1H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

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.

Role of Metabolomics in Pathogenesis and Prompt Diagnosis of Gastric Cancer Metastasis-A Systematic Review

INTRODUCTION

Gastric cancer is the fourth most frequently diagnosed form of cancer and the third leading cause of cancer-related mortality worldwide. The aim of this review is to identify individual metabolic biomarkers and their association with accurate diagnostic values, which can predict gastric cancer metastasis.

MATERIALS AND METHODS

After searching the keywords, 83 articles were found over a period of 13 years. One was eliminated because it was not written in English, and two were published outside the selected period. Seven scientific papers were qualified for this investigation after eliminating duplicates, non-related articles, systematic reviews, and restricted access studies.

RESULTS

New metabolic biomarkers with predictive value for gastric cancer metastasis and for elucidating metabolic pathways of the metastatic process have been found. The pathogenic processes can be outlined as follows: pro-oxidant capacity, T-cell inactivation, cell cycle arrest, energy production and mitochondrial enzyme impairment, cell viability and pro-apoptotic effect, enhanced degradation of collagen extracellular matrix, migration, invasion, structural protein synthesis, and tumoral angiogenesis.

CONCLUSION

Metabolic biomarkers have been recognized as independent risk factors in the molecular process of gastric cancer metastasis, with good diagnostic and prognostic value.

Metabolic codependencies in the tumor microenvironment and gastric cancer: Difficulties and opportunities

Oncogenesis and the development of tumors affect metabolism throughout the body. Metabolic reprogramming (also known as metabolic remodeling) is a feature of malignant tumors that is driven by oncogenic changes in the cancer cells themselves as well as by cytokines in the tumor microenvironment. These include endothelial cells, matrix fibroblasts, immune cells, and malignant tumor cells. The heterogeneity of mutant clones is affected by the actions of other cells in the tumor and by metabolites and cytokines in the microenvironment. Metabolism can also influence immune cell phenotype and function. Metabolic reprogramming of cancer cells is the result of a convergence of both internal and external signals. The basal metabolic state is maintained by internal signaling, while external signaling fine-tunes the metabolic process based on metabolite availability and cellular needs. This paper reviews the metabolic characteristics of gastric cancer, focusing on the intrinsic and extrinsic mechanisms that drive cancer metabolism in the tumor microenvironment, and interactions between tumor cell metabolic changes and microenvironment metabolic changes. This information will be helpful for the individualized metabolic treatment of gastric cancers.

Emerging metabolomic tools to study cancer metastasis

Metastasis is responsible for 90% of deaths in patients with cancer. Understanding the role of metabolism during metastasis has been limited by the development of robust and sensitive technologies that capture metabolic processes in metastasizing cancer cells. We discuss the current technologies available to study (i) metabolism in primary and metastatic cancer cells and (ii) metabolic interactions between cancer cells and the tumor microenvironment (TME) at different stages of the metastatic cascade. We identify advantages and disadvantages of each method and discuss how these tools and technologies will further improve our understanding of metastasis. Studies investigating the complex metabolic rewiring of different cells using state-of-the-art metabolomic technologies have the potential to reveal novel biological processes and therapeutic interventions for human cancers.

Serum metabolomic profiling for patients with adenocarcinoma of the esophagogastric junction

INTRODUCTION

The incidence of adenocarcinoma in the esophagogastric junction (AEG) has increased in the recent years. AEG is reported to have a worse prognosis compared with tumor confined to the stomach (non-AEG). Although the metabolic changes of non-AEG have been investigated in extensive studies, little effort focused on the metabolic profiling of AEG serum.

OBJECTIVES

Here we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to explore the abnormal metabolism underlying AEG.

METHODS

GC-MS-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of serum samples from AEG patients (n = 70), non-AEG patients (n = 70) and health controls (n = 71).

RESULTS

A novel serum metabolic profiling of 18 metabolites from patients of AEG and non-AEG was indicated, in comparison with health controls. Moreover, AEG and non-AEG were also well-classified with 9 distinguishing metabolites including hypoxanthine, alanine, proline, pyroglutamate, glycine, lactate, succinic acid, glutamate and kynurenine, which produced a discriminatory model with an area under the Receiver Operating Characteristic (ROC) curve of 0.852, suggesting a distinct metabolic signature of AEG. Importantly, lactate and glutamate disclosed outcome-prediction values by multivariate cox-proportional hazard model and Kaplan-Meier method based on follow-up information for 2-5 years.

CONCLUSION

This is the first metabolomics study to identify serum metabolic signature of AEG. The distinguishing metabolites show a promising application on clinical diagnose and outcome prediction, and allow us to unveil several key metabolic variations coexisting in AEG, which may aid to understand the underlying metabolic mechanisms.

Protein Phosphatase 1 Regulatory Subunit 3: A Prognostic Biomarker in Stomach Adenocarcinoma

Purpose

This study aimed to determine the potential application of the protein phosphatase 1 regulatory subunit 3 (PPP1R3B) gene as a prognostic marker in stomach adenocarcinoma (STAD), as well as its potential mediating biological processes and pathways.

Materials and Methods

Differential expression analyses were performed using the TIMER2.0 and UALCAN databases. Complete RNA-seq data and other relevant clinical and survival data were acquired from The Cancer Genome Atlas (TCGA). Univariate survival analyses, Cox regression modelling, and Kaplan–Meier curves were implemented to investigate the associations between PPP1R3B gene expression and clinical pathologic features. A genome wide gene set enrichment analysis (GSEA) was conducted to define the underlying molecular mechanisms mediating the observed associations between the PPP1R3B gene and STAD development.

Results

We found that PPP1R3B was overexpressed in STAD tissues, and that higher PPP1R3B expression correlated with worse prognoses in patients with STAD. Comprehensive survival analyses suggested that PPP1R3B might be an independent predictive factor for survival time in patients with STAD. The prognostic relationship between PPP1R3B and STAD was also verified using Kaplan–Meier curves. Patients with higher PPP1R3B levels had a shorter clinical survival time on average. Additionally, a GSEA demonstrated that PPP1R3B might be involved in multiple biological processes and pathways.

Conclusion

Our findings demonstrate that the PPP1R3B gene has utility as a potential molecular marker for STAD prognoses.

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.

Metabolomic Study on the Therapeutic Effect of the Jianpi Yangzheng Xiaozheng Decoction on Gastric Cancer Treated with Chemotherapy Based on GC-TOFMS Analysis

Objective

This study aimed to find new biomarkers of prognosis and metabolomic therapy for gastric carcinoma (GC) treated with chemotherapy and investigate the metabolic mechanism of the Jianpi Yangzheng Xiaozheng (JPYZXZ) decoction in the treatment of GC.

Methods

First, 36 patients with GC were randomly assigned to the treatment (chemotherapy plus JPYZXZ) and control (chemotherapy alone) groups. The clinical efficacy, side effects, and quality of life of patients in the two groups were evaluated after treatment. Then, the serum samples taken from 16 randomly selected patients (eight treatment cases and eight control cases with no evident pattern characters) and eight healthy volunteers were tested to identify the differential metabolite under the gas chromatography-time-of-fight mass spectrometry platform. The relevant metabolic pathways of differential substances were analyzed using multidimensional statistical analysis.

Results

JPYZXZ combined with chemotherapy resulted in a lower risk of leucopenia, thrombocytopenia, and gastrointestinal reaction (P < 0.05). Additionally, patients in the treatment group showed a higher Karnofsky (KPS) scale (P < 0.05). Compared with healthy persons, patients with GC were found to have 26 significant differential metabolites after chemotherapy; these metabolites are mainly involved in 12 metabolic pathways, such as valine, leucine, and isoleucine biosynthesis. JPYZXZ primarily influences the pentose phosphate pathway; glutathione metabolism; glyoxylate and dicarboxylate metabolism; porphyrin and chlorophyll metabolism; and glycine, serine, and threonine metabolism of patients with GC treated with chemotherapy.

Conclusions

The metabolic characteristics of patients with GC after chemotherapy are mainly various amino acid metabolic defects, especially L-glutamine, L-leucine, L-alloisoleucine, and L-valine. These defects lead to a series of problems, such as decreased tolerance and effectiveness of chemotherapy, increased side effects, decreased immunity, and shortened survival time. In addition, the remarkable upregulation of the gluconolactone level in patients with GC suggests the high proliferative activity of GC cells. Thus, gluconolactone may be used as a potential prognostic and diagnostic evaluation index. Moreover, JPYZXZ can reduce the incidence of ADRs and improve the life quality of patients by the correction of L-glutamine, L-leucine, L-alloisoleucine, and L-valine metabolism deficiency. In addition, gluconolactone metabolism is inhibited by JPYZXZ. Such inhibition may be one of the antitumor mechanisms of JPYZXZ.

Brain metabolites in cholinergic and glutamatergic pathways are altered by pancreatic cancer cachexia

BACKGROUND

Cachexia is a major cause of morbidity in pancreatic ductal adenocarcinoma (PDAC) patients. Our purpose was to understand the impact of PDAC-induced cachexia on brain metabolism in PDAC xenograft studies, to gain new insights into the causes of cachexia-induced morbidity. Changes in mouse and human plasma metabolites were characterized to identify underlying causes of brain metabolic changes.

METHODS

We quantified metabolites, detected with high-resolution ¹ H magnetic resonance spectroscopy, in the brain and plasma of normal mice (n = 10) and mice bearing cachexia (n = 10) or non-cachexia (n = 9) inducing PDAC xenografts as well as in human plasma obtained from normal individuals (n = 24) and from individuals with benign pancreatic disease (n = 20) and PDAC (n = 20). Statistical significance was defined as a P value ≤0.05.

RESULTS

The brain metabolic signature of cachexia-inducing PDAC was characterized by a significant depletion of choline of -27% and -21% as well as increases of glutamine of 13% and 9% and formate of 21% and 14%, relative to normal controls and non-cachectic tumour-bearing mice, respectively. Good to moderate correlations with percent weight change were found for choline (r = 0.70), glutamine (r = -0.58), and formate (r = -0.43). Significant choline depletion of -38% and -30%, relative to normal controls and non-cachectic tumour-bearing mice, respectively, detected in the plasma of cachectic mice likely contributed to decreased brain choline in cachectic mice. Similarly, relative to normal controls and patients with benign disease, choline levels in human plasma samples of PDAC patients were significantly lower by -12% and -20% respectively. A comparison of plasma metabolites from PDAC patients with and without weight loss identified significant changes in glutamine metabolism.

CONCLUSIONS

Disturbances in metabolites of the choline/cholinergic and glutamine/glutamate/glutamatergic neurotransmitter pathways may contribute to morbidity. Metabolic normalization may provide strategies to reduce morbidity. The human plasma metabolite changes observed may lead to the development of companion diagnostic markers to detect PDAC and PDAC-induced cachexia.

Nuclear magnetic resonance-based tissue metabolomic analysis clarifies molecular mechanisms of gastric carcinogenesis

Gastric cancer (GC) is one of the deadliest cancers worldwide, and the progression of gastric carcinogenesis (GCG) covers multiple complicated pathological stages. Molecular mechanisms of GCG are still unclear. Here, we undertook NMR-based metabolomic analysis of aqueous metabolites extracted from gastric tissues in an established rat model of GCG. We showed that the metabolic profiles were clearly distinguished among 5 histologically classified groups: control, gastritis, low-grade gastric dysplasia, high-grade gastric dysplasia (HGD), and GC. Furthermore, we carried out metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with the 4 pathological stages, including oxidation stress, choline phosphorylation, amino acid metabolism, Krebs cycle, and glycolysis. Three metabolic pathways were continually disturbed during the progression of GCG, including taurine and hypotaurine metabolism, glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism. Both the Krebs cycle and glycine, serine, and threonine metabolism were profoundly impaired in both the HGD and GC stages, potentially due to abnormal energy supply for tumor cell proliferation and growth. Furthermore, valine, leucine, and isoleucine biosynthesis and glycolysis were significantly disturbed in the GC stage for higher energy requirement of the rapid growth of tumor cells. Additionally, we identified potential gastric tissue biomarkers for metabolically discriminating the 4 pathological stages, which also showed good discriminant capabilities for their serum counterparts. This work sheds light on the molecular mechanisms of GCG and is of benefit to the exploration of potential biomarkers for clinically diagnosing and monitoring the progression of GCG.

Prognostic significance of branched-chain amino acid transferase 1 and CD133 in triple-negative breast cancer

BACKGROUND

Previous studies have shown that branched-chain amino acid transferase 1 (BCAT1) is associated with tumour progression in triple-negative breast cancer (TNBC). Furthermore, CD133 has emerged as a novel cancer stem cell marker for indicating tumour progression. However, the prognostic significance of these two markers remains to be verified. This study was conducted to investigate the correlation between BCAT1 and CD133 expression and clinicopathological features, as well as the prognosis of patients with TNBC.

METHODS

The study cohort included 291 patients with TNBC. Tissue microarrays were constructed for both cancer and normal tissues. The expression of BCAT1 and CD133 was detected by immunohistochemical staining, and the levels were evaluated using an H-scoring system. Cut-off points for BCAT1 and CD133 expression were determined using receiver operating characteristic curves.

RESULTS

The median follow-up time for the study participants was 68.73 months (range: 1.37-103.6 months). The 5-year disease-free survival (DFS) and overall survival (OS) rates of the 291 patients with TNBC were 72.51 and 82.47%, respectively. Higher levels of BCAT1 and CD133 expression independently indicated shorter DFS and OS. High levels of both BCAT1 and CD133 expression were detected in 36 (12.37%) patients, who had significantly shorter DFS and OS (both P < 0.001) compared to other patients.

CONCLUSION

BCAT1 and CD133 can be considered as biomarkers with prognostic significance for TNBC.

Metabolomics as a potential method for predicting thyroid malignancy in children and adolescents

PURPOSE

To identify potential metabolic biomarkers for distinguishing malignant and benign thyroid nodules in children and adolescents using a metabolomics approach.

METHODS

A total of 96 consecutive patients (median age 14.29 ± 2.31 years, range 9-18 years) who underwent thyroidectomy and 40 healthy controls were enrolled. Patients were assigned to the papillary thyroid carcinoma and benign thyroid adenoma groups according to postoperative pathologic biopsy. Plasma samples were preoperatively collected, and multivariate analysis was performed to identify differential metabolites.

RESULTS

Papillary thyroid carcinoma could be distinguished not only from healthy serum but also from benign thyroid adenoma according to the metabolic profiles. A total of 17 metabolites were identified. Compared with those from benign thyroid adenoma patients and healthy controls, the metabolites from papillary thyroid carcinoma patients, including leucine, lactate, alanine, glycine, acetate, lysine and choline, were increased, while glucose was decreased.

CONCLUSION

The metabolomics method based on proton nuclear magnetic resonance has great potential for identifying papillary thyroid carcinoma in children and adolescents. Lactate and glycine may be used as potential serum markers for the diagnosis of papillary thyroid carcinoma.

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

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