Untargeted GC-MS-Based Metabolomics for Early Detection of Colorectal Cancer

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I-IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Serum Metabolomics Uncovers the Mechanisms of Inulin in Preventing Non-Alcoholic Fatty Liver Disease

Inulin may be a promising therapeutic molecule for treating non-alcoholic fatty liver disease (NAFLD). However, the underlying mechanisms of its therapeutic activity remain unclear. To address this issue, a high-fat-diet-induced NAFLD mouse model was developed and treated with inulin. The NAFLD phenotype was evaluated via histopathological analysis and biochemical parameters, including serum levels of alanine aminotransferase, aspartate aminotransferase, liver triglycerides, etc. A serum metabolomics study was conducted using ultra-performance liquid chromatography coupled with tandem mass spectrometry. The results revealed that inulin mitigated NAFLD symptoms such as histopathological changes and liver cholesterol levels. Through the serum metabolomics study, 347 differential metabolites were identified between the model and control groups, and 139 differential metabolites were identified between the inulin and model groups. Additionally, 48 differential metabolites (such as phosphatidylserine, dihomo-γ-linolenic acid, L-carnitine, and 13-HODE) were identified as candidate targets of inulin and subjected to pathway enrichment analysis. The results revealed that these 48 differential metabolites were enriched in several metabolic pathways such as fatty acid biosynthesis and cardiolipin biosynthesis. Taken together, our results suggest that inulin might attenuate NAFLD partially by modulating 48 differential metabolites and their correlated metabolic pathways, constituting information that might help us find novel therapies for NAFLD.

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or  ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Identification of Volatile Markers of Colorectal Cancer from Tumor Tissues Using Volatilomic Approach

The human body releases numerous volatile organic compounds (VOCs) through tissues and various body fluids, including breath. These compounds form a specific chemical profile that may be used to detect the colorectal cancer CRC-related changes in human metabolism and thereby diagnose this type of cancer. The main goal of this study was to investigate the volatile signatures formed by VOCs released from the CRC tissue. For this purpose, headspace solid-phase microextraction gas chromatography-mass spectrometry was applied. In total, 163 compounds were detected. Both cancerous and non-cancerous tissues emitted 138 common VOCs. Ten volatiles (2-butanone; dodecane; benzaldehyde; pyridine; octane; 2-pentanone; toluene; p-xylene; n-pentane; 2-methyl-2-propanol) occurred in at least 90% of both types of samples; 1-propanol in cancer tissue (86% in normal one), acetone in normal tissue (82% in cancer one). Four compounds (1-propanol, pyridine, isoprene, methyl thiolacetate) were found to have increased emissions from cancer tissue, whereas eleven showed reduced release from this type of tissue (2-butanone; 2-pentanone; 2-methyl-2-propanol; ethyl acetate; 3-methyl-1-butanol; d-limonene; tetradecane; dodecanal; tridecane; 2-ethyl-1-hexanol; cyclohexanone). The outcomes of this study provide evidence that the VOCs signature of the CRC tissue is altered by the CRC. The volatile constituents of this distinct signature can be emitted through exhalation and serve as potential biomarkers for identifying the presence of CRC. Reliable identification of the VOCs associated with CRC is essential to guide and tune the development of advanced sensor technologies that can effectively and sensitively detect and quantify these markers.

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

Background

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

Methods

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

Results

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

Conclusion

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

Determination of the Minimum Sample Amount for Capillary Electrophoresis-Fourier Transform Mass Spectrometry (CE-FTMS)-Based Metabolomics of Colorectal Cancer Biopsies

The minimum sample volume for capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) useful for analyzing hydrophilic metabolites was investigated using samples obtained from colorectal cancer patients. One, two, five, and ten biopsies were collected from tumor and nontumor parts of the surgically removed specimens from each of the five patients who had undergone colorectal cancer surgery. Metabolomics was performed on the collected samples using CE-FTMS. To determine the minimum number of specimens based on data volume and biological interpretability, we compared the number of annotated metabolites in each sample with different numbers of biopsies and conducted principal component analysis (PCA), hierarchical cluster analysis (HCA), quantitative enrichment analysis (QEA), and random forest analysis (RFA). The number of metabolites detected in one biopsy was significantly lower than those in 2, 5, and 10 biopsies, whereas those detected among 2, 5, and 10 pieces were not significantly different. Moreover, a binary classification model developed by RFA based on 2-biopsy data perfectly distinguished tumor and nontumor samples with 5- and 10-biopsy data. Taken together, two biopsies would be sufficient for CE-FTMS-based metabolomics from a data content and biological interpretability viewpoint, which opens the gate of biopsy metabolomics for practical clinical applications.

A new strategy for searching determinants in colorectal cancer progression through whole-part relationship combined with multi-omics

The high incidence and mortality of colorectal cancer (CRC) and the lack of adequate diagnostic molecules have led to poor treatment outcomes for colorectal cancer, making it particularly important to develop methods to obtain molecular with significant diagnostic effects. Here, we proposed a whole and part study strategy (early-stage colorectal cancer as "part" and colorectal cancer as "whole") to identify specific and co-pathways of change in early-stage and colorectal cancers and to discover the determinants of colorectal cancer development. Metabolite biomarkers discovered in plasma may not necessarily reflect the pathological status of tumor tissue. To explore the determinant biomarkers associated with plasma and tumor tissue in the CRC progression, multi-omics were performed on three phases of biomarker discovery studies (discovery, identification and validation) including 128 plasma metabolomes and 84 tissue transcriptomes. Importantly, we observe that the metabolic levels of oleic acid and FA (18:2) in patients with colorectal cancer were much higher than in healthy people. Finally, biofunctional verification confirmed that oleic acid and FA (18:2) can promote the growth of colorectal cancer tumor cells and be used as plasma biomarkers for early-stage colorectal cancer. We propose a novel research strategy to discover co-pathways and important biomarkers that may be targeted for a potential role in early colorectal cancer, and our work provides a promising tool for the clinical diagnosis of colorectal cancer.

[Integrated analysis of serum untargeted metabolomics and targeted bile acid metabolomics for identification of diagnostic biomarkers for colorectal cancer]

OBJECTIVE

To identify potential diagnostic biomarkers of colorectal cancer (CRC) using serum metabolomic technology for minimally invasive and efficient screening for CRC.

METHODS

Serum samples from 79 healthy individuals and 82 CRC patients were analyzed by metabolomics using ultra-high-performance liquid chromatography-tandem highresolution mass spectrometry (UHPLC-HRMS). The differential metabolites between the two groups were analyzed using principal component analysis and orthogonal partial least squares discriminant analysis (OPLS-DA). Receiver operating characteristic curve (ROC) analysis was performed to identify the differential metabolites with good diagnostic performance (AUC>0.80) for CRC, and targeted bile acid metabolomics was used to verify the selected bile acids as biomarkers.

RESULTS

Serum metabolic profiles differed significantly between the healthy individuals and CRC patients, and a total of 82 differential metabolites (mostly fatty acids and glycerophospholipids) were selected. ROC analysis identified 10 differential metabolites, including adenine, bilirubin, ACar 12:0, ACar 10:1, ACar 9:0, PC 18:2e, deoxycholic acid, chenodeoxycholic acid, ACar 14:1 and palmitoylcarnitine. One of these metabolites was significantly up-regulated and 9 were down-regulated in the serum of CRC patients (P < 0.05). Multivariate ROC analysis with support vector machine algorithm showed that the biomarker panel consisting of 7 differential metabolites had an AUC of 0.94 for CRC diagnosis. The results of targeted bile acid metabolomics were consistent with those of untargeted metabolomics. The serum levels of deoxycholic acid and chenodeoxycholic acid were significantly down-regulated in patients with CRC as compared with the healthy individuals (P < 0.05).

CONCLUSION

Metabolic disorders of fatty acids and glycerophospholipids are closely related wigh tumorigenesis of CRC. Ten differential metabolites show good performance for CRC diagnosis, and the panel consisting 7 of these metabolites has important diagnostic value for CRC. Deoxycholic acid and chenodeoxycholic acid may serve as potential diagnostic biomarkers of CRC.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

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

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

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

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

Functional and embedding feature analysis for pan-cancer classification

With the increasing number of people suffering from cancer, this illness has become a major health problem worldwide. Exploring the biological functions and signaling pathways of carcinogenesis is essential for cancer detection and research. In this study, a mutation dataset for eleven cancer types was first obtained from a web-based resource called cBioPortal for Cancer Genomics, followed by extracting 21,049 features from three aspects: relationship to GO and KEGG (enrichment features), mutated genes learned by word2vec (text features), and protein-protein interaction network analyzed by node2vec (network features). Irrelevant features were then excluded using the Boruta feature filtering method, and the retained relevant features were ranked by four feature selection methods (least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Monte Carlo feature selection and light gradient boosting machine) to generate four feature-ranked lists. Incremental feature selection was used to determine the optimal number of features based on these feature lists to build the optimal classifiers and derive interpretable classification rules. The results of four feature-ranking methods were integrated to identify key functional pathways, such as olfactory transduction (hsa04740) and colorectal cancer (hsa05210), and the roles of these functional pathways in cancers were discussed in reference to literature. Overall, this machine learning-based study revealed the altered biological functions of cancers and provided a reference for the mechanisms of different cancers.

Extracellular vesicles expressing CEACAM proteins in the urine of bladder cancer patients

Early detection and long‐term monitoring are important for urothelial carcinoma of the bladder (UCB). Urine cytology and existing markers have insufficient diagnostic performance. Here, we examined medium‐sized extracellular vesicles (EVs) in urine to identify specific markers for UCB and evaluated their usefulness as diagnostic material. To identify specific markers in urinary EVs derived from UCB, we undertook shotgun proteomics using urine from four UCB patients and four healthy subjects. Next, 29 healthy specimens, 18 noncancer specimens, and 33 UCB specimens, all from men, were analyzed for urinary EVs by flow cytometry to evaluate the diagnostic performance of UCB‐specific EVs. Nanoparticle‐tracking analysis indicated that the size of EVs extracted from urine was mostly <400 nm. By shotgun proteomics, we detected several proteins characteristic of UCB and found that carcinoembryonic antigen‐related adhesion molecule (CEACAM) proteins were increased in patients. Flow cytometric analysis revealed that the degree of expression of CEACAM1, CEACAM5, and CEACAM6 proteins on the surface of EVs varied among patients. Extracellular vesicles expressing CEACAM proteins also expressed mucin 1, suggesting that they were derived from tumorigenic uroepithelial cells. The number of EVs expressing CEACAM1, 5, and 6 proteins was significantly increased in UCB (mean ± SD, 8.6 ± 13%) compared to non‐UCB (0.69 ± 0.46) and healthy (0.46 ± 0.34) by flow cytometry. The results of receiver operating characteristic (ROC) analysis showed a good score of area under the ROC curve of 0.907. We identified EVs that specifically express CEACAM proteins in urine and have potential for diagnostic applications. These EVs are potential targets in a new liquid biopsy test for UCB patients.