Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool

Pharmacodynamic Mechanisms of Cicadae Periostracum in Parkinson's Disease: A Metabolomics-Based Study

Cicadae Periostracum (CP) is a traditional Chinese animal-derived medicine with the potential to treat Parkinson's disease (PD). This study aims to explore the pharmacodynamic mechanisms of CP against PD-based on metabolomics technology and provide a theoretical basis for developing new anti-PD medicine. First, MPP+-induced SH-SY5Y cells were used to evaluate the anti-PD activity of CP. In the animal study, an MPTP-induced PD mouse model was employed to assess CP's therapeutic effects. Immunofluorescence (IF) staining and Western blotting (WB) were used to evaluate its neuroprotective activity on neurons. A Serum metabolomics analysis was conducted to examine CP's regulatory effects on metabolites and to identify vital metabolic pathways. Finally, cellular experiments were performed to validate the critical pathways. Cellular activity experiments demonstrated that CP mitigates MPP+-induced SH-SY5Y cytotoxicity, inhibits apoptosis, and restores mitochondrial homeostasis. Animal experiments revealed that CP significantly alleviates dyskinesia in PD mice, enhances motor performance, and restores neuronal integrity while reducing α-synuclein (α-syn) aggregation in the striatum (STR), showing its strong anti-PD effect. Metabolomic analysis revealed that CP can significantly improve the metabolic disorders of ten biomarkers that are mainly involved in amino acid metabolism and fatty acid β-oxidation and are closely related to oxidative stress pathways. Finally, pathway verification was performed, and the results show that CP exerted neuroprotective effects against PD through the dual signaling pathways of Bcl-2/Bax/Caspase-3 and Nrf2/HO-1. This study provides a comprehensive strategy for elucidating the mechanisms by which CP exerts its therapeutic effects against PD, highlighting its potential in developing anti-PD drugs.

Colorectal cancer cells establish metabolic reprogramming with cancer-associated fibroblasts (CAFs) through lactate shuttle to enhance invasion, migration, and angiogenesis

Fibroblasts undergo metabolic reprogramming after contact with cancer cells in tumor microenvironment, producing lactate to provide a metabolic substrate for neighboring tumor cells. The exchange of lactate between cancer cells and fibroblasts via monocarboxylate transporters (MCTs) is known as the lactate shuttle. Colorectal cancer cells may establish a metabolic coupling akin to the lactate shuttle in collaboration with cancer-associated fibroblasts (CAFs) to augment their invasive and migratory capabilities. However, the specific phenomena and underlying mechanisms are not clear. In this study, we investigated the phenomena and explored the correlation and possible mechanism between CAFs and the invasion and migration of colorectal cancer cells by using two different co-culture models. The results showed that colorectal cancer cells established a lactate metabolic coupling with fibroblasts through the oxidative stress effect, triggering the metabolic reprogramming process of themselves and those of fibroblasts. In addition, lactate enhanced the invasion and migration of colorectal cancer by stabilizing the protein expression levels of nuclear factor kappa-B (NF-κB) and hypoxia-inducible factor-1α (HIF-1α). Blocking oxidative stress and lactate metabolic coupling with reactive oxygen species removers and MCT1-specific inhibitors, respectively, could effectively suppress metastasis in colorectal cancer. These findings suggest that targeting the lactate metabolic coupling between tumor cells and CAFs will offer a new strategy to combat colorectal cancer.

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

An atlas on risk factors for gastrointestinal cancers: A systematic review of Mendelian randomization studies

OBJECTIVE

Gastrointestinal cancers are one of the most frequent cancer types and seriously threaten human life and health. Recent studies attribute the occurrence of gastrointestinal cancers to both genetic and environmental factors, yet the intrinsic etiology remains unclear. Mendelian randomization is a powerful well-established statistical method that is based on genome-wide association study (GWAS) to evaluate the causal relationship between exposures and outcomes. In the present study, we aimed to conduct a systematic review of Mendelian randomization studies investigating any causal risk factors for gastrointestinal cancers.

METHODS

We systematically searched Mendelian randomization studies that addressed the associations of genetically predicted exposures with five main gastrointestinal cancers from September 2014 to March 2024, as well as testing the research quality and validity.

RESULTS

Our findings suggested robust and consistent causal effects of body mass index (BMI), basal metabolic rate, fatty acids, total cholesterol, total bilirubin, insulin like growth factor-1, eosinophil counts, interleukin 2, alcohol consumption, coffee consumption, apolipoprotein B on colorectal cancer risks, BMI, waist circumference, low-density lipoprotein (LDL), total testosterone, smoking on gastric cancer risks, BMI, fasting insulin, LDL, waist circumference, visceral adipose tissue (VAT), immune cells, type 2 diabetes mellitus (T2DM) on pancreatic cancer risks, waist circumference, smoking, T2DM on esophageal adenocarcinoma risks, and VAT, ferritin, transferrin, alcohol consumption, hepatitis B virus infection, rheumatoid arthritis on liver cancer risks, respectively.

CONCLUSION

Larger, well-designed Mendelian randomization studies are practical in determining the causal status of risk factors for diseases.

Gut microbial and metabolomics profiles reveal the potential mechanism of fecal microbiota transplantation in modulating the progression of colitis-associated colorectal cancer in mice

PURPOSE

Intestinal flora promotes the pathogenesis of colorectal cancer (CRC) through microorganisms and their metabolites. This study aimed to investigate the composition of intestinal flora in different stages of CRC progression and the effect of fecal microbiota transplantation (FMT) on CRC mice.

METHODS

The fecal microbiome from healthy volunteers (HC), colorectal adenoma (CRA), inflammatory bowel disease (IBD), and CRC patients were analyzed by 16s rRNA gene sequencing. In an azoxymethane (AOM)/dextran-sulfate-sodium (DSS)-induced CRC mouse, the effect of FMT from HC, CRA, CRC, and IBD patients on CRC mice was assessed by histological analysis. Expression of inflammation- EMT-associated proteins and Wnt/β-catenin pathway were assessed using qRT-PCR and western blot. The ratio of the fecal microorganisms and metabolomics alteration after FMT were also assessed.

RESULT

Prevotella, Faecalibacterium, Phascolarctobacterium, Veillonella, Alistipes, Fusobacterium, Oscillibacter, Blautia, and Ruminococcus abundance was different among HC, IBD, CRC, and CRA patients. HC-FMT alleviated disease progression and inflammatory response in CRC mice, inhibited splenic T help (Th)1 and Th17 cell numbers, and suppressed the EMT and Wnt/β-catenin pathways in tumor tissues of CRC mice. IBD-FMT, CRA-FMT, and CRC-FMT played deleterious roles; the CRC-FMT mice exhibited the most malignant phenotype. Compared with the non-FMT CRC mice, Muribaculaceae abundance was lower after FMT, especially lowest in the IBD-FMT group; while Lactobacillus abundance was higher after FMT and especially high in HC-FMT. Akkermansia and Ileibacterium abundance increased after FMT-HC compared to other groups. Metabolite correlation analysis revealed that Muribaculaceae abundance was significantly correlated with metabolites such as Betaine, LysoPC, and Soyasaponin III. Lactobacillus abundance was positively correlated with Taurocholic acid 3-sulfate, and Ileibacterium abundance was positively correlated with Linoleoyl ethanolamide.

CONCLUSION

The different intestinal microbiota communities of HC, IBD, CRA, and CRC patients may be attributed to the different modulation effects of FMT on CRC mice. CRC-FMT promoted, while HC-FMT inhibited the progress of CRC. Increased linoleoyl ethanolamide levels and abundance of Muribaculaceae, Akkermansia, and Ileibacterium and reduced Fusobacterium might participate in inhibiting CRC initiation and development. This study demonstrated that FMT intervention could restore the intestinal microbiota and metabolomics of CRC mice, suggesting FMT as a potential strategy for CRC therapy.

Serum Metabolomic Markers of Artificially Sweetened Beverage Consumption

BACKGROUND

The consumption of artificially sweetened beverages is on the rise. Use of artificial sweeteners has been associated with adverse health outcomes. There is a need to identify novel objective biomarkers of artificially sweetened beverages in order to improve dietary assessment and to provide insight into their metabolic impact.

OBJECTIVES

We aimed to identify serum metabolites that are associated with artificially sweetened beverage consumption.

METHODS

In the Atherosclerosis Risk in Communities (ARIC) study, consumption of artificially sweetened beverages was assessed using a food frequency questionnaire and fasting serum samples were collected during the first study visit (1987-1989). Participants were categorized as nonusers if they reported almost never consumption of artificially sweetened beverages, moderate users for 1 glass/mo to 6 glasses/wk, and heavy users for ≥1 glasses/d. Untargeted metabolomic profiling was conducted in 2 subgroups (subgroup 1: n = 1866, profiled in 2010; subgroup 2 profiled in 2014: n = 2072), and 360 metabolites were analyzed. In this secondary data analysis, multivariable linear regression models were used, adjusting for demographics, health behaviors, health status, and dietary factors. Analyses were conducted in each subgroup and results meta-analyzed.

RESULTS

In a meta-analysis of 3938 generally healthy participants (mean age, 54 y; 60% women; 62% Black participants) from ARIC study visit 1, 11 serum metabolites were significantly associated with artificially sweetened beverage consumption. Heavier consumption of artificially sweetened beverages was associated with higher concentrations of 10 metabolites (saccharin, threonate, erythronate, glycerate, gluconate, mannitol, glucose, tryptophan betaine, trehalose, and N6-acetyllysine) and lower concentrations of glycocholenate sulfate.

CONCLUSIONS

Eleven serum metabolites are related to artificially sweetened beverage intake, which consist of known sugar substitutes, processed food additives, glucose-related compounds, and gut microbiome-related metabolites. These findings enhance our knowledge of the metabolic activity of artificial sweeteners and suggests new biomarkers for monitoring intake.

Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR). Gut microbiome-associated metabolites were identified through a comprehensive search of Pubmed, Exposome Explorer and Human Metabolome Database. Single Nucleotide Polymorphisms (SNPs) associated by Genome-Wide Association Studies (GWAS) with circulating levels of 109 of these metabolites were collated from Pubmed and the GWAS catalogue. SNPs for 119 taxonomically defined gut genera were selected from a meta-analysis performed by the MiBioGen consortium. Two-sample MR was conducted using GWAS summary statistics from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including a total of 8,769 cases and 7,055 controls. Inverse variance-weighted MR analyses were performed along with sensitivity analyses to assess potential violations of MR assumptions. Nominally significant associations were noted for genetically predicted circulating concentrations of mannitol (odds ratio per standard deviation [ORSD] = 0.97; 95% confidence interval [CI]: 0.95-0.99, p = 0.006), methionine (ORSD= 0.97; 95%CI: 0.94-1.00, p = 0.031), stearic acid (ORSD= 0.93; 95%CI: 0.87-0.99, p = 0.027), carnitine = (ORSD=1.01; 95% CI: 1.00-1.03, p = 0.027), hippuric acid (ORSD= 1.02; 95%CI: 1.00-1.04, p = 0.038) and 3-methylhistidine (ORSD= 1.05; 95%CI: 1.01-1.10, p = 0.02). Two gut microbiome genera were associated with reduced PDAC risk; Clostridium sensu stricto 1 (OR: 0.88; 95%CI: 0.78-0.99, p = 0.027) and Romboutsia (OR: 0.87; 95%CI: 0.80-0.96, p = 0.004). These results, though based only on genetically predicted gut microbiome characteristics and circulating bacteria-related metabolite concentrations, provide evidence for causal associations with pancreatic carcinogenesis.

Discovery and analysis of the relationship between organic components in exhaled breath and bronchiectasis

The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy (ACC) of breath biomarkers (BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE. Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning (ML) method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without nontuberculous mycobacterium (NTM),P. aeruginosa(P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed. The top ten BBs based ML model achieved an area under the curve of 0.940, sensitivity of 90.7%, specificity of 85%, and ACC of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection,P.aisolation, the number of involved lobes, and three lung functional parameters in BE patients. BBs-based BE detection model showed good ACC for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for BE screening and personalized management.

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

PURPOSE

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach.

METHODS

Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles.

RESULTS

Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points.

CONCLUSION

The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

In vitro digestive properties and the bioactive effect of walnut green husk on human gut microbiota

Introduction

Walnut green husk (WGH) is a waste byproduct from walnut industry. However, it is not well-known about its bioactive effect on human gut health.

Methods

This study conducted in vitro digestion and fermentation experiments to study the bioactive effect of WGH.

Results

Microbial fermentation was the primary mechanism to efficiently release phenolics and flavonoids, resulting in more excellent antioxidant capacities (DPPH, ABTS, and FRAP assays), which reached a highest value with 14.82 ± 0.01 mg VcE/g DW, 3.47 ± 0.01 mmol TE/g DW, and 0.96 ± 0.07 mmol FeSO4·7H2O/g DW, respectively. The surface microstructure of WGH became loose and fragmented after microbial fermentation. The analytical results of gut microbiota demonstrated that WGH could significantly increase the relative abundance of Proteobacteria in phylum level and Phascolarctobacterium in genus level while certain pro-inflammatory bacteria (such as Clostridium_sensu_stricto_1, Dorea, Alistipes, and Bilophila) was inhibited. Additionally, 1,373 differential metabolites were identified and enriched in 283 KEGG pathways. Of which some metabolites were significantly upregulated including ferulic acid, chlorogenic acid, umbelliferone, scopolin, muricholic acid, and so forth.

Discussion

These results indicated that WGH could have antioxidant and anti-inflammatory activities in the human gut, which could improve the economical value of WGH in the food industry.

APOA5 alleviates reactive oxygen species to promote oxaliplatin resistance in PIK3CA-mutated colorectal cancer

Although platinum-based chemotherapy is the frontline regimen for colorectal cancer (CRC), drug resistance remains a major challenge affecting its therapeutic efficiency. However, there is limited research on the correlation between chemotherapy resistance and lipid metabolism, including PIK3CA mutant tumors. In this present study, we found that PIK3CA-E545K mutation attenuated cell apoptosis and increased the cell viability of CRC with L-OHP treatment in vitro and in vivo. Mechanistically, PIK3CA-E545K mutation promoted the nuclear accumulation of SREBP1, which promoted the transcription of Apolipoprotein A5 (APOA5). APOA5 activated the PPARγ signaling pathway to alleviate reactive oxygen species (ROS) production following L-OHP treatment, which contributed to cell survival of CRC cells. Moreover, APOA5 overexpression enhanced the stemness-related traits of CRC cells. Increased APOA5 expression was associated with PIK3CA mutation in tumor specimens and poor response to first-line chemotherapy, which was an independent detrimental factor for chemotherapy sensitivity in CRC patients. Taken together, this study indicated that PIK3CA-E545K mutation promoted L-OHP resistance by upregulating APOA5 transcription in CRC, which could be a potent target for improving L-OHP chemotherapeutic efficiency. Our study shed light to improve chemotherapy sensitivity through nutrient management in CRC.

Role of the Microbiome in the Diagnosis and Management of Gastroesophageal Cancers

PURPOSE

Stomach and esophageal cancers are among the highest mortality from cancers worldwide. Microbiota has an interplaying role within the human gastrointestinal (GI) tract. Dysbiosis occurs when a disruption of the balance between the microbiota and the host happens. With this narrative review, we discuss the main alterations in the microbiome of gastroesophageal cancer, revealing its potential role in the pathogenesis, early detection, and treatment.

RESULTS

Helicobacter pylori plays a major role the development of a cascade of preneoplastic conditions ranging from atrophic gastritis to metaplasia and dysplasia, ultimately culminating in gastric cancer, while other pathogenic agents are Fusobacterium nucleatum, Bacteroides fragilis, Escherichia coli, and Lactobacillus. Campylobacter species (spp.)'s role in the progression of esophageal adenocarcinoma may parallel that of Helicobacter pylori in the context of gastric cancer, with other esophageal carcinogenic agents being Escherichia coli, Bacteroides fragilis, and Fusobacterium nucleatum. Moreover, gut microbiome could significantly alter the outcomes of chemotherapy and immunotherapy. The gut microbiome can be modulated through interventions such as antibiotics, probiotics, or prebiotics intake. Fecal microbiota transplantation has emerged as a therapeutic strategy as well.

CONCLUSIONS

Nowadays, it is widely accepted that changes in the normal gut microbiome causing dysbiosis and immune dysregulation play a role gastroesophageal cancer. Different interventions, including probiotics and prebiotics intake are being developed to improve therapeutic outcomes and mitigate toxicities associated with anticancer treatment. Further studies are required in order to introduce the microbiome among the available tools of precision medicine in the field of anticancer treatment.

Driver Mutations in Pancreatic Cancer and Opportunities for Targeted Therapy

Pancreatic cancer is the sixth leading cause of cancer-related mortality globally. As the most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC) represents up to 95% of all pancreatic cancer cases, accounting for more than 300,000 deaths annually. Due to the lack of early diagnoses and the high refractory response to the currently available treatments, PDAC has a very poor prognosis, with a 5-year overall survival rate of less than 10%. Targeted therapy and immunotherapy are highly effective and have been used for the treatment of many types of cancer; however, they offer limited benefits in pancreatic cancer patients due to tumor-intrinsic and extrinsic factors that culminate in drug resistance. The identification of key factors responsible for PDAC growth and resistance to different treatments is highly valuable in developing new effective therapeutic strategies. In this review, we discuss some molecules which promote PDAC initiation and progression, and their potential as targets for PDAC treatment. We also evaluate the challenges associated with patient outcomes in clinical trials and implications for future research.

Hierarchical superstructure aerogels for in situ biofluid metabolomics

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

The role of gut microbiota and probiotics in preventing, treating, and boosting the immune system in colorectal cancer

The gut microbiome plays a significant role in developing colorectal cancer (CRC). The gut microbiome usually acts as a protective barrier against harmful pathogens and infections in the intestine, while also regulating inflammation by affecting the human immune system. The gut microbiota and probiotics play a role not only in intestinal inflammation associated with tumor formation but also in regulating anti-cancer immune response. As a result, they associated with tumor progression and the effectiveness of anti-cancer therapies. Research indicates that gut microbiota and probiotics can be used as biomarkers to predict the impact of immunotherapy and enhance its efficacy in treating CRC by regulating it. This review examines the importance of gut microbiota and probiotics in the development and progression of CRC, as well as their synergistic impact on anti-cancer treatments.

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

The causal association between serum metabolites and lung cancer based on multivariate Mendelian randomization

This study seeks to understand the causal association between serum metabolites and different lung cancer types, an area yet to be extensively studied. We Used a two-sample Mendelian randomization (TSMR) approach, utilizing 486 blood metabolites as exposures and 3 distinct lung cancer types genome-wide association studies datasets as outcomes. We employed inverse variance weighting, MR-Egger, weighted median, simple mode, and weighted mode to estimate causal effects. We performed sensitivity analyses using Cochran Q test, MR-Egger intercept test, and MR-pleiotropy residual sum and outlier (MR-PRESSO). Linkage disequilibrium score (LDSC) analysis was conducted on the selected metabolites, and common confounding single nucleotide polymorphisms were eliminated using the human genotype-phenotype association Database. Metabolic pathway analysis was performed with MetaboAnalyst 5.0 software. Subsequently, a multivariate Mendelian randomization analysis was conducted to ascertain independent risk exposures. Our findings suggest independent risk factors for specific types of lung cancer: 7-methylxanthine and isoleucine for lung adenocarcinoma, cysteine and 1-arachidonoylglycerophosphocholine are identified as independent protective and risk factors for squamous lung cancer. Undecanoate (11:0) with Linoleate (18:2n6) showed a protective effect for small cell lung cancer. Additionally, 11 metabolic pathways were associated with lung cancer. This novel perspective offers a multidimensional understanding of lung cancer phenotypes, providing valuable guidance for identifying and screening of diverse lung cancer phenotypes.

Multi-omic integration of microbiome data for identifying disease-associated modules

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.

Current Research on Molecular Biomarkers for Colorectal Cancer in Stool Samples

Colorectal cancer (CRC) is one of the most diagnosed cancers worldwide, with a high incidence and mortality rate when diagnosed late. Currently, the methods used in healthcare to diagnose CRC are the fecal occult blood test, flexible sigmoidoscopy, and colonoscopy. However, the lack of sensitivity and specificity and low population adherence are driving the need to implement other technologies that can identify biomarkers that not only help with early CRC detection but allow for the selection of more personalized treatment options. In this regard, the implementation of omics technologies, which can screen large pools of biological molecules, coupled with molecular validation, stands out as a promising tool for the discovery of new biomarkers from biopsied tissues or body fluids. This review delves into the current state of the art in the identification of novel CRC biomarkers that can distinguish cancerous tissue, specifically from fecal samples, as this could be the least invasive approach.

Metabolomics analyses of cancer tissue from patients with colorectal cancer

The alteration of metabolism is essential for the initiation and progression of numerous types of cancer, including colorectal cancer (CRC). Metabolomics has been used to study CRC. At present, the reprogramming of the metabolism in CRC remains to be fully elucidated. In the present study, comprehensive untargeted metabolomics analysis was performed on the paired CRC tissues and adjacent normal tissues from patients with CRC (n=35) using ultra‑high‑performance liquid chromatography‑mass spectrometry. Subsequently, bioinformatic analysis was performed on the differentially expressed metabolites. The changes in these differential metabolites were compared among groups of patients based on sex, anatomical tumor location, grade of tumor differentiation and stage of disease. A total of 927 metabolites were detected in the tissue samples, and 24 metabolites in the CRC tissue were significantly different compared with the adjacent normal tissue. The present study revealed that the levels of three amino acid metabolites were increased in the CRC tissue, specifically, N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, cyclo(Glu‑Glu) and cyclo(Phe‑Glu). The metabolites with decreased levels in the CRC tissue included quinaldic acid (also referred to as quinoline‑2‑carboxilic acid), 17α‑ and 17β‑estradiol, which are associated with tumor suppression activities, as well as other metabolites such as, anhydro‑β‑glucose, Asp‑Arg, lysophosphatidylcholine, lysophosphatidylethanolamine (lysoPE), lysophosphatidylinositol, carnitine, 5'‑deoxy‑5'‑(methylthio) adenosine, 2'‑deoxyinosine‑5'‑monophosphate and thiamine monophosphate. There was no difference in the levels of the differential metabolites between male and female patients. The differentiation of CRC also showed no impact on the levels of the differential metabolites. The levels of lysoPE were increased in the right side of the colon compared with the left side of the colon and rectum. Analysis of the different tumor stages indicated that 2‑aminobenzenesulfonic acid, P‑sulfanilic acid and quinoline‑4‑carboxylic acid were decreased in stage I CRC tissue compared with stage II, III and IV CRC tissue. The levels of N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, methylcysteine and 5'‑deoxy‑5'‑(methylthio) adenosine varied at different stages of tumorigenesis. These differential metabolites were implicated in multiple metabolism pathways, including carbohydrate, amino acid, lipid, nucleotide and hormone. In conclusion, the present study demonstrated that CRC tumors had altered metabolites compared with normal tissue. The data from the metabolic profile of CRC tissues in the present study provided supportive evidence to understand tumorigenesis.

Intratumoral metabolic heterogeneity of colorectal cancer

Although the metabolic phenotype within tumors is known to differ significantly from that of the surrounding normal tissue, the importance of this heterogeneity is just becoming widely recognized. Colorectal cancer (CRC) is often classified as the Warburg phenotype, a metabolic type in which the glycolytic system is predominant over oxidative phosphorylation (OXPHOS) in mitochondria for energy production. However, this dichotomy (glycolysis vs. OXPHOS) may be too simplistic and not accurately represent the metabolic characteristics of CRC. Therefore, in this review, we decompose metabolic phenomena into factors based on their source/origin and reclassify them into two categories: extrinsic and intrinsic. In the CRC context, extrinsic factors include those based on the environment, such as hypoxia, nutrient deprivation, and the tumor microenvironment, whereas intrinsic factors include those based on subpopulations, such as pathological subtypes and cancer stem cells. These factors form multiple layers inside and outside the tumor, affecting them additively, dominantly, or mutually exclusively. Consequently, the metabolic phenotype is a heterogeneous and fluid phenomenon reflecting the spatial distribution and temporal continuity of these factors. This allowed us to redefine the characteristics of specific metabolism-related factors in CRC and summarize and update our accumulated knowledge of their heterogeneity. Furthermore, we positioned tumor budding in CRC as an intrinsic factor and a novel form of metabolic heterogeneity, and predicted its metabolic dynamics, noting its similarity to circulating tumor cells and epithelial-mesenchymal transition. Finally, the possibilities and limitations of using human tumor tissue as research material to investigate and assess metabolic heterogeneity are discussed.

Characterization of Salivary and Plasma Metabolites as Biomarkers for HCC: A Pilot Study

(1) Background: The incidence of hepatocellular carcinoma (HCC) is rising, and current screening methods lack sensitivity. This study aimed to identify distinct and overlapping metabolites in saliva and plasma that are significantly associated with HCC. (2) Methods: Saliva samples were collected from 42 individuals (HCC = 16, cirrhosis = 12, healthy = 14), with plasma samples from 22 (HCC = 14, cirrhosis = 2, healthy = 6). We performed untargeted mass spectrometry on blood and plasma, tested metabolites for associations with HCC or cirrhosis using a logistic regression, and identified enriched pathways with Metaboanalyst. Pearson's correlation was employed to test for correlations between salivary and plasma metabolites. (3) Results: Six salivary metabolites (1-hexadecanol, isooctanol, malonic acid, N-acetyl-valine, octadecanol, and succinic acid) and ten plasma metabolites (glycine, 3-(4-hydroxyphenyl)propionic acid, aconitic acid, isocitric acid, tagatose, cellobiose, fucose, glyceric acid, isocitric acid, isothreonic acid, and phenylacetic acid) were associated with HCC. Malonic acid was correlated between the paired saliva and plasma samples. Pathway analysis highlighted deregulation of the 'The Citric Acid Cycle' in both biospecimens. (4) Conclusions: Our study suggests that salivary and plasma metabolites may serve as independent sources for HCC detection. Despite the lack of correlation between individual metabolites, they converge on 'The Citric Acid Cycle' pathway, implicated in HCC pathogenesis.

Integrated multi-omic analyses provide insight into colon adenoma susceptibility modulation by the gut microbiota

Colon cancer onset is strongly associated with the differences in microbial taxa in the gastrointestinal tract. Although recent studies highlight the role of individual taxa, the effect of a complex gut microbiome (GM) on the metabolome and host transcriptome is still unknown. We used a multi-omics approach to determine how differences in the GM affect the susceptibility to adenoma development in a rat model of human colon cancer. Ultra-high performance liquid chromatography mass spectrometry of feces collected prior to observable disease onset identified putative metabolite profiles that likely predict future disease severity. Transcriptome analyses performed after disease onset from normal colonic epithelium and tumor tissues show a correlation between GM and host gene expression. Integrated pathway analyses of the metabolome and transcriptome based on putatively identified metabolic features indicate that bile acid biosynthesis is enriched in rats with high tumors along with increased fatty acid metabolism and mucin biosynthesis. Targeted pyrosequencing of the Pirc allele indicates that the GM alters the mechanism of adenoma development and may drive an epigenetic pathway of tumor suppressor silencing. This study reveals how untargeted metabolomics identifies signatures of susceptibility and integrated analyses uncover pathways of differential mechanisms of loss of tumor suppressor gene function and for potential prevention and therapeutic intervention. IMPORTANCE The association between the gut microbiome and colon cancer is significant but difficult to test in model systems. This study highlights the association of differences in the pathogen-free gut microbiome to changes in the host transcriptome and metabolome that correlate with colon adenoma initiation and development in a rat genetic model of early colon cancer. The utilization of a multi-omics approach integrating metabolomics and transcriptomics reveals differences in pathways including bile acid biosynthesis and fatty acid metabolism. The study also shows that differences in gut microbiomes significantly alter the mechanism of adenoma formation, shifting from genetic changes to epigenetic changes that initiate the early loss of tumor suppressor function. These findings enhance our understanding of the gut microbiome's role in colon cancer susceptibility, offer insights into potential biomarkers and therapeutic targets, and may pave the way for future prevention and intervention strategies.

The role of the symbiotic microecosystem in cancer: gut microbiota, metabolome, and host immunome

The gut microbiota is not just a simple nutritional symbiosis that parasitizes the host; it is a complex and dynamic ecosystem that coevolves actively with the host and is involved in a variety of biological activities such as circadian rhythm regulation, energy metabolism, and immune response. The development of the immune system and immunological functions are significantly influenced by the interaction between the host and the microbiota. The interactions between gut microbiota and cancer are of a complex nature. The critical role that the gut microbiota plays in tumor occurrence, progression, and treatment is not clear despite the already done research. The development of precision medicine and cancer immunotherapy further emphasizes the importance and significance of the question of how the microbiota takes part in cancer development, progression, and treatment. This review summarizes recent literature on the relationship between the gut microbiome and cancer immunology. The findings suggest the existence of a "symbiotic microecosystem" formed by gut microbiota, metabolome, and host immunome that is fundamental for the pathogenesis analysis and the development of therapeutic strategies for cancer.

GC-MS Based Metabolomics Analysis to Evaluate Short-Term Effect of Tumor Removal on Patients with Early-Stage Breast Cancer

In this study, it was aimed to demonstrate the short-term effect of breast cancer surgery and tumor removal on the metabolomic profiles of patients with early-stage breast cancer. This cohort consisted of 18 early-stage breast carcinoma patients who had breast cancer surgery to remove tumor and surrounding tissues. The blood samples obtained preoperatively and 24 h after surgery were used in this investigation. Gas chromatography-mass spectrometry (GC-MS) based metabolomic analysis was performed to determine the metabolites. The GC-MS-based metabolomics profile enabled the identification of 162 metabolites in the plasma samples. Postoperatively, glyceric acid, phosphoric acid, O-phosphocolamine, 2-hydroxyethyliminodiacetic acid, N-acetyl-D-mannosamine, N-acetyl-5-hydroxytryptamine, methyl stearate, methyl oleate, iminodiacetic acid, glycerol 1-phosphate, β-glycerol phosphate and aspartic acid were found to be significantly increased (P < 0.05 for all), whereas saccharic acid, leucrose, gluconic acid, citramalic acid and acetol were significantly decreased (P < 0.05 for all). Breast cancer surgery and tumor removal has an impact on the metabolomic profiles of patients with early-stage breast cancer. These findings can be used for understanding the pathogenesis of breast cancer biology and screening the success of the surgery.

Strain specificity in fusobacterial co-aggregation with colorectal cancer-relevant species

OBJECTIVES

The purpose of the present study was to characterize co-aggregation interactions between isolates of Fusobacterium nucleatum subsp. animalis and other colorectal cancer (CRC)-relevant species.

METHODS

Co-aggregation interactions were assessed by comparing optical density values following 2-h stationary strain co-incubations to strain optical density values when incubated alone. Co-aggregation was characterized between strains from a previously isolated, CRC biopsy-derived community and F. nucleatum subsp. animalis, a species linked to CRC and known to be highly aggregative. Interactions were also investigated between the fusobacterial isolates and strains sourced from alternate human gastrointestinal samples whose closest species match aligned with species in the CRC biopsy-derived community.

RESULTS

Co-aggregation interactions were observed to be strain-specific, varying between both F. nucleatum subsp. animalis strains and different strains of the same co-aggregation partner species. F. nucleatum subsp. animalis strains were observed to co-aggregate strongly with several taxa linked to CRC: Campylobacter concisus, Gemella spp., Hungatella hathewayi, and Parvimonas micra.

CONCLUSIONS

Co-aggregation interactions suggest the ability to encourage the formation of biofilms, and colonic biofilms, in turn, have been linked to promotion and/or progression of CRC. Co-aggregation between F. nucleatum subsp. animalis and CRC-linked species such as C. concisus, Gemella spp., H. hathewayi, and P. micra may contribute to both biofilm formation along CRC lesions and to disease progression.

Faecal metabolome and its determinants in inflammatory bowel disease

OBJECTIVE

Inflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohn's disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD.

DESIGN

We measured 1684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 patients with IBD and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participant's lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels.

RESULTS

We identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC=0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism.

CONCLUSION

In this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.

Distinct Signatures of Tumor-Associated Microbiota and Metabolome in Low-Grade vs. High-Grade Dysplastic Colon Polyps: Inference of Their Role in Tumor Initiation and Progression

According to the driver-passenger model for colorectal cancer (CRC), the tumor-associated microbiota is a dynamic ecosystem of bacterial species where bacteria with carcinogenic features linked to CRC initiation are defined as "drivers", while opportunistic bacteria colonizing more advanced tumor stages are known as "passengers". We reasoned that also gut microbiota-associated metabolites may be differentially enriched according to tumor stage, and be potential determinants of CRC development. Thus, we characterized the mucosa- and lumen-associated microbiota (MAM and LAM, respectively) and mucosa-associated metabolites in low- vs. high-grade dysplastic colon polyps from 78 patients. We show that MAM, obtained with a new biopsy-preserving approach, and LAM differ in composition and α/β-diversity. By stratifying patients for polyp histology, we found that bacteria proposed as passengers by previous studies colonized high-grade dysplastic adenomas, whereas driver taxa were enriched in low-grade polyps. Furthermore, we report altered "mucosa-associated metabolite" levels in low- vs. high-grade groups. Integrated microbiota-metabolome analysis suggests the involvement of the gut microbiota in the production and consumption of these metabolites. Altogether, our findings support the involvement of bacterial species and associated metabolites in CRC mucosal homeostasis in a tumor-stage-specific manner. These distinct signatures may be used to distinguish low-grade from high-grade dysplastic polyps.

Serum metabolic signatures for acute pulmonary embolism identified by untargeted metabolomics

Background and aims

The important metabolic features of acute pulmonary embolism (APE) risk stratification and their underlying biological basis remain elusive. Our study aims to develop early diagnostic models and classification models by analyzing the plasma metabolic profile of patients with APE.

Materials and methods

Serum samples were collected from 68 subjects, including 19 patients with confirmed APE, 35 patients with confirmed NSTEMI, and 14 healthy individuals. A comprehensive metabolic assessment was performed using ultra-performance liquid chromatography-mass spectrometry based on an untargeted metabolomics approach. In addition, an integrated machine learning strategy based on LASSO and logistic regression was used for feature selection and model building.

Results

The metabolic profiles of patients with acute pulmonary embolism and NSTEMI is significantly altered relative to that of healthy individuals. KEGG pathway enrichment analysis revealed differential metabolites between acute pulmonary embolism and healthy individuals mainly involving glycerophosphate shuttle, riboflavin metabolism, and glycerolipid metabolism. A panel of biomarkers was defined to distinguish acute pulmonary embolism, NSTEMI, and healthy individuals with an area under the receiver operating characteristic curve exceeding 0.9 and higher than that of D-dimers.

Conclusion

This study contributes to a better understanding of the pathogenesis of APE and facilitates the discovery of new therapeutic targets. The metabolite panel can be used as a potential non-invasive diagnostic and risk stratification tool for APE.

Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer

BACKGROUND AND AIMS

The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes.

METHODS

A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months.

RESULTS

Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (P_FDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (P_FDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (P_FDR = 2.61 × 10^-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (P_FDR = 1.30 × 10^-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7.

CONCLUSIONS

Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.

Increased ENT2 expression and its association with altered purine metabolism in cell lines derived from different stages of colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent malignant cancer types worldwide. Although the purine metabolism pathway is vital for cancer cell survival, little is known about the role of equilibrative nucleoside transporter 2 (ENT2) in CRC development and its association with purine metabolites. The aim of the present study was to evaluate the levels of hypoxanthine phosphoribosyl transferase (HPRT), hypoxanthine and uric acid (UA), as well as xanthine oxidase (XO) activity, and investigate their association with ENT2 expression levels in a normal human colon cell line and CRC cell lines derived from different stages of CRC. These analyses were performed using the normal colon CCD-841CoN cell line and a panel of human CRC cell lines comprising SW480, HCT15 and HCT116, which represent Dukes' B, C and D stages, respectively. Reverse transcription-quantitative PCR was performed to determine the level of ENT2 mRNA expression. In cells of all CRC stages, the levels of HPRT and hypoxanthine were significantly higher (P<0.05), while XO activity and UA levels were significantly decreased (P<0.05), compared with those in the CCD-841CoN cell line. ENT2 expression was found to be elevated in cells derived from all stages of CRC. The Dukes' D stage cell line had higher levels of HPRT and hypoxanthine, although its ENT2 level was not significantly lower than that of the Dukes' B and C stage cell lines. Increased levels of HPRT and hypoxanthine in various stages of CRC may indicate an increase in the activity of the salvage pathway. The increased expression of ENT2 implies the importance of the ENT2 protein in facilitating hypoxanthine transport, which is required for enhanced DNA synthesis via hypoxanthine recycling. In conclusion, ENT2 may have potential as a target in the development of CRC therapeutics.

Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers

BACKGROUND AND AIMS

Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current "gold standard" techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management.

METHODS

Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity).

RESULTS

GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions.

CONCLUSION

Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research.

Implication of Obesity and Gut Microbiome Dysbiosis in the Etiology of Colorectal Cancer

The complexity and variety of gut microbiomes within and among individuals have been extensively studied in recent years in connection to human health and diseases. Our growing understanding of the bidirectional communication between metabolic diseases and the gut microbiome has also highlighted the significance of gut microbiome dysbiosis in the genesis and development of obesity-related cancers. Therefore, it is crucial to comprehend the possible role of the gut microbiota in the crosstalk between obesity and colorectal cancer (CRC). Through the induction of gut microbial dysbiosis, gut epithelial barrier impairment, metabolomic dysregulation, chronic inflammation, or dysregulation in energy harvesting, obesity may promote the development of colorectal tumors. It is well known that strategies for cancer prevention and treatment are most effective when combined with a healthy diet, physical activity, and active lifestyle choices. Recent studies also suggest that an improved understanding of the complex linkages between the gut microbiome and various cancers as well as metabolic diseases can potentially improve cancer treatments and overall outcomes. In this context, we herein review and summarize the clinical and experimental evidence supporting the functional role of the gut microbiome in the pathogenesis and progression of CRC concerning obesity and its metabolic correlates, which may pave the way for the development of novel prognostic tools for CRC prevention. Therapeutic approaches for restoring the microbiome homeostasis in conjunction with cancer treatments are also discussed herein.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer

BACKGROUND

Multiple primary lung cancer (MPLC) is becoming increasingly common in clinical practice. Imaging examination is sometimes difficult to differentiate from intrapulmonary metastasis (IM) or single primary lung cancer (SPLC) before surgery. There is a lack of effective blood biomarkers as an auxiliary diagnostic method.

PARTICIPANTS AND METHODS

A total of 179 patients who were hospitalized and operated in our department from January to June 2019 were collected, and they were divided into SPLC with 136 patients, MPLC with 24 patients, and IM with 19 patients. In total, 96 healthy people without lung cancer were enrolled. Medical history, imaging, and pathology data were assembled from all participants. Plasma metabolomics analysis was performed by quadrupole time-of-flight tandem mass spectrometry, and data were analyzed using SPSS19.0/Simca 14.1/MetaboAnalyst5.0 software. Significant metabolites were selected by variable importance in projection, P value, and fold change. The area under the receiver operating characteristic curve was used to evaluate their diagnostic ability.

RESULTS

There were significant differences in plasma metabolite profiles between IM and MPLC. Seven metabolites were screened out. Two metabolites had higher levels in IM, and five metabolites had higher levels in MPLC. All had favorable discriminating capacity. Phosphatidyl ethanolamine (38:5) showed the highest sensitivity (0.95) and specificity (0.92). It was followed by l -histidine with sensitivity 0.92 and specificity 0.84. l -tyrosine can be used to identify SPLC and MPLC. The panel composed of related metabolites exhibited higher diagnostic ability. Eight principal metabolites caused remarkable differences between healthy people and MPLC, and five of them had area under the curves greater than 0.85, showing good discriminating power.

CONCLUSION

Through the study of plasma metabolomics, it was found that there were obvious differences in the metabolite profiles of MPLC, IM, SPLC, and the healthy population. Some discovered metabolites possessed excellent diagnostic competence with high sensitivity and specificity. They had the potential to act as biomarkers for the screening and differential diagnosis of MPLCs.

Metabolomic Analyses to Identify Candidate Biomarkers of Cystinosis

Cystinosis is a rare, devastating hereditary disease secondary to recessive CTNS gene mutations. The most commonly used diagnostic method is confirmation of an elevated leukocyte cystine level; however, this method is expensive and difficult to perform. This study aimed to identify candidate biomarkers for the diagnosis and follow-up of cystinosis based on multiomics studies. The study included three groups: newly-diagnosed cystinosis patients (patient group, n = 14); cystinosis patients under treatment (treatment group, n = 19); and healthy controls (control group, n = 30). Plasma metabolomics analysis identified 10 metabolites as candidate biomarkers that differed between the patient and control groups [L-serine, taurine, lyxose, 4-trimethylammoniobutanoic acid, orotic acid, glutathione, PE(O-18:1(9Z)/0:0), 2-hydroxyphenyl acetic acid, acetyl-N-formil-5-metoxikinuramine, 3-indoxyl sulphate]. As compared to the healthy control group, in the treatment group, hypotaurine, phosphatidylethanolamine, N-acetyl-d-mannosamine, 3-indolacetic acid, p-cresol, phenylethylamine, 5-aminovaleric acid, glycine, creatinine, and saccharic acid levels were significantly higher, and the metabolites quinic acid, capric acid, lenticin, xanthotoxin, glucose-6-phosphate, taurine, uric acid, glyceric acid, alpha-D-glucosamine phosphate, and serine levels were significantly lower. Urinary metabolomic analysis clearly differentiated the patient group from the control group by means of higher allo-inositol, talose, glucose, 2-hydroxybutiric acid, cystine, pyruvic acid, valine, and phenylalanine levels, and lower metabolite (N-acetyl-L-glutamic acid, 3-aminopropionitrile, ribitol, hydroquinone, glucuronic acid, 3-phosphoglycerate, xanthine, creatinine, and 5-aminovaleric acid) levels in the patient group. Urine metabolites were also found to be significantly different in the treatment group than in the control group. Thus, this study identified candidate biomarkers that could be used for the diagnosis and follow-up of cystinosis.

Metabolic profiling reveals metabolic features of consolidation therapy in pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) and its treatment continue to pose substantial risks. To understand ALL more deeply, the metabolome in fasting plasma of 27 ALL patients before and after high-dose methotrexate therapies (consolidation therapy) including methotrexate and 6-mercaptopurine (6-MP) was investigated. Plasma metabolites were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS). Orthogonal projections to latent structures discriminant analysis and significance analysis of microarrays were used to evaluate the metabolic changes. Pathway enrichment and co-expression network analyses were performed to identify clusters of molecules, and 2826 metabolites were identified. Among them, 38 metabolites were identified by univariate analysis, and 7 metabolites that were altered by conditioning therapy were identified by multivariate analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used for pathway enrichment analysis. Among the enriched KEGG pathways, the 3 significantly altered metabolic pathways were pyrimidine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; and phenylalanine metabolism. In addition, L-phenylalanine was significantly correlated with blood urea nitrogen (BUN), and palmitoylcarnitine was correlated with aspartate aminotransferase (AST). In summary, consolidation therapy significantly affected pyrimidine- and phenylalanine-associated metabolic pathways in pediatric ALL patients. These findings may provide an insight into the role of metabolic profiling in consolidation treatment and as a potential for pediatric ALL patients.

Microbial metabolites in colorectal tumorigenesis and cancer therapy

Trillions of microbes are indigenous to the human gastrointestinal tract, together forming an ecological community known as the gut microbiota. The gut microbiota is involved in dietary digestion to produce various metabolites. In healthy condition, microbial metabolites have unneglectable roles in regulating host physiology and intestinal homeostasis. However, increasing studies have reported the correlation between metabolites and the development of colorectal cancer (CRC), with the identification of oncometabolites. Meanwhile, metabolites can also influence the efficacy of cancer treatments. In this review, metabolites derived from microbes-mediated metabolism of dietary carbohydrates, proteins, and cholesterol, are introduced. The roles of pro-tumorigenic (secondary bile acids and polyamines) and anti-tumorigenic (short-chain fatty acids and indole derivatives) metabolites in CRC development are then discussed. The impacts of metabolites on chemotherapy and immunotherapy are further elucidated. Collectively, given the importance of microbial metabolites in CRC, therapeutic approaches that target metabolites may be promising to improve patient outcome.

Systematic Review: Contribution of the Gut Microbiome to the Volatile Metabolic Fingerprint of Colorectal Neoplasia

Colorectal cancer (CRC) has been associated with changes in volatile metabolic profiles in several human biological matrices. This enables its non-invasive detection, but the origin of these volatile organic compounds (VOCs) and their relation to the gut microbiome are not yet fully understood. This systematic review provides an overview of the current understanding of this topic. A systematic search using PubMed, Embase, Medline, Cochrane Library, and the Web of Science according to PRISMA guidelines resulted in seventy-one included studies. In addition, a systematic search was conducted that identified five systematic reviews from which CRC-associated gut microbiota data were extracted. The included studies analyzed VOCs in feces, urine, breath, blood, tissue, and saliva. Eight studies performed microbiota analysis in addition to VOC analysis. The most frequently reported dysregulations over all matrices included short-chain fatty acids, amino acids, proteolytic fermentation products, and products related to the tricarboxylic acid cycle and Warburg metabolism. Many of these dysregulations could be related to the shifts in CRC-associated microbiota, and thus the gut microbiota presumably contributes to the metabolic fingerprint of VOC in CRC. Future research involving VOCs analysis should include simultaneous gut microbiota analysis.

Biochemical alterations in the follicular fluid of bovine peri-ovulatory follicles and association with final oocyte maturation

Follicular fluid (FF), a product of vascular transudate and granulosa and thecal cell secretions, is the milieu that has evolved to support oocyte growth and maturation which plays a central role in oocyte quality determination. Therefore, a suboptimal FF composition may be reflected in compromised oocyte progression through maturation, fertilization or embryo development. To date, the composition of bovine FF remains understudied. To address this, we comprehensively characterized the metabolomic constituency of bovine FF in the period during which the oocyte undergoes meiotic maturation. More specifically, FF from pre (-24 h) and peri (-2 h) -ovulatory follicles was profiled by high-throughput untargeted ultra-high-performance liquid chromatography tandem mass spectroscopy. A total of 634 metabolites were identified, comprising: lipids (37.1%), amino acids (30.0%), xenobiotics (11.5%), nucleotides (6.8%), carbohydrates (4.4%), cofactors and vitamins (4.4%), peptides (3.6%) and energy substrates (2.1%). The concentrations of 67 metabolites were significantly affected by stage of follicle development, 33.3% (n=21) were reduced (P≤0.05) by a mean of 9.0-fold, whereas 46 were elevated (P≤0.05) by a mean of 1.7-fold in peri vs. pre -ovulatory FF. The most pronounced individual metabolite concentration decreases were hypoxanthine (98.9-fold), xanthine (65.7-fold), 17β-oestradiol (12.4-fold), and inosine (4.6-fold). In contrast, the greatest increases were in retinal (4.9-fold), 1-methyl-5-imidazoleacetate (2.7-fold), and isovalerylcarnitine (2.7-fold). This global metabolomic analysis of bovine FF temporal dynamics provides new information for understanding the environment supporting oocyte maturation and facilitating ovulation, that has the potential for improving oocyte quality both in vivo and in vitro.

Metagenomics and metabolomics analysis to investigate the effect of Shugan decoction on intestinal microbiota in irritable bowel syndrome rats

BACKGROUND

The effect of Shugan Decoction (SGD) on intestinal motility and visceral hypersensitivity in Water avoid stress (WAS)-induced diarrhea predominant irritable bowel syndrome (IBS-D) model rats has been confirmed. However, the mechanisms of its action involved in the treatment of IBS-D need to be further studied. Intestinal microbiota plays an important role in maintaining intestinal homeostasis and normal physiological function. Changes in the intestinal microbiota and its metabolites are thought to participate in the pathophysiological process of IBS.

AIM

This study aimed to analyze the influence of SGD on intestinal microbiota and fecal metabolites in IBS-D rats by multiple omics techniques, including metagenomic sequencing and metabolomics.

METHODS

We measured the intestinal motility and visceral sensitivity of three groups of rats by fecal pellets output and colorectal distension (CRD) experiment. In addition, metagenome sequencing analysis was performed to explore the changes in the number and types of intestinal microbiota in IBS-D model rats after SGD treatment. Finally, we also used untargeted metabolomic sequencing to screen the metabolites and metabolic pathways closely related to the therapeutic effect of SGD.

RESULTS

We found that compared with the rats in the control group, the fecal pellets output of the rats in the WAS group increased and the visceral sensitivity threshold was decreased (P < 0.05). Compared with the rats in the WAS group, the fecal pellets output of the SGD group was significantly decreased, and the visceral sensitivity threshold increased (P < 0.05). Besides, compared with the rats in the WAS group, the relative abundance of Bacteroidetes increased in SGD group, while that of Firmicutes decreased at the phylum level, and at the species level, the relative abundance of Bacteroides sp. CAG:714, Lactobacillus reuteri and Bacteroides Barnesiae in SGD group increased, but that of bacterium D42-87 decreased. In addition, compared with the WAS group, several metabolic pathways were significantly changed in SGD group, including Taurine and hypotaurine metabolism, Purine metabolism, Sulfur metabolism, ABC transporters, Arginine and proline metabolism and Bile secretion.

CONCLUSION

SGD can regulate specific intestinal microbiota and some metabolic pathways, which may explain its effect of alleviating visceral hypersensitivity and abnormal intestinal motility in WAS-induced IBS-D rats.

The metabolomic differential plasma profile between dialysates. Pursuing to understand the mechanisms of citrate dialysate clinical benefits

Background: Currently, bicarbonate-based dialysate needs a buffer to prevent precipitation of bicarbonate salts with the bivalent cations, and acetate at 3-4 mmol/L is the most used. However, citrate is being postulated as a preferred option because of its association with better clinical results by poorly understood mechanisms. In that sense, this hypothesis-generating study aims to identify potential metabolites that could biologically explain these improvements found in patients using citrate dialysate. Methods: A unicentric, cross-over, prospective untargeted metabolomics study was designed to analyze the differences between two dialysates only differing in their buffer, one containing 4 mmol/L of acetate (AD) and the other 1 mmol/L of citrate (CD). Blood samples were collected in four moments (i.e., pre-, mid-, post-, and 30-min-post-dialysis) and analyzed in an untargeted metabolomics approach based on UPLC-Q-ToF mass spectrometry. Results: The 31 most discriminant metabolomic variables from the plasma samples of the 21 participants screened by their potential clinical implications show that, after dialysis with CD, some uremic toxins appear to be better cleared, the lysine degradation pathway is affected, and branched-chain amino acids post-dialysis levels are 9-10 times higher than with AD; and, on its part, dialysis with AD affects acylcarnitine clearance. Conclusion: Although most metabolic changes seen in this study could be attributable to the dialysis treatment itself, this study successfully identifies some metabolic variables that differ between CD and AD, which raise new hypotheses that may unveil the mechanisms involved in the clinical improvements observed with citrate in future research.

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments.

Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography–mass spectrometry (LC–MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls

BACKGROUND

Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development.

METHODS

Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates.

RESULTS

A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5'-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol.

CONCLUSIONS

Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.

Dietary Energy Intake and Presence of Aberrant Crypt Foci Are Associated with Phospholipid, Purine, and Taurine Metabolite Abundances in C57BL/6N Mouse Colon

SCOPE

Colon metabolomes associated with high-fat (H) versus energy-restricted (E) diets in early colorectal cancer (CRC) models have never been directly compared. The objectives of this study are to elucidate metabolites associated with diet, aberrant crypt foci (ACF), and diet:ACF interaction, using a lifetime murine model.

METHODS AND RESULTS

Three-week-old mice consumed control (C), E, or H initiation diets for 18 weeks. ACF formation is initiated weeks 16-21 with azoxymethane injections, followed by progression diet crossover (to C, E, or H) through week 60. Colon extracts are analyzed using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Metabolites associated with diet, ACF, or diet:ACF are determined using regression models (FDR-adjusted p-value <0.05). No metabolites are significantly associated with initiation diets, but concentrations of acylcarnitines and phospholipids are associated with C, E, and H progression diets. Purines, taurine, and phospholipids are associated with ACF presence. No significant associations between metabolites and diet:ACF interaction are observed.

CONCLUSIONS

These results suggest that recent, rather than early-life, diet is more closely associated with the colon metabolome, particularly lipid metabolism. Results from this study also provide candidate biomarkers of early CRC development and provide support for the importance of early diet on influencing pre-CRC risk.