Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: a systematic review

Comprehensive metabolomic and epigenomic characterization of microsatellite stable BRAF-mutated colorectal cancer

Oncogenic codon V600E mutations of the BRAF gene affect 10-15% of colorectal cancers, resulting in activation of the MAPK/ERK signaling pathway and increased cell proliferation and survival. BRAF-mutated colorectal tumors are often microsatellite unstable and characterized by high DNA methylation levels. However, the mechanistic link between BRAF mutations and hypermethylation remains controversial. Understanding this link, particularly in microsatellite stable tumors is of great interest as these often show poor survival. We characterized the metabolomic, epigenetic and transcriptomic patterns of altogether 39 microsatellite stable BRAF-mutated colorectal cancers. Metabolomic analysis of tumor tissue showed low levels of vitamin C and its metabolites in BRAF-mutated tumors. Gene expression analysis indicated dysregulation of vitamin C antioxidant activity in these lesions. As vitamin C is an important cofactor for the activity of TET DNA demethylase enzymes, low vitamin C levels could directly contribute to the high methylation levels in these tumors by decreasing enzymatic TET activity. Vitamin C transporter gene SLC23A1 expression, as well as vitamin C metabolite levels, were inversely correlated with DNA methylation levels. This work proposes a new mechanistic link between BRAF mutations and hypermethylation, inspiring further work on the role of vitamin C in the genesis of BRAF-mutated colorectal cancer.

Harnessing the Power of Metabolomics for Precision Oncology: Current Advances and Future Directions

Metabolic reprogramming is a hallmark of cancer, with cancer cells acquiring many unique metabolic traits to support malignant growth, and extensive intra- and inter-tumour metabolic heterogeneity. Understanding these metabolic characteristics presents opportunities in precision medicine for both diagnosis and therapy. However, despite its potential, metabolic phenotyping has lagged behind genetic, transcriptomic, and immunohistochemical profiling in clinical applications. This is partly due to the lack of a single experimental technique capable of profiling the entire metabolome, necessitating the use of multiple technologies and approaches to capture the full range of cancer metabolic plasticity. This review examines the repertoire of tools available for profiling cancer metabolism, demonstrating their applications in preclinical and clinical settings. It also presents case studies illustrating how metabolomic profiling has been integrated with other omics technologies to gain insights into tumour biology and guide treatment strategies. This information aims to assist researchers in selecting the most effective tools for their studies and highlights the importance of combining different metabolic profiling techniques to comprehensively understand tumour metabolism.

Tumor-exosomal miR-205-5p as a diagnostic biomarker for colorectal cancer

BACKGROUND

Tumor-derived exosomal miRNAs play crucial roles in cancer diagnosis. Current studies aim to identify exosomal miRNAs associated with colorectal cancer (CRC) that are noninvasive, sensitive, and specific.

PATIENTS AND METHODS

Exosomes were extracted from CRC patients and healthy donors via ultracentrifugation, followed by verification via transmission electron microscopy (TEM), qNano, and Western blot analysis. The differential expression levels and clinical characteristics of miR-205-5p were analyzed in CRC via data from The Cancer Genome Atlas (TCGA). Real-time quantitative PCR was used to assess the expression levels of exosomal miRNAs in 157 primary CRC patients, 20 patients with benign diseases, and 135 healthy donors. Predictions regarding target genes were made to guide further exploration of the disease's etiopathogenesis through bioinformatics.

RESULTS

Compared with that in healthy donors, the expression of miR-205-5p in colorectal cancer (CRC) patients was significantly lower, as determined through analysis of the TCGA database. We conducted a prediction and analysis of the functional enrichment of downstream target genes regulated by miR-205-5p. A lower level of exosomal miR-205-5p in the serum of CRC patients than in that of healthy controls (p < 0.0001) and patients with benign disease (p < 0.0001) was observed. Furthermore, the expression levels of exosomal miR-205-5p were significantly lower in early-stage CRC patients than in the comparison groups (p<0.001 and p < 0.0001). Notably, the expression levels of exosomal miR-205-5p significantly increased postoperatively (p = 0.0053).

CONCLUSIONS

The present study demonstrated that serum exosomal miR-205-5p may be a diagnostic biomarker for CRC.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Faecal Metabolome Profiles in Individuals Diagnosed with Hyperplastic Polyps and Conventional Adenomas

Colorectal cancer (CRC) development is a gradual process in which progressive histological alterations of the intestinal mucosa damage occur over years. This process can be influenced by modifiable external factors such as lifestyle and diet. Most CRC cases (>80%) originate from conventional adenomas through the adenomatous pathway and usually harbour dysplastic cells, whereas the serrated pathway is less frequent (<20% cases) and comprises hyperplastic polyps and other polyps containing dysplastic cells. The aim of the present work was to shed light on alterations of the faecal metabolome associated with hyperplastic polyps and conventional adenomas. Metabolites were analysed by Reversed-Phase High-Performance Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometry (RP/HPLC-Q/TOF-MS/MS) and Hydrophilic Interaction Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometry (HILIC-Q/TOF-MS/MS) and the results were integrated. Comparisons were performed between controls without mucosal lesions and the polyps' group, hyperplastic polyps versus conventional adenomas, and hyperplastic polyps or conventional adenomas versus controls. Alterations of metabolites in specific biochemical modules differentiated hyperplastic polyps and conventional adenomas. The metabolome of the hyperplastic polyps was characterized by an enrichment in glycerophospholipids and an altered metabolism of the degradation pathways of xanthines/purines and pyrimidines, whereas the enrichment in some phenolic compounds and disaccharides, all of them from exogenous origin, was the main differential faecal signature of conventional adenomas. Further research could help to elucidate the contribution of diet and the intestinal microbiota to these metabolomics alterations.

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients

BACKGROUND

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.

METHODS

A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor.

RESULTS

There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively.

CONCLUSION

We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes

BACKGROUND

Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition.

RESULTS

In this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results.

CONCLUSIONS

These results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups.

Clinical significance of ribosomal protein S15 expression in patients with colorectal cancer liver metastases

BACKGROUND

Liver metastasis is the most frequently observed distant metastasis of colorectal cancer, and the residual liver recurrence rate after hepatic resection is still high. To explore the mechanism of liver metastasis to discover potential new treatments, we assessed the relationship between the expression of differentially expressed genes (DEGs) and prognosis in patients with colorectal cancer liver metastasis (CRLM).

METHODS

The gene expression dataset was extracted from The Cancer Genome Atlas and the Gene Expression Omnibus. Significance analysis of DEGs between tumor and normal samples of colorectum, liver, and lung was conducted. A total of 80 CRLM patients were studied to assess the expression of RPS15, characteristics, and outcomes. We examined the relationships of RPS15 expression to cell viability and apoptosis in vitro and vivo.

RESULTS

Significance analysis identified 33 DEGs. In our cohorts, the overall survival rates were significantly lower in the high-RPS15-expression group, and high expression of RPS15 was an independent and unfavorable prognostic factor in recurrence-free survival and overall survival. Knockdown of RPS15 expression reduced the proliferative capacity of colorectal cancer cells and increased BAX-induced apoptotic cell death.

CONCLUSIONS

RPS15 expression is an independent prognostic factor for CRLM patients and might be a novel therapeutic target for CRLM.

Development of a point-of-care colorimetric metabolomic sensor platform

Metabolomics is the large-scale study of small molecule metabolites within a biological system. It has applications in measuring dietary intake, predicting heart disease risk, and diagnosing cancer. Metabolites are often measured using high-end analytical tools such as mass spectrometers or large spectrophotometers. However, due to their size, cost, and need for skilled operators, using such equipment at the bedside is not practical. To address this issue, we have developed a low-cost, portable, optical color sensor platform for metabolite detection. This platform includes LEDs, sensors, microcontrollers, a power source, and a Bluetooth chip enclosed within a 3D-printed light-tight case. We evaluated the color sensor's performance using both a range of dyed water samples as well as well-established colorimetric reactions for specific metabolite detection. The sensor accurately measured creatinine, L-carnitine, ascorbate, and succinate well within normal human urine levels with accuracy and sensitivity equal to or better than a standard laboratory spectrophotometer. Our color sensor offers a cost-effective, portable alternative for measuring metabolites via colorimetric assays, thereby enabling low-cost, point-of-care metabolite testing.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Iodine-131 intervention in hyperthyroidism with hepatic insufficiency: Metabolomic evaluation

Hyperthyroidism, often accompanied by hepatic insufficiency (HI), poses significant clinical challenges, highlighting the necessity for identifying optimal treatment strategies and early diagnostic biomarkers to improve patient outcomes. This study aimed to determine the optimal iodine-131 (131I) intervention dose for alleviating hyperthyroidism with HI and to identify serum metabolic biomarkers for early diagnosis using UPLC-Q/TOF-MS technology. A mouse model for early 131I intervention was established to monitor changes in physiological response, body weight, fur condition, thyroid, and liver function. Metabolite identification was achieved through UPLC-Q/TOF-MS and further analyzed via MetaboAnalyst. Six biomarkers were identified and subjected to ROC analysis. Early intervention with 80 μCi 131I per gram of thyroid tissue effectively controlled hyperthyroidism and improved liver function. Metabolomics analysis uncovered 63 differentially abundant metabolites, six of which (L-kynurenine, Taurochenodesoxycholic acid, Glycocholic acid, Phytosphingosine, Tryptamine, and Betaine) were identified as early warning biomarkers. Post-intervention, these biomarkers progressively returned to normal levels. This study demonstrates the efficacy of UPLC-Q/TOF-MS in identifying metabolic biomarkers for early diagnosis of hyperthyroidism with HI and highlights the therapeutic potential of early 131I intervention in normalizing these biomarkers.

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.

Comparative Metabolomic Profiles of Vascular Involvement in Behçet's Disease

BACKGROUND

Behçet's disease is a systemic, inflammatory disease affecting multiple organs. Vascular involvement is the main cause of morbidity and mortality in Behçet's disease patients. Though clinically well-defined, there is limited information related to disease pathogenesis and vascular incidence in this patient group. The aim of this study is to investigate the unique metabolic signatures of Behçet's disease patients with vascular involvement.

METHODS

Metabolomic profiling was performed on serum samples of 48 Behçet's disease patients (18 with vascular involvement) and 40 healthy controls using gas chromatography-mass spectrometrybased untargeted metabolomics analysis. Multivariate and univariate statistical analyses were performed to find altered metabolites and pathways.

RESULTS

Untargeted metabolomics results showed that a total of 168 metabolites were identified. The comparison between the groups of Behçet's disease, vascular involvement in Behçet's disease, and the healthy control group showed that altered amino acid and oxidative stress pathways, especially with glutathione synthesis, could be an important stage for developing Behçet's disease.

CONCLUSION

In the present work, the untargeted metabolomics approach provided new molecular insights for a better understanding of Behçet's disease pathogenesis and also developing vascular involvement in Behçet's disease at the metabolite level. The results showed that vascular involvement in Behçet's disease could be highly linked with amino acid metabolism and also the antioxidant system, and these disease-related pathways could be evaluated with further experiments for diagnosis and prognosis of Behçet's disease and also for vascular involvement in Behçet's disease.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur

16S rRNA is the universal gene of microbes, and it is often used as a target gene to obtain profiles of microbial communities via next-generation sequencing (NGS) technology. Traditionally, sequences are clustered into operational taxonomic units (OTUs) at a 97% threshold based on the taxonomic standard using 16S rRNA, and methods for the reduction of sequencing errors are bypassed, which may lead to false classification units. Several denoising algorithms have been published to solve this problem, such as DADA2 and Deblur, which can correct sequencing errors at single-nucleotide resolution by generating amplicon sequence variants (ASVs). As high-resolution ASVs are becoming more popular than OTUs and only one analysis method is usually selected in a particular study, there is a need for a thorough comparison of OTU clustering and denoising pipelines. In this study, three of the most widely used 16S rRNA methods (two denoising algorithms, DADA2 and Deblur, along with de novo OTU clustering) were thoroughly compared using 16S rRNA amplification sequencing data generated from 358 clinical stool samples from the Colorectal Cancer (CRC) Screening Cohort. Our findings indicated that all approaches led to similar taxonomic profiles (with P > 0.05 in PERMNAOVA and P <0.001 in the Mantel test), although the number of ASVs/OTUs and the alpha-diversity indices varied considerably. Despite considerable differences in disease-related markers identified, disease-related analysis showed that all methods could result in similar conclusions. Fusobacterium, Streptococcus, Peptostreptococcus, Parvimonas, Gemella, and Haemophilus were identified by all three methods as enriched in the CRC group, while Roseburia, Faecalibacterium, Butyricicoccus, and Blautia were identified by all three methods as enriched in the healthy group. In addition, disease-diagnostic models generated using machine learning algorithms based on the data from these different methods all achieved good diagnostic efficiency (AUC: 0.87-0.89), with the model based on DADA2 producing the highest AUC (0.8944 and 0.8907 in the training set and test set, respectively). However, there was no significant difference in performance between the models (P >0.05). In conclusion, this study demonstrates that DADA2, Deblur, and de novo OTU clustering display similar power levels in taxa assignment and can produce similar conclusions in the case of the CRC cohort.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Comprehensive Insight into Colorectal Cancer Metabolites and Lipids for Human Serum: A Proof-of-Concept Study

Colorectal cancer (CRC) ranks as the third most frequently diagnosed cancer and the second leading cause of cancer-related deaths. The current endoscopic-based or stool-based diagnostic techniques are either highly invasive or lack sufficient sensitivity. Thus, there is a need for less invasive and more sensitive screening approaches. We, therefore, conducted a study on 64 human serum samples representing three different groups (adenocarcinoma, adenoma, and control) using cutting-edge GC×GC-LR/HR-TOFMS (comprehensive two-dimensional gas chromatography coupled with low/high-resolution time-of-flight mass spectrometry). We analyzed samples with two different specifically tailored sample preparation approaches for lipidomics (fatty acids) (25 μL serum) and metabolomics (50 μL serum). In-depth chemometric screening with supervised and unsupervised approaches and metabolic pathway analysis were applied to both datasets. A lipidomics study revealed that specific PUFA (ω-3) molecules are inversely associated with increased odds of CRC, while some PUFA (ω-6) analytes show a positive correlation. The metabolomics approach revealed downregulation of amino acids (alanine, glutamate, methionine, threonine, tyrosine, and valine) and myo-inositol in CRC, while 3-hydroxybutyrate levels were increased. This unique study provides comprehensive insight into molecular-level changes associated with CRC and allows for a comparison of the efficiency of two different analytical approaches for CRC screening using same serum samples and single instrumentation.

Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis

BACKGROUND

The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease.

METHODS

We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes.

RESULTS

In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2.

CONCLUSION

Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.

A Novel Approach on the Use of Samples from Faecal Occult Blood Screening Kits for Metabolomics Analysis: Application in Colorectal Cancer Population

The incidence of colorectal cancer (CRC) is increasing, and currently it is the third most common cancer. Early CRC diagnosis is still difficult and relies on an invasive colonoscopy and tissue biopsy. The globally observed tendency demands non-invasive, specific, and accurate diagnostic tools for early diagnosis and prognosis. In this work, the main aim was to evaluate for the first time the feasibility of using extracts from the non-invasive sample collection from faecal occult blood (FOB) kits for its use in metabolomics studies taking advantage in this way of the high sensitivity of this technology. Then, a cohort of 131 samples from control individuals (CTL), adenoma (AD) and CRC patients were analysed using a semitargeted approach by ultra-high-performance liquid chromatography-time-of-flight-mass spectrometry (UHPLC-ToF-MS). Multivariate and univariate statistical analysis revealed that cholesteryl esters (ChoE) with polyunsaturated fatty acids (PUFAs) together with FOB were relevant metabolites that could clearly separate CRC patients from AD and CTL individuals, whereas the metabolic profiles of CTL and AD were very similar. These results are in agreement with previous findings and reveal the advantage of using the same FOBT samples for several analyses, which would facilitate sample collection and improve direct connection between FOB measurements and metabolomics analysis. Although the sample size and the number of metabolites should be enhanced to cover a wider range of metabolites, alterations in lipid metabolism clearly point out for future perspectives.

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (TCRC) and normal paired tissues (THC) along with 10 matched fecal samples (FCRC) and 10 healthy controls (FHC). The highest microbial phyla abundance from THC and TCRC were Firmicutes, while the dominant phyla from FHC and FCRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in THC than in TCRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in FHC than in FCRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between THC and TCRC groups and 700 metabolites different between FHC and FCRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection

Purpose

In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria.

Experimental design

Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier.

Results

Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy.

Conclusion

Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.

Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer

INTRODUCTION

Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT.

OBJECTIVES

This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients.

METHODS

Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models.

RESULTS

Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively.

CONCLUSION

This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine^TM and Ulib^MS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

Association of levels of metabolites with the safe margin of rectal cancer surgery: a metabolomics study

Background

Rectal cancer is one of the most lethal of gastrointestinal malignancies. Metabonomics has gradually developed as a convenient, inexpensive and non-destructive technique for the study of cancers.

Methods

A total of 150 tissue samples from 25 rectal cancer patients were analyzed by liquid chromatography–mass spectrometry (LC–MS), and 6 tissue samples were collected from each patient (group 1: tumor; group 2: 0.5 cm from tumor; group 3:1 cm from tumor; group 4:2 cm from tumor; group 5:3 cm from tumor and group 6:5 cm from tumor). The differential metabolites of tumor tissues and 5 cm from the tumor (normal tissues) were first selected. The differential metabolites between tumor tissues and normal tissues were regrouped by hierarchical clustering analysis, and further selected by discriminant analysis according to the regrouping of clustering results. The potential safe margin of clinical T(cT)1,cT2 stage rectal cancer and cT3,cT4 stage rectal cancer at the metabolomic level was further identified by observing the changes in the level of differential metabolites within the samples from group 1 to group 6.

Results

We found 22 specific metabolites to distinguish tumor tissue and normal tissue. The most significant changes in metabolite levels were observed at 0.5 cm (cT1, cT2) and 2.0 cm (cT3, cT4) from the tumor, while the changes in the tissues afterwards showed a stable trend.

Conclusions

There are differential metabolites between tumor tissues and normal tissues in rectal cancer. Based on our limited sample size, the safe distal incision margin for rectal cancer surgery in metabolites may be 0.5 cm in patients with cT1 and cT2 stage rectal cancer and 2.0 cm in patients with cT3 and cT4 stage rectal cancer.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753-0.9825), 0.7 and 1, respectively.

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.

Lipidomic biomarkers: Potential mediators of associations between urinary bisphenol A exposure and colorectal cancer

Previous research reported associations between bisphenol A (BPA) exposure and some malignant tumor incidences, yet the underlying mechanism remains largely uncertain. This investigation was aimed to explore the association of BPA exposure burden with colorectal cancer (CRC) and the role of tumor tissue lipid metabolism in the associations between BPA and CRC using lipidomic approach. Urinary BPA levels in CRC cases were significantly higher than those in controls (P value < 0.05). BPA was positively correlated with all three serum CRC biomarkers, with an estimated odds ratio (OR) of 4.45 (95% confidence interval (95% CI): [1.31, 15.14]) between the highest and lowest tertiles of exposure. Lipidomic screening of tumor samples suggested significant perturbation in the glycerophospholipid metabolism pathway, of which phosphatidylcholine (PC 34:0), phosphatidylcholine (PC 37:1), phosphatidylethanolamine (PE 34:2), triacylglycerol (TG 56:4) demonstrated mediation contribution of 21.9%, 18.7%, 18.4% and 27.39%, respectively, in the association between BPA exposure and CRC. Our work provides novel findings that cancer tissue metabolites may be playing vital mediating roles in the associations between BPA exposure burden and CRC risk. These findings contribute to better understanding of the etiology of CRC induced by environmental stressors.

LncRNA MSTO2P promotes colorectal cancer progression through epigenetically silencing CDKN1A mediated by EZH2

BACKGROUND

Pseudogene-derived long non-coding RNAs (lncRNAs) have been reported to act as key regulatory factors of cancers. However, the study focused on pseudogene misato family member 2 (MSTO2P) in the occurrence and development of colorectal cancer (CRC) remains unclear.

METHODS

CCK-8, colony formation, and transwell assays clarified HT-29 and SW480 cell proliferation and invasion. Furthermore, flow cytometry was carried out to detect cell cycle and cell apoptosis. Subcellular localization assay indicated the location of MSTO2P in HT-29 cells. RIP and CHIP assays clarified the relationship of MSTO2P with target protein and gene in HT-29 cells.

RESULTS

MSTO2P expression was upregulated in CRC tissues and cells. Functional experiments revealed that inhibition of MSTO2P suppressed HT-29 and SW480 cell proliferation and invasion, and promoted cell cycle arrest and cell apoptosis. Besides, MSTO2P epigenetically down-regulated cyclin-dependent kinase inhibitor 1A (CDKN1A) via binding to the enhancer of zeste homolog 2 (EZH2) in the nucleus. At last, rescue experiments proved the anti-tumor effect of inhibition of MSTO2P was partially recovered due to the knockdown of CDKN1A in HT-29 cells.

CONCLUSION

LncRNA MSTO2P promoted colorectal cancer progression through epigenetically silencing CDKN1A mediated by EZH2.

Metabolomic Pathway Activity with Genomic Single-Nucleotide Polymorphisms Associated with Colorectal Cancer Recurrence and 5-Year Overall Survival

PURPOSE

Metabolomic analysis in colorectal cancer (CRC) is an emerging research area with both prognostic and therapeutic targeting potential. We aimed to identify metabolomic pathway activity prognostic for CRC recurrence and overall survival and cross-reference such metabolomic data with prognostic genomic single-nucleotide polymorphisms (SNPs).

METHODS

A systematic search of PubMed, Embase and Cochrane Library was performed for studies reporting prognostic metabolomic pathway activity in CRC in keeping with PRISMA guidelines. The QUADOMICS tool was used to assess study quality. MetaboAnalyst software (version4.0) was used to map metabolites that were associated with recurrence and survival in CRC to recognise metabolic pathways and identify genomic SNPs associated with CRC prognosis, referencing the following databases: Human Metabolome Database (HMDB), the Small Molecule Pathway Database (SMPDB), PubChem and Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathway Database.

RESULTS

Nine studies met the inclusion criteria, reporting on 1117 patients. Increased metabolic activity in the urea cycle (p = 0.002, FDR = 0.198), ammonia recycling (p = 0.004, FDR = 0.359) and glycine and serine metabolism (p = 0.004, FDR = 0.374) was prognostic of CRC recurrence. Increased activity in aspartate metabolism (p < 0.001, FDR = 0.079) and ammonia recycling (p = 0.004, FDR = 0.345) was prognostic of survival. Eight resulting SNPs were prognostic for CRC recurrence (rs2194980, rs1392880, rs2567397, rs715, rs169712, rs2300701, rs313408, rs7018169) and three for survival (rs2194980, rs169712, rs12106698) of which two overlapped with recurrence (rs2194980, rs169712).

CONCLUSIONS

With a caveat on study heterogeneity, specific metabolites and metabolic pathway activity appear evident in the setting of poor prognostic colorectal cancers and such metabolic signatures are associated with specific genomic SNPs.

Multi-Omics Prognostic Signatures Based on Lipid Metabolism for Colorectal Cancer

Background: The potential biological processes and laws of the biological components in malignant tumors can be understood more systematically and comprehensively through multi-omics analysis. This study elaborately explored the role of lipid metabolism in the prognosis of colorectal cancer (CRC) from the metabonomics and transcriptomics.

Methods: We performed K-means unsupervised clustering algorithm and t test to identify the differential lipid metabolites determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) in the serum of 236 CRC patients of the First Hospital of Jilin University (JLUFH). Cox regression analysis was used to identify prognosis-associated lipid metabolites and to construct multi-lipid-metabolite prognostic signature. The composite nomogram composed of independent prognostic factors was utilized to individually predict the outcome of CRC patients. Glycerophospholipid metabolism was the most significant enrichment pathway for lipid metabolites in CRC, whose related hub genes (GMRHGs) were distinguished by gene set variation analysis (GSVA) and weighted gene co-expression network analysis (WGCNA). Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis were utilized to develop the prognostic signature.

Results: Six-lipid-metabolite and five-GMRHG prognostic signatures were developed, indicating favorable survival stratification effects on CRC patients. Using the independent prognostic factors as variables, we established a composite nomogram to individually evaluate the prognosis of CRC patients. The AUCs of one-, three-, and five-year ROC curves were 0.815, 0.815, and 0.805, respectively, showing auspicious prognostic accuracy. Furthermore, we explored the potential relationship between tumor microenvironment (TME) and immune infiltration. Moreover, the mutational frequency of TP53 in the high-risk group was significantly higher than that in the low-risk group (p < 0.001), while in the coordinate mutational status of TP53, the overall survival of CRC patients in the high-risk group was significantly lower than that in low-risk group with statistical differences.

Conclusion: We identified the significance of lipid metabolism for the prognosis of CRC from the aspects of metabonomics and transcriptomics, which can provide a novel perspective for promoting individualized treatment and revealing the potential molecular biological characteristics of CRC. The composite nomogram including a six-lipid-metabolite prognostic signature is a promising predictor of the prognosis of CRC patients.

Asparagine Metabolism in Tumors Is Linked to Poor Survival in Females with Colorectal Cancer: A Cohort Study

The interplay between the sex-specific differences in tumor metabolome and colorectal cancer (CRC) prognosis has never been studied and represents an opportunity to improve patient outcomes. This study examines the link between tumor metabolome and prognosis by sex for CRC patients. Using untargeted metabolomics analysis, abundances of 91 metabolites were obtained from primary tumor tissues from 197 patients (N = 95 females, N = 102 males) after surgical colectomy for stage I-III CRC. Cox Proportional hazard (PH) regression models estimated the associations between tumor metabolome and 5-year overall survival (OS) and recurrence-free survival (RFS), and their interactions with sex. Eleven metabolites had significant sex differences in their associations with 5-year OS, and five metabolites for 5-year RFS. The metabolites asparagine and serine had sex interactions for both OS and RFS. Furthermore, in the asparagine synthetase (ASNS)-catalyzed asparagine synthesis pathway, asparagine was associated with substantially poorer OS (HR (95% CI): 6.39 (1.78–22.91)) and RFS (HR (95% CI): 4.36 (1.39–13.68)) for female patients only. Similar prognostic disadvantages in females were seen in lysophospholipid and polyamine synthesis. Unique metabolite profiles indicated that increased asparagine synthesis was associated with poorer prognosis for females only, providing insight into precision medicine for CRC treatment stratified by sex.

Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.

GC-MS based metabolite fingerprinting of serous ovarian carcinoma and benign ovarian tumor

The aim of this study is to identify urinary metabolomic profile of benign and malign ovarian tumors patients. Samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and metabolomic tools to define biomarkers that cause differentiation between groups. 7 metabolites were found to be different in patients with ovarian cancer (OC) and benign tumors (BT). R2Y and Q2 values were found to be 0.670 and 0.459, respectively. L-tyrosine, glycine, stearic acid, turanose and L-threonine metabolites were defined as prominent biomarkers. The sensitivity of the model was calculated as 90.72% and the specificity as 82.09%. In the pathway analysis, glutathione metabolism, aminoacyl-tRNA biosynthesis, glycine serine and threonine metabolic pathway, primary bile acid biosynthesis pathways were found to be important. According to the t-test, 29 metabolites were found to be significant in urine samples of OC patients and healthy controls (HC). R2Y and Q2 values were found to be 0.8170 and 0.749, respectively. These results showed that the model has high compatibility and predictive power. Benzoic acid, L-threonine, L-pyroglutamic acid, creatinine and 3,4-dihydroxyphenylacetic acid metabolites were determined as prominent biomarkers. The sensitivity of the model was calculated as 93.81% and the specificity as 98.59%. Glycine serine and threonine metabolic pathway, glutathione metabolism and aminoacyl-tRNA biosynthesis pathways were determined important in OC patients and HC. The R2Y, Q2, sensitivity and specificity values in the urine samples of BT patients and HC were found to be 0.869, 0.794, 91.75, 97.01% and 97.18%, respectively. L-threonine, L-pyroglutamic acid, benzoic acid, creatinine and pentadecanol metabolites were determined as prominent biomarkers. Valine, leucine and isoleucine biosynthesis and aminoacyl-tRNA biosynthesis were significant. In this study, thanks to the untargeted metabolomic approach and chemometric methods, every group was differentiated from the others and prominent biomarkers were determined.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

Metabolic characterization and biomarkers screening for visceral leishmaniasis in golden hamsters

A better understanding of the changes in metabolic molecules during visceral leishmaniasis (VL) is essential to develop new strategies for diagnosis and treatment. Previous metabolomics studies on Leishmania have increased our knowledge of leishmaniasis and its causative pathogen. As these studies were mainly carried out in vitro, to go further, we conducted this global metabolomics analysis on the serum of golden hamsters. Serum samples were detected over a time course of 2, 4, 8 and 12 weeks post infection. Our results revealed that under extensively disturbed metabolomes between the infection group and controls, glycerophospholipid (GPL) metabolism was most affected over the infection time, followed by α-linoleic acid metabolism and arachidonic acid metabolism. Within GPLs, phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were found to be significantly increased, while their enzyme-catalysed metabolites lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) showed no significant changes. Moreover, eight differential metabolites were selected. The ability of these metabolites to be used as a diagnostic biomarker panel was supported by receiver operating characteristic (ROC) analysis. Our findings revealed that GPL metabolism might play an important role in the response of the host to Leishmania infection. The metabolism of PC and PE might be crucial in the in vivo progression of VL. The panel of eight potential biomarkers might contribute to the diagnosis of VL.

Subcellular metabolomics: Isolation, measurement, and applications

Metabolomics, a technique that profiles global small molecules in biological samples, has been a pivotal tool for disease diagnosis and mechanism research. The sample type in metabolomics covers a wide range, including a variety of body fluids, tissues, and cells. However, little attention was paid to the smaller, relatively independent partition systems in cells, namely the organelles. The organelles are specific compartments/places where diverse metabolic activities are happening in an orderly manner. Metabolic disorders of organelles were found to occur in various pathological conditions such as inherited metabolic diseases, diabetes, cancer, and neurodegenerative diseases. However, at the cellular level, the metabolic outcomes of organelles and cytoplasm are superimposed interactively, making it difficult to describe the changes in subcellular compartments. Therefore, characterizing the metabolic pool in the compartmentalized system is of great significance for understanding the role of organelles in physiological functions and diseases. So far, there are very few research articles or reviews related to subcellular metabolomics. In this review, subcellular fractionation and metabolite analysis methods, as well as the application of subcellular metabolomics in the physiological and pathological studies are systematically reviewed, as a practical reference to promote the continued advancement in subcellular metabolomics.

Predictive model for recurrence of renal cell carcinoma by comparing pre- and postoperative urinary metabolite concentrations

To improve treatment outcomes in real practice, useful biomarkers are desired when predicting postoperative recurrence for renal cell carcinoma (RCC). We collected data from patients who underwent definitive surgery for RCC and for benign urological tumor at our department between November 2016 and December 2019. We evaluated the differences in pre‐ and postoperative urinary metabolites with our precise quantitative method and identified predictive factors for RCC recurrence. Additionally, to clarify the significance of metabolites, we measured the intracellular metabolite concentration of three RCC cell lines. Among the 56 patients with RCC, nine had a recurrence (16.0%). When comparing 27 patients with T1a RCC and 10 with benign tumor, a significant difference was observed between pre‐ and postoperative concentrations among 10 urinary metabolites. In these 10 metabolites, multiple logistic regression analysis identified five metabolites (lactic acid, glycine, 2‐hydroxyglutarate, succinic acid, and kynurenic acid) as factors to build our recurrence prediction model. The values of area under the receiver operating characteristic curve, sensitivity, and specificity in this predictive model were 0.894%, 88.9%, and 88.0%, respectively. When stratified into low and high risk groups of recurrence based on this model, we found a significant drop of recurrence‐free survival rates among the high risk group. In in vitro studies, intracellular metabolite concentrations of metastatic tumor cell lines were much higher than those of primary tumor cell lines. By using our quantitative evaluation of urinary metabolites, we could predict postoperative recurrence with high sensitivity and specificity. Urinary metabolites could be noninvasive biomarkers to improve patient outcome.

WDR74 promotes proliferation and metastasis in colorectal cancer cells through regulating the Wnt/β-catenin signaling pathway

Colon cancer (CRC) is a common type of cancer and has a high incidence worldwide. Protein 74 (WDR74), which consists of the WD repetition sequence, has been previously associated with tumor tumorigenesis. However, its mechanism of action in CRC remains unclear. Here, we found that WDR74 expression was upregulated in CRC tissues and cells. Downregulation of WDR74 repressed the proliferation and cell cycles in CRC cells. In addition, WDR74 knockdown induced cell apoptosis and suppressed both cell metastasis and invasion. Mechanistically, WDR74 decreased the phosphorylation of β-catenin and induced nuclear β-catenin accumulation, activating the Wnt/β-catenin signaling pathway in CRC cells. Further investigation showed that blocking the Wnt/β-catenin signaling pathway by XAV-939 reversed the effects of WDR74 on cell proliferation, migration, and invasion in HCT116 cells. Overall, WDR74 induced β-catenin translocation to the nucleus and activated the Wnt/β-Catenin, thus facilitated CRC cell proliferation and metastasis. In summary, WDR74 could be a potential target for the intervention of CRC.

A Positive Feedback Loop of lncRNA MIR31HG-miR-361-3p -YY1 Accelerates Colorectal Cancer Progression Through Modulating Proliferation, Angiogenesis, and Glycolysis

Background

Colorectal cancer (CRC) is a common malignant tumor with high metastatic and recurrent rates. This study probes the effect and mechanism of long non-coding RNA MIR31HG on the progression of CRC cells.

Materials and Methods

Quantitative real-time PCR (qRT-PCR) was used to analyze the expression of MIR31HG and miR-361-3p in CRC tissues and normal tissues. Gain- or loss-of-function assays were conducted to examine the roles of MIR31HG, miR-361-3p and YY1 transcription factor (YY1) in the CRC progression. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and colony formation experiment were conducted to test CRC cell proliferation. CRC cell invasion was determined by Transwell assay. The glucose detection kit and lactic acid detection kit were utilized to monitor the levels of glucose and lactate in CRC cells. The glycolysis level in CRC cells was examined by the glycolytic stress experiment. Western blot was performed to compare the expression of glycolysis-related proteins (PKM2, GLUT1 and HK2) and angiogenesis-related proteins (including VEGFA, ANGPT1, HIF1A and TIMP1) in HUVECs. The binding relationships between MIR31HG and miR-361-3p, miR-361-3p and YY1 were evaluated by the dual-luciferase reporter assay and RNA immunoprecipitation (RIP).

Results

MIR31HG was up-regulated in CRC tissues and was associated with poorer prognosis of CRC patients. The in-vitro and in-vivo experiments confirmed that overexpressing MIR31HG heightened the proliferation, growth, invasion, glycolysis and lung metastasis of CRC cells as well as the angiogenesis of HUVECs. In addition, MIR3HG overexpression promoted YY1 mRNA and protein level, and forced overexpression of YY1 enhanced MIR31HG level. Overexpressing YY1 reversed the tumor-suppressive effect mediated by MIR31HG knockdown. miR-361-3p, which was inhibited by MIR31HG overexpression, repressed the malignant behaviors of CRC cells. miR-361-3p-mediated anti-tumor effects were mostly reversed by upregulating MIR31HG. Further mechanism studies illustrated that miR-361-3p targeted and negatively regulated the expression of YY1.

Conclusion

This study reveals that MIR31HG functions as an oncogenic gene in CRC via forming a positive feedback loop of MIR31HG-miR-361-3p-YY1.

Integration of global metabolomics and lipidomics approaches reveals the molecular mechanisms and the potential biomarkers for postoperative recurrence in early-stage cholangiocarcinoma

Background

Cholangiocarcioma (CCA) treatment is challenging because most of the patients are diagnosed when the disease is advanced, and cancer recurrence is the main problem after treatment, leading to low survival rates. Therefore, our understanding of the mechanism underlying CCA recurrence is essential in order to prevent CCA recurrence and improve patient outcomes.

Methods

We performed ¹H-NMR and UPLC-MS-based metabolomics on the CCA serum. The differential metabolites were further analyzed using pathway analysis and potential biomarker identification.

Results

At an early stage, the metabolites involved in energy metabolisms, such as pyruvate metabolism, and the TCA cycle, are downregulated, while most lipids, including TGs, PCs, PEs, and PAs, are upregulated in recurrence patients. This metabolic feature has been described in cancer stem-like cell (CSC) metabolism. In addition, the CSC markers CD44v6 and CD44v8-10 are associated with CD36 (a protein involved in lipid uptake) as well as with recurrence-free survival. We also found that citrate, sarcosine, succinate, creatine, creatinine and pyruvate, and TGs have good predictive values for CCA recurrence.

Conclusion

Our study demonstrates the possible molecular mechanisms underlying CCA recurrence, and these may associate with the existence of CSCs. The metabolic change involved in the recurrence pathway might be used to determine biomarkers for predicting CCA recurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00266-5.

A pilot study for colorectal carcinoma screening by instant metabolomic profiles using conductive polymer spray ionization mass spectrometry

BACKGROUND

The rapid and accurate discrimination of colorectal carcinoma (CRC) and polyps at the molecular level enables early intervention of CRC, which can greatly improve the 5-year survival rate of patients. Here we reported the potential of conductive polymer spray ionization mass spectrometry (CPSI-MS) in successfully screening CRC according to the serum metabolic profile.

METHODS

Trace intravenous blood (50 μL) was collected from 60 colorectal carcinoma (CRC) and 60 polyp patients, respectively. After centrifugation, serum (2 μL) was loaded onto the tip of conductive polymer to form a dried serum spot. When the 5 μL methanol-water (1:1, v/v) extraction solvent was spiked onto the dried serum spot followed with +4.5 kV high voltage applied on the polymer tip, the extracted components will be ionized and carried into the MS system for direct metabolic profiling.

FINDINGS

There were 51 metabolites discovered to be significantly changed in CRC serum compared to polyps. Combining these metabolites as the characteristic panel, the ideal diagnostic performance was achieved by Lasso regression model with the accuracy of 88.3%.

INTERPRETATION

This pilot study demonstrated the potential of CPSI-MS as a cost-effective tool in large-scale CRC screening in the high-risk population.

Metabolomics in cancer research and emerging applications in clinical oncology

Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.

Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms

Analysis of metabolomics has been suggested as a promising approach for early detection of colorectal cancer and advanced adenomas. We investigated and compared the metabolomics profile in blood, stool, and urine samples of screening colonoscopy participants and aimed to evaluate differences in metabolite concentrations between people with advanced colorectal neoplasms and those without neoplasms. Various types of bio-samples (plasma, feces, and urine) from 400 participants of screening colonoscopy were investigated using the MxP® Quant 500 kit (Biocrates, Innsbruck, Austria). We detected a broad range of metabolites in blood, stool, and urine samples (504, 331, and 131, respectively). Significant correlations were found between concentrations in blood and stool, blood and urine, and stool and urine for 93, 154, and 102 metabolites, of which 68 (73%), 126 (82%), and 39 (38%) were positive correlations. We found significant differences between participants with and without advanced colorectal neoplasms for concentrations of 123, 49, and 28 metabolites in blood, stool and urine samples, respectively. We detected mostly positive correlations between metabolite concentrations in blood samples and urine or stool samples, and mostly negative correlations between urine and stool samples. Differences between subjects with and without advanced colorectal neoplasms were found for metabolite concentrations in each of the three bio-fluids.

Lipidomic Signatures for Colorectal Cancer Diagnosis and Progression Using UPLC-QTOF-ESI+MS

Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I to IV) compared with 16 controls (disease-free, non-CRC patients), using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI⁺ MS). More than 400 metabolites were separated and identified, then all data were processed by the advanced Metaboanalyst 5.0 online software, using multi- and univariate analysis, including specificity/sensitivity relationships (area under the curve (AUC) values), enrichment and pathway analysis, identifying the specific pathways affected by cancer progression in the different stages. Several sub-classes of lipids including phosphatidylglycerols (phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and PAs), fatty acids and sterol esters as well as ceramides confirmed the “lipogenic phenotype” specific to CRC development, namely the upregulated lipogenesis associated with tumor progression. Both multivariate and univariate bioinformatics confirmed the relevance of some putative lipid biomarkers to be responsible for the altered metabolic pathways in colorectal cancer.

Pre-Diagnostic Circulating Metabolites and Colorectal Cancer Risk in the Cancer Prevention Study-II Nutrition Cohort

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through 30 June 2015. In this nested case–control study, controls were matched 1:1 to cases on age, sex, race and date of blood draw. Mass spectroscopy-based metabolomic analyses of pre-diagnostic plasma identified 886 named metabolites, after quality control exclusions. Conditional logistic regression models estimated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) for 1 standard deviation (SD) increase in each metabolite with risk of colorectal cancer. Six metabolites were associated with colorectal cancer risk at a false discovery rate < 0.20. These metabolites were of several classes, including cofactors and vitamins, nucleotides, xenobiotics, lipids and amino acids. Five metabolites (guanidinoacetate, 2’-O-methylcytidine, vanillylmandelate, bilirubin (E,E) and N-palmitoylglycine) were positively associated (OR per 1 SD = 1.29 to 1.32), and one (3-methylxanthine) was inversely associated with CRC risk (OR = 0.79, 95% CI, 0.69–0.89). We did not replicate findings from two earlier prospective studies of 250 cases each after adjusting for multiple comparisons. Large pooled prospective analyses are warranted to confirm or refute these findings and to discover and replicate metabolites associated with colorectal cancer risk.

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method

Introduction: Colorectal cancer (CRC) is one of the most important health problems in the Western world. In order to reduce the burden of the disease, two strategies are proposed: screening and prompt detection in symptomatic patients. Although diagnosis and prevention are mainly based on colonoscopy, fecal hemoglobin detection has been widely implemented as a noninvasive strategy. Various studies aiming to discover blood-based biomarkers have recently emerged.Areas covered: The burgeoning omics field provides diverse high-throughput approaches for CRC blood-based biomarker discovery. In this review, we appraise the most robust and commonly used technologies within the fields of genomics, transcriptomics, epigenomics, proteomics, and metabolomics, together with their targeted validation approaches. We summarize the transference process from the discovery phase until clinical translation. Finally, we review the best candidate biomarkers and their potential clinical applicability.Expert opinion: Some available biomarkers are promising, especially in the field of epigenomics: DNA methylation and microRNA. Transference requires the joint collaboration of basic researchers, intellectual property experts, technology transfer officers and clinicians. Blood-based biomarkers will be selected not only based on their diagnostic accuracy and cost but also on their reliability, applicability to clinical analysis laboratories and their acceptance by the population.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review

OBJECTIVE

This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making.

BACKGROUND

Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants.

METHODS

PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods.

RESULTS

Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses.

CONCLUSIONS

Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.