Plasma sample based analysis of gastric cancer progression using targeted metabolomics

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

The fruit of Rosa odorata sweet var. gigantea (Coll. et Hemsl.) Rehd. et Wils attenuates chronic atrophic gastritis induced by MNNG and its potential mechanism

ETHNOPHARMACOLOGICAL RELEVANCE

Rosa odorata Sweet var. gigantea (Coll. et Hemsl.) Rehd. et Wils is a commonly utilized traditional medicine among the Yi nationality, also known as "Gugongguo", for the treatment of gastrointestinal disorders. Previous studies have indicated that the extract of Rosa odorata sweet var. gigantea (FOE) fruit has demonstrated a protective effect on the stomach; however, its impact on chronic atrophic gastritis (CAG) with severe disease remains unknown.

AIM OF THE STUDY

This study aimed to investigate the impact of FOE on CAG and its underlying mechanisms both in vitro and in vivo.

MATERIALS AND METHODS

By employing Ultra Performance Liquid Chromatography/Quadrupole-Time of Flight Mass Spectrometry (UPLC-QTOF-MS/MS) and network pharmacology, the primary active compounds and action targets of FOE were identified. In vitro, the impact of FOE on CAG was investigated through scratch, migration, and invasion assays. Subsequently, guided by network pharmacology, EMT and TGF-β signaling pathway-related proteins were assessed using Western blot and immunofluorescence experiments. Additionally, an in vivo CAG rat model was established to validate the effects of FOE and confirm its mechanism of action through hematoxylin-eosin (H&E), immunohistochemistry, Western blot, as well as untargeted metabolomics analysis of rat serum. It was observed that FOE inhibited scratch healing abilities, migration, invasion capabilities, as well as the expression of EMT-related proteins (E-cadherin, N-cadherin, Snail, Vimentin) in CAG model cells (MC cells), providing initial evidence for its efficacy.

RESULTS

Through the analysis of UPLC-QTOF-MS/MS, a total of 51 major compounds were identified in the FOE. Subsequent network pharmacological analysis suggested that FOE may regulate Epithelial mesenchymal transition (EMT) through the transforming growth factor β (TGF-β) pathway. Furthermore, experimental verification demonstrated that FOE inhibited the protein expression of TGF-β1 and its downstream protein Smad2/3 in vitro. In vivo findings also indicated similar mechanisms in MC cells, suggesting a reversal of the CAG process and significant inhibition of EMT and TGF-β signaling pathways. Additionally, untargeted metabolomics of rat serum confirmed the therapeutic effect of FOE on CAG and predicted its potential involvement in the arachidonic acid metabolic pathway.

CONCLUSION

This study initially demonstrated that FOE effectively reverses the process of EMT through the TGF-β1/Smad2/3 signaling pathway, thereby providing a therapeutic benefit for CAG.

Comprehensive multiomics analysis of the signatures of gastric mucosal bacteria and plasma metabolites across different stomach microhabitats in the development of gastric cancer

PURPOSE

As an important component of the microenvironment, the gastric microbiota and its metabolites are associated with tumour occurrence, progression, and metastasis. However, the relationship between the gastric microbiota and the development of gastric cancer is unclear. The present study investigated the role of the gastric mucosa microbiome and metabolites as aetiological factors in gastric carcinogenesis.

METHODS

Gastric biopsies from different stomach microhabitats (n = 70) were subjected to 16S rRNA gene sequencing, and blood samples (n = 95) were subjected to untargeted metabolome (gas chromatography‒mass spectrometry, GC‒MS) analyses. The datasets were analysed using various bioinformatics approaches.

RESULTS

The microbiota diversity and community composition markedly changed during gastric carcinogenesis. High Helicobacter. pylori colonization modified the overall diversity and composition of the microbiota associated with gastritis and cancer in the stomach. Most importantly, analysis of the functional features of the microbiota revealed that nitrate reductase genes were significantly enriched in the tumoral microbiota, while urease-producing genes were significantly enriched in the microbiota of H. pylori-positive patients. A panel of 81 metabolites was constructed to discriminate gastric cancer patients from gastritis patients, and a panel of 15 metabolites was constructed to discriminate H. pylori-positive patients from H. pylori-negative patients. receiver operator characteristic (ROC) curve analysis identified a series of gastric microbes and plasma metabolites as potential biomarkers of gastric cancer.

CONCLUSION

The present study identified a series of signatures that may play important roles in gastric carcinogenesis and have the potential to be used as biomarkers for diagnosis and for the surveillance of gastric cancer patients with minimal invasiveness.

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.

Searching molecular biomarkers correlating with BSID-III at 24 months in infants with neonatal hypoxic-ischemic encephalopathy

An early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. The objectives were (i) to analyze the characteristics of miRNA expression and metabolic patterns of neonates with NE and (ii) to assess their predictive performance for neurodevelopmental outcomes. Plasma samples from moderate/severe NE patients (N = 92) of the HYPOTOP study were collected before, during, and after therapeutic hypothermia (TH) and compared to a control group (healthy term infants). The expression of miRNAs and concentrations of metabolites (hypoxia-related and energy, steroid, and tryptophan metabolisms) were analyzed. Neurodevelopmental outcomes were evaluated at 24 months postnatal age using Bayley Scales of Infant Development, ed. III, BSID-III. Differences in miRNA and metabolic profiles were found between NE vs. control infants, abnormal (i.e., mildly and moderately abnormal and severe) vs. normal, and severe vs. non-severe (i.e., normal and mildly and moderately abnormal) BSID-III. 4-Androstene-3,17-dione, testosterone, betaine, xanthine, and lactate were suitable for BSID-III outcome prediction (receiver operating characteristic areas under the curve (AUCs) ≥ 0.6), as well as 68 miRNAs (AUCs of 0.5-0.9). Significant partial correlations of xanthine and betaine levels and the expression of several miRNAs with BSID-III sub-scales were found. Conclusion: We have identified metabolites/miRNAs that might be useful to support the prediction of middle-term neurodevelopmental outcomes of NE. What is known and what is new: • The early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. • Alterations of the metabolome and miRNAs had been observed in NE. • We performed miRNA sequencing and quantified selected metabolites (i.e., lactate, pyruvate, ketone bodies, Krebs cycle intermediates, tryptophan pathway, hypoxia-related metabolites, and steroids) by GC- and LC-MS. • Specific miRNAs and metabolites that allow prediction of middle-term neurodevelopmental outcomes of newborns with NE undergoing hypothermia treatment were identified.

Abnormal arginine synthesis confers worse prognosis in patients with middle third gastric cancer

BACKGROUND

Gastric cancer at different locations has distinct prognoses and biological behaviors, but the specific mechanism is unclear.

METHODS

Non-targeted metabolomics was performed to examine the differential metabolite phenotypes that may be associated with the effects of tumor location on the prognosis of gastric cancer. And silencing of the rate-limiting enzyme to evaluate the effect of abnormal changes in metabolic pathway on the functional biological assays of gastric cancer cells HGC-27 and MKN28.

RESULTS

In a retrospective study of 94 gastric cancer patients, the average survival time of patients with gastric cancer in the middle third of the stomach was significantly lower than that of patients with gastric cancer in other locations (p < 0.05). The middle third location was also found to be an independent risk factor for poor prognosis (HR = 2.723, 95%CI 1.334-5.520), which was closely associated with larger tumors in this location. Non-targeted metabolomic analysis showed that the differential metabolites affected 16 signaling pathways including arginine synthesis, retrograde endocannabinoid signaling, arginine biosynthesis, and alanine and aspartate and glutamate metabolism between gastric cancer and normal tissue, as well as between tumors located in the middle third of the stomach and other locations. Argininosuccinate synthetase 1 (ASS1), the rate-limiting enzyme of the arginine biosynthesis pathway, catalyzes the production of argininosuccinic acid. Here, knockdown of ASS1 significantly inhibited the proliferation, colony formation, and migration/invasion of gastric cancer cells, and promoted apoptosis.

CONCLUSIONS

Our study suggests that abnormal arginine synthesis may lead to larger tumor size and worse prognosis in gastric cancer located in the middle third position of the stomach. These findings may provide the basis for the stratification and targeted treatment of gastric cancer in different locations.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

OBJECTIVE

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

Development of a Signature Based on Eight Metastatic-Related Genes for Prognosis of GC Patients

Gastric cancer (GC) has been a common tumor type with high mortality. Distal metastasis is one of the main causes of death in GC patients, which is also related to poor prognosis. The mRNA profiles and clinical information of GC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Univariate Cox and LASSO Cox analyses were used to screen the optimal metastasis-related genes (MRGs) to establish a prognostic Risk Score model for GC patients. The nomogram was used to visualize the Risk Score and predict the 1-, 3-, 5-year survival rate. The immune cell infiltration was analyzed by CIBERSORT and the ratio of immune-stromal component was calculated by the ESTIMATE algorithm. A total of 142 differentially expressed genes were identified between metastatic and non-metastatic GC samples. The optimal 8 genes, comprising GAMT (guanidinoacetate N-methyltransferase), ABCB5 (ATP-binding cassette subfamily B member 5), ITIH3 (inter-alpha-trypsin inhibitor heavy chain 3), GDF3 (growth differentiation factor 3), VSTM2L (V-set and transmembrane domain-containing 2 like), CIDEA (cell death inducing DFFA like effector a), NPTX1 (neuronal pentraxin-1), and UMOD (uromodulin), were further screened to establish a prognostic Risk Score, which proved to be an independent prognostic factor. Patients in high-risk group had a poor prognosis. There were significant differences in the proportion of 11 tumor-infiltrating immune cells between high-risk and low-risk subgroups. In addition, the StromalScore, ImmuneScore, and ESTIMATEScore in high-risk group were higher than those in low-risk group, indicating that the tumor microenvironment of the high-risk group was more complex. A Risk Score model based on eight metastasis-related genes could clearly distinguish the prognosis of GC patients. The poor prognosis of patients with high-Risk Score might be associated with the complex tumor microenvironments.

Multi-omics analysis reveals the involvement of origin recognition complex subunit 6 in tumor immune regulation and malignant progression

Background

Origin recognition complex 6 (ORC6) is one of the six highly conserved subunit proteins required for DNA replication and is essential for maintaining genome stability during cell division. Recent research shows that ORC6 regulates the advancement of multiple cancers; however, it remains unclear what regulatory impact it has on the tumor immune microenvironment.

Methods

Unpaired Wilcoxon rank sum and signed rank tests were used to analyze the differences in the expression of ORC6 in normal tissues and corresponding tumor tissues. Multiple online databases have evaluated the genetic alterations, protein expression and localization, and clinical relevance of ORC6. To evaluate the potential prognostic impact and diagnostic significance of ORC6 expression, we carried out log-rank, univariate Cox regression, and receiver operating characteristic curve analysis. The ICGC-LIRI-JP cohort, CGGA-301 cohort, CGGA-325 cohort, CGGA-693 cohort, and GSE13041 cohort were used for external validation of the study findings. The associations between ORC6 expression and immune cell infiltration, immune checkpoint expression, and immunotherapy cohorts was further analyzed. To explore the functional and signaling pathways related to ORC6 expression, gene set enrichment analysis was performed. To clarify the expression and function of ORC6 in hepatocellular carcinoma (LIHC) and glioma, we conducted in vitro experiments.

Results

Expression of ORC6 is upregulated in the majority of cancer types and is associated with poor patient prognosis, notably in cases of LIHC and gliomas. In addition, ORC6 may be involved in multiple signaling pathways related to cancer progression and immune regulation. High expression of ORC6 correlates with an immunosuppressive state in the tumor microenvironment. The results of further immunotherapy cohort analysis suggested that patients in the ORC6 high-expression group benefited from immunotherapy. Inhibiting ORC6 expression suppressed the proliferative and migratory abilities of LIHC and glioma cells.

Conclusion

High expression of ORC6 may be used as a biomarker to predict the poor prognosis of most tumor patients. The high expression of ORC6 may be involved in the regulation of the tumor immunosuppressive environment, and it is expected to become a molecular target for inhibiting tumor progression.

Plasma serotonergic biomarkers are associated with hypoxemia events in preterm neonates

BACKGROUND

Hypoxemia is a physiological manifestation of immature respiratory control in preterm neonates, which is likely impacted by neurotransmitter imbalances. We investigated relationships between plasma levels of the neurotransmitter serotonin (5-HT), metabolites of tryptophan (TRP), and parameters of hypoxemia in preterm neonates.

METHODS

TRP, 5-HT, 5-hydroxyindoleacetic acid (5-HIAA), and kynurenic acid (KA) were analyzed in platelet-poor plasma at ~1 week and ~1 month of life from a prospective cohort of 168 preterm neonates <31 weeks gestational age (GA). Frequency of intermittent hypoxemia (IH) events and percent time hypoxemic (<80%) were analyzed in a 6 h window after the blood draw.

RESULTS

At 1 week, infants with detectable plasma 5-HT had fewer IH events (OR (95% CI) = 0.52 (0.29, 0.31)) and less percent time <80% (OR (95% CI) = 0.54 (0.31, 0.95)) compared to infants with undetectable 5-HT. A similar relationship occurred at 1 month. At 1 week, infants with higher KA showed greater percent time <80% (OR (95% CI) = 1.90 (1.03, 3.50)). TRP, 5-HIAA or KA were not associated with IH frequency at either postnatal age. IH frequency and percent time <80% were positively associated with GA < 29 weeks.

CONCLUSIONS

Circulating neuromodulators 5-HT and KA might represent biomarkers of immature respiratory control contributing to hypoxemia in preterm neonates.

IMPACT

Hypoxemia events are frequent in preterm infants and are associated with poor outcomes. Mechanisms driving hypoxemia such as immature respiratory control may include central and peripheral imbalances in modulatory neurotransmitters. This study found associations between the plasma neuromodulators serotonin and kynurenic acid and parameters of hypoxemia in preterm neonates. Imbalances in plasma biomarkers affecting respiratory control may help identify neonates at risk of short- and long-term adverse outcomes.

Plasma metabolome identifies potential biomarkers of gastric precancerous lesions and gastric cancer risk

OBJECTIVES

Currently, metabolic biomarkers with great practicability of gastric cancer (GC) and gastric precancerous lesions (GPL) are scarce. Thus, we are devoted to determining the plasma metabolic profiles of patients with GPL or GC and validate candidate biomarkers for disease diagnosis.

METHODS

In this hospital-based case-control study, 68 plasma samples from 27 non-atrophic gastritis (NAG, control), 31 GPL, and 10 GC patients were collected for targeted metabolomics analysis. Univariate and multivariate analyses were used for selecting the differential metabolites. A receiver operating characteristic curve combined with binary logistic regression analysis was performed to test the diagnostic performance of the differential metabolites. Dietary data were obtained using a semiquantitative food frequency questionnaire.

RESULTS

Distinct metabolomic profiles were noted for NAG, GPL, and GC. Compared to the NAG patients, the levels of 5 metabolites in the GPL group and 4 metabolites in the GC group were found to significantly elevate. Compared with the model involving 9 traditional risk factors (AUC: 0.89, 95%CI: 0.78-1.00), Trimethylamine N-oxide, the most significant metabolite (P = 2.00 × 10-5, FDR = 0.003, FC > 2, VIP > 2), showed a good diagnostic performance for the patients with GC (AUC: 0.90, 95%CI: 0.78-1.00), and its diagnostic performance has been further improved with the integration of Rhamnose (AUC: 0.96, 95%CI: 0.89-1.00).

CONCLUSION

In our study, 9 defined metabolites might serve as meaningful biomarkers for identifying the high-risk population of GPL and GC, possibly enhancing the prevention and control of GPL and GC.

Study on the mechanism of Fufang E'jiao Jiang on precancerous lesions of gastric cancer based on network pharmacology and metabolomics

ETHNOPHARMACOLOGICAL RELEVANCE

Fufang E'jiao Jiang (FEJ) is a prominent traditional Chinese medicine prescription, which consists of Asini Corii Colla (Donkey-hide gelatin prepared by stewing and concentrating from the hide of Equus asinus Linnaeus., ACC), Codonopsis Radix (the dried roots of Codonopsis pilosula (Franch.) Nannf., CR), Ginseng Radix et Rhizoma Rubra (the steamed and dried root of Panax ginseng C.A. Mey., GRR), Crataegi Fructus (the mature fruits of Crataegus pinnatifida Bunge., CF), and Rehmanniae Radix Praeparata (the steamed and sun dried tuber of Rehmannia glutinosa (Gaertn.) Libosch. ex Fisch. & C.A. Mey., RRP). It is a popularly used prescription for "nourishing Qi and nourishing blood".

AIM OF THE STUDY

To explore the potential mechanism of FEJ on precancerous lesion of gastric cancer in rats by combining network pharmacology and metabolomics.

METHODS

Traditional Chinese Medicine Systems Pharmacology and Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine were used to identify the ingredients and potential targets of FEJ. GeneCards database was used to define PLGC-associated targets. We built a herb-component-disease-target network and analyzed the protein-protein interaction network. Underlying mechanisms were identified using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. In addition, 40% ethanol, N-methyl-N'-nitro-N-nitroguanidine and irregular eating were used to establish PLGC rats model. We also evaluated the efficacy of FEJ on MNNG-induced PLGC rats by body weight, histopathology, blood routine and cytokine levels, while the predicted pathway was determined by the Western blot. Ultra-performance liquid chromatography-tandem mass spectrometry-based serum non-targeted metabolomics was used to select potential biomarkers and relevant pathways for FEJ in the treatment of PLGC.

RESULTS

Network pharmacology showed that FEJ exhibited anti-PLGC effects through regulating ALB, TNF, VEGFA, TP53, AKT1 and other targets, and the potential pathways mainly involved cancer-related, TNF, PI3K-AKT, HIF-1, and other signaling pathways. Animal experiments illustrated that FEJ could suppress inflammation, regulate gastrointestinal hormones, and inhibit the expression of PI3K/AKT/HIF-1α pathway-related proteins. Based on serum non-targeted metabolomics analysis, 12 differential metabolites responding to FEJ treatment were identified, and metabolic pathway analysis showed that the role of FEJ was concentrated in 6 metabolic pathways.

CONCLUSION

Based on network pharmacology, animal experiments and metabolomics, we found that FEJ might ameliorate gastric mucosal injury in PLGC rats by regulating gastrointestinal hormones and inhibiting inflammation, and its mechanism of action is related to the inhibition of excessive activation of PI3K/AKT/HIF-1α signaling pathway and regulation of disorders of body energy metabolism. This comprehensive strategy also provided a reasonable way for unveiling the pharmacodynamic mechanisms of multi-components, multi-targets, and multi-pathways in Traditional Chinese Medicine.

Circulating Proteins and Metabolite Biomarkers in Gastric Cancer: A Systematic Review and Meta-analysis

BACKGROUND

Gastric cancer (GC) is often diagnosed at an advanced stage and thus patients have a poor prognosis. This implies that early detection of this cancer will improve patient prognosis and survival. This systematic review explored the association of circulating protein and metabolite biomarkers with GC development.

METHODS

A literature search was conducted until November 2021 on Medline, Embase, Cochrane library, and Web of Science databases. Studies were included if they assessed circulating proteins and metabolites in blood, urine, or saliva and determined their association with GC risk. Quality of identified studies was determined using the Newcastle-Ottawa scale for cohort studies. Random and fixed effects meta-analyses were performed to calculate pooled odds ratio.

RESULTS

A total of 53 studies were included. High levels of anti-Helicobacter pylORi IgG levels, pepsinogen I (PGI) <30 µg/L and serum pepsinogen I/ pepsinogen II (PGI/II) ratio<3 were positively associated with risk of developing GC (pooled odds ratio (OR): 2.70; 95% CI: 1.44-5.04, 5.96, 95% CI: 2.65-13.42 and 4.43; 95% CI: 3.04-6.47). In addition, an inverse relationship was found between ferritin, iron and transferrin levels and risk of developing GC (OR: 0.62; 95% CI: 0.38-1,0.97; 95% CI: 0.94-1 and 0.85; 95% CI: 0.76-0.94). However, there was no association between levels of glucose, cholesterol, vitamin C, vitamin B12, vitamin A, α-Carotene, β-Carotene, α-Tocopherol, γ-Tocopherol, and GC risk.

CONCLUSION

The pooled analysis demonstrated that high levels of anti-Helicobacter pylORi IgG, PGI<30µg/L and serum PGI/II ratio <3 and low levels of ferritin, iron and transferrin were associated with risk of GC.

Progress with Metabolomic Blood Tests for Gastrointestinal Cancer Diagnosis-An Assessment of Biomarker Translation

There is an urgent need for cost-effective, non-invasive tools to detect early stages of gastrointestinal cancer (colorectal, gastric, and esophageal cancers). Despite many publications suggesting circulating metabolites acting as accurate cancer biomarkers, few have reached the clinic. In upper gastrointestinal cancer this is critically important, as there is no test to complement gold-standard endoscopic evaluation in patients with mild symptoms that do not meet referral criteria. Therefore, this study aimed to describe and solve this translational gap. Studies reporting diagnostic accuracy of metabolomic blood-based gastrointestinal cancer biomarkers from 2007 to 2020 were systematically reviewed and progress of each biomarker along the discovery-validation-adoption pathway was mapped. Successful biomarker translation was defined as a composite endpoint, including patent protection/FDA approval/recommendation in national guidelines. The review found 77 biomarker panels of gastrointestinal cancer, including 25 with an AUROC >0.9. All but one was stalled at the discovery phase, 9.09% were patented and none were clinically approved, confirming the extent of biomarker translational gap. In addition, there were numerous "re-discoveries," including histidine, discovered in 7 colorectal studies. Finally, this study quantitatively supports the presence of a translational gap between discovery and clinical adoption, despite clear evidence of highly performing biomarkers with significant potential clinical value.

Plasma metabolomics for the assessment of the progression of non-small cell lung cancer

Objectives

Non-small cell lung cancer (NSCLC) is a leading type of lung cancer with a high mortality rate worldwide. Although many procedures for the diagnosis and prognosis assessment of lung cancer exist, they are often laborious, expensive, and invasive. This study aimed to develop an ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS)-based analysis method for the plasma biomarkers of NSCLC with the potential to indicate the stages and progression of this malignancy conveniently and reliably.

Methods

A total of 53 patients with NSCLC in early stages (I–III) and advanced stage (IV) were classified into the early and advanced groups based on the tumor node metastasis staging system. A comprehensive metabolomic analysis of plasma from patients with NSCLC was performed via UPLC–MS/MS. Principal component analysis and partial least squares–discriminant analysis were conducted for statistical analysis. Potential biomarkers were evaluated and screened through receiver operating characteristic analyses and correlation analysis. Main differential metabolic pathways were also identified by utilizing metaboanalyst.

Results

A total of 129 differential metabolites were detected in accordance with the criteria of VIP ≥ 1 and a P-value of ≤ 0.05. The receiver operating characteristic curves indicated that 11 of these metabolites have the potential to be promising markers of disease progression. Apparent correlated metabolites were also filtered out. Furthermore, the 11 most predominant metabolic pathways with alterations involved in NSCLC were identified.

Conclusion

Our study focused on the plasma metabolomic changes in patients with NSCLC. These changes may be used for the prediction of the stage and progression of NSCLC. Moreover, we discussed the metabolic pathways wherein the altered metabolites were mainly enriched.

Uncovering the Mechanisms of Active Components from Toad Venom against Hepatocellular Carcinoma Using Untargeted Metabolomics

Toad venom, a dried product of secretion from Bufo bufo gargarizans Cantor or Bufo melanostictus Schneider, has had the therapeutic effects of hepatocellular carcinoma confirmed. Bufalin and cinobufagin were considered as the two most representative antitumor active components in toad venom. However, the underlying mechanisms of this antitumor effect have not been fully implemented, especially the changes in endogenous small molecules after treatment. Therefore, this study was designed to explore the intrinsic mechanism on hepatocellular carcinoma after the cotreatment of bufalin and cinobufagin based on untargeted tumor metabolomics. Ultraperformance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) was performed to identify the absorbed components of toad venom in rat plasma. In vitro experiments were determined to evaluate the therapeutic effects of bufalin and cinobufagin and screen the optimal ratio between them. An in vivo HepG2 tumor-bearing nude mice model was established, and a series of pharmacodynamic indicators were determined, including the body weight of mice, tumor volume, tumor weight, and histopathological examination of tumor. Further, the entire metabolic alterations in tumor after treating with bufalin and cinobufagin were also profiled by UHPLC-MS/MS. Twenty-seven active components from toad venom were absorbed in rat plasma. We found that the cotreatment of bufalin and cinobufagin exerted significant antitumor effects both in vitro and in vivo, which were reflected in inhibiting proliferation and inducing apoptosis of HepG2 cells and thereby causing cell necrosis. After cotherapy of bufalin and cinobufagin for twenty days, compared with the normal group, fifty-six endogenous metabolites were obviously changed on HepG2 tumor-bearing nude mice. Meanwhile, the abundance of α-linolenic acid and phenethylamine after the bufalin and cinobufagin intervention was significantly upregulated, which involved phenylalanine metabolism and α-linolenic acid metabolism. Furthermore, we noticed that amino acid metabolites were also altered in HepG2 tumor after drug intervention, such as norvaline and Leu-Ala. Taken together, the cotreatment of bufalin and cinobufagin has significant antitumor effects on HepG2 tumor-bearing nude mice. Our work demonstrated that the in-depth mechanism of antitumor activity was mainly through the regulation of phenylalanine metabolism and α-Linolenic acid metabolism.

Targeted metabolomic profiles of serum amino acids and acylcarnitines related to gastric cancer

Background

Early diagnosis and treatment are imperative for improving survival in gastric cancer (GC). This work aimed to assess the ability of human serum amino acid and acylcarnitine profiles in distinguishing GC cases from atrophic gastritis (AG) and control superficial gastritis (SG) patients.

Methods

Sixty-nine GC, seventy-four AG and seventy-two SG control patients treated from May 2018 to May 2019 in Gansu Provincial Hospitalwere included. The levels of 42 serum metabolites in the GC, AG and SG groups were detected by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Then, orthogonal partial least squares discriminant analysis (OPLS-DA) and the Kruskal-Wallis H test were used to identify a metabolomic signature among the three groups. Metabolites with highest significance were examined for further validation. Receiver operating characteristic (ROC) curve analysis was carried out for evaluating diagnostic utility.

Results

The metabolomic analysis found adipylcarnitine (C6DC), 3-hydroxy-hexadecanoylcarnitine (C16OH), hexanoylcarnitine (C6), free carnitine (C0) and arginine (ARG) were differentially expressed (all VIP >1) and could distinguish GC patients from AG and SG cases. In comparison with the AG and SG groups, GC cases had significantly higher C6DC, C16OH, C6, C0 and ARG amounts. Jointly quantitating these five metabolites had specificity and sensitivity in GC diagnosis of 98.55% and 99.32%, respectively, with an area under the ROC curve (AUC) of 0.9977.

Conclusion

This study indicates C6DC, C16OH, C6, C0 and ARG could effectively differentiate GC cases from AG and SG patients, and may jointly serve as a valuable circulating multi-marker panel for GC detection.

Metabolite biomarker discovery for human gastric cancer using dried blood spot mass spectrometry metabolomic approach

As one of the most common malignancies, gastric cancer (GC) is the third leading cause of cancer-related deaths in China. GC is asymptomatic in early stages, and the majority of GC mortality is due to delayed symptoms. It is an urgent task to find reliable biomarkers for the identification of GC in order to improve outcomes. A combination of dried blood spot sampling and direct infusion mass spectrometry (MS) technology was used to measure blood metabolic profiles for 166 patients with GC and 183 healthy individuals, and 93 metabolites including amino acids, carnitine/acylcarnitines and their derivatives, and related ratios were quantified. Multiple algorithms were used to characterize the changes of metabolic profiles in patients with GC compared to healthy individuals. A biomarker panel was identified in training set, and assessed by tenfold cross-validation and external test data set. After systematic selection of 93 metabolites, a biomarker panel consisting of Ala, Arg, Gly, Orn, Tyr/Cit, Val/Phe, C4-OH, C5/C3, C10:2 shows the potential to distinguish patients with GC from healthy individuals in tenfold cross-validation model (sensitivity: 0.8750, specificity: 0.9006) and test set (sensitivity: 0.9545, specificity: 0.8636). This metabolomic analysis makes contribution to the identification of disease-associated biomarkers and to the development of new diagnostic tools for patients with GC.

Alterations in the gastric microbiota and metabolites in gastric cancer: An update review

Gastric cancer (GC) is one of the leading causes of cancer mortality worldwide. Numerous studies have shown that the gastric microbiota can contribute to the occurrence and development of GC by generating harmful microbial metabolites, suggesting the possibility of discovering biomarkers. Metabolomics has emerged as an advanced promising analytical method for the analysis of microbiota-derived metabolites, which have greatly accelerated our understanding of host-microbiota metabolic interactions in GC. In this review, we briefly compiled recent research progress on the changes of gastric microbiota and its metabolites associated with GC. And we further explored the application of metabolomics and gastric microbiome association analysis in the diagnosis, prevention and treatment of GC.

Metabolomics Profiles Associated with the Treatment of Zuojin Pill on Patients with Chronic Nonatrophic Gastritis

Objective: Chronic nonatrophic gastritis (CNG) is the most common digestive disease. In China, Zuojin pill (ZJP) is considered an effective medicine formula for CNG. However, its efficacy and mechanism have never been explored. In order to understand how and why ZJP demonstrates therapeutic effect on CNG, a clinical trial was conducted. Metabolomics was used to explore its deep mechanism.

Methods: A total of 14 patients with CNG were recruited from October 2020 to March 2021 (ChiCTR2000040549). The endoscopy and histopathological changes were evaluated as efficacy. Serum samples were prepared and detected by performing widely targeted metabolome using UPLC. Multivariate statistical analysis was conducted to identify potential differential metabolites and signaling pathways. Last, the signal-related inflammatory factors containing COX-2, IL-4, and IL-17 were confirmed via immunohistochemical staining and enzyme-linked immunosorbent assay.

Results: ZJP was able to alleviate several indexes of mucosal injury under endoscopy and histology. Erosion and bile reflux, but not red plaques and hemorrhage, were downregulated by ZJP. In addition, it could remarkably alleviate active chronic inflammation. A total of 14 potential metabolites, namely, hypoxanthine, adipic acid, D-ribono-1,4-lactone, L-sepiapterin, imidazoleacetic acid, sebacate, ADP-ribose, 4-hydroxybenzyl alcohol, 11,12-EET, 15-OxoETE, 12-OxoETE, (±)8-HETE, glycyrrhizinate, and DL-aminopimelic acid, were discriminated by metabolomics. Moreover, certain amino acid metabolism got significance during the disease progress and treatment. The related inflammatory factors including COX-2, IL-4, and IL-17 were inhibited by ZJP in both mucosa and serum.

Conclusion: All these results indicated that ZJP partially acts as an inflammatory suppressor to regulate comprehensive metabolism disorders. This might be an important mechanism of ZJP in the treatment of CNG.

Intestinal Candida albicans Promotes Hepatocarcinogenesis by Up-Regulating NLRP6

Hepatocellular carcinoma (HCC), a primary liver cancer, is closely associated with the gut microbiota. However, the role of gut fungi in the development of HCC remains unclear. The aim of this study was to explore the influence of intestinal Candida albicans on HCC. Here, We found that patients with HCC showed significantly decreased diversity of the gut mycobiome and increased abundance of C. albicans, compared to the patients with liver cirrhosis. The gavage of C. albicans in the WT models increased the tumor size and weight and influenced the plasma metabolome, which was indicated by alterations in 117 metabolites, such as L-carnitine and L-acetylcarnitine, and several KEGG enriched pathways, such as phenylalanine metabolism and citrate cycle. Moreover, the expression of nucleotide oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6) in the intestinal tissues and primary intestinal epithelial cells of the WT mice interacted with C. albicans increased. Notably, the colonization of C. albicans had no effect on tumor growth in Nlrp6^–/– mice. In conclusion, the abnormal colonization of C. albicans reprogrammed HCC metabolism and contributed to the progression of HCC dependent on NLRP6, which provided new targets for the treatment of HCC.

Development of a prognostic metabolic signature in stomach adenocarcinoma

PURPOSE

The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD.

METHODS

An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan-Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping.

RESULTS

A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA.

CONCLUSION

We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.

Abnormal arginine metabolism is associated with prognosis in patients of gastric cancer

Background

Metabolic disorder is a key factor in the occurrence and development of tumors. Metabolomics methods can explore a variety of prognostic markers for tumors.

Methods

The 454 patients included in this study comprised 92 cases of gastric cancer, 51 cases of gastric ulcers, 206 cases of gastric polyps, and 105 cases of gastritis. The plasma levels of 23 amino acids in patients before treatment were detected by liquid chromatography-tandem mass spectrometry, and t-test was used to determine the difference of amino acids levels between the gastric cancer group and other groups. Shared different amino acids were selected to analyze their relationship with staging, differentiation and prognosis. The TCGA database was used to explore the changes of genes expression related to the synthesis and degradation of different amino acids, and the relationship between the genes and stage, differentiation and prognosis.

Results

The plasma arginine level in the gastric cancer group was significantly higher than that in the gastric ulcer, gastric polyp, and gastritis groups (P values 0.0065, 0.0306, 0.0004, respectively).The level of plasma arginine in patients with non-metastatic gastric cancer was significantly higher than that in patients with metastatic gastric cancer (P=0.0013). Compared with the normal control, the key metabolic enzyme ASS1 gene was highly expressed in gastric cancer, and the survival time of gastric cancer patients with high expression of ASS1 was longer. Patients with high arginine expression had significantly longer survival (log-rank test P=0.0003).

Conclusions

Increased plasma arginine level in gastric cancer patients was related to overexpression of ASS1 by TCGA database analysis. High expression of ASS1 prolonged the overall survival of gastric cancer patients, and the arginine level before treatment could be used as a prognostic factor.

OUP accepted manuscript

Circulating biomarkers have emerged as valuable surrogates for evaluating disease states in solid malignancies. Their relative ease of access and rapid turnover has bolstered clinical applications in monitoring treatment efficacy and cancer progression. In this review, the roles of various circulating biomarkers in monitoring treatment response are described. Non-specific markers of disease burden, tumor markers (eg CA 125, CEA, PSA, etc.), circulating tumor cells, nucleic acids, exosomes, and metabolomic arrays are highlighted. Specifically, the discovery of each of these markers is reviewed, with examples illustrating their use in influencing treatment decisions, and barriers to their application noted where these exist. Finally, opportunities for future work using these circulating biomarkers are discussed.

Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA

PURPOSE

The aim of the present study was to screen differential metabolites of gastric cancer (GC) and identify the key metabolic pathways of GC.

METHODS

GC (n=28) and matched paracancerous (PC) tissues were collected, and LC-MS/MS analysis were performed to detect metabolites of GC and PC tissues. Metabolite pathways based on differential metabolites were enriched by MetaboAnalyst, and genes related to metabolite pathways were identified using the KEGGREST function of the R software package. Transcriptomics data from The Cancer Genome Atlas (TCGA) was analyzed to obtain differentially expressed genes (DEGs) of GC. Overlapping genes were acquired from metabonimics and transcriptomics data. Pathway enrichment analysis was performed using String. The protein expression of genes was validated by the Human Protein Atlas (HPA) database.

RESULTS

A total of 325 key metabolites were identified, 111 of which were differentially expressed between the GC and PC groups. Seven metabolite pathways enriched by MetaboAnalyst were chosen, and 361 genes were identified by KEGGREST. A total of 2831 DEGs were identified from the TCGA cohort. Of these, 1317 were down-regulated, and 1636 were up-regulated. Twenty-two overlapping genes were identified between genes related to metabolism and DEGs. Glycerophospholipid (GPL) metabolism is likely associated with GC, of which AGPAT9 and ETNPPL showed lower expressed in GC tissues.

CONCLUSIONS

We investigated the tissue-based metabolomics profile of GC, and several differential metabolites were identified. GPL metabolism may affect on progression of GC.

Effect of Helicobacter Pylori on Plasma Metabolic Phenotype in Patients With Gastric Cancer

BACKGROUND

Although Helicobacter pylori (Hp) as high risk factor for gastric cancer have been investigated from human trial, present data is inadequate to explain the effect of Hp on the changes of metabolic phenotype of gastric cancer in different stages.

PURPOSE

Herein, plasma of human superficial gastritis (Hp negative and positive), early gastric cancer and advanced gastric cancer analyzed by UPLC-HDMS metabolomics can not only reveal metabolic phenotype changes in patients with gastric cancer of different degrees (30 Hp negative, 30 Hp positive, 20 early gastric cancer patients, and 10 advanced gastric cancer patients), but also auxiliarily diagnose gastric cancer.

RESULTS

Combined with multivariate statistical analysis, the results represented biomarkers different from Hp negative, Hp positive, and the alterations of metabolic phenotype of gastric cancer patients. Forty-three metabolites are involved in amino acid metabolism, and lipid and fatty acid metabolism pathways in the process of cancer occurrence, especially 2 biomarkers glycerophosphocholine and neopterin, were screened in this study. Neopterin was consistently increased with gastric cancer progression and glycerophosphocholine tended to consistently decrease from Hp negative to advanced gastric cancer.

CONCLUSION

This method could be used for the development of rapid targeted methods for biomarker identification and a potential diagnosis of gastric cancer.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

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.

Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery

PURPOSE

Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes.

MATERIALS/METHODS

Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis.

RESULTS

A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades.

CONCLUSIONS

This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.

Association of metabolomic markers and response to nutritional support: A secondary analysis of the EFFORT trial using an untargeted metabolomics approach

BACKGROUND & AIMS

The EFFORT trial reported a substantial risk reduction for adverse events and mortality in medical in-patients receiving a nutritional support intervention. With the use of an untargeted metabolomics approach, we investigated the prognostic and therapeutic potential of metabolomic markers to understand, whether there are distinct metabolic patterns associated with malnutrition risk as assessed by the Nutritional Risk screening (NRS 2002) score, the risk of 30-day mortality and the response to nutritional support, respectively.

METHODS

Out of the 2088 samples we randomly selected 120 blood samples drawn on day 1 after hospital admission and before treatment initiation. Samples were stratified by NRS 2002, treatment allocation (intervention vs. control), and mortality at 30 days, but not on the type of medical illness. We performed untargeted analysis by liquid chromatography mass spectrometry (LC-MS/MS).

RESULTS

We measured 1389 metabolites in 120 patients of which 81 (67.5%) survived until day 30. After filtering, 371 metabolites remained, and 200 were matched to one or more Human Metabolome Data Base (HMDB) entries. Between group analysis showed a slight distinction between the treatment groups for patients with a NRS 3, but not for those with NRS 4 and ≥ 5. C-statistic between those who died and survived at day 30 ranged from 0.49 (95% confidence interval 0.35-0.68) for a combination of 5 metabolites/predictors to 0.66 (95% confidence interval 0.53-0.79) for a combination of 100 metabolites. Pathway analysis found significant enrichment in the pathways for nitrogen, vitamin B3 (nicotinate and nicotinamide), leukotriene, and arachidonic acid metabolisms in nutritional support responders compared to non-responders.

CONCLUSION

In our heterogenous population of medical inpatients with different illnesses and comorbidities, metabolomic markers showed little prognostic and therapeutic potential for better phenotyping malnutrition and response to nutritional therapy. Future studies should focus on more selected patient populations to understand whether a metabolomic approach can advance the nutritional care of patients.

Metabolome and microbiome alterations in tongue coating of gastric precancerous lesion patients

Objective: This paper seeks to provide mechanistic insight into the pathological transition through the analysis of metabolites and microorganisms in the tongue coating of gastric precancerous lesions (GPL) patients.Methods: GC-TOF-MS and UHPLC-QE-MS metabolomics, combined with 16S rRNA microbiome techniques, were performed to explore the changes in metabolites and microorganisms in the tongue coating of GPL patients.Results: When compared with 15 controls, 133 metabolites were found to be differentially expressed in 60 GPL cases, of which could be divided into ten categories. Among them, most of the differentially expressed metabolites identified were lipids or lipid-like molecules. These metabolites were implicated in 6 metabolic pathways including glycine, serine and threonine metabolism, arginine and proline metabolism, sphingolipid metabolism, valine, leucine and isoleucine degradation, arachidonic acid metabolism, and tyrosine metabolism. The relative abundances of Alloprevotella, Solobacterium, Rothia, Eikenella, and Aggregatibacter in the GPL group increased significantly relative to the controls and were associated with lipids and lipid-like molecules, organic nitrogen compounds, organic oxygen compounds, phenylpropanoids and polyketides, and organoheterocyclic compounds, respectively.Conclusions: Compared with healthy people, the changes of tongue coating metabolites in GPL patients were mainly characterized by alterations in lipid metabolism and were associated with localized changes in the microbiome.

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.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

Efficacy of Analytical Technologies in Metabolomics Studies of the Gastrointestinal Cancers

According to the reports of the World Health Organization and the International Agency for Research on Cancer, cancer is the second leading cause of human death worldwide. However, early-stage detection of cancers can efficiently enhance the chance of therapy and saving lives. Metabolomics strategies apply a variety of approaches to discover new potential diagnoses, prognoses, and/or therapeutic biomarkers of various diseases. Metabolomics aims to identify and measure different low-molecular-weight biomolecules in physiological environments. In these studies, special metabolites are extracted from biological samples and identified using analytical techniques. Afterward, using data processing programs discovering significantly associated biomarkers is pursued. In the present review, we aimed to discuss recently reported analytical approaches on the metabolomics studies of gastrointestinal cancers including gastric, colorectal, and esophageal cancers. The gas- and liquid-chromatography with different detectors have been shown that are the main analytical techniques and for metabolites quantification, nuclear magnetic resonance has been utilized as a master method.

Serum Metabolic Profiling Analysis of Chronic Gastritis and Gastric Cancer by Untargeted Metabolomics

Purpose

Gastric cancer is a common tumor of the digestive system. Identification of potential molecules associated with gastric cancer progression and validation of potential biomarkers for gastric cancer diagnosis are very important. Thus, the aim of our study was to determine the serum metabolic characteristics of the serum of patients with chronic gastritis (CG) or gastric cancer (GC) and validate candidate biomarkers for disease diagnosis.

Experimental Design

A total of 123 human serum samples from patients with CG or GC were collected for untargeted metabolomic analysis via UHPLC-Q-TOF/MS to determine characteristics of the serum. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and heat map were used for multivariate analysis. In addition, commercial databases were used to identify the pathways of metabolites. Differential metabolites were identified based on a heat map with a t-test threshold (p < 0.05), fold-change threshold (FC > 1.5 or FC < 2/3) and variable importance in the projection (VIP >1). Then, differential metabolites were analyzed by receiver operating characteristic (ROC) curve to determine candidate biomarkers. All samples were analyzed for fasting lipid profiles.

Results

Analysis of serum metabolomic profiles indicated that most of the altered metabolic pathways in the three groups were associated with lipid metabolism (p < 0.05) and lipids and lipid-like molecules were the predominating metabolites within the top 100 differential metabolites (p < 0.05, FC > 1.5 or FC < 2/3, and VIP >1). Moreover, differential metabolites, including hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine had high diagnostic performance according to PLS-DA. In addition, fasting lipid profile analysis showed the serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (Apo-A1) were decreased concomitant to the progression of the progression of the disease compared with those in the control group (p < 0.05).

Conclusions

Thus, this study demonstrated that lipid metabolism may influence the development of CG to GC. Hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine were selected as candidate diagnostic markers for CG and GC.

The Distinctive Serum Metabolomes of Gastric, Esophageal and Colorectal Cancers

Simple Summary

Cancer of the stomach, esophagus and colon are often fatal. Ways are being sought to establish patient-friendly screening tests that would allow these cancers to be detected earlier. Examination of the metabolomics results of cancer patient’s serum for certain metabolites unique for a particular cancer was the goal of this review. From studies conducted within the past five years several metabolites were found to be changed in cancer compared to non-cancer patients for each of the three cancers. Further confirmation of what was discovered in this review coupled with establishment of standard protocols may allow for cancer screening on patient blood samples to become routine clinical tests.

Abstract

Three of the most lethal cancers in the world are the gastrointestinal cancers—gastric (GC), esophageal (EC) and colorectal cancer (CRC)—which are ranked as third, sixth and fourth in cancer deaths globally. Early detection of these cancers is difficult, and a quest is currently on to find non-invasive screening tests to detect these cancers. The reprogramming of energy metabolism is a hallmark of cancer, notably, an increased dependence on aerobic glycolysis which is often referred to as the Warburg effect. This metabolic change results in a unique metabolic profile that distinguishes cancer cells from normal cells. Serum metabolomics analyses allow one to measure the end products of both host and microbiota metabolism present at the time of sample collection. It is a non-invasive procedure requiring only blood collection which encourages greater patient compliance to have more frequent screenings for cancer. In the following review we will examine some of the most current serum metabolomics studies in order to compare their results and test a hypothesis that different tumors, notably, from EC, GC and CRC, have distinguishing serum metabolite profiles.

Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

One of the major hallmarks of cancer is the derailment of a cell's metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

Pickled Vegetables Intake Impacts the Metabolites for Gastric Cancer

Purpose

An increased risk of gastric cancer (GC) for pickled vegetables intake has been suggested, but a complete understanding of its pathogenic origin is still lacking, especially from a metabolic viewpoint. We investigated the plasma metabolites and metabolic pathway alteration of GC related to pickled vegetables intake.

Methods

We analyzed plasma samples collected from 365 gastric cancer patients and 347 healthy individuals, and divided them into three subgroups according to the intake of pickled vegetables. Plasma samples were detected by untargeted metabolomics.

Results

Nine metabolites were significantly altered in GC patients among pickled vegetables intake groups (FDR P-value<0.05). All of them were associated with the risk of gastric cancer adjusted for gender, age, smoking status, Helicobacter pylori infection. Pathway analysis showed significant alteration in the folate biosynthesis pathway.

Conclusion

In short, we provide new insights from a metabolic perspective on the relationship between pickled vegetables intake and the occurrence of gastric cancer.

Undetectable Free Aromatic Amino Acids in Nails of Breast Carcinoma: Biomarker Discovery by a Novel Metabolite Purification VTGE System

Background: Metabolic reprogramming in breast cancer is depicted as a crucial change in the tumor microenvironment. Besides the molecular understanding of metabolic heterogeneity, appreciable attention is drawn to characterizing metabolite profiles in tumor tissue and derived biological fluids and tissue materials. Several findings reported on the metabolic alterations of free aromatic amino acids (FAAAs) and other metabolites in biological fluids. Furthermore, there is a significant gap in the development of a suitable method for the purification and analysis of metabolite biomarkers in nails of cancer patients. Methods: To address the metabolite alterations, specifically FAAA levels in nails, fingernail clippings of breast cancer patients (N = 10) and healthy subjects (N-12) were used for extraction and purification of metabolites. Here, we reported a novel and specifically designed vertical tube gel electrophoresis (VTGE) system that helped in the purification of metabolites in the range of 100-1,000 Da from nail materials. Here, the VTGE system uses 15% polyacrylamide under non-denaturing and non-reducing conditions, which makes eluted metabolites directly compatible with LC-HRMS and other analytical techniques. Qualitative and quantitative determination of FAAAs in nail lysates was done in positive ESI mode of the Agilent LC-HRMS platform. Results: The analysis on collected data of nail metabolites clearly suggested that FAAAs including tryptophan, tyrosine, phenylalanine, and histidine were undetectable in nail lysates of breast cancer over healthy subjects. This is a first report that showed highly reduced levels of FAAAs in nails of breast cancer patients. Furthermore, the present observation is in consonance with previous findings that showed cancer cachexia and high amino acid catabolism in breast cancer patients that drive metabolite-led cancer growth and proliferation. Conclusion: This paper provides a proof of concept for a novel and specifically developed VTGE process that showed first evidence on the undetectable level of FAAAs in nails of breast cancer patients as metabolite biomarkers. Here, the authors propose the potential use of a VTGE-assisted process to achieve metabolomic discovery in nails of breast cancer and other tumor types.

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.

Metabolomic profiling in neuroblastoma

BACKGROUND AND OBJECTIVES

Previous studies on several cancer types show that metabolomics provides a potentially useful noninvasive screening approach for outcome prediction and accurate response to treatment assessment. Neuroblastoma (NB) accounts for at least 15% of cancer-related deaths in children. Although current risk-based treatment approaches in NB have resulted in improved outcome, survival for high-risk patients remains poor. This study aims to evaluate the use of metabolomics for improving patients' risk-group stratification and outcome prediction in NB.

DESIGN AND METHODS

Plasma samples from 110 patients with NB were collected at diagnosis prior to starting therapy and at the end of treatment if available. Metabolomic analysis of samples was carried out by ultra-performance liquid chromatography-time of flight mass spectrometry (UPLC-MS).

RESULTS

The metabolomic analysis was able to identify different plasma metabolic profiles in high-risk and low-risk NB patients at diagnosis. The metabolic model correctly classified 16 high-risk and 15 low-risk samples in an external validation set providing 84.2% sensitivity (60.4-96.6, 95% CI) and 93.7% specificity (69.8-99.8, 95% CI). Metabolomic profiling could also discriminate high-risk patients with active disease from those in remission. Notably, a plasma metabolomic signature at diagnosis identified a subset of high-risk NB patients who progressed during treatment.

CONCLUSIONS

To the best of our knowledge, this is the largest NB study investigating the prognostic power of plasma metabolomics. Our results support the potential of metabolomic profiling for improving NB risk-group stratification and outcome prediction. Additional validating studies with a large cohort are needed.

Detection of Helicobacter pylori Microevolution and Multiple Infection from Gastric Biopsies by Housekeeping Gene Amplicon Sequencing

Despite the great efforts devoted to research on Helicobacter pylori, the prevalence of single-strain infection or H. pylori mixed infection and its implications in the mode of transmission of this bacterium are still controversial. In this study, we explored the usefulness of housekeeping gene amplicon sequencing in the detection of H. pylori microevolution and multiple infections. DNA was extracted from five gastric biopsies from four patients infected with distinct histopathological diagnoses. PCR amplification of six H. pylori-specific housekeeping genes was then assessed on each sample. Optimal results were obtained for the cgt and luxS genes, which were selected for amplicon sequencing. A total of 11,833 cgt and 403 luxS amplicon sequences were obtained, 2042 and 112 of which were unique sequences, respectively. All cgt and luxS sequences were clustered at 97% to 9 and 13 operational taxonomic units (OTUs), respectively. For each sample from a different patient, a single OTU comprised the majority of sequences in both genes, but more than one OTU was detected in all samples. These results suggest that multiple infections with a predominant strain together with other minority strains are the main way by which H. pylori colonizes the human stomach.

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.

Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics

Kit-based assays, such as AbsoluteIDQ^TM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving Lipidyzer^TM platform, analyzing 223 samples in parallel to the AbsoluteIDQ^TM. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.

Serum Metabolomics Study of Nonsmoking Female Patients with Non-Small Cell Lung Cancer Using Gas Chromatography-Mass Spectrometry

The incidence of nonsmoking female patients with non-small cell lung cancer (NSCLC) has increased in recent decades; however, the pathogenesis of patients is unclear, and early diagnosis biomarkers are in urgent need. In this study, 136 nonsmoking female subjects (65 patients with NSCLC, 6 patients with benign lung tumors, and 65 healthy controls) were enrolled, and their metabolic profiling was investigated by using pseudotargeted gas chromatography-mass spectrometry. A total of 56 annotated metabolites were found and verified to be significantly different in nonsmoking females with NSCLC compared with the control. The metabolic profiling was featured by disturbed energy metabolism, amino acid metabolism, oxidative stress, lipid metabolism, and so on. Cysteine, serine, and 1-monooleoylglycerol were defined as the biomarker panel for the diagnosis of NSCLC patients. 98.5 and 91.4% of subjects were correctly distinguished in the discovery and validation sets, respectively. The biomarker panel was also useful for the diagnosis of in situ malignancy patients, with an accuracy of 97.7 and 97.8% in the discovery and validation sets, respectively. The study provides a biomarker panel for the auxiliary diagnosis of nonsmoking females with NSCLC.

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.

Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells

Recently, there has been increased attention on the analysis of circulating tumor cells (CTCs), also known as liquid biopsy, owing to its potential benefits in cancer diagnosis and treatment. Circulating tumor cells are released from primary tumor lesions into the blood stream and eventually metastasize to distant body organs. However, a major hurdle with CTC analysis is their natural scarcity. Existing methods lack sensitivity, specificity, or reproducibility required in CTC characterization and detection. Here, we report untargeted molecular profiling of single CTCs obtained from gastric cancer and colorectal cancer patients, using live single cell mass spectrometry integrated with microfluidics-based cell enrichment techniques. Using this approach, we showed the difference in the metabolomic profile between CTCs originating from different cancer groups. Moreover, potential biomarkers were putatively annotated to be specific to each cancer type.