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

Analysis of metabolic characteristics of metabolic syndrome in elderly patients with gastric cancer by non-targeted metabolomics

BACKGROUND

The relationship between metabolic syndrome (MetS) and gastric cancer (GC), which is a common metabolic disease, has attracted much attention. However, the specific metabolic characteristics of MetS in elderly patients with GC remain unclear.

AIM

To investigate the differentially abundant metabolites and metabolic pathways between preoperative frailty and MetS in elderly patients with GC based on nontargeted metabolomics techniques.

METHODS

In this study, 125 patients with nonfrail nonmeal GC were selected as the control group, and 50 patients with GC in the frail group were selected as the frail group. Sixty-five patients with GC combined with MetS alone were included in the MetS group, and 50 patients with GC combined with MetS were included in the MetS group. Nontargeted metabolomics techniques were used to measure plasma metabolite levels by ultrahigh-performance liquid chromatography-mass spectrometry. Multivariate statistical analysis was performed by principal component analysis, orthogonal partial least squares, pattern recognition analysis, cluster analysis, and metabolic pathway annotation.

RESULTS

A total of 125 different metabolites, including amino acids, glycerophospholipids, sphingolipids, fatty acids, sugars, nucleosides and nucleotides, and acidic compounds, were identified via nontargeted metabolomics techniques. Compared with those in the control group, there were 41, 32, and 52 different metabolites in the MetS group, the debilitated group, and the combined group, respectively. Lipid metabolites were significantly increased in the MetS group. In the weak group, amino acids and most glycerol phospholipid metabolites decreased significantly, and fatty acids and sphingosine increased significantly. The combined group was characterized by significantly increased levels of nucleotide metabolites and acidic compounds. The alanine, aspartic acid, and glutamate metabolic pathways were obviously enriched in the asthenic group, and the glycerol and phospholipid metabolic pathways were obviously enriched in the combined group.

CONCLUSION

Elderly GC patients with simple frailty, simple combined MetS, and frailty combined with MetS have different metabolic characteristics, among which amino acid and glycerophospholipid metabolite levels are significantly lower in frail elderly GC patients, and comprehensive supplementation of fat and protein should be considered. Many kinds of metabolites, such as amino acids, lipids, nucleotides, and acidic compounds, are abnormally abundant in patients with MetS combined with fthenia, which may be related to tumor-related metabolic disorders.

Analysis of metabolic characteristics of metabolic syndrome in elderly patients with gastric cancer by non-targeted metabolomics

BACKGROUND

The relationship between metabolic syndrome (MetS) and gastric cancer (GC), which is a common metabolic disease, has attracted much attention. However, the specific metabolic characteristics of MetS in elderly patients with GC remain unclear.

AIM

To investigate the differentially abundant metabolites and metabolic pathways between preoperative frailty and MetS in elderly patients with GC based on nontargeted metabolomics techniques.

METHODS

In this study, 125 patients with nonfrail nonmeal GC were selected as the control group, and 50 patients with GC in the frail group were selected as the frail group. Sixty-five patients with GC combined with MetS alone were included in the MetS group, and 50 patients with GC combined with MetS were included in the MetS group. Nontargeted metabolomics techniques were used to measure plasma metabolite levels by ultrahigh-performance liquid chromatography-mass spectrometry. Multivariate statistical analysis was performed by principal component analysis, orthogonal partial least squares, pattern recognition analysis, cluster analysis, and metabolic pathway annotation.

RESULTS

A total of 125 different metabolites, including amino acids, glycerophospholipids, sphingolipids, fatty acids, sugars, nucleosides and nucleotides, and acidic compounds, were identified via nontargeted metabolomics techniques. Compared with those in the control group, there were 41, 32, and 52 different metabolites in the MetS group, the debilitated group, and the combined group, respectively. Lipid metabolites were significantly increased in the MetS group. In the weak group, amino acids and most glycerol phospholipid metabolites decreased significantly, and fatty acids and sphingosine increased significantly. The combined group was characterized by significantly increased levels of nucleotide metabolites and acidic compounds. The alanine, aspartic acid, and glutamate metabolic pathways were obviously enriched in the asthenic group, and the glycerol and phospholipid metabolic pathways were obviously enriched in the combined group.

CONCLUSION

Elderly GC patients with simple frailty, simple combined MetS, and frailty combined with MetS have different metabolic characteristics, among which amino acid and glycerophospholipid metabolite levels are significantly lower in frail elderly GC patients, and comprehensive supplementation of fat and protein should be considered. Many kinds of metabolites, such as amino acids, lipids, nucleotides, and acidic compounds, are abnormally abundant in patients with MetS combined with fthenia, which may be related to tumor-related metabolic disorders.

Comprehensive analysis of the oral microbiota and metabolome change in patients of burning mouth syndrome with psychiatric symptoms

Background

Burning mouth syndrome (BMS) is a chronic idiopathic facial pain with intraoral burning or dysesthesia. BMS patients regularly suffer from anxiety/depression, and the association of psychiatric symptoms with BMS has received considerable attention in recent years. The aims of this study were to investigate the potential interplay between psychiatric symptoms and BMS.

Methods

Using 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC/MS) to evaluate the oral microbiota and saliva metabolism of 40 BMS patients [including 29 BMS patients with depression or anxiety symptoms (DBMS)] and 40 age matched healthy control (HC).

Results

The oral microbiota composition in BMS exhibited no significant differences from HC, although DBMS manifested decreased α-diversity relative to HC. Noteworthy was the discernible elevation in the abundance of proinflammatory microorganisms within the oral microbiome of individuals with DBMS. Parallel findings in LC/MS analyses revealed discernible disparities in metabolites between DBMS and HC groups. Principal differential metabolites were notably enriched in amino acid metabolism and lipid metabolism, exhibiting associations with infectious and immunological diseases. Furthermore, the integrated analysis underscores a definitive association between the oral microbiome and metabolism in DBMS.

Conclusions

This study suggests possible future modalities for better understanding the pathogenesis and personalized treatment plans of BMS.

Untargeted metabolomics in gastric and colorectal cancer patients - preliminary results

Introduction

Recent years, microbiota-associated aspects have been analysed in multiple disorders regarding cancers. Existing evidence pints that gut microorganisms might take part in tumour origin and therapy efficacy. Nevertheless, to date, data on faecal metabolomics in cancer patients is still strongly limited. Therefore, we aimed to analyse gut untargeted metabolome in gastrointestinal cancer patients (i.e., gastric and colorectal cancer).

Patients and methods

There were 12 patients with either gastric (n=4) or colorectal cancer (n=8) enrolled and 8 analysed (n=4 each). Stool samples were collected prior to anti-cancer treatments. Untargeted metabolomics analyses were conducted by means of mass spectrometry.

Results

A plethora of metabolites in cancer patients we analysed were noted, with higher homogenity in case of gastric cancer patients. We found that the level of Deoxyguanosine,m/z 266.091,[M-H]-, Uridine,m/z 245.075,[M+H]+, Deoxyguanosine,m/z 268.104,[M]+, 3-Indoleacetic acid,m/z 176.07,[M+H]+, Indoxyl,m/z 132.031,[M-H]-, L-Phenylalanine,m/z 164.073,[M-H]-, L-Methionine,m/z 150.058,[M+NH4]+, was significantly higher in colorectal cancer patients and Ethyl hydrogen malonate,m/z 133.031,[M+H]+ in gastric cancer.

Conclusion

The overall insights into untargeted metabolomics showed that most often higher levels of analysed metabolites were detected in colorectal cancer patients compared to gastric cancer patients. The link between gut metabolome and both local and distal metastasis might exist, however it requires confirmation in further multi-centre studies regarding larger sample size.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Appraising associations between signature lipidomic biomarkers and digestive system cancer risk: novel evidences from a prospective cohort study of UK Biobank and Mendelian randomization analyses

BACKGROUND

The roles of serum lipids on digestive system cancer (DSC) risk were still inconclusive. In this study, we systematically assessed indicative effects of signature lipidomic biomarkers (high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)) on DSC (oesophagus, stomach, colorectal, liver, gallbladder, and pancreas cancers) risk.

METHODS

HDL-C, LDL-C, and TG concentration measurements were respectively analyzed with enzyme immunoinhibition, enzymatic selective protection, and GPO-POD methods in AU5800 supplied from Beckman Coulter. The diagnoses of DSCs were coded using International Classification of Diseases, Tenth Revision (ICD-10) codes updated until October 2022 in the UK Biobank (UKB). In this study, we assessed phenotypic association patterns between signature lipidomic biomarkers and DSC risk using restricted cubic splines (RCSs) in multivariable-adjusted Cox proportional hazards regression models. Moreover, linear and nonlinear causal association patterns of signature lipidomic biomarkers with DSC risk were determined by linear and nonlinear Mendelian randomization (MR) analyses.

RESULTS

A median follow-up time of 11.8 years was recorded for 319,568 participants including 6916 DSC cases. A suggestive independent nonlinear phenotypic association was observed between LDL-C concentration and stomach cancer risk (Pnonlinearity < 0.05, Poverall < 0.05). Meanwhile, a remarkable independent linear negative phenotypic association was demonstrated between HDL-C concentration and stomach cancer risk (Pnonlinearity > 0.05, Poverall < 0.008 (0.05/6 outcomes, Bonferroni-adjusted P)), and suggestive independent linear positive associations were observed between HDL-C concentration and colorectal cancer risk, and between TG concentration and gallbladder cancer risk (Pnonlinearity > 0.05, Poverall < 0.05). Furthermore, based on nonlinear and linear MR-based evidences, we observed an suggestive independent negative causal association (hazard ratio (HR) per 1 mmol/L increase: 0.340 (0.137-0.843), P = 0.020) between LDL-C and stomach cancer risk without a nonlinear pattern (Quadratic P = 0.901, Cochran Q P = 0.434). Meanwhile, subgroup and stratified MR analyses both supported the category of LDL-C ≥ 4.1 mmol/L was suggestively protective against stomach cancer risk, especially among female participants (HR: 0.789 (0.637-0.977), P = 0.030) and participants aged 60 years or older (HR: 0.786 (0.638-0.969), P = 0.024), and the category of TG ≥ 2.2 mmol/L concluded to be a suggestive risk factor for gallbladder cancer risk in male participants (HR: 1.447 (1.020-2.052), P = 0.038) and participants aged 60 years or older (HR: 1.264 (1.003-1.593), P = 0.047).

CONCLUSIONS

Our findings confirmed indicative roles of signature lipidomic biomarkers on DSC risk, notably detecting suggestive evidences for a protective effect of high LDL-C concentration on stomach cancer risk, and a detrimental effect of high TG concentration on gallbladder cancer risk among given participants.

Utilizing serum metabolomics for assessing postoperative efficacy and monitoring recurrence in gastric cancer patients

OBJECTIVE

(1) This study aims to identify distinct serum metabolites in gastric cancer patients compared to healthy individuals, providing valuable insights into postoperative efficacy evaluation and monitoring of gastric cancer recurrence; (2) Methods: Serum samples were collected from 15 healthy individuals, 16 gastric cancer patients before surgery, 3 months after surgery, 6 months after surgery, and 15 gastric cancer recurrence patients. T-test and analysis of variance (ANOVA) were performed to screen 489 differential metabolites between the preoperative group and the healthy control group. Based on the level of the above metabolites in the recurrence, preoperative, three-month postoperative, and six-month postoperative groups, we further selected 18 significant differential metabolites by ANOVA and partial least squares discriminant analysis (PLS-DA). The result of hierarchical clustering analysis about the above metabolites showed that the samples were regrouped into the tumor-bearing group (comprising the original recurrence and preoperative groups) and the tumor-free group (comprising the original three-month postoperative and six-month postoperative groups). Based on the results of PLS-DA, 7 differential metabolites (VIP > 1.0) were further selected to distinguish the tumor-bearing group and the tumor-free group. Finally, the results of hierarchical clustering analysis showed that these 7 metabolites could well identify gastric cancer recurrence; (3) Results: Lysophosphatidic acids, triglycerides, lysine, and sphingosine-1-phosphate were significantly elevated in the three-month postoperative, six-month postoperative, and healthy control groups, compared to the preoperative and recurrence groups. Conversely, phosphatidylcholine, oxidized ceramide, and phosphatidylglycerol were significantly reduced in the three-month postoperative, six-month postoperative, and healthy control groups compared to the preoperative and recurrence groups. However, these substances did not show significant differences between the preoperative and recurrence groups, nor between the three-month postoperative, six-month postoperative, and healthy control groups; (4) Conclusions: Our findings demonstrate the presence of distinct metabolites in the serum of gastric cancer patients compared to healthy individuals. Lysophosphatidic acid, triglycerides, lysine, sphingosine-1-phosphate, phosphatidylcholine, oxidized ceramide, and phosphatidylglycerol hold potential as biomarkers for evaluating postoperative efficacy and monitoring recurrence in gastric cancer patients. These metabolites exhibit varying concentrations across different sample categories.

Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review

From a global perspective, gastric cancer (GC) persists as a significant healthcare issue. In the Western world, the majority of cases are discovered at late stages, when the treatment is generally unsuccessful. There are no organized screening programs outside of Asia (Japan and Republic of Korea). Traditional diagnosis techniques (such as upper endoscopy), conventional tumor markers (CEA, CA19-9, and CA72-4), radiographic imaging, and CT scanning all have drawbacks. The gold standard for the earliest detection of cancer and related premalignant lesions is still endoscopy with a proper biopsy follow-up. Since there are currently no clinically approved biomarkers for the early diagnosis of GC, the identification of non-invasive biomarkers is expected to help improve the prognosis and survival rate of these patients. The search for new screening biomarkers is currently underway. These include genetic biomarkers, such as circulating tumor cells, microRNAs, and exosomes, as well as metabolic biomarkers obtained from biofluids. Meanwhile, cutting-edge high-resolution endoscopic technologies are demonstrating promising outcomes in the visual diagnosis of mucosal lesions with the aid of linked color imaging and machine learning models. Following the PRISMA guidelines, this study examined the articles in databases such as PubMed, resulting in 167 included articles. This review discusses the currently available and emerging methods for diagnosing GC early on, as well as new developments in the endoscopic detection of early lesions of the stomach.

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.

Comprehensive analysis of the microbiome and metabolome in pus from pyogenic liver abscess patients with and without diabetes mellitus

Introduction

Pyogenic liver abscess (PLA) patients combined with diabetes mellitus (DM) tend to have more severe clinical manifestations than without DM. The mechanism responsible for this phenomenon is not entirely clear. The current study therefore aimed to comprehensively analyze the microbiome composition and metabolome in pus from PLA patients with and without DM, to determine the potential reasons for these differences.

Methods

Clinical data from 290 PLA patients were collected retrospectively. We analyzed the pus microbiota using 16S rDNA sequencing in 62 PLA patients. In addition, the pus metabolomes of 38 pus samples were characterized by untargeted metabolomics analysis. Correlation analyses of microbiota, metabolites and laboratory findings were performed to identify significant associations.

Results

PLA patients with DM had more severe clinical manifestations than PLA patients without DM. There were 17 discriminating genera between the two groups at the genus level, among which Klebsiella was the most discriminating taxa. The ABC transporters was the most significant differential metabolic pathway predicted by PICRUSt2. Untargeted metabolomics analysis showed that concentrations of various metabolites were significantly different between the two groups and seven metabolites were enriched in the ABC transporters pathway. Phosphoric acid, taurine, and orthophosphate in the ABC transporters pathway were negatively correlated with the relative abundance of Klebsiella and the blood glucose level.

Discussion

The results showed that the relative abundance of Klebsiella in the pus cavity of PLA patients with DM was higher than those without DM, accompanied by changes of various metabolites and metabolic pathways, which may be associated with more severe clinical manifestations.

Micafungin effect on Pseudomonas aeruginosa metabolome, virulence and biofilm: potential quorum sensing inhibitor

The prevalence of antibiotic resistance in Pseudomonas aeruginosa places a heavy burden on the health care sectors urging the need to find alternative, non-antibiotic strategies. The interference with the P. aeruginosa quorum sensing (QS) system represents a promising alternative strategy to attenuate the bacterial virulency and its ability to form biofilms. Micafungin has been reported to impede the pseudomonal biofilm formation. However, the influences of micafungin on the biochemical composition and metabolites levels of P. aeruginosa have not been explored. In this study, the effect of micafungin (100 µg/mL) on the virulence factors, QS signal molecules and the metabolome of P. aeruginosa was studied using exofactor assay and mass spectrometry-based metabolomics approaches. Furthermore, confocal laser scanning microscopy (CLSM) using the fluorescent dyes ConA-FITC and SYPRO® Ruby was used to visualize micafungin disturbing effects on the pseudomonal glycocalyx and protein biofilm-constituents, respectively. Our findings showed that micafungin significantly decreased the production of various QS-controlled virulence factors (pyocyanin, pyoverdine, pyochelin and rhamnolipid), along with a dysregulation in the level of various metabolites involved in QS system, lysine degradation, tryptophan biosynthesis, TCA cycle, and biotin metabolism. In addition, the CLSM examination showed an altered matrix distribution. The presented findings highlight the promising role of micafungin as a potential quorum sensing inhibitor (QSI) and anti-biofilm agent to attenuate P. aeruginosa pathogenicity. In addition, they point to the promising role of metabolomics study in investigating the altered biochemical pathways in P. aeruginosa.

Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the primary contributors to deaths that are due to cancer globally. At the moment, STAD does not have any universally acknowledged biological markers, and its predictive, preventive, and personalized medicine (PPPM) remains sufficient. Oxidative stress can promote cancer by increasing mutagenicity, genomic instability, cell survival, proliferation, and stress resistance pathways. As a direct and indirect result of oncogenic mutations, cancer depends on cellular metabolic reprogramming. However, their roles in STAD remain unclear.

METHOD

743 STAD samples from GEO and TCGA platforms were selected. Oxidative stress and metabolism-related genes (OMRGs) were acquired from the GeneCard Database. A pan-cancer analysis of 22 OMRGs was first performed. We categorized STAD samples by OMRG mRNA levels. Additionally, we explored the link between oxidative metabolism scores and prognosis, immune checkpoints, immune cell infiltration, and sensitivity to targeted drugs. A series of bioinformatics technologies were employed to further construct the OMRG-based prognostic model and clinical-associated nomogram.

RESULTS

We identified 22 OMRGs that could evaluate the prognoses of patients with STAD. Pan-cancer analysis concluded and highlighted the crucial part of OMRGs in the appearance and development of STAD. Subsequently, 743 STAD samples were categorized into three clusters with the enrichment scores being C2 (upregulated) > C3 (normal) > C1 (downregulated). Patients in C2 had the lowest OS rate, while C1 had the opposite. Oxidative metabolic score significantly correlates with immune cells and immune checkpoints. Drug sensitivity results reveal that a more tailored treatment can be designed based on OMRG. The OMRG-based molecular signature and clinical nomogram have good accuracy for predicting the adverse events of patients with STAD. Both transcriptional and translational levels of ANXA5, APOD, and SLC25A15 exhibited significantly higher in STAD samples.

CONCLUSION

The OMRG clusters and risk model accurately predicted prognosis and personalized medicine. Based on this model, high-risk patients might be identified in the early stage so that they can receive specialized care and preventative measures, and choose targeted drug beneficiaries to deliver individualized medical services. Our results showed oxidative metabolism in STAD and led to a new route for improving PPPM for STAD.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

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.

MAGEA11 as a STAD Prognostic Biomarker Associated with Immune Infiltration

Expression of MAGE family member A11 (MAGEA11) is upregulated in different tumors. However, in gastric cancer, the prognostic significance of MAGEA11 and its relationship with immune infiltration remain largely unknown. The expression of MAGEA11 in pan-cancer and the receiver operating characteristic (ROC) and survival impact of gastric cancer were evaluated by The Cancer Genome Atlas (TCGA). Whether MAGEA11 was an independent risk factor was assessed by Cox analysis. Nomograms were constructed from MAGEA11 and clinical variables. Gene functional pathway enrichment was obtained based on MAGEA11 differential analysis. The relationship between MAGEA11 and immune infiltration was determined by the Tumor Immunity Estimation Resource (TIMER) and the Tumor Immune System Interaction Database (TISIDB). Finally, MAGEA11-sensitive drugs were predicted based on the CellMiner database. The results showed that the expression of MAGEA11 mRNA in gastric cancer tissues was significantly higher than that in normal tissues. The ROC curve indicated an AUC value of 0.667. Survival analysis showed that patients with high MAGEA11 had poor prognosis (HR = 1.43, p = 0.034). In correlation analysis, MAGEA11 mRNA expression was found to be associated with tumor purity and immune invasion. Finally, drug sensitivity analysis found that the expression of MAGEA11 was correlated with seven drugs. Our study found that upregulated MAGEA11 in gastric cancer was significantly associated with lower survival and invasion by immune infiltration. It is suggested that MAGEA11 may be a potential biomarker and immunotherapy target for gastric cancer.

Metabolic profiling reveals new serum signatures to discriminate lupus nephritis from systemic lupus erythematosus

Background

Lupus nephritis (LN) occurs in 50% of patients with systemic lupus erythematosus (SLE), causing considerable morbidity and even mortality. Previous studies had shown the potential of metabolic profiling in the diagnosis of SLE or LN. However, few metabonomics studies have attempted to distinguish SLE from LN based on metabolic changes. The current study was designed to find new candidate serum signatures that could differentiate LN from SLE patients using a non-targeted metabonomics method based on ultra high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS).

Method

Metabolic profiling of sera obtained from 21 healthy controls, 52 SLE patients and 43 LN patients. We used SPSS 25.0 for statistical analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and metabolic pathway analysis were used to analyze the metabolic data.

Results

Upon comparison of SLE and LN groups, 28 differential metabolites were detected, the majority of which were lipids and amino acids. Glycerolphospholipid metabolism, pentose and glucuronate interconversions and porphyrin and chlorophyll metabolism were obviously enriched in LN patients versus those with SLE. Among the 28 characteristic metabolites, five key serum metabolites including SM d34:2, DG (18:3(9Z,12Z,15Z)/20:5(5Z,8Z,11Z,14Z,17Z)/0:0), nervonic acid, Cer-NS d27:4, and PC (18:3(6Z,9Z,12Z)/18:3(6Z,9Z,12Z) performed higher diagnostic performance in discriminating LN from SLE (all AUC &gt; 0.75). Moreover, combined analysis of neuritic acid, C1q, and CysC (AUC = 0.916) produced the best combined diagnosis.

Conclusion

This study identified five serum metabolites that are potential indicators for the differential diagnosis of SLE and LN. Glycerolphospholipid metabolism may play an important role in the development of SLE to LN. The metabolites we screened can provide more references for the diagnosis of LN and more support for the pathophysiological study of SLE progressed to LN.

Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis.

METHODS

This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning.

RESULTS

Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), L-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879).

CONCLUSIONS

This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer.

Integration of transcriptomics, proteomics, and metabolomics data to reveal HER2-associated metabolic heterogeneity in gastric cancer with response to immunotherapy and neoadjuvant chemotherapy

Background

Currently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine.

Methods

The integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis.

Results

In this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that alanine, aspartate and glutamate (AAG) metabolism was significantly associated with the prevalence and progression of GC. Weighted metabolite correlation network analysis revealed that glycolysis/gluconeogenesis (GG) and AAG metabolism served as HER2-coexpressed metabolic pathways. Consensus clustering was used to stratify patients with GC into four subtypes with different metabolic characteristics (i.e. quiescent, GG, AAG and mixed subtypes). The GG subtype was characterised by a lower level of ERBB2 expression, a higher proportion of the inflammatory phenotype and the worst prognosis. However, contradictory features were found in the mixed subtype with the best prognosis. The GG and mixed subtypes were found to be highly sensitive to chemotherapy, whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.

Conclusions

Transcriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC. Metabolomics analysis highlighted the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC. The novel integrated multi-omics strategy used in this study may facilitate the development of a more tailored approach to GC therapy.

Correlation Analysis Between Trace Elements and Colorectal Cancer Metabolism by Integrated Serum Proteome and Metabolome

Colorectal cancer (CRC) is currently the third most common cancer with a high mortality rate. The underlying molecular mechanism of CRC, especially advanced CRC, remains poorly understood, resulting in few available therapeutic plans. To expand our knowledge of the molecular characteristics of advanced CRC and explore possible new therapeutic strategies, we herein conducted integrated proteomics and metabolomics analyses of 40 serum samples collected from 20 advanced CRC patients before and after treatment. The mass spectrometry-based proteomics analysis was performed under data-independent acquisition (DIA), and the metabolomics analysis was performed by ultra-performance liquid chromatography coupled with time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS). Trace elements including Mg, Zn, and Fe were measured by inductively coupled plasma spectrometry (ICP-MS) analysis. Four of the 20 patients had progressive disease (PD) after treatment, and clinical test results indicated that they all had impaired liver functions. In the proteomics analysis, 64 proteins were discovered to be significantly altered after treatment. These proteins were enriched in cancer-related pathways and pathways participating immune responses, such as MAPK signaling pathway and complement/coagulation cascades. In the metabolomics analysis, 128 metabolites were found to be significantly changed after treatment, and most of them are enriched in pathways associated with lipid metabolism. The cholesterol metabolism pathway was significantly enriched in both the proteomics and metabolomics pathway enrichment analyses. The concentrations of Mg in the serums of CRC patients were significantly lower than those in healthy individuals, which returned to the normal range after treatment. Correlation analysis linked key lipids, proteins, and Mg as immune modulators in the development of advanced CRC. The results of this study not only extended our knowledge on the molecular basis of advanced CRC but also provided potential novel therapeutic targets for CRC treatment.

Comprehensive Analysis of the Relationships Between the Gut Microbiota and Fecal Metabolome in Individuals With Primary Sjogren's Syndrome by 16S rRNA Sequencing and LC-MS-Based Metabolomics

The gut microbiota has been associated with primary Sjogren’s syndrome (pSS), yet the biological implications of these associations are often elusive. We analyzed the fecal microbiota through 16S rRNA gene amplification and sequencing in 30 patients with pSS and 20 healthy controls (HCs); At the same time, the fecal metabolome was characterized by ultrahigh-performance liquid chromatography–mass spectrometry. In addition, correlation analyses of microbiota and metabolome data were performed to identify meaningful associations. We found that the microbiota composition of pSS patients was significantly different from that of HCs. The pSS gut microbiota is characterized by increased abundances of proinflammatory microbes, especially Escherichia-Shigella, and decreased abundances of anti-inflammatory microbes. Concerning the metabolome, a multivariate model with 33 metabolites efficiently distinguished cases from controls. Through KEGG enrichment analysis, we found that these metabolites were mainly involved in amino acid metabolism and lipid metabolism. The correlation analysis indicated that there were certain correlations between the microbiota and metabolism in pSS patients. In addition, an abundance of Escherichia-Shigella was found to be correlated with high levels of four metabolites (aflatoxin M1, glycocholic acid, L-histidine and phenylglyoxylic acid). Our research suggests that in pSS patients, the gut microbiota is characterized by a specific combination of proinflammatory changes and metabolic states. Escherichia-Shigella is a factor related to gut dysbiosis, which may promote intestinal damage and affect amino acid metabolism.

Proteomic and metabolomic profiling combined with in vitro studies reveal the antiproliferative mechanism of silver nanoparticles in MDA-MB-231 breast carcinoma cells

Silver nanoparticles, shaped and stabilized by various means, are known to alter biological systems and promote cytotoxicity. However, the precise mechanism by which they induce toxic outcomes in cancer cells is poorly understood. Using a combination of cellular and biophysical assays and proteomic and metabolomic analyses, we report the cytotoxic mechanism of action of tryptone-stabilized silver nanoparticles (T-AgNPs). After their facile synthesis and characterization using an assortment of spectroscopic techniques and transmission electron microscopy, the mechanism of action of the particles was elucidated using MDA-MB-231 breast cancer cells as the cell model. The nanoparticles inhibited the proliferative (IC₅₀:100 ± 3 μg mL^-1) and clonogenic potential of the cells. Flow cytometry analyses revealed an absence of phase-specific cell cycle arrest but extensive cell death in the treated cells. The mechanism of action of the particles consisted of their direct binding to the microtubule-building protein tubulin and the disruption of its helical integrity, as confirmed via fluorometric analysis and far-UV spectropolarimetry, respectively. The binding hampered the assembly of microtubules, as confirmed via polymer mass analysis of in vitro assembled, purified tubulin and immunofluorescence imaging of cellular microtubules. Proteomic and metabolomic analyses revealed the downregulation of lipid metabolism to be a synergistic contributor to cell death. Taken together, we report a novel antiproliferative mechanism of action of T-AgNPs that involves tubulin disruption and the downregulation of lipid metabolism.

Urinary metabolomics for discovering metabolic biomarkers of bladder cancer by UPLC-MS

Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.

Aberrant lipid metabolism in cancer cells and tumor microenvironment: the player rather than bystander in cancer progression and metastasis

As the primary cause of cancer-induced fatality and morbidity, cancer metastasis has been a hard nut to crack. Existing studies indicate that lipid metabolism reprogramming occurring in cancer cells and surrounding cells in TME also endows the aggressive and spreading properties with malignant cells. In this review we describe the lipid metabolic reprogramming of cancer cells at different steps along the metastatic process, we also summarize the altered lipid metabolism of non-cancer cells in TME during tumor metastasis. Additionally, we reveal both intrinsic and extrinsic factors which influence the cellular lipid metabolism reprogramming.

Roseburia hominis Increases Intestinal Melatonin Level by Activating p-CREB-AANAT Pathway

Intestinal melatonin exerts diverse biological effects on the body. Our previous research showed that the abundance of the butyrate-producing bacteria, Roseburia, is positively related to the expression of colonic mucosal melatonin. However, the detailed relationship is unclear. Therefore, we aimed to explore whether Roseburia regulates intestinal melatonin and its underlying mechanisms. Male Sprague–Dawley germfree rats were orally administered with or without Roseburia hominis. R. hominis treatment significantly increased the intestinal melatonin level. The concentrations of propionate and butyrate in the intestinal contents were significantly elevated after gavage of R. hominis. Propionate or butyrate treatment increased melatonin, 5-hydroxytryptamine (5-HT), arylalkylamine N-acetyltransferase (AANAT), and phosphorylated cAMP-response element-binding protein (p-CREB) levels. When pretreated with telotristat ethyl, the inhibitor of tryptophan hydroxylase (TPH), or siRNA of Aanat, or 666-15, i.e., an inhibitor of CREB, propionate, or butyrate, could not promote melatonin production in the pheochromocytoma cell line BON-1. Metabolomics analysis showed that propionate and butyrate stimulation regulated levels of some metabolites and some metabolic pathways in BON-1 cell supernatants. In conclusion, propionate and butyrate, i.e., metabolites of R. hominis, can promote intestinal melatonin synthesis by increasing 5-HT levels and promoting p-CREB-mediated Aanat transcription, thereby offering a potential target for ameliorating intestinal diseases.

Metabolomics Study of Isocaloric Different Dietary Patterns on the Life Span in Healthy Population

Purpose

How to prolong life by diet has been widely concerned. There are many reports about the effects of different dietary patterns on life span, but the results are not consistent. The main reason may be that total energy intake has not been considered. This study aims to explore the effects of isocaloric different dietary patterns on population life span.

Materials and Methods

From the data of the follow-up population, eligible participators were divided into normal control (NC) group (28.31% fat, 12.37% protein, 62.30% carbohydrate), isocaloric high-fat (IHF) group (38.39% fat, 12.21% protein, 51.32% carbohydrate), isocaloric high-protein (IHP) group (33.41% fat, 17.10% protein, 52.67% carbohydrate) and isocaloric high-carbohydrate (IHC) group (22.23% fat, 10.52% protein, 70.13% carbohydrate) according to the dietary structure and the age stratification. Global serum metabolic profiling analysis by UPLC−Q-TOF-MS/MS technology, fatty acid and amino acid profiles in serum were determined by GC-MS and UPLC-TQ-MS technology. One-way ANOVA followed by Dunnett post hoc test and receiver operating characteristic (ROC) curve analysis were used to statistical analysis.

Results

Non-targeted metabolomics was to identify 18 potential metabolites related to longevity. ROC curve analysis to identify biomarkers indicated that the areas under the ROC (AUC) of the 12 of 18 biomarkers are above 0.9. The 12 biomarkers were mainly enriched in three metabolic pathways: lipid metabolism, amino acid metabolism and tricarboxylic acid cycle. Compared to control, 11 and 10 of 12 biomarkers showed the same trend with aging in IHP and IHC groups, respectively. Conversely, no differences were observed between IHF group and NC group.

Conclusion

Without consideration of the nature of carbohydrates, fats and proteins, IHP and IHC diets might shorten life span by influencing amino acid metabolism, lipid metabolism and tricarboxylic acid cycle metabolism, while the isocaloric IHF diet has no effects on longevity.

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.