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

The regulatory mechanism of m6A modification in gastric cancer

To the best of our knowledge, N6-Methyladenosine (m6A) exerts a significant role in the occurrence and development of various tumors. Gastric cancer (GC), originating from the mucosal epithelium in the digestive tract, is the fifth most common cancer and the third most common cause of cancer death around the world. Therefore, it is urgent to explore the specific mechanism of tumorigenesis of GC. As we all know, m6A modification as the most common RNA modification, is involved in the modification of mRNA and ncRNA at the post-transcriptional level, which played a regulatory role in various biological processes. As identified by numerous studies, the m6A modification are able to influence the proliferation, apoptosis, migration, and invasion of GC. What's more, m6A modification are associated with EMT, drug resistance, and aerobic glycolysis in GC. m6A related-ncRNAs may be a valuable biomarker used by the prediction of GC diagnosis in the future. This review summarizes the role of m6A modification in the mechanism of gastric cancer, with the aim of identifying biological progress.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study

BACKGROUND

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.

METHODS

To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.

RESULTS

A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.

CONCLUSION

These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

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.

Glucose and lactate levels are lower in EUS-aspirated cyst fluid of mucinous vs non-mucinous pancreatic cystic lesions

BACKGROUND

Distinguishing mucinous (M) pancreatic cystic neoplasms (PCNs) from non-mucinous (NM) is challenging but crucial. Low intracystic glucose level has shown diagnostic tool promise, however further investigation is needed to understand metabolic processes.

AIMS

To compare the diagnostic accuracy of intracystic glucose and CEA levels in a large cohort and explore lactate levels as potential marker.

METHODS

PCNs≥15 mm which underwent EUS-fine needle aspiration were prospectively enrolled. Glucose, CEA and lactate levels were measured. Diagnostic accuracy for M-PCN diagnosis was evaluated using surgical/cytology reports or multidisciplinary evaluations.

RESULTS

169 PCNs were included (64 % M-PCNs). Median intracystic glucose was significantly lower in M-PCNs (1 mg/dL) compared to NM-PCNs (101 mg/dL); mean intracystic CEA was significantly higher in M-PCNs (152.5 ng/mL) compared to NM-PCNs (0.3 ng/mL). ROC curve analysis revealed best glucose cut-off ≤58 mg/dL (accuracy 93.5 %) and CEA cut-off >2.5 ng/mL (accuracy 90.5 %) for M-PCNs. Intracystic lactates were significantly lower in M-PCNs correlating directly with glucose. Single glucose dosage evidenced best diagnostic accuracy respect markers combination.

CONCLUSION

Intracystic glucose demonstrated high diagnostic utility for M-PCNs differentiation, surpassing CEA. Lactate levels correlated with glucose, suggesting their uptake by M-PCNs cells. These findings contribute to a better metabolic landscape understanding glucose use as diagnostic marker.

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study

Background

Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms.

Methods

Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software.

Results

A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Genetic and molecular characterization of metabolic pathway-based clusters in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

Burdens of stomach and esophageal cancer from 1990 to 2019 and projection to 2030 in China: Findings from the 2019 Global Burden of Disease Study

Background

Stomach and esophageal cancer exhibit high morbidity and mortality rate in China, resulting in substantial disease burdens. It is imperative to identify the temporal trends of stomach and esophageal cancer from 1990 to 2019 and project future trends until 2030, which can provide valuable information for planning effective management and prevention strategies.

Methods

We collected and analysed data from the Global Burden of Disease (GBD) between 1990 and 2019, including incidence, mortality, disability-adjusted life years (DALYs), age-standardised incidence rate (ASIR), mortality rate (ASMR) and DALYs rate. We also calculated and reported the proportion of mortality and DALYs attributable to risk factors by sex in China and different regions. The Bayesian age-period-cohort model was applied to project future trends until 2030.

Results

The new cases, deaths and DALYs of stomach and esophageal cancer increased from 1990 to 2019. However, the ASIR, ASMR and age-standardised DALYs rates for stomach and esophageal cancer all decreased during the same period. These changes may be related to risks, such as smoking and diet. Furthermore, we utilised the projection model to estimate that the ASIR and ASMR of stomach and esophageal cancer among females will likely follow steady downward trends, while the ASMR of stomach cancer among males is expected to exhibit a significant decline. However, the ASIR of stomach and esophageal cancer and the ASMR of esophageal cancer among males are projected to display slight upward trends until 2030.

Conclusions

The analysis of stomach and esophageal cancer trends in China from 1990 to 2030 reveals a general decline. However, it is crucial to acknowledge the persistent high burden of both cancers in the country. Adopting healthy lifestyle practices, including the reduction of tobacco and alcohol intake, avoidance of moldy foods and increased consumption of fresh fruits and vegetables can contribute to mitigating the risk of stomach and esophageal cancer. Significantly, the formulation and implementation of well-founded and efficacious public health policies are imperative for alleviating the disease burden in China.

Effect of metabolic reprogramming on the immune microenvironment in gastric cancer

Gastric cancer (GC) is a malignant tumor of the gastrointestinal tract with a high mortality rate worldwide, a low early detection rate and a poor prognosis. The rise of metabolomics has facilitated the early detection and treatment of GC. Metabolism in the GC tumor microenvironment (TME) mainly includes glucose metabolism, lipid metabolism and amino acid metabolism, which provide energy and nutrients for GC cell proliferation and migration. Abnormal tumor metabolism can influence tumor progression by regulating the functions of immune cells and immune molecules in the TME, thereby contributing to tumor immune escape. Thus, in this review, we summarize the impact of metabolism on the TME during GC progression. We also propose novel strategies to modulate antitumor immune responses by targeting metabolism.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer

BACKGROUND

Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC).

METHODS

A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC.

RESULTS

We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805).

CONCLUSIONS

These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing

Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches.

SHMT2 regulates esophageal cancer cell progression and immune Escape by mediating m6A modification of c-myc

BACKGROUND

In recent years, the role of altered cellular metabolism in tumor progression has attracted widespread attention. Related metabolic enzymes have also been considered as potential cancer therapeutic targets. Serine hydroxymethyltransferase 2 (SHMT2) has been reported to be upregulated in several cancers and associated with poor prognosis. However, there are few studies of SHMT2 in esophageal cancer (EC), and the related functions and mechanisms also need to be further explored.

METHODS

In this study, we first analyzed SHMT2 expression in EC by online database and clinical samples. Then, the biological functions of SHMT2 in EC were investigated by cell and animal experiments. The intracellular m6A methylation modification levels were also evaluated by MeRIP. Linked genes and mechanisms of SHMT2 were analyzed by bioinformatics and rescue experiments.

RESULTS

We found that SHMT2 expression was abnormally upregulated in EC and associated with poor prognosis. Functionally, SHMT2 silencing suppressed c-myc expression in an m6A-dependent manner, thereby blocking the proliferation, migration, invasion and immune escape abilities of EC cells. Mechanistically, SHMT2 encouraged the accumulation of methyl donor SAM through a one-carbon metabolic network, thereby regulating the m6A modification and stability of c-myc mRNA in a METTL3/FTO/ALKBH5/IGF2BP2-dependent way. In vivo animal experiments also demonstrated that SHMT2 mediated MYC expression by m6A-methylation modification, thus boosting EC tumorigenesis.

CONCLUSION

In conclusion, our data illustrated that SHMT2 regulated malignant progression and immune escape of EC cell through c-myc m6A modification. These revealed mechanisms related to SHMT2 in EC and maybe offer promise for the development of new therapeutic approaches.

Microbial-Related Metabolites May Be Involved in Eight Major Biological Processes and Represent Potential Diagnostic Markers in Gastric Cancer

Metabolites associated with microbes regulate human immunity, inhibit bacterial colonization, and promote pathogenicity. Integrating microbe and metabolome research in GC provides a direction for understanding the microbe-associated pathophysiological process of metabolic changes and disease occurrence. The present study included 30 GC patients with 30 cancerous tissues and paired non-cancerous tissues (NCs) as controls. LC-MS/MS metabolomics and 16S rRNA sequencing were performed to obtain the metabolic and microbial characteristics. Integrated analysis of the microbes and metabolomes was conducted to explore the coexistence relationship between the microbial and metabolic characteristics of GC and to identify microbial-related metabolite diagnostic markers. The metabolic analysis showed that the overall metabolite distribution differed between the GC tissues and the NC tissues: 25 metabolites were enriched in the NC tissues and 42 metabolites were enriched in the GC tissues. The α and β microbial diversities were higher in the GC tissues than in the NC tissues, with 11 differential phyla and 52 differential genera. In the correlation and coexistence integrated analysis, 66 differential metabolites were correlated and coexisted, with specific differential microbes. The microbes in the GC tissue likely regulated eight metabolic pathways. In the efficacy evaluation of the microbial-related differential metabolites in the diagnosis of GC, 12 differential metabolites (area under the curve [AUC] >0.9) exerted relatively high diagnostic efficiency, and the combined diagnostic efficacy of 5 to 6 microbial-related differential metabolites was higher than the diagnostic efficacy of a single feature. Therefore, microbial diversity and metabolite distribution differed between the GC tissues and the NC tissues. Microbial-related metabolites may be involved in eight major metabolism-based biological processes in GC and represent potential diagnostic markers.

Advancements in Analyzing Tumor Metabolites through Chemical Derivatization-Based Chromatography

Understanding the metabolic abnormalities of tumors is crucial for early diagnosis, prognosis, and treatment. Accurate identification and quantification of metabolites in biological samples are essential to investigate the relationship between metabolite variations and tumor development. Common techniques like LC-MS and GC-MS face challenges in measuring aberrant metabolites in tumors due to their strong polarity, isomerism, or low ionization efficiency during MS detection. Chemical derivatization of metabolites offers an effective solution to overcome these challenges. This review focuses on the difficulties encountered in analyzing aberrant metabolites in tumors, the principles behind chemical derivatization methods, and the advancements in analyzing tumor metabolites using derivatization-based chromatography. It serves as a comprehensive reference for understanding the analysis and detection of tumor metabolites, particularly those that are highly polar and exhibit low ionization efficiency.

Patterns of the lymph node metastasis and the influencing factors in esophagogastric junction cancers

OBJECTIVE

A retrospective analysis of 214 cases of esophagogastric junction cancers (EGJCs) in Kunming, Yunnan Province, was conducted to investigate the lymph node metastasis (LNM) pattern for EGJCs and its associated risk factors (RFs), as well as the predictive value of common clinical metabolic indicators for it.

METHODS

The clinical data of 214 patients diagnosed with EGJCs by electronic gastroscope and postoperative pathology between 2013 and 2021 at the First Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, and the Second Affiliated Hospital of Kunming Medical University were retrospectively examined. Preoperative gastroscopy, imaging, biochemical data, and postoperative pathological findings analysis in EGJCs were statistically analyzed to determine the RFs of LNM.

RESULTS

At presentation, 92.5% of EGJCs were progressive malignancies, including 68.2% LNMs, 79.5% abdominal lymph nodes (ALN), 20.1% mediastinal lymph nodes (MLN), and 27.1% distal metastases. The ratio of Siewert subtypes was approximately 2:11:7 (type I to type II to type III). In terms of age, disease duration at initial presentation, history, tumor length, pathological biopsy histology, degree of differentiation, depth of infiltration, LNM status, MLN metastasis, and surgical route, the differences between the three Siewert subgroups were statistically significant (p < 0.05). Multifactor analysis revealed that the proportion of patients aged <65 at the time of consultation was significantly more significant in Siewert II and Siewert III than in Siewert I. Significantly more patients than in Siewert I had <2 months of disease duration at the time of their first consultation. The proportion of patients with tumors <3 Cm in length was significantly higher than in Siewert I. For the RFs analysis of LNM, Siewert staging (type I and type II), depth of infiltration, and distant metastasis were the independent RFs for LNM. The depth of infiltration and family history of the tumor were the independent RFs for ALN metastasis, and the number of lymph nodes cleared in the abdominal and mediastinal regions was a protective factor for ALN metastasis. Siewert staging(type I and type II), infiltration depth, invasion of the esophagus by the tumor, tumor length, and distant metastasis were independent risk factors for MLN metastasis. Among the metabolic variables evaluated, BMI was an independent RF for LNM, fasting glucose was an independent RF for ALN metastasis, and triacylglycerol was a protective factor for MLN metastasis.

CONCLUSIONS

EGJCs are frequently advanced at presentation, characterized by minimal differentiation and a high incidence of LNM. The Siewert subtype is concentrated near the stomach. Different Siewert subtypes exhibit distinct clinicopathological characteristics. LNM and MLN metastasis risk are considerably higher in type I tumors compared to types II and III. There is a strong correlation between LNM and MLN metastasis and distant metastasis in EGJCs, so Siewert I is more aggressive and associated with a worse prognosis. EGJCs have numerous RFs associated with LNM, and there are similarities and differences in the RFs affecting their LNM, ALN metastases, and MLN metastases, which are related to their unique anatomical features. There is a close relationship between metabolic factors and EGJCs with some predictive value.

A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma

Background

There is a heterogenous clinical response following chemoradiotherapy (CRT) in esophageal squamous cell carcinoma (ESCC). Therefore, we aimed to study signaling pathway genes that affect CRT sensitivity and prognosis.

Methods

Gene expression analyses were performed in the GEO and TCGA datasets. A immunohistochemistry (IHC) analysis was performed in pretreatment biopsies.

Results

MMP13 was found to be highly expressed in the "Pathologic Complete Response (pCR)" and "Complete Remission (CR)" and "Alive" groups. Th17 cells and MMP9/13 showed a negative correlation in immune infiltration analysis. In GSEA analysis, IL-4 and IL-13 signaling pathways were highly enriched in patients exhibiting high MMP expression in pCR and CR groups. IHC results suggested higher MMP13 & IL-4 and lower IL-17A & RORC expression in the CR group compared to the 0.70, and the model could well distinguish high-risk and low-risk subgroups.

Conclusion

The above results may provide guidance for developing novel treatment and prognostic strategies in ESCC patients.

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.

Rs15285, a functional polymorphism located in lipoprotein lipase, predicts the risk and prognosis of gastric cancer

Lipoprotein lipase (LPL), a crucial gene in lipid metabolism, has a significant role in the progression of malignant tumors. The purpose of this research was to investigate the impact of rs15285 found in the LPL gene's 3'UTR region on the risk, biological behavior, and gastric cancer (GC) prognosis as well as to examine its potential function. Genotyping of rs15285 in 888 GC cases and 874 controls was conducted by SNaPshot technology. We used bioinformatics analysis and in vitro experiments to study the role of rs15285. First, this study revealed for the first time that polymorphism rs15285 increases the risk of GC (OR = 1.48, 95%CI = 1.16-1.89, P = 0.002). Although no relationship was found between rs12585 and the pathological features of GC, the prognosis of individuals with the rs12585 TT genotype was poorer than that of patients with the CC or CC+CT genotype (HR = 2.39 for TT vs. CC, P = 0.025; HR = 2.38 for TT vs. CC+CT, P = 0.025). In addition, bioinformatics analysis showed rs12585 may affect the binding of miRNAs to LPL, resulting in an increase of LPL expression to promote cancer progression. Ultimately, in vitro tests revealed that the rs15285 T allele increased LPL expression on the mRNA as well as the protein levels, promoting GC cell proliferation, invasion, and metastasis. The LPL rs12528 TT genotype increased the risk of GC and predicted a poor prognosis. Mechanistically, the rs15285 T allele could improve the expression of LPL, and thus promotes the malignant phenotype of GC. Therefore, our study may provide new biological predictors and a theoretical basis for the prognosis and customized therapy of stomach cancer patients. KEY POINTS: • Rs15285 polymorphism is a risk factor for GC. • Rs12585 TT genotype predicts a bad outcome in GC individuals. • Rs15285 T allele enhances GC cells malignant biological behavior.

Metabolomics Reveals Novel Serum Metabolic Signatures in Gastric Cancer by a Mass Spectrometry Platform

Gastric cancer (GAS) is one of the malignant tumors of the gastrointestinal system. Alterations in metabolite composition can reflect pathological processes of GAS and constitute a basis for diagnosis and treatment improvements. In this study, a total of 301 serum samples from 150 GAS patients at different tumor-node-metastasis (TNM) stages and 151 healthy controls were collected. Mass spectrometry platforms were performed to investigate the changes in GAS-related metabolites and explore the new potential serum biomarkers and the metabolic dysregulation associated with GAS progression. Twelve differential metabolites (ethyl 2,4-dimethyl-1,3-dioxolane-2-acetate, D-urobilinogen, 14-HDoHE, 13-hydroxy-9-methoxy-10-oxo-11-octadecenoic acid, 5,6-dihydroxyprostaglandin F1a, 9'-carboxy-gamma-tocotrienol, glutaric acid, alanine, tyrosine, C18:2(FFA), adipic acid, and suberic acid) were identified to establish the diagnosis model for GAS. The defined biomarker panel was also statistically significant for GAS progression with different TNM stages. KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment revealed the metabolic dysregulation associated with GAS progression. In conclusion, a diagnostic panel was established and validated, which could be used to further stage the early and advanced GAS patients from healthy controls. These findings may provide useful information for explaining the GAS metabolic alterations and try to facilitate the characterization of GAS patients in the early stage.

Tumour catabolism independent of malnutrition and inflammation in upper GI cancer patients revealed by longitudinal metabolomics

BACKGROUND

The detrimental impact of malnutrition and cachexia in cancer patients subjected to surgical resection is well established. However, how systemic and local metabolic alterations in cancer patients impact the serum metabolite signature, thereby leading to cancer-specific differences, is poorly defined. In order to implement metabolomics as a potential tool in clinical diagnostics and disease follow-up, targeted metabolite profiling based on quantitative measurements is essential. We hypothesized that the quantitative metabolic profile assessed by ¹ H nuclear magnetic resonance (NMR) spectroscopy can be used to identify cancer-induced catabolism and potentially distinguish between specific tumour entities. Importantly, to prove tumour dependency and assess metabolic normalization, we additionally analysed the metabolome of patients' sera longitudinally post-surgery in order to assess metabolic normalization.

METHODS

Forty two metabolites in sera of patients with tumour entities known to cause malnutrition and cachexia, namely, upper gastrointestinal cancer and pancreatic cancer, as well as sera of healthy controls, were quantified by ¹ H NMR spectroscopy.

RESULTS

Comparing serum metabolites of patients with gastrointestinal cancer with healthy controls and pancreatic cancer patients, we identified at least 15 significantly changed metabolites in each comparison. Principal component and pathway analysis tools showed a catabolic signature in preoperative upper gastrointestinal cancer patients. The most specifically upregulated metabolite group in gastrointestinal cancer patients was ketone bodies (3-hydroxybutyrate, P < 0.0001; acetoacetate, P < 0.0001; acetone, P < 0.0001; false discovery rate [FDR] adjusted). Increased glycerol levels (P < 0.0001), increased concentration of the ketogenic amino acid lysine (P = 0.03) and a significant correlation of 3-hydroxybutyrate levels with branched-chained amino acids (leucine, P = 0.02; isoleucine, P = 0.04 [FDR adjusted]) suggested that ketone body synthesis was driven by lipolysis and amino acid breakdown. Interestingly, the catabolic signature was independent of the body mass index, clinically assessed malnutrition using the nutritional risk screening score, and systemic inflammation assessed by CRP and leukocyte count. Longitudinal measurements and principal component analyses revealed a quick normalization of key metabolic alterations seven days post-surgery, including ketosis.

CONCLUSIONS

Together, the quantitative metabolic profile obtained by ¹ H NMR spectroscopy identified a tumour-induced catabolic signature specific to upper gastrointestinal cancer patients and enabled monitoring restoration of metabolic homeostasis after surgery. This approach was critical to identify the obtained metabolic profile as an upper gastrointestinal cancer-specific signature independent of malnutrition and inflammation.

Polyurea-Modified Magnetic Particles with Versatile Probes for Chemoselective Capture of Carbonyl Metabolites and Biomarker Discovery

Playing a great role in human physiologies and pathologies, carbonyl metabolites are intimately associated with a variety of diseases, though the effective analysis method of them remains a challenge. A hydrazide-terminated polyurea-modified magnetic particle (HPMP) with versatile probes is developed to address this issue. The capture ability of HPMPs for carbonyl metabolite is more than 1200 µmol g^-1 , which is increased by 4 orders of magnitude via the introduction of polyurea. With a broad linear range of over 4 orders of magnitude, remarkably improved sensitivity, and limit of detection at attomole quantities, HPMPs are applied in relative quantification of more than 1500 carbonyl metabolites in 113 human serum samples with high throughput and high coverage. The combined indicators of these metabolites demonstrates a great diagnostic accuracy for distinguishing between health and disease subjects as well as differentiating the patients with benign lung disease and lung cancer. Combining powerful capture ability, low-cost preparation, and convenient operation, the HPMPs demonstrate extensive application in biomarker discovery and the detailed study of the biochemical landscape.

METTL3-induced DLGAP1-AS2 promotes non-small cell lung cancer tumorigenesis through m6A/c-Myc-dependent aerobic glycolysis

The critical roles of N6-methyladenosine (m6A) modification have been demonstrated by more and more evidence. However, the cross talk of m6A and long noncoding RNAs (lncRNAs) in non-small cell lung cancer (NSCLC) tumorigenesis is still unclear. Here, this work focused on the functions and molecular mechanism of m6A-modified lncRNA DLGAP1 antisense RNA 2 (DLGAP1-AS2) in NSCLC. Microarray analysis found that lncRNA DLGAP1-AS2 is upregulated in NSCLC cells. Clinical data showed that DLGAP1-AS2 high-expression was correlated with advanced pathological stage and poor prognosis. Functionally, DLGAP1-AS2 overexpression promoted the aerobic glycolysis and DLGAP1-AS2 knockdown suppressed the tumor growth of NSCLC cells. Mechanistically, m6A methyltransferase METTL3 enhanced the stability of DLGAP1-AS2 via m6A site binding. Moreover, DLGAP1-AS2 interacted with YTHDF1 to enhance the stability of c-Myc mRNA through DLGAP1-AS2/YTHDF1/m6A/c-Myc mRNA. In conclusion, our work indicates the functions of m6A-modified DLGAP1-AS2 in the NSCLC aerobic glycolysis, disclosing a potential m6A-dependent manner for NSCLC treatment.

Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses

Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting therapeutic efficacy. Obviously, a systematic analysis of lipid metabolism characterizations of gastric cancer (GC) needs to be reported.

Experimental design: Based on two proposed computational algorithms (TCGA-STAD and GSE84437), the lipid metabolism characterization of 367 GC patients and its systematic relationship with genomic characteristics, clinicopathologic features, and clinical outcomes of GC were analyzed in our study. Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore for GC. To verify the specific changes in lipid metabolism, a total of 90 serum, 30 tumor, and non-tumor adjacent tissues from GC patients, were included for pseudotargeted metabolomics analysis via SCIEX triple quad 5500 LC-MS/MS system.

Results: Five lipid metabolism signature genes were identified from a total of 3,104 DEGs. The LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic drugs. Additionally, significant changes in sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue from GC patients by pseudotargeted metabolomics.

Conclusion: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the characteristics of lipid metabolism may help to provide insights into the pathogenesis of GC, interpret the responses of gastric tumors to therapies, and achieve a better outcome in the treatment of GC. In addition, significant alterations of sphingolipid metabolism and increased levels of sphingolipids, in particular, sphingosine (d16:1) and ceramide, were discovered in GC tissue by lipidome pseudotargeted metabolomics, and most of the sphingolipid molecules have the potential to be diagnostic biomarkers for GC.

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.

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.

Lipid Metabolic-Related Signature CYP19A1 is a Potential Biomarker for Prognosis and Immune Cell Infiltration in Gastric Cancer

Background

Altered lipid metabolism is associated with gastric cancer (GC) progression. Comprehensive analysis to identify critical lipid metabolic drivers for predicting overall survival (OS) is not fully elucidated in GC. Our study aim to explore a novel lipid metabolism-related prognostic marker for GC.

Methods

Transcriptional status and clinical features were obtained from the TCGA-STAD database. The differentially expressed lipid metabolic genes and the risk prognostic model were developed by using bioinformatics and Cox regression analyses. ROC and Kaplan-Meier analysis were established to assess the performance of the risk predictive score model. GSE84437 dataset was used for external validation. Immunochemistry (IHC) was used to examine the expression of CYP19A1 in GC patients. Gene Set Enrichment Analysis (GSEA) was conducted to elucidate the underlying enriched mechanisms. TIMER and CIBERSORT analysis were performed to explore the relationship between CYP19A1 and immune microenvironment.

Results

A novel lipid metabolic gene signature (including MTTP, CYP19A1, MYB, SERPINE1), and specifically CYP19A1, might be a promising prognostic factor for GC. Using the validation cohort, ROC curves indicate a good showing of our risk model. Based on the signature yielded a significant difference OS time between the low- and high-risk groups. Cox regression indicates that the signature is an independent prognostic variable. ROC curves present better and reliability predictive accuracy. The IHC data validate that high expression of CYP19A1 was found in GC tissues. GSEA analysis reveals that higher expression of CYP19A1 may significantly up-regulate genes involved in fatty acid metabolism and glycerolipid metabolism. CIBERSORT analysis suggests that CYP19A1 is related to the infiltration of multiple immune cells.

Conclusion

CYP19A1 could be an independent prognostic factor and a novel metabolic-targeted treatment strategy for gastric cancer.

Effects of Helicobacter pylori eradication on the profiles of blood metabolites and their associations with the progression of gastric lesions: a prospective follow-up study

Objective:

This study aimed at examining the alterations in metabolomic profiles caused by treatment of H. pylori infection, and the associations between key plasma metabolites and the risk of gastric lesion progression during follow-up after treatment.

Methods:

An intervention trial was performed in 183 participants, 117 of whom were H. pylori positive participants receiving treatment for H. pylori infection. H. pylori positive participants were prospectively followed for 182 to 1,289 days. Untargeted metabolomics assays were conducted on plasma samples collected at baseline, 6 months after treatment, and during continued follow-up.

Results:

We identified 59 metabolites with differential posttreatment changes between participants with successful and failed H. pylori eradication, 17 metabolites significantly distinguished participants with successful vs. failed eradication. Two metabolites [PC(18:1(11Z)/14:1(9Z)) and (2S)-6-amino-2-formamidohexanamide] showed posttreatment changes positively associated with successful H. pylori eradication, and were inversely associated with the risk of gastric lesion progression among participants with successful eradication. In contrast, 9-decenoic acid showed posttreatment changes inversely associated with successful eradication: its level was positively associated with the risk of gastric lesion progression among participants with successful eradication. Although the identified metabolites showed a temporary but significant decline after treatment, the trend generally reversed during continued follow-up, and pretreatment levels were restored.

Conclusions:

Treatment of H. pylori infection significantly altered plasma metabolic profiles in the short term, and key metabolites were capable of distinguishing participants with successful vs. failed eradication, but might not substantially affect metabolic regulation in the long term. Several plasma metabolites were differentially associated with the risk of gastric lesion progression among participants with successful or failed eradication.

MIR-147B Regulated Proliferation and Apoptosis of Gastric Cancer Cells by Targeting CPEB2 Via the PTEN Pathway

The present study has been performed to illustrate the role and mechanism of microRNA-147b (miR-147b) in the cellular viability and apoptosis of gastric cancer (GC) cells. The GC tissues of 50 patients with complete data and the adjacent tissues were selected from Shanxi Cancer Hospital, and 3 pairs of tissues were randomly selected for microarray detection of high-expressing microRNAs. The expressions of miR-147b were quantified in numerous GC cell lines, i.e., BGC-823, SGC-7901, AGS, MGC-803 and MKN-45, normal tissue cell lines and 50 pairs of gastric cancer tissues. Moreover, two cell lines of miR-147b high-expressing used PCR quantitative analysis were selected for transfection experiments. The differentially expressed miR-147b was screened from 3 pairs of samples by miRNA chip. The expression ofmiR-147b was found highly expressed in gastric cancer tissues of 50 pairs of gastric cancer and adjacent tissues. The miR-147b found in diverse range in each of GC cell line. Therefore, two cell lines, BGC-823 and MGC-803, with relatively high expression levels of miR-147b were selected for further analysis and research. Scratch analysis results showed that compared with miR-147b NC, the miR-147b inhibitor group inhibited GC cell growth and reduced cell migration. The early apoptosis of MGC-803, and BGC-823 cells was enhanced by miR-147b inhibitor. miR-147b inhibitor significantly repressed the proliferation of BGC-823 and MGC-803 cells. Our study showed that the high expression of miR-147b is positively correlated with the occurrence and development of gastric cancer.

Spatial metabolomics identifies distinct tumor-specific subtypes in gastric cancer patients

PURPOSE

Current systems of gastric cancer (GC) molecular classification include genomic, molecular, and morphological features. GC classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct GC subtypes and identify their clinicopathological and molecular characteristics.

EXPERIMENTAL DESIGN

Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 GC patients. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses to trastuzumab treatment were investigated across the subtypes by introducing an independent patient cohort with HER2-positive GC from a multicenter observational study.

RESULTS

Three tumor- and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2+MIB+CD3+) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2-MIB-CD3-) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR+) positively correlated with pEGFR. Patients with tumor subtype T1(HER2+MIB+CD3+) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2(HER2-MIB-CD3-) and T3(pEGFR+). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however linked to distinct metabolic pathways and molecular features.

CONCLUSIONS

Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing GC molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.

The N6-methyladenosine:mechanisms, diagnostic value, immunotherapy prospec-ts and challenges in gastric cancer

The N6-methyladenosine(m⁶A) RNA modification is important in post-transcriptional regulation of RNA and are regulated reversibly by methyltransferases (writers), demethylases (erasers) and m⁶A recognition proteins (readers). Changes in the structure and function of key RNAs contribute to the development of diseases, particularly tumors. Many abnormal expressions of molecules related to m⁶A RNA methylation modification are discovered in gastric cancer(GC), which changes the methylation level and stability of target genes after transcription, and then regulates related metabolic pathways, affecting the occurrence and progression of GC. Therefore, an in-depth study of m⁶A RNA modification in GC is conducive to the development of new tumor therapies and the achieve of individualized treatment. At present, both basic and clinical studies indicate that m⁶A plays a complex and contentious role in GC. In this paper, we not only review the roles and mechanisms of m⁶A modified related proteins, but also discuss the value of m⁶A modulators in the clinical applications and current challenges of GC, aiming to provide research clues for the early diagnosis and explore the feasibility of m⁶A related proteins as specific targets for GC immunotherapy.

Metabolomic markers of biological fluid in women with reproductive failure: a systematic review of current literatures

Understanding metabolic changes in reproductive failure, including early miscarriage (EM), recurrent miscarriage (RM) and repeated implantation failure (RIF), may be beneficial to understand the pathophysiology, thus improving pregnancy outcomes. Nine metabolomic profiling studies in women with reproductive failures (4 for EM, 3 for RM and 2 for RIF) were included for systematic review. In total 78, 75 and 25 significant metabolites were identified and 40, 40 and 34 metabolic pathways were enriched in EM, RM and RIF, respectively. Among them, 7 and 11 metabolites, and 28 and 28 pathways were shared between EM and RM and between RM and RIF, respectively. Notably, histidine metabolism has the highest impact in EM; phenylalanine, tyrosine and tryptophan biosynthesis. Ubiquinone and other terpenoid-quinone biosynthesis metabolism have the highest impact factor in RM; alanine, aspartate and glutamate metabolism have the highest impact factor in RIF. This study not only summarized the common and distinct metabolites and metabolic pathways in different reproductive failures but also summarized limitations of the study designs and methodologies. Hence, further investigations and validations of these metabolites are still urgently needed to understand the underlying metabolic mechanism for the development and treatment of reproductive failures.

Inhibition of Musashi-1 enhances chemotherapeutic sensitivity in gastric cancer patient-derived xenografts

Musashi-1 (MSI1), a neural RNA-binding protein, is considered a gastric and intestinal stem cell marker. Although the function of MSI1 in gastric cancer has attracted increasing interest, it is not known whether MSI1 can be used as a biomarker to monitor gastric cancer development and response to treatment. Here, the role of MSI1 in the chemotherapeutic sensitivity of gastric cancer was investigated. Patients with high MSI1 levels had poor outcomes, implicating the gene in the development and progression of the disease. We overexpressed and silenced MSI1 in the human gastric cancer cell lines MKN45 and HGC27, finding that knockdown reduced proliferation, invasion, and migration, while promoting apoptosis. A patient-derived xenograft gastric cancer model was constructed in which mice received chemical drugs, si-MSI1, or a drug-si-MSI1 combination. It was found that blocking MSI1 expression reduced gastric cancer drug tolerance. The combination treatment with si-MSI1 was superior to 5F-dUMP and cisplatin, either separately or in combination, indicating that including si-MSI1 was better than drug therapy alone. Transcriptome sequencing analysis showed that MSI1 altered cell cycle regulation and growth signal transduction, including that of blood vessel epicardial substance (BVES). These results suggest that MSI1 reduces the tolerance of gastric cancer to chemical drugs through modulation of MSI1/BVES signaling.

Kynurenine and Anthranilic Acid in the Peritoneum Correlate With the Stage of Gastric Cancer Disease

Background:

This study aimed to assess the importance of selected kynurenines measured in peritoneal fluid, lavage washings, and blood serum in patients with advanced gastric cancer (GC) based on the clinical and pathological staging of TNM for a more precise evaluation of the stage of the disease.

Methods:

Data were collected from a prospectively maintained database of all patients operated on advanced GC between July 2018 and August 2020. In total, 98 patients were eligible for the analysis according to the REMARK guidelines.

Results:

Among the various kynurenines analyzed in this study, we found that the median concentration of anthranilic acid (AA) in the peritoneal lavage washings was significantly higher in patients with positive nodes (pN1-3) compared to those with negative nodes (pN0) (P = 0.0100). Based on the ROC analysis, AA showed diagnostic utility in the differentiation of the pN staging (P = 0.0047). Furthermore, there was a positive correlation between AA in peritoneal fluid with stage pN (P = 0.0116) and a positive correlation between AA in peritoneal lavage washings with stage cT (P = 0.0101). We found that the median concentration of kynurenine (Kyn) in peritoneal lavage washings was significantly higher in patients with cM1 compared to cM0 patients (P = 0.0047). Based on the ROC analysis, Kyn showed diagnostic utility in cM staging differentiation (P < 0.0001). There was a positive correlation between peritoneal Kyn and stage of cM (P = 0.0079).

Conclusions:

AA and Kyn measured in peritoneal lavage indicate advanced GC and may be considered in the future as valuable adjunct tools in TNM staging of advanced GC.

The interplay of immunology and cachexia in infection and cancer

Diverse inflammatory diseases, infections and malignancies are associated with wasting syndromes. In many of these conditions, the standards for diagnosis and treatment are lacking due to our limited understanding of the causative molecular mechanisms. Here, we discuss the complex immunological context of cachexia, a systemic catabolic syndrome that depletes both fat and muscle mass with profound consequences for patient prognosis. We highlight the main cytokine and immune cell-driven pathways that have been linked to weight loss and tissue wasting in the context of cancer-associated and infection-associated cachexia. Moreover, we discuss the potential immunometabolic consequences of cachexia on the basis of newly identified pathways and explore the multilayered area of immunometabolic crosstalk both upstream and downstream of tissue catabolism. Collectively, this Review highlights the intricate relationship of the immune system with cachexia in the context of malignant and infectious diseases.

Chronic inflammation and long-lasting changes in the gastric mucosa after Helicobacter pylori infection involved in gastric cancer

OBJECTIVE

Helicobacter pylori (H. pylori) infects approximately half of the world's population, as one of the most common chronic infections. H. pylori infection has been widely recognized as a major risk factor for gastric cancer (GC).

METHODS

Eradication treatment is considered to abolish the inflammatory response and prevent progression to GC. However, only 1-3% of H. pylori-infected patients develop GC, whereas GC can occur even after eradicating H. pylori. In addition, the incidence of GC following H. pylori infection is significantly higher compared to the gross incidence induced by all causes, although eradicating H. pylori reduces the risk of developing GC.

RESULTS

Therefore, it is reasonable to hypothesize that H. pylori infection results in changes that persist even after its eradication. Several of these changes may not be reversible within a short time, including the status of inflammation, the dysfunction of immunity and apoptosis, mitochondrial changes, aging and gastric dysbacteriosis.

CONCLUSION

The present review article aimed to discuss these potential long-lasting changes induced by H. pylori infection that may follow the eradication of H. pylori and contribute to the development of GC.

Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer.

METHODS

Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics.

RESULTS

An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset.

CONCLUSIONS

In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients.

Metabolomic Characterization Reveals ILF2 and ILF3 Affected Metabolic Adaptions in Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) is a common malignant disease in eastern countries. However, a study of the metabolomic characteristics associated with other biological factors in esophageal squamous cell carcinoma (ESCC) is limited. Interleukin enhancer binding factor 2 (ILF2) and ILF3, double-stranded RNA-binding proteins, have been reported to contribute to the occurrence and development of various types of malignancy. Nevertheless, the underlying functions of ILF2 and ILF3 in ESCC metabolic reprogramming have never been reported. This study aimed to contribute to the metabolic characterization of ESCC and to investigate the metabolomic alterations associated with ILF2 and ILF3 in ESCC tissues. Here, we identified 112 differential metabolites, which were mainly enriched in phosphatidylcholine biosynthesis, fatty acid metabolism, and amino acid metabolism pathways, based on liquid chromatography–mass spectrometry and capillary electrophoresis–mass spectrometry approaches using ESCC tissues and paired para-cancer tissues from twenty-eight ESCC patients. In addition, ILF2 and ILF3 expression were significantly elevated in EC tissues compared to the histologically normal samples, and closely associated with PI3K/AKT and MAPK signaling pathways in ESCC. Moreover, in ESCC tissues with a high ILF2 expression, several short-chain acyl-carnitines (C3:0, C4:0, and C5:0) related to the BCAA metabolic pathway and long-chain acyl-carnitines (C14:0, C16:0, C16:0-OH, and C18:0) involved in the oxidation of fatty acids were obviously upregulated. Additionally, a series of intermediate metabolites involved in the glycolysis pathway, including G6P/F6P, F1,6BP, DHAP, G3P, and 2,3BPG, were remarkably downregulated in highly ILF3-expressed ESCC tissues compared with the corresponding para-cancer tissues. Overall, these findings may provide evidence for the roles of ILF2 and ILF3 during the process of ESCC metabolic alterations, and new insights into the development of early diagnosis and treatment for ESCC. Further investigation is needed to clarify the underlying mechanism of ILF2 and ILF3 on acyl-carnitines and the glycolysis pathway, respectively.

mGWAS identification of six novel single nucleotide polymorphism loci with strong correlation to gastric cancer

BACKGROUND

Metabolite genome-wide association studies (mGWAS) are key for understanding the genetic regulation of metabolites in complex diseases including cancers. Although mGWAS has revealed hundreds of metabolomics quantitative trait loci (mQTLs) in the general population, data relating to gastric cancer (GC) are still incomplete.

METHODS

We identified mQTLs associated with GC by analyzing genome-wide and metabolome-wide datasets generated from 233 GC patients and 233 healthy controls.

RESULTS

Twenty-two metabolites were statistically different between GC cases and healthy controls, and all of them were associated with the risk of gastric cancer. mGWAS analyses further revealed that 9 single nucleotide polymorphisms (SNPs) were significantly associated with 3 metabolites. Of these 9 SNPs, 6 loci were never reported in the previous mGWAS studies. Surprisingly, 4 of 9 SNPs were significantly enriched in genes involved in the T cell receptor signaling pathway.

CONCLUSIONS

Our study unveiled several novel GC metabolite and genetic biomarkers, which may be implicated in the prevention and diagnosis of gastric cancer.

Transgelin Inhibits the Malignant Progression of Esophageal Squamous Cell Carcinomas by Regulating Epithelial-Mesenchymal Transition

Objective

This article investigates the role of Transgelin (TAGLN) in the epithelial–mesenchymal transition (EMT) of esophageal squamous cell carcinomas (ESCC) and its possible mechanism of inhibiting the invasion of these cancers.

Methods

Tissue specimens and clinical information of patients with ESCC were collected to analyze the relationship between Transgelin expression level and prognosis of patients with ESCC. Transgelin siRNA was used to knock down Transgelin expression. The expression of Transgelin in Eca-109 and KYSE-150 cells was overexpressed by Transgelin-overexpressing plasmid. The effects of Transgelin overexpression and knockdown on the proliferation of Eca-109 and KYSE-150 cells were examined by Transwell chamber, scratch assay, and CCK-8 cell activity assay. RT-PCR and Western blot were used to detect the effect of Transgelin overexpression or knockdown on the mRNA and protein expressions of E-cadherin and Vimentin. TCGA data were used to analyze Transgelin co-expressed genes and further study the GO and KEGG enrichment analysis results under the influence of Transgelin.

Results

The expression of Transgelin was low in ESCC, and its expression level was positively correlated with the prognosis of patients with ESCC. The targeted Transgelin siRNA and Transgelin-overexpressing plasmid can effectively regulate the expression of Transgelin mRNA and protein in Eca-109 and KYSE-150 cells. After overexpression of Transgelin, the invasion and proliferation abilities of Eca-109 and KYSE-150 cells were significantly decreased compared with those of the control group (P < 0.05). However, Transgelin knockdown could promote the proliferation, migration, and invasion of ESCC cells. The overexpression of Transgelin inhibits EMT in ESCC. With the increase of Transgelin expression in Eca-109 and KYSE-150 cells, the expression of E-cadherin increased, while the expression of Vimentin decreased, and the difference was statistically significant (P < 0.05).

Conclusion

Transgelin can inhibit the malignant progression of ESCC by inhibiting the occurrence of EMT.

Excess Body Weight and Gastrointestinal Cancer Risk

Background

Excess body weight (EBW), defined as a body mass index (BMI) ≥25 kg/m², has become the second most important as well as a potentially modifiable risk factor for cancer in many industrialized countries. The more excess weight people have, the higher the risk of certain cancers. Over the past several decades, EBW has been increasing globally not only among adults, but also among children and adolescents.

Summary

EBW is causally associated with colorectal, esophageal (adenocarcinoma), gastric (cardia), pancreatic, biliary and hepatocellular cancer. EBW when combined with tobacco smoking, risky alcohol use, or diabetes can act synergistically to cause gastrointestinal cancer. In recent years, more and more young adults (20-40 years old) were diagnosed with EBW-associated neoplasms. People with EBW should be encouraged to join cancer screening programs.

Key Messages

Keeping a healthy weight is a major public health concern and reduces the risk of cancer.

Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

BACKGROUND

Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds.

AIM OF REVIEW

The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.

Integrating microbiome, transcriptome and metabolome data to investigate gastric disease pathogenesis: a concise review

Microbiome, the study of microbial communities in specific environments, has developed significantly since the Human Microbiome Project began. Microbiomes have been associated with changes within environmental niches and the development of various diseases. The development of high-throughput technology such as next-generation sequencing has also allowed us to perform transcriptome studies, which provide accurate functional profiling data. Metabolome studies, which analyse the metabolites found in the environment, are the most direct environmental condition indicator. Although each dataset provides valuable information on its own, the integration of multiple datasets provides a deeper understanding of the relationship between the host, agent and environment. Therefore, network analysis using multiple datasets might give a clearer understanding of disease pathogenesis.

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes in Gastric Cancer

Background: Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism–related genes in GC patients.

Materials and Methods: Two GC datasets from the Gene Expression Atlas, GSE62254 (n = 300) and GSE26942 (n = 217), were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes was evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors.

Results: A risk predictive score model was established based on the expression of 19 lipid metabolism–related genes (LPL, IPMK, PLCB3, CDIPT, PIK3CA, DPM2, PIGZ, GPD2, GPX3, LTC4S, CYP1A2, GALC, SGMS1, SMPD2, SMPD3, FUT6, ST3GAL1, B4GALNT1, and ACADS). The time-dependent ROC analysis revealed that the risk predictive score model was stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A higher risk score was associated with more aggressive features, including a higher tumor grade, a more advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the high–risk and low–risk score groups. A nomogram containing patients’ age, tumor stage, adjuvant chemotherapy, and the risk predictive score could accurately predict the survival probability of patients at 1, 3, and 5 years.

Conclusion: A novel 19-gene risk predictive score model was developed based on the lipid metabolism–related genes, which could be a potential prognostic indicator and therapeutic target of GC.

Identification and Validation of Plasma Metabolomic Signatures in Precancerous Gastric Lesions That Progress to Cancer

IMPORTANCE

Metabolic deregulation plays an important role in gastric cancer (GC) development. To date, no studies have comprehensively explored the metabolomic profiles along the cascade of gastric lesions toward GC.

OBJECTIVE

To draw a metabolic landscape and define metabolomic signatures associated with the progression of gastric lesions and risk of early GC.

DESIGN, SETTING, AND PARTICIPANTS

A 2-stage, population-based cohort study was initiated in 2017 in Linqu County, Shandong Province, China, a high-risk area for GC. Prospective follow-up was conducted during the validation stage (June 20, 2017, to May 27, 2020). A total of 400 individuals were included based on the National Upper Gastrointestinal Cancer Early Detection Program in China. The discovery stage involved 200 individuals with different gastric lesions or GC (high-grade intraepithelial neoplasia or invasive GC). The validation stage prospectively enrolled 152 individuals with gastric lesions who were followed up for 118 to 1063 days and 48 individuals with GC.

EXPOSURES

Metabolomic profiles and metabolite signatures were examined based on untargeted plasma metabolomics assay.

MAIN OUTCOMES AND MEASURES

The risk of GC overall and early GC (high-grade intraepithelial neoplasia), and progression of gastric lesions.

RESULTS

Of the 400 participants, 124 of 200 (62.0%) in the discovery set were men; mean (SD) age was 56.8 (7.5) years. In the validation set, 136 of 200 (68.0%) were men; mean (SD) age was 57.5 (8.1) years. Distinct metabolomic profiles were noted for gastric lesions and GC. Six metabolites, including α-linolenic acid, linoleic acid, palmitic acid, arachidonic acid, sn-1 lysophosphatidylcholine (LysoPC)(18:3), and sn-2 LysoPC(20:3) were significantly inversely associated with risk of GC overall and early GC (high-grade intraepithelial neoplasia). Among these metabolites, the first 3 were significantly inversely associated with gastric lesion progression, especially for the progression of intestinal metaplasia (α-linolenic acid: OR, 0.42; 95% CI, 0.18-0.98; linoleic acid: OR, 0.43; 95% CI, 0.19-1.00; and palmitic acid: OR, 0.32; 95% CI, 0.13-0.78). Compared with models including only age, sex, Helicobacter pylori infection, and gastric histopathologic findings, integrating these metabolites significantly improved the performance for predicting the progression of gastric lesions (area under the curve [AUC], 0.86; 95% CI, 0.70-1.00 vs AUC, 0.69; 95% CI, 0.50-0.88; P = .02) and risk of early GC (AUC, 0.83; 95% CI, 0.58-1.00 vs AUC, 0.61; 95% CI, 0.31-0.91; P = .03).

CONCLUSIONS AND RELEVANCE

This study defined metabolite signatures that might serve as meaningful biomarkers for assessing high-risk populations and early diagnosis of GC, possibly advancing targeted GC prevention and control.

Differences in Several Factors in the Development of Erosive Esophagitis Among Patients at Various Stages of Metabolic Syndrome: A Cross-Sectional Study

BACKGROUND

Erosive esophagitis (EE) is strongly associated with metabolic syndrome (MS), but is not always recognized in individuals with MS and the prevalence of EE in individuals with non-MS is not low.

AIM

To examine the differences in clinical factors associated with EE at various stages of MS, as well as the differences in metabolites between subjects with MS, with and without EE.

METHODS

A total of 7,097 persons who underwent health checkups including esophagogastroduodenoscopy were analyzed. We examined the differences in clinical factors for EE among subjects with non-MS, pre-MS, and MS and compared metabolites between 34 subjects with MS, with and without EE.

RESULTS

EE prevalence was significantly higher in the MS and pre-MS groups than in the non-MS group (p < 0.001). EE severity was higher in the MS group than in the pre-MS and non-MS groups (p < 0.001). In the non-MS group, there were significant differences between subjects with and without EE with respect to Helicobacter pylori (H. pylori) and smoking. In the pre-MS and MS groups, there were significant differences in H. pylori, hiatal hernia, and drinking in those with and without EE. The levels of glutamine, hypoxanthine, and lactic acid metabolites were significantly different between subjects with MS, with and without EE (all p < 0.05).

CONCLUSION

Although H. pylori and lifestyle factors such as smoking and drinking are important for EE, differences in these factors should be considered at various stages of MS. Additionally, several metabolites may be involved in the development of EE in MS.

A Prospective Investigation of Circulating Metabolome Identifies Potential Biomarkers for Gastric Cancer Risk

BACKGROUND

Metabolomics is widely used to identify potential novel biomarkers for cancer risk. No investigation, however, has been conducted to prospectively evaluate the role of perturbation of metabolome in gastric cancer development.

METHODS

250 incident cases diagnosed with primary gastric cancer were selected from the Shanghai Women's Health and the Shanghai Men's Health Study, and each was individually matched to one control by incidence density sampling. An untargeted global profiling platform was used to measure approximately 1,000 metabolites in prediagnostic plasma. Conditional logistic regression was utilized to generate ORs and P values.

RESULTS

Eighteen metabolites were associated with gastric cancer risk at P < 0.01. Among them, 11 metabolites were lysophospholipids or lipids of other classes; for example, 1-(1-enyl-palmitoyl)-GPE (P-16:0) (OR = 1.56; P = 1.89 × 10^-4). Levels of methylmalonate, a suggested biomarker of vitamin B12 deficiency, was correlated with increased gastric cancer risk (OR = 1.42; P = 0.004). Inverse associations were found for three biomarkers for coffee/tea consumption (3-hydroxypyridine sulfate, quinate and N-(2-furoyl) glycine), although the associations were only significant when comparing cases that were diagnosed within 5 years after the blood collection to matched controls. Most of the identified associations were more profound in women and never smokers than their male or ever smoking counterparts and some with notable significant interactions.

CONCLUSIONS

Our study identified multiple potential risk biomarkers for gastric cancer independent of Helicobacter pylori infection and other major risk factors.

IMPACT

New risk-assessment tools to identify high-risk population could be developed to improve prevention of gastric cancer.See related commentary by Drew et al., p. 1601.

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.

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.

Gastric Metabolomics Detects Helicobacter pylori Correlated Loss of Numerous Metabolites in Both the Corpus and Antrum

Helicobacter pylori is a chronic bacterial pathogen that thrives in several regions of the stomach, causing inflammation that can vary by site and result in distinct disease outcomes. Whether the regions differ in terms of host-derived metabolites is not known. We thus characterized the regional variation of the metabolomes of mouse gastric corpus and antrum organoids and tissue. The uninfected secreted organoid metabolites differed between the corpus and antrum in only seven metabolites as follows: lactic acid, malic acid, phosphoethanolamine, alanine, uridine, glycerol, and isoleucine. Several of the secreted chemicals were depleted upon H. pylori infection in both regions, including urea, cholesterol, glutamine, fumaric acid, lactic acid, citric acid, malic acid, and multiple nonessential amino acids. These results suggest a model in which H. pylori preferentially uses carboxylic acids and amino acids in complex environments, and these are found in both the corpus and antrum. When organoid metabolites were compared to mouse tissue, there was little overlap. The tissue corpus and antrum metabolomes were distinct, including antrum-elevated 5-methoxytryptamine, lactic acid, and caprylic acid, and corpus-elevated phospholipid products. The corpus and antrum remained distinct over an 8-month infection time course. The antrum displayed no significant changes between the time points in contrast to the corpus, which exhibited metabolite changes that were consistent with stress, tissue damage, and depletion of key nutrients, such as glutamine and fructose-6-phosphate. Overall, our results suggest that the corpus and antrum have largely but not completely overlapping metabolomes that change moderately upon H. pylori infection.