in signal transduction between cells and body metabolism. With the continuous development and innovation of lipidomics technology, many studies have shown that the relationship between lipids and cancer is steadily increasing, involving cancer occurrence, proliferation, migration, and apoptosis. Breast cancer has seriously affected the safety and quality of life of human beings worldwide and has become a significant public health problem in modern society, with an especially high incidence among women. Therefore, the issue has inspired scientific researchers to study the link between lipids and breast cancer. This article reviews the research progress of lipidomics, the biological characteristics of lipid molecules, and the relationship between some lipids and cancer drug resistance. Furthermore, this work summarizes the lipid molecules related to breast cancer diagnosis and prognosis, and then it clarifies their impact on the occurrence and development of breast cancer The discussion revolves around the current research hotspot long-chain non-coding RNAs (lncRNAs), summarizes and explains their impact on tumor lipid metabolism, and provides more scientific basis for future cancer research studies.

The Diagnostic and Prognostic Value of a Liquid Biopsy for Esophageal Cancer: A Systematic Review and Meta-Analysis

Simple Summary

The “liquid biopsy” is a novel concept for detecting circulating biomarkers in the peripheral blood of patients with various cancers, including esophageal cancer. There are two main methods to identify circulating cancer related biomarkers such as morphological techniques or molecular biological techniques. There are some differences in the sensitivity and specificity for detecting circulating tumor cells (CTCs) or circulating markers between each method. Although it is still challenging to determine strong candidates for early diagnosis and predicting prognosis in patients with esophageal cancer, our meta-analysis might be a milestone for the future development of liquid biopsies in use with esophageal cancer.

Abstract

Esophageal cancer is among the most aggressive diseases, and circulating tumor cells (CTCs) have been recognized as novel biomarkers for various cancers over the past two decades, including esophageal cancer. CTCs might provide crucial clinical information for predicting cancer prognosis, monitoring therapeutic responses or recurrences, or elucidating the mechanism of metastasis. The isolation of CTCs is among the applications of a “liquid biopsy”. There are various technologies for liquid biopsies, and they are classified into two main methods: cytometric or non-cytometric techniques. Here, we review a total of 57 eligible articles to summarize various technologies for the use of a liquid biopsy in esophageal cancer and perform a meta-analysis to assess the clinical utility of liquid biopsies as a prognostic and diagnostic biomarker technique. For prognostic evaluation, the pooled hazard ratio in the cytometric assay is relatively higher than that of the non-cytometric assay. On the other hand, a combination of multiple molecules, using a non-cytometric assay, might be a favorable biomarker technique for the early diagnosis of esophageal cancer. Although determining strong evidence for a biomarker by using a liquid biopsy is still challenging, our meta-analysis might be a milestone for the future development of liquid biopsies in use with esophageal cancer.

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.

A multinational review: Oesophageal cancer in low to middle-income countries

Oesophageal cancer (OC) is an aggressive neoplasm that manifests in the gastrointestinal tract and is the result of numerous factors that can contribute to the development of the disease. These may include old age, nutritional deficiencies, oesophageal obstruction and food ingestion difficulties. Environmental factors serve a large role in increasing the risk of developing OC. Two factors that serve an increasing risk of developing OC are the use of tobacco and the consumption of alcohol. Genetic factors also exhibit a large effect on the risk of developing OC, for example, the causative genes in Black Africans differ from other races. OC is 3–4 times more common among men than women. OC has been previously reported in >450 000 individuals worldwide, and its incidence is increasing. The current review compares OC in low to middle-income countries with developed countries. The incidence of OC, particularly squamous cell carcinoma (SCC) is high in low and middle-income countries. In developed countries, the incidence of SCC is low compared with adenocarcinoma. The majority of OC cases are diagnosed in the late stages of the disease, leading to high mortality rates. The current review aimed to discuss factors that contribute to the development of this disease in different geographical areas and genetic mechanisms governing these findings. The current review also aims to discuss the preventative treatment options for the disease, and also discusses the diagnosis and surveillance in five LMICs, including South Africa, China, Tanzania, India and Brazil.

Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses

Tyrosine is mainly degraded in the liver by a series of enzymatic reactions. Abnormal expression of the tyrosine catabolic enzyme tyrosine aminotransferase (TAT) has been reported in patients with hepatocellular carcinoma (HCC). Despite this, aberration in tyrosine metabolism has not been investigated in cancer development. In this work, we conduct comprehensive cross-platform study to obtain foundation for discoveries of potential therapeutics and preventative biomarkers of HCC. We explore data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine and Kaplan Meier plotter (KM plotter) and performed integrated analyses to evaluate the clinical significance and prognostic values of the tyrosine catabolic genes in HCC. We find that five tyrosine catabolic enzymes are downregulated in HCC compared to normal liver at mRNA and protein level. Moreover, low expression of these enzymes correlates with poorer survival in patients with HCC. Notably, we identify pathways and upstream regulators that might involve in tyrosine catabolic reprogramming and further drive HCC development. In total, our results underscore tyrosine metabolism alteration in HCC and lay foundation for incorporating these pathway components in therapeutics and preventative strategies.

Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System

Aim

To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC.

Method

Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained.

Results

The nonlesional tissues showed peaks at 891 cm⁻¹, 1103 cm⁻¹, 1417 cm⁻¹, 1206 cm⁻¹, 1234 cm⁻¹, 1479 cm⁻¹, 1560 cm⁻¹, and 1678 cm⁻¹. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm⁻¹ was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P < 0.05). Considering the peak intensity ratio of I1560 cm⁻¹/I1103 cm⁻¹ as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 98.8%, 93.9%, and 91.9%, respectively. Considering the integral energy ratio (noncontinuous frequency band and continuous frequency band) as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 99.2-99.6%, 93.9-97.0%, and 95.5%, respectively.

Conclusions

The integral energy ratio of the Raman spectrum could be considered an effective indicator for the diagnosis of EGC.

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most fatal diseases worldwide. Because early diagnosis is difficult, ESCC is mostly diagnosed at an advanced stage, leading to a poor overall prognosis. The purpose of this study was to explore the differences between plasma metabolic profiles in ESCC patients and healthy controls and to establish a diagnostic model of ESCC.

Methods: In this study, a cohort of 310 subjects, containing 140 ESCC patients and 170 healthy controls (HC), was recruited. Participants were randomly separated into a training set (80 ESCCs, 80 HCs) and a validation set (60 ESCCs, 90 HCs) and their plasma metabolomics profiles were analyzed by ultra-performance liquid chromatography-tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF/MS) technique. Univariate statistical analysis and multivariate analysis (MVA) methods were used to identify differential metabolites. Finally, the dysregulated pathways associated with ESCC were further explored and the diagnostic performance of the biomarker panel was evaluated.

Results: Metabolic analyses identified 34 significant metabolites involved in the metabolism of amino acids, phospholipids, fatty acids, purine, and choline. Farthermore, an effective diagnostic model for ESCC was constructed based on eight metabolites. This panel of biomarkers consisted of hypoxanthine, proline betaine, indoleacrylic acid, inosine, 9-decenoylcarnitine, tetracosahexaenoic acid, LPE (20:4), and LPC (20:5). The model was verified and evaluated in the validation set. The AUC value of the ROC curve was 0.991(95% CI: 0.981-1.000, CI, Confidence interval), with a sensitivity (SE) of 98.8% and a specificity (SP) of 94.9% for the training set and 0.965(95% CI: 0.936-0.993), with a SE of 88.3% and a SP of 88.9% for the validation set. Among them, three biomarkers, indoleacrylic acid, LPC (20:5), and LPE (20:4), exhibited a trend associated with the ESCC progression.

Conclusions: Our study identified a novel plasma biomarker panel, which clearly distinguishes ESCC patients and provides insight into the mechanisms of ESCC. This finding may form the basis for the development of a minimally invasive method for ESCC detection.

Metabolomics studies in gastrointestinal cancer: a systematic review

Introduction: This systemic review provides an overview of metabolic perturbations and possible mechanisms in gastrointestinal cancer. The authors discuss emerging challenges of technical and clinical applications.Areas covered: In this systemic review, the authors summarized the currently available results of metabolomic biomarkers linked to GI cancer, and discussed the altered metabolism pathways including carbohydrate metabolism, amino acid metabolism, lipids, and nucleotide metabolism and other metabolisms. Furthermore, future efforts need to adhere to normalize analysis procedures, validate with the larger cohort and utilize multiple-omics technologies. The search was conducted in PubMed with the following search terms (biomarker, gastrointestinal cancer, colorectal cancer, and esophageal cancer) from 2013 to 2019.Expert opinion: This systemic review summarized the currently available results of metabolomic biomarkers linked to gastrointestinal cancer, and discussed the altered metabolism pathways. The authors believe that metabolomics will benefit deeper understandings of the pathogenic mechanism, discovery of biomarkers and aid the search for drug targets as we move toward the era of personalized medicine. Personalized medication for tumors can improve the curative effect, avoid side effects and medical resource waste. As a promisingtool, metabolomics that targets the entire cancer-specific metabolite network should be applied more widely in cancer research.

Plasma metabolites as possible biomarkers for diagnosis of breast cancer

Metabolomic approaches have been used to identify new diagnostic biomarkers for various types of cancers, including breast cancer. In this study, we aimed to identify potential biomarkers of breast cancer using plasma metabolic profiling. Furthermore, we analyzed whether these biomarkers had relationships with clinicopathological characteristics of breast cancer. Our study used two liquid chromatography-mass spectrometry sets: a discovery set (40 breast cancer patients and 30 healthy controls) and a validation set (30 breast cancer patients and 16 healthy controls). All breast cancer patients were randomly selected from among stage I–III patients who underwent surgery between 2011 and 2016. First, metabolites distinguishing cancer patients from healthy controls were identified in the discovery set. Then, consistent and reproducible metabolites were evaluated in terms of their utility as possible biomarkers of breast cancer. Receiver operating characteristic (ROC) analysis was applied to the discovery set, and ROC cut-off values for the identified metabolites derived therein were applied to the validation set to determine their diagnostic performance. Ultimately, four candidate biomarkers (L-octanoylcarnitine, 5-oxoproline, hypoxanthine, and docosahexaenoic acid) were identified. L-octanoylcarnitine showed the best diagnostic performance, with a 100.0% positive predictive value. Also, L-octanoylcarnitine levels differed according to tumor size and hormone receptor expression. The plasma metabolites identified in this study show potential as biomarkers allowing early diagnosis of breast cancer. However, the diagnostic performance of the metabolites needs to be confirmed in further studies with larger sample sizes.

Hyperspectral Raman microscopy can accurately differentiate single cells of different human thyroid nodules

We report on the use of line-scan hyperspectral Raman microscopy in combination with multivariate statistical analyses for identifying and classifying single cells isolated from clinical samples of human thyroid nodules based on their intrinsic Raman spectral signatures. A total of 248 hyperspectral Raman images of single cells from benign thyroid (n = 127) and classic variant of papillary carcinoma (n = 121) nodules were collected. Spectral differences attributed to phenylalanine, tryptophan, proteins, lipids, and nucleic acids were identified for benign and papillary carcinoma cells. Using principal component analysis and linear discriminant analysis, cells were identified with 97% diagnostic accuracy. In addition, preliminary data of cells from follicular adenoma (n = 20), follicular carcinoma (n = 25), and follicular variant of papillary carcinoma (n = 18) nodules suggest the feasibility of further discrimination of subtypes. Our findings indicate that hyperspectral Raman microscopy can potentially be developed into an objective approach for analyzing single cells from fine needle aspiration (FNA) biopsies to enable the minimally invasive diagnosis of "indeterminate" thyroid nodules and other challenging cases.

Cerumenogram: a new frontier in cancer diagnosis in humans

Cancer is the deadliest human disease and the development of new diagnosis methods is important to increase the chances of a cure. In this work it was developed a new method, named here for the first time as cerumenogram, using cerumen (earwax) as a new biomatrix for diagnosis. Earwax samples collected from cancer patients (cancer group) and cancer-free patients (control group) were analyzed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS), following with multivariate analysis steps to process the raw data generated. In total, 158 volatile organic metabolites (VOMs) were identified in the cerumen samples. The 27 selected as potential VOMs biomarkers for cancer provided 100% discrimination between the cancer and control groups. This new test can thus be routinely employed for cancer diagnoses that is non-invasive, fast, cheap, and highly accurate.

Genome-wide tracts of homozygosity and exome analyses reveal repetitive elements with Barrets esophagus/esophageal adenocarcinoma risk

BACKGROUND

Barrett's esophagus (BE) is most commonly seen as the condition in which the normal squamous epithelium lining of the esophagus is replaced by goblet cells. Many studies show that BE is a predisposing factor for the development of esophageal adenocarcinoma (EAC), a particularly lethal cancer. The use of single nucleotide polymorphisms (SNPs) to map BE/EAC genes has previously provided insufficient genetic information to fully characterize the heterogeneous nature of the disease. We therefore hypothesize that rigorous interrogation of other types of genomic changes, e.g. tracts of homozygosity (TOH), repetitive elements, and insertion/deletions, may provide a comprehensive understanding of the development of BE/EAC.

RESULTS

First, we used a case-control framework to identify TOHs by using SNPs and tested for association with BE/EAC. Second, we used a case only approach on a validation series of eight samples subjected to exome sequencing to identify repeat elements and insertion/deletions. Third, insertion/deletions and repeat elements identified in the exomes were then mapped onto genes in the significant TOH regions. Overall, 24 TOH regions were significantly differentially represented among cases, as compared to controls (adjusted-P = 0.002-0.039). Interestingly, four BE/EAC-associated genes within the TOH regions consistently showed insertions and deletions that overlapped across eight exomes. Predictive functional analysis identified NOTCH, WNT, and G-protein inflammation pathways that affect BE and EAC.

CONCLUSIONS

The integration of common TOHs (cTOHs) with repetitive elements, insertions, and deletions within exomes can help functionally prioritize factors contributing to low to moderate penetrance predisposition to BE/EAC.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Normalization of abnormal plasma amino acid profile-based indexes in patients with gynecological malignant tumors after curative treatment

BACKGROUND

We developed a novel plasma amino acid profile-based index (API) to detect ovarian, uterine, cervical, and endometrial cancers. In this study, we aimed to evaluate whether abnormal API values could be normalized after curative treatment in patients with gynecological malignant tumors.

METHODS

Patients with gynecological cancer with abnormal API values were included in this study. Pre-operative absolute API values were compared with those after curative treatment. The normalization rates of API values in patients negative for the expression of three well-known tumor markers (SCC, CA125, and CA19-9) were also evaluated. In addition, related amino acid profiles in healthy controls and patients under pre- and post-treatment conditions were analyzed.

RESULTS

Among 94 patients with abnormal pre-operative API values, the median API value was decreased from 9.52 to 2.17 after treatment (normalization rate: 88.3%). The decreased ranges were similar in patients with adenocarcinoma (6.28; 95% confidence interval [CI]: 5.43-6.95) and squamous carcinoma (7.44; 95% CI: 3.04-8.46). In 93.5% (43/46) of patients negative for tumor markers prior to operation, API values were normalized after the successful treatment. In addition, some pre-operative abnormal amino acid profiles, including Ile, Trp, and His, were reversibly normalized after treatment.

CONCLUSION

The API is a promising tumor marker in gynecological malignancies for the diagnosis of remission, particularly in patients negative for general tumor markers. Further studies are needed to explore the mechanisms related to the normalization of abnormal amino acid profiles.

Endometrium metabolomic profiling reveals potential biomarkers for diagnosis of endometriosis at minimal-mild stages

BACKGROUND

The sensitivity and specificity of non-invasive diagnostic methods for endometriosis, especially at early stages, are not optimal. The clinical diagnostic indicator cancer antigen 125 (CA125) performs poorly in the diagnosis of minimal endometriosis, with a sensitivity of 24%. Therefore, it is urgent to explore novel diagnostic biomarkers. We evaluated the metabolomic profile variation of the eutopic endometrium between minimal-mild endometriosis patients and healthy women by ultra-high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (UHPLC-ESI-HRMS).

METHODS

Our study comprised 29 patients with laparoscopically confirmed endometriosis at stages I-II and 37 infertile women who underwent diagnostic laparoscopy combined with hysteroscopy from January 2014 to January 2015. Eutopic endometrium samples were collected by pipelle endometrial biopsy. The metabolites were quantified by UHPLC-ESI-HRMS. The best combination of biomarkers was then selected by performing step-wise logistic regression analysis with backward elimination.

RESULTS

Twelve metabolites were identified as endometriosis-associated biomarkers. The eutopic endometrium metabolomic profile of the endometriosis patients was characterized by a significant increase in the concentration of hypoxanthine, L-arginine, L-tyrosine, leucine, lysine, inosine, omega-3 arachidonic acid, guanosine, xanthosine, lysophosphatidylethanolamine and asparagine. In contrast, the concentration of uric acid was decreased. Metabolites were filtered by step-wise logistic regression with backward elimination, and a model containing uric acid, hypoxanthine, and lysophosphatidylethanolamine was constructed. Receiver-operating characteristic (ROC) analysis confirmed the prognostic value of these parameters for the diagnosis of minimal/mild endometriosis with a sensitivity of 66.7% and a specificity of 90.0%.

CONCLUSIONS

Metabolomics analysis of the eutopic endometrium in endometriosis was effectively characterized by UHPLC-ESI-HRMS-based metabolomics. Our study supports the importance of purine and amino acid metabolites in the pathophysiology of endometriosis and provides potential biomarkers for semi-invasive diagnosis of early-stage endometriosis.

A Metabolomic Approach to Predict Breast Cancer Behavior and Chemotherapy Response

Although the classification of breast carcinomas into molecular or immunohistochemical subtypes has contributed to a better categorization of women into different therapeutic regimens, breast cancer nevertheless still progresses or recurs in a remarkable number of patients. Identifying women who would benefit from chemotherapy could potentially increase treatment effectiveness, which has important implications for long-term survival. Metabolomic analyses of fluids and tissues from cancer patients improve our knowledge of the reprogramming of metabolic pathways involved in resistance to chemotherapy. This review evaluates how recent metabolomic approaches have contributed to understanding the relationship between breast cancer and the acquisition of resistance. We focus on the advantages and challenges of cancer treatment and the use of new strategies in clinical care, which helps us comprehend drug resistance and predict responses to treatment.

The Epidemiology of Esophageal Adenocarcinoma

The incidence of esophageal adenocarcinoma (EAC) has increased in many Western countries and is higher in men than women. Some risk factors for EAC have been identified-mainly gastroesophageal reflux disease, Barrett's esophagus, obesity, and tobacco smoking. It is not clear whether interventions to address these factors can reduce risk of EAC, although some evidence exists for smoking cessation. Although consumption of alcohol is not associated with EAC risk, other exposures, such as physical activity, nutrition, and medication use, require further study. Genetic variants have been associated with risk for EAC, but their overall contribution is low. Studies are needed to investigate associations between risk factors and the molecular subtypes of EAC. The prognosis for patients with EAC has slightly improved, but remains poor-screening and surveillance trials of high-risk individuals are needed.

Liquid Biopsy for Investigation of Cancer DNA in Esophageal Adenocarcinoma: Cell-Free Plasma DNA and Exosome-Associated DNA

Liquid biopsy of cancers is an area of increasing interest in medical practice for the surveillance, management, and potential detection of malignant cells, using minimally invasive collection of body fluids. A liquid biopsy is particularly useful for metastatic cancers, which may be difficult to be sampled by core biopsy, due to difficulty of access or an occult location. Access to DNA shed from esophageal adenocarcinoma can enable the detection of mutations confirming the presence of malignant cells or the evolution of clonal lines with altered treatment response profiles. In this chapter, we detail a method for the isolation of cell-free DNA from blood plasma and DNA associated with exosomes in blood from patients with esophageal adenocarcinoma.

Overexpression of the human DEK oncogene reprograms cellular metabolism and promotes glycolysis

The DEK oncogene is overexpressed in many human malignancies including at early tumor stages. Our reported in vitro and in vivo models of squamous cell carcinoma have demonstrated that DEK contributes functionally to cellular and tumor survival and to proliferation. However, the underlying molecular mechanisms remain poorly understood. Based on recent RNA sequencing experiments, DEK expression was necessary for the transcription of several metabolic enzymes involved in anabolic pathways. This identified a possible mechanism whereby DEK may drive cellular metabolism to enable cell proliferation. Functional metabolic Seahorse analysis demonstrated increased baseline and maximum extracellular acidification rates, a readout of glycolysis, in DEK-overexpressing keratinocytes and squamous cell carcinoma cells. DEK overexpression also increased the maximum rate of oxygen consumption and therefore increased the potential for oxidative phosphorylation (OxPhos). To detect small metabolites that participate in glycolysis and the tricarboxylic acid cycle (TCA) that supplies substrate for OxPhos, we carried out NMR-based metabolomics studies. We found that high levels of DEK significantly reprogrammed cellular metabolism and altered the abundances of amino acids, TCA cycle intermediates and the glycolytic end products lactate, alanine and NAD+. Taken together, these data support a scenario whereby overexpression of the human DEK oncogene reprograms keratinocyte metabolism to fulfill energy and macromolecule demands required to enable and sustain cancer cell growth.

Serum Metabolomic Profiles for Breast Cancer Diagnosis, Grading and Staging by Gas Chromatography-Mass Spectrometry

Detection of metabolic signature for breast cancer (BC) has the potential to improve patient prognosis. This study identified potentially significant metabolites differentiating between breast cancer patients and healthy controls to help in diagnosis, grading, staging and determination of neoadjuvant status. Serum was collected from 152 pre-operative breast cancer (BC) patients and 155 healthy controls in this case-controlled study. Gas chromatography-mass spectrometry (GC-MS) was used to obtain metabolic profiles followed by chemometric analysis with the identification of significantly differentiated metabolites including 7 for diagnosis, 18 for grading, 23 for staging, 15 for stage III subcategory and 10 for neoadjuvant status (p-value < 0.05). Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between healthy controls and BC patients with a sensitivity of 96% and specificity of 100% on external validation. Models for grading, staging and neoadjuvant status were built with Decision Tree Algorithm with predictive accuracy of 71.5%, 71.3% and 79.8% respectively. Pathway analysis revealed increased glycolysis, lipogenesis, and production of volatile organic metabolites indicating the metabolic alterations in breast cancer.