Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS)

Black raspberry-mediated metabolic changes in patients with familial adenomatous polyposis associated with rectal polyp regression

Familial adenomatous polyposis (FAP) patients face an almost certain 100% risk of developing colorectal cancer, necessitating prophylactic colectomy to prevent disease progression. A crucial goal is to hinder this progression. In a recent clinical trial involving 14 FAP patients, half received 60 g of black raspberry (BRB) powder orally and BRB suppositories at bedtime, while the other half received only BRB suppositories at bedtime over 9 months. This intervention led to a notable reduction in rectal polyps for 11 patients, although 3 showed no response. In this study, we delved into the metabolic changes induced by BRBs in the same patient cohort. Employing mass spectrometry-based non-targeted metabolomics, we analyzed pre- and post-BRB urinary and plasma samples from the 11 responders. The results showed significant alterations in 23 urinary and 6 plasma metabolites, influencing various pathways including polyamine, glutathione metabolism, the tricarboxylic acid cycle, inositol metabolism, and benzoate production. BRBs notably elevated levels of several metabolites associated with these pathways, suggesting a potential mechanism through which BRBs facilitate rectal polyp regression in FAP patients by modulating multiple metabolic pathways. Notably, metabolites derived from BRB polyphenols were significantly increased post-BRB intervention, emphasizing the potential therapeutic value of BRBs in FAP management.

Metabolic Signatures: Pioneering the Frontier of Rectal Cancer Diagnosis and Response to Neoadjuvant Treatment with Biomarkers-A Systematic Review

Colorectal cancer (CRC) is one of the most aggressive, heterogenous, and fatal types of human cancer for which screening, and more effective therapeutic drugs are urgently needed. Early-stage detection and treatment greatly improve the 5-year survival rate. In the era of targeted therapies for all types of cancer, a complete metabolomic profile is mandatory before neoadjuvant therapy to assign the correct drugs and check the response to the treatment given. The aim of this study is to discover specific metabolic biomarkers or a sequence of metabolomic indicators that possess precise diagnostic capabilities in predicting the efficacy of neoadjuvant therapy. After searching the keywords, a total of 108 articles were identified during a timeframe of 10 years (2013-2023). Within this set, one article was excluded due to the use of non-English language. Six scientific papers were qualified for this investigation after eliminating all duplicates, publications not referring to the subject matter, open access restriction papers, and those not applicable to humans. Biomolecular analysis found a correlation between metabolomic analysis of colorectal cancer samples and poor progression-free survival rates. Biomarkers are instrumental in predicting a patient's response to specific treatments, guiding the selection of targeted therapies, and indicating resistance to certain drugs.

Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients

Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.

Research progress of metabolomics in cervical cancer

INTRODUCTION

Cervical cancer threatens women's health seriously. In recent years, the incidence of cervical cancer is on the rise, and the age of onset tends to be younger. Prevention, early diagnosis and specific treatment have become the main means to change the prognosis of cervical cancer patients. Metabolomics research can directly reflect the changes of biochemical processes and microenvironment in the body, which can provide a comprehensive understanding of the changes of metabolites in the process of disease occurrence and development, and provide new ways for the prevention and diagnosis of diseases.

OBJECTIVES

The aim of this study is to review the metabolic changes in cervical cancer and the application of metabolomics in the diagnosis and treatment.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2022.

RESULTS

With the emergence of metabolomics, metabolic regulation and cancer research are further becoming a focus of attention. By directly reflecting the changes in the microenvironment of the body, metabolomics research can provide a comprehensive understanding of the patterns of metabolites in the occurrence and development of diseases, thus providing new ideas for disease prevention and diagnosis.

CONCLUSION

With the continuous, in-depth research on metabolomics research technology, it will bring more benefits in the screening, diagnosis and treatment of cervical cancer with its advantages of holistic and dynamic nature.

NMR-Based Mitochondria Metabolomic Profiling: A New Approach To Reveal Cancer-Associated Alterations

Studying metabolism may assist in understanding the relationship between normal and dysfunctional mitochondrial activity and various diseases, such as neurodegenerative, cardiovascular, autoimmune, psychiatric, and cancer. Nuclear magnetic resonance-based metabolomics represents a powerful method to characterize the chemical content of complex samples and has been successfully applied to studying a range of conditions. However, an optimized methodology is lacking for analyzing isolated organelles, such as mitochondria. In this study, we report the development of a protocol to metabolically profile mitochondria from healthy, tumoral, and metastatic tissues. Encouragingly, this approach provided quantitative information about up to 45 metabolites in one comprehensive and robust analysis. Our results revealed significant differences between whole-cell and mitochondrial metabolites, which supports a more refined approach to metabolic analysis. We applied our optimized methodology to investigate aggressive and metastatic breast cancer in mouse tissues, discovering that lung mitochondria exhibit an altered metabolic fingerprint. Specific amino acids, organic acids, and lipids showed significant increases in levels when compared with mitochondria from healthy tissues. Our optimized methodology could promote a better understanding of the molecular mechanisms underlying breast cancer aggressiveness and mitochondrial-related diseases and support the optimization of new advanced therapies.

Differences in the metabolomic profile of the human palatine tonsil between pediatrics and adults

Palatine tonsils (PT) are B cell-predominant lymphoid organs that provide primary immune responses to airborne and dietary pathogens. Numerous histopathological and immunological studies have been conducted on PT, yet no investigations have been conducted on its metabolic profile. We performed high-resolution magic angle spinning nuclear magnetic resonance spectroscopy-based metabolic profiling in 35 pediatric and 28 adult human palatine tonsillar tissue samples. A total of 36 metabolites were identified, and the levels of 10 metabolites were significantly different depending on age. Among them, partial correlation analysis shows that glucose levels increased with age, whereas glycine, phosphocholine, phosphoethanolamine, and ascorbate levels decreased with age. We confirmed the decrease in immunometabolic activity in adults through metabolomic analysis, which had been anticipated from previous histological and immunological studies on the PT. These results improve our understanding of metabolic changes in the PT with aging and serve as a basis for future tonsil-related metabolomic studies.

Metabolic Regulation of Copper Toxicity during Marine Mussel Embryogenesis

The development of new tools for assessing the health of cultured shellfish larvae is crucial for aquaculture industries to develop and refine hatchery methodologies. We established a large-volume ecotoxicology/health stressor trial, exposing mussel (Perna canaliculus) embryos to copper in the presence of ethylenediaminetetraacetic acid (EDTA). GC/MS-based metabolomics was applied to identify potential biomarkers for monitoring embryonic/larval health and to characterise mechanisms of metal toxicity. Cellular viability, developmental abnormalities, larval behaviour, mortality, and a targeted analysis of proteins involved in the regulation of reactive oxygen species were simultaneously evaluated to provide a complementary framework for interpretative purposes and authenticate the metabolomics data. Trace metal analysis and speciation modelling verified EDTA as an effective copper chelator. Toxicity thresholds for P. canaliculus were low, with 10% developmental abnormalities in D-stage larvae being recorded upon exposure to 1.10 μg·L-1 bioavailable copper for 66 h. Sublethal levels of bioavailable copper (0.04 and 1.10 μg·L-1) caused coordinated fluctuations in metabolite profiles, which were dependent on development stage, treatment level, and exposure duration. Larvae appeared to successfully employ various mechanisms involving the biosynthesis of antioxidants and a restructuring of energy-related metabolism to alleviate the toxic effects of copper on cells and developing tissues. These results suggest that regulation of trace metal-induced toxicity is tightly linked with metabolism during the early ontogenic development of marine mussels. Lethal-level bioavailable copper (50.3 μg·L-1) caused severe metabolic dysregulation after 3 h of exposure, which worsened with time, substantially delayed embryonic development, induced critical oxidative damage, initiated the apoptotic pathway, and resulted in cell/organism death shortly after 18 h of exposure. Metabolite profiling is a useful approach to (1) assess the health status of marine invertebrate embryos and larvae, (2) detect early warning biomarkers for trace metal contamination, and (3) identify novel regulatory mechanisms of copper-induced toxicity.

LC-MS-based serum metabolomics analysis for the screening and monitoring of colorectal cancer

Background

Colorectal Cancer (CRC) is a prevalent digestive system tumour with significant mortality and recurrence rates. Serum metabolomics, with its high sensitivity and high throughput, has shown potential as a tool to discover biomarkers for clinical screening and monitoring of the CRC patients.

Methods

Serum metabolites of 61 sex and age-matched healthy controls and 62 CRC patients (before and after surgical intervention) were analyzed using a ultra-performance liquid chromatography-high resolution mass spectrometer (UPLC-MS). Statistical methods and pathway enrichment analysis were used to identify potential biomarkers and altered metabolic pathways.

Results

Our analysis revealed a clear distinction in the serum metabolic profile between CRC patients and healthy controls (HCs). Pathway analysis indicated a significant association with arginine biosynthesis, pyrimidine metabolism, pantothenate, and CoA biosynthesis. Univariate and multivariate statistical analysis showed that 9 metabolites had significant diagnostic value for CRC, among them, Guanosine with Area Under the Curve (AUC) values of 0.951 for the training group and0.998 for the validation group. Furthermore, analysis of four specific metabolites (N-Phenylacetylasparticacid, Tyrosyl-Gamma-glutamate, Tyr-Ser and Sphingosine) in serum samples of CRC patients before and after surgery indicated a return to healthy levels after an intervention.

Conclusion

Our results suggest that serum metabolomics may be a valuable tool for the screening and monitoring of CRC patients.

Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Background

Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated.

Methods

By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms.

Results

GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential.

Conclusion

In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

Urinary volatile organic compounds for colorectal cancer screening, a systematic review and meta-analysis

BACKGROUND

The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia.

METHODS

A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database.

RESULTS

Sixteen studies-involving 837 CRC patients and 1618 controls-were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73-91%) and 70% (95% CI 63-77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied.

CONCLUSION

Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes.

Warburg Effect as a Novel Mechanism for Homocysteine-Induced Features of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a major cause of blindness. Recent studies have reported impaired glycolysis in AMD patients with a high lactate/pyruvate ratio. Elevated homocysteine (Hcy) (Hyperhomocysteinemia, HHcy) was observed in several clinical studies, reporting an association between HHcy and AMD. We established the effect of HHcy on barrier function, retinal pigment epithelium (RPE) structure, and induced choroidal neovascularization (CNV) in mice. We hypothesize that HHcy contributes to AMD by inducing a metabolic switch in the mitochondria, in which cells predominantly produce energy by the high rate of glycolysis, or "Warburg", effect. Increased glycolysis results in an increased production of lactate, cellular acidity, activation of angiogenesis, RPE barrier dysfunction, and CNV. Evaluation of cellular energy production under HHcy was assessed by seahorse analysis, immunofluorescence, and western blot experiments. The seahorse analysis evaluated the extracellular acidification rate (ECAR) as indicative of glycolysis. HHcy showed a significant increase in ECAR both in vivo using (Cystathionine β-synthase) cbs^+/- and cbs^-/- mice retinas and in vitro (Hcy-treated ARPE-19) compared to wild-type mice and RPE cells. Moreover, HHcy up-regulated glycolytic enzyme (Glucose transporter-1 (GlUT-1), lactate dehydrogenase (LDH), and hexokinase 1 (HK1)) in Hcy-treated ARPE-19 and primary RPE cells isolated from cbs^+/+, cbs^+/-, and cbs^-/- mice retinas. Inhibition of GLUT-1 or blocking of N-methyl-D-aspartate receptors (NMDAR) reduced glycolysis in Hcy-treated RPE and improved albumin leakage and CNV induction in Hcy-injected mice eyes. The current study suggests that HHcy causes a metabolic switch in the RPE cells from mitochondrial respiration to glycolysis during AMD and confirms the involvement of NMDAR in this process. Therefore, targeting Glycolysis or NMDAR could be a novel therapeutic target for AMD.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Relationship between [18F]FDG PET/CT and metabolomics in patients with colorectal cancer

INTRODUCTION

Advances in metabolomics have significantly improved cancer detection, diagnosis, treatment, and prognosis.

OBJECTIVES

To investigate the relationship between metabolic tumor volume (MTV) using 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG) positron emission tomography (PET)/ computed tomography (CT) and metabolomics data in patients with colorectal cancer (CRC).

METHODS

The metabolome in tumor tissues was analyzed using capillary electrophoresis time-of-flight mass spectrometry in 33 patients with newly diagnosed CRC who underwent FDG PET/CT before treatment and had tumor tissue post-surgery. Based on the FDG PET data, MTV was calculated and was dichotomized according to the median value, and tumors were divided into low-MTV and high-MTV tumors. Metabolomics data were compared between the low-MTV and high-MTV tumors.

RESULTS

The levels of most glycolysis-related metabolites were not different between low-MTV and high-MTV tumors. The level of component of the initial part of the tricarboxylic acid (TCA) cycle, citrate, was significantly lower in the high-MTV tumor than in the low-MTV tumor. The TCA intermediate succinate level was significantly higher in the high-MTV tumor than in the low-MTV tumor. In contrast, the TCA intermediate fumarate level was significantly lower in the high-MTV tumor than in the low-MTV tumor. The levels of many amino acids were significantly higher in the high-MTV tumor than in the low-MTV tumor.

CONCLUSIONS

Although preliminary, these results suggest that tumors with high FDG metabolism in CRC may obtain more energy by using a reverse reaction of the TCA cycle and amino-acid metabolism. However, further research is required to clarify this relationship.

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.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Distinct Urinary Metabolic Biomarkers of Human Colorectal Cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with high mortality rate due to its poor diagnosis in the early stage. Here, we report a urinary metabolomic study on a cohort of CRC patients (n =67) and healthy controls (n =21) using ultraperformance liquid chromatography triple quadrupole mass spectrometry. Pathway analysis showed that a series of pathways that belong to amino acid metabolism, carbohydrate metabolism, and lipid metabolism were dysregulated, for instance the glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, glycolysis, and TCA cycle. A total of 48 differential metabolites were identified in CRC compared to controls. A panel of 12 biomarkers composed of chenodeoxycholic acid, vanillic acid, adenosine monophosphate, glycolic acid, histidine, azelaic acid, hydroxypropionic acid, glycine, 3,4-dihydroxymandelic acid, 4-hydroxybenzoic acid, oxoglutaric acid, and homocitrulline were identified by Random Forest (RF), Support Vector Machine (SVM), and Boruta analysis classification model and validated by Gradient Boosting (GB), Logistic Regression (LR), and Random Forest diagnostic model, which were able to discriminate CRC subjects from healthy controls. These urinary metabolic biomarkers provided a novel and promising molecular approach for the early diagnosis of CRC.

Fusobacterium nucleatum Affects Cell Apoptosis by Regulating Intestinal Flora and Metabolites to Promote the Development of Colorectal Cancer

Background/Aims

Intestinal flora, especially Fusobacterium nucleatum (Fn), can affect the development of colorectal cancer (CRC). In this study, we examined the composition of intestinal flora and their metabolites in the tissues, serum and feces of CRC patients.

Materials and Methods

CRC tissues, adjacent normal colonic tissues, fecal and serum samples were collected from CRC patients who received surgical treatment between January 2018 and January 2020. Fecal and serum samples were collected from healthy individuals for comparison. In addition, fecal samples were collected from BALB/c female mice. SW480, a human CRC cell line, was utilized for in vitro studies. The experiments involved ¹H-NMR-based metabolomics analysis, targeted and untargeted mass spectrometry analysis, and intestinal flora 16S rDNA V4 region sequencing.

Results

The abundance of Bacteroides and propionic acid concentration were decreased and that of Lactobacillus and lactic acid concentration were increased in CRC tissues. In addition, the abundances of Ruminococcus, Prevotella, and Sutterell were decreased in CRC patients. The levels of leucine and isoleucine were decreased in the serum and tumor tissues of CRC patients. Aspartate, glutamate and glutathione levels were elevated in the tissues of CRC patients only. The serum glutamine, tyrosine, valine, alanine, and histidine levels were decreased significantly. Lactic acid inhibited and propionic acid promoted apoptosis among SW480 CRC cells.

Conclusion

Fn affected the apoptosis of CRC cells and promoted the progression of CRC by affecting the distribution of intestinal flora, which altered the concentrations of metabolites such as lactic acid, propionic acid. Intestinal flora could regulate amino acid metabolism.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Exopolysaccharide Produced by Lactiplantibacillus plantarum-12 Alleviates Intestinal Inflammation and Colon Cancer Symptoms by Modulating the Gut Microbiome and Metabolites of C57BL/6 Mice Treated by Azoxymethane/Dextran Sulfate Sodium Salt

Exopolysaccharide produced by Lactiplantibacillus plantarum-12 (LPEPS) exhibited the anti-proliferating effect on human colon cancer cell line HT-29 in vitro. The purpose of the study was to determine the alleviating effects of LPEPS on colon cancer development of the C57BL/6 mice treated by azoxymethane/dextran sulfate sodium salt (AOM/DSS). The C57BL/6 mice treated by AOM/DSS were orally administered LPEPS daily for 85 days. The results showed that LPEPS oral administration enhanced colon tight-junction protein expression and ameliorated colon shortening and tumor burden of the AOM/DSS treated mice. Furthermore, LPEPS oral administration significantly reduced pro-inflammatory factors TNF-α, IL-8, and IL-1β levels and increased anti-inflammatory factor IL-10 level in the serum of the AOM/DSS-treated mice. LPEPS oral administration reversed the alterations of gut flora in AOM/DSS-treated mice, as evidenced by the increasing of the abundance of Bacteroidetes, Bacteroidetes/Firmicutes ratio, Muribaculaceae, Burkholderiaceae, and norank_o__Rhodospirillales and the decreasing of the abundance of Firmicutes, Desulfovibrionaceae, Erysipelotrichaceae, and Helicobacteraceae. The fecal metabolites of the AOM/DSS-treated mice were altered by LPEPS oral administration, involving lipid metabolism and amino acid metabolism. Together, these results suggested that LPEPS oral administration alleviated AOM/DSS-induced colon cancer symptoms of the C57BL/6 mice by modulating gut microbiota and metabolites, enhancing intestine barrier, inhibiting NF-κB pathway, and activating caspase cascade.

Microbiome-Mediated Immune Signaling in Inflammatory Bowel Disease and Colorectal Cancer: Support From Meta-omics Data

Chronic intestinal inflammation and microbial dysbiosis are hallmarks of colorectal cancer (CRC) and inflammatory bowel diseases (IBD), such as Crohn’s disease and ulcerative colitis. However, the mechanistic relationship between gut dysbiosis and disease has not yet been fully characterized. Although the “trigger” of intestinal inflammation remains unknown, a wealth of evidence supports the role of the gut microbiome as a mutualistic pseudo-organ that significantly influences intestinal homeostasis and is capable of regulating host immunity. In recent years, culture-independent methods for assessing microbial communities as a whole (termed meta-omics) have grown beyond taxonomic identification and genome characterization (metagenomics) into new fields of research that collectively expand our knowledge of microbiomes. Metatranscriptomics, metaproteomics, and metabolomics are meta-omics techniques that aim to describe and quantify the functional activity of the gut microbiome. Uncovering microbial metabolic contributions in the context of IBD and CRC using these approaches provides insight into how the metabolic microenvironment of the GI tract shapes microbial community structure and how the microbiome, in turn, influences the surrounding ecosystem. Immunological studies in germ-free and wild-type mice have described several host-microbiome interactions that may play a role in autoinflammation. Chronic colitis is a precursor to CRC, and changes in the gut microbiome may be an important link triggering the neoplastic process in chronic colitis. In this review, we describe several microbiome-mediated mechanisms of host immune signaling, such as short-chain fatty acid (SCFA) and bile acid metabolism, inflammasome activation, and cytokine regulation in the context of IBD and CRC, and discuss the supporting role for these mechanisms by meta-omics data.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Metabolic Reprogramming of Colorectal Cancer Cells and the Microenvironment: Implication for Therapy

Colorectal carcinoma (CRC) is one of the most frequently diagnosed carcinomas and one of the leading causes of cancer-related death worldwide. Metabolic reprogramming, a hallmark of cancer, is closely related to the initiation and progression of carcinomas, including CRC. Accumulating evidence shows that activation of oncogenic pathways and loss of tumor suppressor genes regulate the metabolic reprogramming that is mainly involved in glycolysis, glutaminolysis, one-carbon metabolism and lipid metabolism. The abnormal metabolic program provides tumor cells with abundant energy, nutrients and redox requirements to support their malignant growth and metastasis, which is accompanied by impaired metabolic flexibility in the tumor microenvironment (TME) and dysbiosis of the gut microbiota. The metabolic crosstalk between the tumor cells, the components of the TME and the intestinal microbiota further facilitates CRC cell proliferation, invasion and metastasis and leads to therapy resistance. Hence, to target the dysregulated tumor metabolism, the TME and the gut microbiota, novel preventive and therapeutic applications are required. In this review, the dysregulation of metabolic programs, molecular pathways, the TME and the intestinal microbiota in CRC is addressed. Possible therapeutic strategies, including metabolic inhibition and immune therapy in CRC, as well as modulation of the aberrant intestinal microbiota, are discussed.

Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

Metabolomics in endometriosis: challenges and perspectives for future studies

Endometriosis is a complex disease characterized by inflammation and the growth of endometrial- like glands and stroma outside the uterine cavity. The pathophysiology of endometriosis is not entirely understood, however, with a prevalence of ~10% of women in their reproductive years, the disease symptoms significantly affect the quality of life of millions of women globally. Metabolomic studies have previously identified specific metabolites that could be a signature of endometriosis. This approach could potentially be used as a non-invasive tool for early diagnosis and provide a better understanding of endometriosis pathophysiology. This review aims to provide insight as to how endometriosis affects the metabolome by reviewing different studies that have used this approach to design follow-up studies. The search query included the term 'endometriosis' in combination with 'metabolomics', 'lipidomics', or 'sphingolipidomics' published between 2012 and 2020. We included studies in humans and animal models. Most studies reported differences in the metabolome of subjects with endometriosis in comparison to healthy controls and used samples taken from serum, endometrial tissue, follicular fluid, urine, peritoneal fluid, or endometrial fluid. Statistically significant metabolites contributed to group separation between patients and healthy controls. Reported metabolites included amino acids, lipids, organic acids, and other organic compounds. Differences in methods, analytical techniques, and the presence of confounding factors can interfere with results and interpretation of data. Metabolomics seems to be a promising tool for identifying significant metabolites in patients with endometriosis. Nonetheless, more investigation is needed in order to understand the significance of the study results.

Lay summary

Endometriosis is a chronic disease affecting the quality of life in one out of every ten women during their reproductive years, causing pain and infertility. It is characterized by inflammation and growth of tissue like the endometrium (uterus lining) outside the uterine cavity. Studies have searched for a predictor of endometriosis-associated changes by observing small molecules necessary for metabolism on a large scale (metabolomics). Metabolomics could serve to resolve one of the biggest challenges that patients with endometriosis face: a delay in diagnosis. In this review, the authors summarize identified potential biomarkers from various bodily fluids and tissues that are characteristic of metabolic processes observed in endometriosis. Biomarkers include cell growth, cell survival, high energy demand, oxidative stress, and fatty acid levels. A metabolomics approach offers promise as a non-invasive tool to identify significant metabolite changes in patients with endometriosis, potentially leading to earlier diagnoses and new opportunities for back-translational strategies.

Lipid profiling of mouse intestinal organoids for studying APC mutations

Inactivating mutations including both germline and somatic mutations in the adenomatous polyposis coli (APC) gene drives most familial and sporadic colorectal cancers. Understanding the metabolic implications of this mutation will aid to establish its wider impact on cellular behaviour and potentially inform clinical decisions. However, to date, alterations in lipid metabolism induced by APC mutations remain unclear. Intestinal organoids have gained widespread popularity in studying colorectal cancer and chemotherapies, because their 3D structure more accurately mimics an in vivo environment. Here, we aimed to investigate intra-cellular lipid disturbances induced by APC gene mutations in intestinal organoids using a reversed-phase ultra-high-performance liquid chromatography mass spectrometry (RP-UHPLC-MS)-based lipid profiling method. Lipids of the organoids grown from either wild-type (WT) or mice with APC mutations (Lgr5-EGFP-IRES-CreERT2Apcfl/fl) were extracted and analysed using RP-UHPLC-MS. Levels of phospholipids (e.g. PC(16:0/16:0), PC(18:1/20:0), PC(38:0), PC(18:1/22:1)), ceramides (e.g. Cer(d18:0/22:0), Cer(d42:0), Cer(d18:1/24:1)) and hexosylceramides (e.g. HexCer(d18:1/16:0), HexCer(d18:1/22:0)) were higher in Apcfl/fl organoids, whereas levels of sphingomyelins (e.g. SM(d18:1/14:0), SM(d18:1/16:0)) were lower compared with WT. These observations indicate that cellular metabolism of sphingomyelin was up-regulated, resulting in the cellular accumulation of ceramides and production of HexCer due to the absence of Apcfl/fl in the organoids. Our observations demonstrated lipid profiling of organoids and provided an enhanced insight into the effects of the APC mutations on lipid metabolism, making for a valuable addition to screening options of the organoid lipidome.

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

Simple Summary

Changes in metabolism are a well-known characteristic of cancer cells. Different cancer types are unique in their genetic aspects, but also in their metabolism, which is in turn, governed by genetics. The aim of our study was to find these differences in metabolic behavior across different cancer types and uncovering intersections between gene expression and metabolic deregulations. We scoured the public domain for metabolomics and transcriptomics data from clinical profiling studies to perform a comprehensive comparison study. By combining evidence from both the genetic and the metabolic aspects, we described the most prominently aberrated pathways across eight different cancer types together with their metabolomic and transcriptomics similarities.

Abstract

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

Neutral Desorption Extractive Electrospray Ionization Mass Spectrometry Analysis Sputum for Non-Invasive Lung Adenocarcinoma Detection

Purpose

Increased use of low-dose spiral computed tomography (LDCT: low-dose computed tomography) screening has contributed to more frequent incidental detection of peripheral lung nodules, part of them were adenocarcinoma, which need to be further evaluated to establish a definitive diagnosis. Here, our primary objective was to evaluate the ambient mass spectrometry (AMS) sputum analysis as a non-invasive lung adenocarcinoma (LAC) diagnosis solution.

Patients and Methods

Neutral desorption extractive electrospray ionization mass spectrometry (ND-EESI-MS) and collision induced dissociation (CID) were used to detect sputum metabolites from 143 spontaneous sputum samples. Partial least squares-discriminant analysis (PLS-DA) was used to refine the biomarker panel, whereas orthogonal PLS-DA (OPLS-DA) was used to operationalize the enhanced biomarker panel for diagnosis.

Results

In this approach, 19 altered metabolites were detected by ND-EESI-MS from 76 cases of LAC and 67 cases of control. Significance testing and receiver operating characteristic (ROC) analysis identified 5 metabolites [hydroxyphenyllactic acid, phytosphingosine, N-nonanoylglycine, sphinganine, S-carboxymethyl-L-cysteine] with p <0.05 and AUC >0.75, respectively. Evaluation of model performance for prediction of LAC resulted in a cross-validation classification accuracy of 87.9%. Metabolic pathway analysis showed that sphingolipid metabolism, fatty acid metabolism, carnitine synthesis and Warburg effect were most impacted in response to disease.

Conclusion

This study indicates that the application of ND-EESI-MS to sputum analysis can be used as a non-invasive detection of peripheral lung nodules. The use of sputum metabolite biomarkers may aid in the development of a further evaluation program for lung adenocarcinoma.

Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives

Colorectal cancer (CRC) ranks third among the most commonly occurring cancers worldwide, and it causes half a million deaths annually. Alongside mechanistic study for CRC detection and treatment by conventional techniques, new technologies have been developed to study CRC. These technologies include genomics, transcriptomics, proteomics, and metabolomics which elucidate DNA markers, RNA transcripts, protein and, metabolites produced inside the colon and rectum part of the gut. All these approaches form the omics arena, which presents a remarkable opportunity for the discovery of novel prognostic, diagnostic and therapeutic biomarkers and also delineate the underlying mechanism of CRC causation, which may further help in devising treatment strategies. This review also mentions the latest developments in metagenomics and culturomics as emerging evidence suggests that metagenomics of gut microbiota has profound implications in the causation, prognosis, and treatment of CRC. A majority of bacteria cannot be studied as they remain unculturable, so culturomics has also been strengthened to develop culture conditions suitable for the growth of unculturable bacteria and identify unknown bacteria. The overall purpose of this review is to succinctly evaluate the application of omics technologies in colorectal cancer research for improving the diagnosis and treatment strategies.

Profiling of polar urine metabolite extracts from Chinese colorectal cancer patients to screen for potential diagnostic and adverse-effect biomarkers

Background: Metabolomics has demonstrated its potential in the early diagnosis, drug safety evaluation and personalized toxicology research of various cancers. Objectives: We aim to screen for potential diagnostic and capecitabine-related adverse effect (CRAE) biomarkers from urinary endogenous metabolites in Chinese colorectal cancer (CRC) patients. Methods: The metabolic profiles of 139 CRC patients and 50 non-neoplastic controls were analyzed using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry. Results: There were 41 metabolites identified between the CRC patients and the non-neoplastic controls, and 19 metabolites were identified between CRC patients with and without CRAE. Based on these identified metabolites, bioinformatic analysis and prediction model construction were completed. Most of these differential metabolites have important roles in cell proliferation and differentiation and the immune system. Based on binary logistic regression, a CRC prediction model, composed of 3-methylhistidine, N-heptanoylglycine, N1,N12-diacetylspermine and hippurate, was established, with an area under curve (AUC) of 0.980 (95% CI: 0.953-1.000; sensitivity: 94.3%; specificity: 92.0%) in the training set, and an AUC of 0.968 (95% CI: 0.933-1.000; sensitivity: 89.9%; specificity: 92.0%) in the testing set. In addition, methionine and 4-pyridoxic acid can be combined to predict hand foot syndrome, with an AUC of 0.884; ubiquinone-1 and 4-pyridoxic acid can be combined to predict anemia, with an AUC of 0.889; and 5-acetamidovalerate and 3,4-methylenesebacic acid can be combined to predict neutropenia, with an AUC of 0.882. Conclusion: The profiling of urine polar metabolites has great potential in the early detection of CRC and the prediction of CRAE.

Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin's lymphoma urinary metabolite markers

Non-Hodgkin's lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease. Graphical Abstract.

Integrating Two-Dimensional Gas and Liquid Chromatography-Mass Spectrometry for Untargeted Colorectal Cancer Metabolomics: A Proof-of-Principle Study

Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability of integration of two-dimensional gas and liquid chromatography-mass spectrometry (GC × GC-MS and 2DLC-MS) platforms to characterizing circulating polar metabolome extracted from plasma collected from 29 individuals with colorectal cancer in comparison with 29 who remained cancer-free. After adjustment of multiple comparisons, 20 metabolites were found to be up-regulated and 8 metabolites were found to be down-regulated, which pointed to the dysregulation in energy metabolism and protein synthesis. While integrating the GC × GC-MS and 2DLC-MS data can dramatically increase the metabolite coverage, this study had a limitation in analyzing the non-polar metabolites. Given the small sample size, these results need to be validated with a larger sample size and with samples collected prior to diagnostic and treatment. Nevertheless, this proof-of-principle study demonstrates the potential applicability of integration of these advanced analytical platforms to improve discrimination between colorectal cancer cases and controls based on metabolite profiles in future studies.

ApcMin/+ tumours and normal mouse small intestines show linear metabolite concentration and DNA cytosine hydroxymethylation gradients from pylorus to colon

Topographical variations of metabolite concentrations have been reported in the duodenum, jejunum and ileum of the small intestine, and in human intestinal tumours from those regions, but there are no published metabolite concentrations measurements correlated with linear position in the mouse small intestine or intestinal tumours. Since DNA methylation dynamics are influenced by metabolite concentrations, they too could show linear anatomical variation. We measured metabolites by HR-MAS ¹H NMR spectroscopy and DNA cytosine modifications by LC/MS, in normal small intestines of C57BL/6J wild-type mice, and in normal and tumour samples from Apc^Min/+ mice. Wild-type mouse intestines showed approximately linear, negative concentration gradations from the pylorus (i.e. the junction with the stomach) of alanine, choline compounds, creatine, leucine and valine. Apc^Min/+ mouse tumours showed negative choline and valine gradients, but a positive glycine gradient. 5-Hydroxymethylcytosine showed a positive gradient in the tumours. The linear gradients we found along the length of the mouse small intestine and in tumours contrast with previous reports of discrete concentration changes in the duodenum, jejunum and ileum. To our knowledge, this is also the first report of a systematic measurement of global levels of DNA cytosine modification in wild-type and Apc^Min/+ mouse small intestine.

Urinary 1H-NMR Metabolic Signature in Subjects Undergoing Colonoscopy for Colon Cancer Diagnosis

Metabolomics represents a promising non-invasive approach that can be applied to identify biochemical changes in colorectal cancer patients (CRC) and is potentially useful for diagnosis and follow-up. Despite the literature regarding metabolomics CRC-specific profiles, discrimination between metabolic changes specifically related to CRC and intra-individual variability is still a problem to be solved. This was a preliminary case-control study, in which 1H-NMR spectroscopy combined with multivariate statistical analysis was used to profile urine metabolites in subjects undergoing colonoscopy for colon cancer diagnosis. To reduce intra-individual variability, metabolic profiles were evaluated in participants’ urine samples, collected just before the colonoscopy and after a short-term dietary regimen required for the endoscopy procedure. Data obtained highlighted different urinary metabolic profiles between CRC and unaffected subjects (C). The metabolites altered in the CRC urine (acetoacetate, creatine, creatinine, histamine, phenylacetylglycine, and tryptophan) significantly correlated with colon cancer and discriminated with accuracy CRC patients from C patients (receiver operator characteristic (ROC) curve with an area under the curve (AUC) of 0.875; 95% CI: 0.667–1). These results confirm that urinary metabolomic analysis can be a valid tool to improve CRC diagnosis, prognosis, and response to therapy, representing a noninvasive approach that could precede more invasive tests.

Simultaneous Determination of 34 Amino Acids in Tumor Tissues from Colorectal Cancer Patients Based on the Targeted UHPLC-MS/MS Method

A targeted ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was established and validated for the simultaneous determination of 34 amino acids in tissue samples from colorectal cancer (CRC) patients. The chromatographic separation was achieved on an Agilent ZORBAX SB-C₁₈ column (3.0 × 150 mm, 5 μm) with a binary gradient elution system (A, 0.02% heptafluorobutyric acid and 0.2% formic acid in water, v/v; B, methanol). The run time was 10 min. The multiple reaction monitoring mode was chosen with an electrospray ionization source operating in the positive ionization mode for data acquisition. The linear correlation coefficients were >0.99 for all the analytes in their corresponding calibration ranges. The sample was pretreated based on tissue homogenate and protein precipitation with a 100 mg aliquot sample. The average recovery and matrix effect for 34 amino acids and 3 internal standards were 39.00%∼146.95% and 49.45%∼173.63%, respectively. The intra- and interday accuracy for all the analytes ranged from -13.52% to 14.21% (RSD ≤8.57%) and from -14.52% to 12.59% (RSD ≤10.31%), respectively. Deviations of stability under different conditions were within ±15% for all the analytes. This method was applied to simultaneous quantification of 34 amino acids in tissue samples from 94 CRC patients.

Effect of daunorubicin on acute promyelocytic leukemia cells using nuclear magnetic resonance spectroscopy-based metabolomics

The aim of this study was to determine several key metabolites as potential biomarkers of daunorubicin (DNR) treatment of acute promyelocytic leukemia (APL) using APL blasts and NB4 cells. Samples which were obtained from 16 newly diagnosed APL patients and human APL NB4 cell lines were exposed to increasing concentrations of DNR (0 μM, 0.1 μM, 0.5 μM and 1.0 μM). Electron microscopy and Nuclear Magnetic Resonance (NMR) spectroscopy confirmed that there were clear differences between controls and DNR-treated groups, with the resultant models having excellent predictive and discriminative abilities. Four metabolites meeting the biomarker requirements were identified. KEGG analyses revealed that these biomarkers were associated with the metabolism of fat, choline, and glucose. These findings offered vital information about the effects of chemotherapies on the whole body biochemistry which might be important for monitoring apoptosis and injury to cells in order to reduce chemotherapies-induced side effects.

Magnetic resonance spectroscopy and its application in colorectal cancer diagnosis and screening: A narrative review

There are several slightly invasive methods to detect colorectal carcinoma (CRC) including colonoscopy and sigmoidoscopy; but there is no noninvasive, accurate screening test. It is recommended to initiate screening at the age of 50 for non-familial CRC. Laboratory tests are routinely suggested if internal observation and imaging are recommended for further evaluation. Spectroscopic-based imaging, such as magnetic resonance spectroscopy (MRS) is an interesting and promising tool with the potential to be an alternative to some minimally invasive procedures, such as biopsy. Accordingly, MRS might be a suitable substitution for invasive methods, such as colonoscopy. This article aimed to review the studies that have evaluated the MRS technique as a screening tool in CRC.

Tumor Tissue-Specific Biomarkers of Colorectal Cancer by Anatomic Location and Stage

The progress in the discovery and validation of metabolite biomarkers for the detection of colorectal cancer (CRC) has been hampered by the lack of reproducibility between study cohorts. The majority of discovery-phase biomarker studies have used patient blood samples to identify disease-related metabolites, but this pre-validation phase is confounded by non-specific disease influences on the metabolome. We therefore propose that metabolite biomarker discovery would have greater success and higher reproducibility for CRC if the discovery phase was conducted in tumor tissues, to find metabolites that have higher specificity to the metabolic consequences of the disease, that are then validated in blood samples. This would thereby eliminate any non-tumor and/or body response effects to the disease. In this study, we performed comprehensive untargeted metabolomics analyses on normal (adjacent) colon and tumor tissues from CRC patients, revealing tumor tissue-specific biomarkers (n = 39/group). We identified 28 highly discriminatory tumor tissue metabolite biomarkers of CRC by orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate analyses (VIP > 1.5, p < 0.05). A stepwise selection procedure was used to identify nine metabolites that were the most predictive of CRC with areas under the curve (AUCs) of >0.96, using various models. We further identified five biomarkers that were specific to the anatomic location of tumors in the colon (n = 236). The combination of these five metabolites (S-adenosyl-L-homocysteine, formylmethionine, fucose 1-phosphate, lactate, and phenylalanine) demonstrated high differentiative capability for left- and right-sided colon cancers at stage I by internal cross-validation (AUC = 0.804, 95% confidence interval, CI 0.670–0.940). This study thus revealed nine discriminatory biomarkers of CRC that are now poised for external validation in a future independent cohort of samples. We also discovered a discrete metabolic signature to determine the anatomic location of the tumor at the earliest stage, thus potentially providing clinicians a means to identify individuals that could be triaged for additional screening regimens.

Metabolomic Markers of Colorectal Tumor With Different Clinicopathological Features

Background: Colorectal cancer (CRC) is the result of complex interactions between the tumor's molecular profile and metabolites produced by its microenvironment. Despite recent studies identifying CRC molecular subtypes, a metabolite classification system is still lacking. We aimed to explore the distinct phenotypes and subtypes of CRC at the metabolite level.

Methods: We conducted an untargeted metabolomics analysis of 51 paired tumor tissues and adjacent mucosa using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. Multivariate analysis including principal component analysis, orthogonal partial least squares discriminant analysis and heat maps, univariate analysis, and pathway analysis were used to identify potential metabolite phenotypes of CRC. Unsupervised consensus clustering was used to identify robust metabolite subtypes, and evaluated their clinical relevance.

Results: A total of 173 metabolites (including nucleotides, carbohydrates, free fatty acids, and choline) were identified between CRC tumor tissue and adjacent mucosa. We found that lipid metabolism was closely related to the occurrence and progression of CRC. In particular, CRC tissues could be divided into three subtypes, and statistically significant correlations between different subtypes and clinical prognosis were observed.

Conclusions: CRC tumor tissue exhibits distinct metabolite phenotypes. Metabolite differences between subtypes may provide a basis and direction for further clinical individualized treatment planning.

Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status

Background

Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore between TMPRSS2-ERG rearrangement-positive and -negative PC subclasses.

Methods

Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (¹H HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by ¹H/³¹P NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort.

Results

The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and α-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in β-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress.

Conclusions

Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.

Identification of potential metabolite markers for colon cancer and rectal cancer using serum metabolomics

Background

To determine the metabolic characteristics of patients with colon cancer (CC) and rectal cancer (RC) using gas chromatography‐mass spectrometry (GC‐MS)‐based metabolomics.

Methods

In this study, serum samples were collected from 22 CC patients and 23 RC patients preoperatively and postoperatively and 45 healthy volunteers (HVs), and subjected to metabolomics analysis by GC‐MS. Differential metabolites in the preoperative RC and CC samples and HVs were identified as potential biomarkers and evaluated for their utilities by receiver operating characteristic analyses.

Results

The different metabolic markers between CC and RC patients were identified, which may assist in distinguishing the two types of cancers. The area under the curve (AUC) was 0.805 for combination of d‐glucose and d‐mannose for CC diagnosis, and 0.889 for combination of 2‐aminobutanoic acid, 3‐hydroxypyridine, d‐glucose, d‐mannose, isoleucine, l‐tryptophan, urea, and uric acid for RC diagnosis. The combinations of metabolite markers showed a better predictability than CEA and CA199 two commonly used protein markers for CRC diagnosis in clinical practice. Combining the metabolite markers with these two protein markers effectively improved the diagnostic accuracy with the AUC reaching 0.936 and 0.937 for CC and RC diagnosis, respectively.

Conclusions

Metabolic profiles are different in the blood samples between CC and RC patients. The study has established a panel of metabolic markers as a predictive and multiplexing signature for CC and RC diagnosis.

Nanostructured Chemoresistive Sensors for Oncological Screening and Tumor Markers Tracking: Single Sensor Approach Applications on Human Blood and Cell Samples

Preventive screening does not only allow to preemptively intervene on pathologies before they can harm the host; but also to reduce the costs of the intervention itself; boosting the efficiency of the NHS (National Health System) by saving resources for other purposes. To improve technology advancements in this field; user-friendly yet low-cost devices are required; and various applications for gas sensors have been tested and proved reliable in past studies. In this work; cell cultures and blood samples have been studied; using nanostructured chemoresistive sensors; to both verify if this technology can reliably detect tumor markers; and if correlations between responses from tumor line metabolites and the screening outcomes on human specimens could be observed. The results showed how sensors responded differently to the emanations from healthy and mutant (for cells) or tumor affected (for blood) samples, and how those results were consistent between them, since the tumoral specimens had higher responses compared to the ones of their healthy counterparts. Even though the patterns in the responses require a bigger population to be defined properly; it appeared that the different macro-groups between the same kind of samples are distinguishable from some of the sensors chosen in the study; giving promising outcomes for further research.

Metabolomic profiling in neuroblastoma

BACKGROUND AND OBJECTIVES

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

DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Metabolic phenotyping to monitor chronic enteritis canceration

INTRODUCTION

Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRC patients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE.

OBJECTIVE

Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers.

METHODS

In this case-control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRC patients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CE patients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE.

RESULTS

Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRC patients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC.

CONCLUSIONS

The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CE patients and are superior to commonly used clinical biomarkers (CEA and CA19-9).

Metabolic models predict bacterial passengers in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment.

METHODS

We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers.

RESULTS

We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome.

CONCLUSION

Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer

Colorectal cancer is one of the main causes of cancer death worldwide, and novel biomarkers are urgently needed for its early diagnosis and treatment. The utilization of metabolomics to identify and quantify metabolites in body fluids may allow the detection of changes in their concentrations that could serve as diagnostic markers for colorectal cancer and may also represent new therapeutic targets. Metabolomics generates a pathophysiological ‘fingerprint’ that is unique to each individual. The purpose of our study was to identify a differential metabolomic signature for metastatic colorectal cancer. Serum samples from 60 healthy controls and 65 patients with metastatic colorectal cancer were studied by liquid chromatography coupled to high-resolution mass spectrometry in an untargeted metabolomic approach. Multivariate analysis revealed a separation between patients with metastatic colorectal cancer and healthy controls, who significantly differed in serum concentrations of one endocannabinoid, two glycerophospholipids, and two sphingolipids. These findings demonstrate that metabolomics using liquid-chromatography coupled to high-resolution mass spectrometry offers a potent diagnostic tool for metastatic colorectal cancer.