The Impact of Beeswax and Glycerol Monolaurate on Camellia Oil Oleogel's Formulation and Application in Food Products.

This study assessed the nutritional profile of camellia oil through its fatty acid composition, highlighting its high oleic acid content (81.4%), followed by linoleic (7.99%) and palmitic acids (7.74%), demonstrating its excellence as an edible oil source. The impact of beeswax (BW) and glycerol monolaurate (GML) on camellia oil oleogels was investigated, revealing that increasing BW or GML concentrations enhanced hardness and springiness, with 10% BW oleogel exhibiting the highest hardness and springiness. FTIR results suggested that the structure of the oleogels was formed by interactions between molecules without altering the chemical composition. In biscuits, 10% BW oleogel provided superior crispness, expansion ratio, texture, and taste, whereas GML imparted a distinct odor. In sausages, no significant differences were observed in color, water retention, and pH between the control and replacement groups; however, the BW group scored higher than the GML group in the sensory evaluation. The findings suggest that the BW oleogel is an effective fat substitute in biscuits and sausages, promoting the application of camellia oil in food products.

eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Evaluation of prediction effect of perfusion index for supraclavicular brachial plexus block in children: protocol for a randomized trial.

BACKGROUND: Pulse perfusion index (PI) reflects blood perfusion. It has been reported that PI can be used to evaluate the effect of nerve block, but currently, it is mainly focused on awake adults. In pediatric general anesthesia, it has been reported that PI can evaluate the effect of the sacral block. Still, there is a lack of relevant research on the impact of brachial plexus blocks. Our objective is to assess the prediction effects of PI on the success of supraclavicular brachial plexus block in pediatric patients under sevoflurane or propofol general anesthesia. METHODS/DESIGN: This is a mono-center, parallel, 2-arm randomized superiority trial. One hundred four children aged 1 month to 12 years who undergo upper limb surgery will be enrolled in this study. According to anesthesia induction and maintenance medication, they will be divided into sevoflurane and propofol groups. The PI values of the index and little finger will be recorded on the blocked and non-blocked sides of supraclavicular brachial plexus block (SCB) in all children. The primary outcome is to assess the effects of PI on the success of supraclavicular brachial plexus block in pediatric patients under sevoflurane or propofol general anesthesia. The secondary outcome includes mean arterial blood pressure (MAP), heart rate (HR), and correlation between baseline PI and 10 min after SCB (PI ratio). DISCUSSION: This trial will provide evidence on the changes in PI after SCB in sevoflurane or propofol anesthesia in children. SCB may lead to changes in PI values under sevoflurane or propofol anesthesia. After the children wake up at the end of the surgery, the changes in PI values on the block side and non-block side may be helpful to judge the effect of nerve block when excluding the influence of anesthetics. TRIAL REGISTRATION: ClinicalTrials.gov NCT04216823 . Registered on 15 July 2020.

Probiotics-fermented blueberry juices as potential antidiabetic product: antioxidant, antimicrobial and antidiabetic potentials.

BACKGROUND: Fermentation is a traditional food-preserving technique. It is an effective process, widely used to enrich the nutrients diversity and bioactivity of the fermented foods since ancient times. This study aimed at investigating the effects of various fermentation starters on the physicochemical, antioxidant, antimicrobial, and antidiabetic properties of blueberry juices. The blueberry juices were fermented by natural fermentation (NFBJ), self-made starters fermentation (SFBJ), and commercial starters fermentation (CFBJ); fresh blueberry juice (BBJ) was processed without fermentation for comparison. RESULTS: Probiotics-fermented blueberry juices (SFBJ and CFBJ) showed less total and reducing sugars, higher titratable acidity, and a wider variety and higher amounts of organic acids than non-fermented blueberry juice (BBJ) did. All the fermented blueberry juices (NFBJ, SFBJ, and CFBJ) showed significantly (P < 0.05) higher antioxidant potentials than that of BBJ measured by 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid, cupric-reducing antioxidant capacity, and ferric-reducing ability power assays. The SFBJ exhibited the highest antibacterial activities against Escherichia coli, Staphylococcus aureus, and Salmonella Typhimurium, with inhibition zone diameters of 38.84 ± 1.74 mm, 34.91 ± 1.53 mm, and 36.18 ± 3.16 mm respectively. Compared with BBJ, the α-glucosidase inhibitory activity of the SFBJ and CFBJ increased by two-to threefold. The α-amylase inhibitory activity of the SFBJ and CFBJ increased by 600%, whereas the spontaneous fermentation showed no improvement. The SFBJ and CFBJ promoted glucose consumption of HepG2 cell lines, indicating the promising potential for a higher glucose bio-utilization. CONCLUSIONS: The SFBJ and CFBJ showed remarkable improvements in the antioxidant, antimicrobial, and antidiabetic activities compared with non-fermented and spontaneous fermented juices, indicating their promising potentials as an antihyperglycemic agent. © 2021 Society of Chemical Industry.

Exploring the potential of novel xanthine oxidase inhibitory peptide (ACECD) derived from Skipjack tuna hydrolysates using affinity-ultrafiltration coupled with HPLC-MALDI-TOF/TOF-MS.

This study aimed to isolate and investigate the potential of the peptide alanine-cysteine-glutamic acid-cysteine-aspartic acid (ACECD), a novel xanthine oxidase inhibitory (XODI) peptide derived from Skipjack tuna hydrolysate (HS). Ultrafiltration membranes were used to obtain HS-based peptides as successive ultrafiltration fractions (of decreasing molecular weight) of UF-1, UF-2, UF-3, and UF-4. Their antioxidant and xanthine oxidase (XOD) inhibitory activities were determined and further characterized by affinity-ultrafiltration coupled with HPLC-MALDI-TOF/TOF-MS and in silico techniques. The results showed that peptides with a molecular weight (MW) cutoff of 600-1000 Da (UF-2) exhibited the highest antioxidant and XODI activities. A novel XODI peptide (ACECD) was identified with an IC50 value of 13.40 mmol/L, which decreased by 21.24% and 51.40% compared to those of UF-2 and HS, respectively. Molecular docking indicated that ACECD inserted into the active center of Mo atoms in XOD, which led to competitive attachment with XOD and caused inhibition. The study findings indicated that the ACECD peptide could be useful as a safe XODI substance to alleviate hyperuricemia.

Magnetic resonance imaging for evaluating tumor resectability in advanced ovarian cancer: A protocol for systematic review and meta-analysis.

BACKGROUND: This study will evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) to investigate tumor resectability at primary debulking surgery among women experiencing advanced-stage ovarian cancer. METHODS: We will systematically search the randomized controlled trials (RCTs) for potentially eligible studies from electronic databases, including 4 English databases (PubMed, EMBASE, Web of Science, and Cochrane Library) and 3 Chinese databases (China National Knowledge Infrastructure, WanFang, and China Biomedical Database). The study language will be restricted to English and Chinese. Also, 2 independent authors will collect and carry out data extraction as well as quality assessment. Data will be synthesized using appropriate statistical methods. RESULTS: We will summarize present study's evidence to assess the diagnostic accuracy of MRI for evaluating tumor resectability at primary debulking surgery in women experiencing advanced-stage ovarian cancer. CONCLUSION: The present study will put forward the latest high-quality evidence for MRI's clinical application for evaluating tumor resectability in women experiencing advanced ovarian cancer. ETHICS AND DISSEMINATION: Since the present study is a systematic review and meta-analysis based on the published literature, ethical approval will not be necessary. PROTOCOL REGISTRATION NUMBER: DOI 10.17605/OSF.IO/UWDRF (https://osf.io/uwdrf/).

Magnetic resonance imaging for the non-invasive diagnosis in patients with ovarian cancer: A protocol for systematic review and meta-analysis.

BACKGROUND: In developed nations, ovarian cancer has resulted in the most fatalities from gynecological cancer. Laparoscopy is primarily utilized as the test to diagnose ovarian cancer. Besides being costly, there are surgical risks associated with laparoscopies. At present, clinical practitioners have access to non-invasive tests for diagnosing ovarian cancer. This study aims to evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) for diagnosing ovarian cancer. METHODS: In order to obtain eligible studies, cross-sectional studies or randomized controlled trials are searched in electronic databases. The databases include 5 English databases (PubMed, the Cochrane Library, PsycINFO, EMBASE, and Web of Science) and 3 Chinese databases (China Biomedical Literature Database, China National Knowledge Infrastructure, and WanFang database). The databases are searched from their origin to October 2020. Quality Assessment of Diagnostic Accuracy Studies-2 is used to assess the methodological quality of the selected studies. RevMan 5.3 and SAS NLMIXED software are used to assess the data synthesis, sensitivity analysis, and risk of bias assessment. RESULTS: This study evaluates the pooled diagnostic value of MRI for diagnosing ovarian cancer. CONCLUSIONS: This study will summarize previously published evidence of MRI in relation to diagnosing ovarian cancer. ETHICS AND DISSEMINATION: Since this study does not utilize data from patients, this protocol does not require ethical approval. PROTOCOL REGISTRATION NUMBER: DOI 10.17605/OSF.IO/A6SPQ (https://osf.io/a6spq).

Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

Electrospun Chitosan/Poly(ethylene oxide)/Lauric Arginate Nanofibrous Film with Enhanced Antimicrobial Activity.

In this study, chitosan/poly(ethylene oxide) (PEO)/lauric arginate (LAE) composite nanofibrous films were fabricated via electrospinning. The addition of LAE did not change the physical properties of chitosan/PEO in acetic aqueous solutions, but increased the fluorescent intensity of chitosan by electrostatic interactions, resulting in uniform and bead-free nanofibers with an average diameter of 150 nm. The Fourier transform infrared spectra and thermal analysis indicated that the LAE molecules were homogeneously dispersed within the chitosan/PEO nanofibers. The formation of electrostatic and hydrogen bonding interactions induced by the LAE addition changed the inter- and intramolecular interactions between PEO and chitosan and further affected the mobility of the polymer molecules, leading to the increased crystallinity and decreased melting point. The hydrophilicity of the nanofibrous films was significantly increased by the incorporation of LAE, as indicated by the decreasing water contact angle from 39° to 10°. Meanwhile, the chitosan/PEO/LAE nanofibrous films showed LAE concentration dependent antimicrobial activity against Escherichia coli and Staphylococcus aureus, suggesting enhanced antimicrobial activity. The fluorescent staining experiments demonstrated that the antimicrobial mechanism of the nanofibrous films was cell membrane damage.

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis.

Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.

Physical characterization and antioxidant activity of thymol solubilized Tween 80 micelles.

Attempts were made to solubilize thymol in Tween 80 micelle to study the solubilization mechanism of thymol and the effect of solubilization on its antioxidant activity. The maximum solubilized concentration of thymol in a 2.0% (w/v) Tween 80 micelle solution is 0.2 wt%. There was no significant difference in Z-average diameter between the empty micelles and thymol solubilized micelles. 1H NMR spectra indicated that 3-H and 4-H on the benzene ring of thymol interacted with the ester group between the hydrophilic head group and the hydrophobic tail group of Tween 80 by Van der Waals' force. Ferric reducing antioxidant potential (FRAP) and cupric ion reducing antioxidant capacity (CUPRAC) assays showed that the reducing antioxidant activity of free thymol did not change after solubilized in Tween 80 micelles. Compared to free thymol, the solubilized thymol showed higher activities to scavenge DPPH (2,2-diphenyl-1-picrylhydrazyl) and hydroxyl radicals. The present study suggested a possible preparation of thymol-carrying micelles with enhanced antioxidant activities that could be applied in food beverages.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls.

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring.

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.

Solubilization of tea seed oil in a food-grade water-dilutable microemulsion.

Food-grade microemulsions containing oleic acid, ethanol, Tween 20, and water were formulated as a carrier system for tea seed oil (Camellia oleifera Abel.). The effect of ethanol on the phase behavior of the microemulsion system was clearly reflected in pseudo-ternary diagrams. The solubilization capacity and solubilization efficiency of tea seed oil dispersions were measured along the dilution line at a 70/30 surfactant/oil mass ratio with Tween 20 as the surfactant and oleic acid and ethanol (1:3, w/w) as the oil phase. The dispersed phase of the microemulsion (1.5% weight ratio of tea seed oil to the total amount of oil, surfactant, and tea seed oil) could be fully diluted with water without phase separation. Differential scanning calorimetry and viscosity measurements indicated that both the carrier and solubilized systems underwent a similar microstructure transition upon dilution. The dispersion phases gradually inverted from the water-in-oil phase (< 35% water) to the bicontinuous phase (40-45% water) and finally to the oil-in-water phase (> 45% water) along the dilution line.

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression.

Despite the fact that colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world, the development of improved and robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC continues to be evasive. In particular, patients with colon polyps are at higher risk of developing colon cancer; however, noninvasive methods to identify these patients suffer from poor performance. In consideration of the challenges involved in identifying metabolite biomarkers in individuals with high risk for colon cancer, we have investigated NMR-based metabolite profiling in combination with numerous demographic parameters to investigate the ability of serum metabolites to differentiate polyp patients from healthy subjects. We also investigated the effect of disease risk on different groups of biologically related metabolites. A powerful statistical approach, seemingly unrelated regression (SUR), was used to model the correlated levels of metabolites in the same biological group. The metabolites were found to be significantly affected by demographic covariates such as gender, BMI, BMI(2), and smoking status. After accounting for the effects of the confounding factors, we then investigated potential of metabolites from serum to differentiate patients with polyps and age matched healthy controls. Our results showed that while only valine was slightly associated, individually, with polyp patients, a number of biologically related groups of metabolites were significantly associated with polyps. These results may explain some of the challenges and promise a novel avenue for future metabolite profiling methodologies.

Colorectal cancer detection using targeted serum metabolic profiling.

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.