Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma

OBJECTIVES

Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis.

METHODS

Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously.

RESULTS

Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9.

CONCLUSION

This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

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

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

Effects of pork sausage on intestinal microecology and metabolism in mice

BACKGROUND

Processed meat, as an important part of the human diet, has been recognized as a carcinogen by the International Agency for Research on Cancer (IARC). Although numerous epidemiological reports supported the IARC's view, the relevant evidence of a direct association between processed meat and carcinogenicity has been insufficient and the mechanism has been unclear. This study aims to investigate the effects of pork sausage (as a representative example of processed meat) intake on gut microbial communities and metabolites of mice. Microbial communities and metabolites from all groups were analyzed using 16S rRNA gene sequencing and Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometer (UPLC-Q-TOF/MS), respectively.

RESULTS

The levels of Bacteroidetes, Bacteroides, Alloprevotella, Lactobacillus, Prevotella_9, Lachnospiraceae_NK4A136_group, Alistipes, Blautia, Proteobacteria, Firmicutes, Allobaculum, Helicobacter, Desulfovibrio, Clostridium_sensu_stricto_1, Ruminococcaceae_UCG-014, Lachnospiraceae_UCG-006 and Streptococcus (P < 0.05) were obviously altered in the mice fed a pork sausage diet. Twenty-seven metabolites from intestinal content samples and fourteen matabolites from whole blood samples were identified as potential biomarkers from multivariate analysis, including Phosphatidic acid (PA), Sphingomyelin (SM), Lysophosphatidylcholine (LysoPC), Diglyceride (DG), D-maltose, N-acylamides and so forth. The significant changes in these biomarkers demonstrate metabonomic variations in pork sausage treated rats, especially carbohydrate metabolism, lipid metabolism, and amino acid metabolism.

CONCLUSION

The present study provided evidence that a processed meat diet can increase the risk of colorectal cancer and other diseases significantly by altering the microbial community structure and disrupting the body's metabolic pathways. © 2023 Society of Chemical Industry.

Trends in confinement-induced cell migration and multi-omics analysis

Cell migration is an essential manner of different cell lines that are involved in embryological development, immune responses, tumorigenesis, and metastasis in vivo. Physical confinement derived from crowded tissue microenvironments has pivotal effects on migratory behaviors. Distinct migration modes under a heterogeneous extracellular matrix (ECM) have been extensively studied, uncovering potential molecular mechanisms involving a series of biological processes. Significantly, multi-omics strategies have been launched to provide multi-angle views of complex biological phenomena, facilitating comprehensive insights into molecular regulatory networks during cell migration. In this review, we describe biomimetic devices developed to explore the migratory behaviors of cells induced by different types of confined microenvironments in vitro. We also discuss the results of multi-omics analysis of intrinsic molecular alterations and critical pathway dysregulations of cell migration under heterogeneous microenvironments, highlighting the significance of physical confinement-triggered intracellular signal transduction in order to regulate cellular behaviors. Finally, we discuss both the challenges and promise of mechanistic analysis in confinement-induced cell migration, promoting the development of early diagnosis and precision therapeutics.

Targeting carnitine palmitoyl transferase 1A (CPT1A) induces ferroptosis and synergizes with immunotherapy in lung cancer

Despite the successful application of immune checkpoint therapy, no response or recurrence is typical in lung cancer. Cancer stem cells (CSCs) have been identified as a crucial player in immunotherapy-related resistance. Ferroptosis, a form of cell death driven by iron-dependent lipid peroxidation, is highly regulated by cellular metabolism remolding and has been shown to have synergistic effects when combined with immunotherapy. Metabolic adaption of CSCs drives tumor resistance, yet the mechanisms of their ferroptosis defense in tumor immune evasion remain elusive. Here, through metabolomics, transcriptomics, a lung epithelial-specific Cpt1a-knockout mouse model, and clinical analysis, we demonstrate that CPT1A, a key rate-limiting enzyme of fatty acid oxidation, acts with L-carnitine, derived from tumor-associated macrophages to drive ferroptosis-resistance and CD8+ T cells inactivation in lung cancer. Mechanistically, CPT1A restrains ubiquitination and degradation of c-Myc, while c-Myc transcriptionally activates CPT1A expression. The CPT1A/c-Myc positive feedback loop further enhances the cellular antioxidant capacity by activating the NRF2/GPX4 system and reduces the amount of phospholipid polyunsaturated fatty acids through ACSL4 downregulating, thereby suppressing ferroptosis in CSCs. Significantly, targeting CPT1A enhances immune checkpoint blockade-induced anti-tumor immunity and tumoral ferroptosis in tumor-bearing mice. The results illustrate the potential of a mechanism-guided therapeutic strategy by targeting a metabolic vulnerability in the ferroptosis of CSCs to improve the efficacy of lung cancer immunotherapy.

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

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

Triptolide inhibits proliferation and invasion of colorectal cancer cells by blocking Nrf2 expression

Triptolide (TPL), the main active ingredient of Tripterygium wilfordii, has anti-inflammatory, immunomodulatory, and antitumor actions. It can also inhibit cell proliferation and metastasis while promoting apoptosis of several tumors, such as colorectal cancer (CRC). However, the mechanism of TPL against CRC is not clear. This study was designed to investigate the effects and molecular mechanisms of TPL on the proliferation and invasion ability of CRC cells. A human CRC cell line (HT29 cell line) cultured in vitro was treated with different concentrations of TPL (0, 25, 50, and 100 nmol/L). The proliferation of cells was detected by MTT, the invasion ability of cells by Transwell, and the apoptosis level by flow cytometry. The protein expression levels of nuclear factor-erythroid 2-related factor 2 (Nrf2), matrix metalloproteinase (MMP)-2, and MMP-9 were detected by western blotting. After transfection with sh-Nrf2, HT29 cells were divided into NC group, NC + TPL group and sh-Nrf2 + TPL group, and the above assays were repeated for each group. TPL significantly inhibited the proliferation and invasion ability of HT29 cells and promoted apoptosis (p < .05). Notably, its inhibitory or promotional effects were concentration-dependent, which were enhanced with increasing drug concentration (p < .05). After silencing Nrf2 expression, the proliferation, and invasion ability of HT29 cells were further significantly inhibited while cells apoptosis was further promoted (p < .05). Besides, the decreased Nrf2 expression reduced the protein expression levels of MMP-2 and MMP-9 (p < .05). TPL can effectively inhibit the proliferation and invasion while promoting apoptosis of HT29 cells. And its mechanism of action may be related to the inhibition of Nrf2 signaling expression.

Molecular subtypes identified by multiomics analysis based on cuproptosis-related genes precisely predict response to immunotherapy and chemotherapy in colorectal cancer

Cuproptosis is a newly reported type of programmed cell death that is involved in the progression of various diseases. Some studies have reported its potential significance in multiple tumors. Colorectal cancer (CRC) is one of the malignant tumors with high incidence and mortality. The purpose of this study was to further explore the importance of cuproptosis in the CRC development and treatment. We analyzed the expression, alterations, and promoter methylation of cuproptosis-related genes (CRGs) in patients with CRC. Three machine learning methods was used to determine cuproptosis-related feature genes and a diagnostic model was built based on them. Using the unsupervised clustering, patients with CRC were classified into distinct clusters. Then, the LASSO method was used to establish a cuproptosis risk model. We analyzed the association of risk scores with outcomes, immune microenvironment, response to immunotherapy, and sensitivity to chemotherapeutic drugs. The results showed that the expression of CRGs was dysregulated in CRC. The diagnostic model based on cuproptosis-related feature genes showed great clinical value. The patients in two clusters displayed different prognosis and microenvironment. Furthermore, the risk score was correlated with clinical characteristics, immune infiltration and response to immunotherapy and chemotherapy. Above all, the present findings revealed the involvement of cuproptosis in CRC development and provided a diagnostic tool to evaluate CRC occurrence risk. The immune infiltration and drug sensitivity analysis results helped to predict the response of patients in different subtypes of CRC to immunotherapy and chemotherapy.

A fast and accurate colorimetric assay for quantifying hippuric acid in human urine

Hippuric acid is an abundant metabolite in human urine. Urinary hippuric acid levels change with toxic exposure to aromatic compounds, consumption of fruits and vegetables, cancers, chronic kidney disease, schizophrenia and Crohn's disease. While urinary hippuric acid can be detected and quantified via mass spectrometry or nuclear magnetic resonance spectroscopy, a colorimetric assay would be preferable for a low-cost, point-of care clinical assay. Two colorimetric methods, that use p-dimethylaminobenzaldehyde (DMAB) or benzenesulfonyl chloride (PhSO2Cl), respectively, have been previously developed to detect hippuric acid but these assays have many limitations. We replaced PhSO2Cl with p-toluenesulfonyl chloride (p-TsCl), to create a simpler, faster and more accurate method that works with human urine. This modified colorimetric assay detects from 60 μM to 1000 μM hippuric acid in urine in 2 min. We also corrected for the effects of interfering compounds present in urine such that the assay works across many urine backgrounds. We validated this improved assay on multiple hippurate-spiked urine samples, observing an excellent correlation (R2 > 0.94) between observed and known hippurate concentrations. These data suggest that this colorimetric assay is accurate and should greatly facilitate the measurement of hippuric acid in urine to detect a variety of human conditions.

Integrated pathway and network analyses of metabolomic alterations in peripheral blood of patients with depression

Depression is a serious mental illness, but the molecular mechanisms of depression remain unclear. Previous research has reported metabolomic changes in the blood of patients with depression, while integrated analysis based on these altered metabolites was still lacking. The objective of this study was to integrate metabolomic changes to reveal the underlying molecular alternations of depression. We retrieved altered metabolites in the blood of patients with depression from the MENDA database. Pathway analysis was conducted to explore enriched pathways based on candidate metabolites. Pathway crosstalk analysis was performed to explore potential correlations of these enriched pathways, based on their shared candidate metabolites. Moreover, potential interactions of candidate metabolites with other biomolecules such as proteins were assessed by network analysis. A total of 854 differential metabolite entries were retrieved in peripheral blood of patients with depression, including 555 unique candidate metabolites. Pathway analysis identified 215 significantly enriched pathways, then pathway crosstalk analysis revealed that these pathways were clustered into four modules, including amino acid metabolism, nucleotide metabolism, energy metabolism and others. Additionally, eight molecular networks were identified in the molecular network analysis. The main functions of these networks involved amino acid metabolism, molecular transport, inflammatory responses and others. Based on integrated analysis, our study revealed pathway-based modules and molecular networks associated with depression. These results will contribute to the underlying knowledge of the molecular mechanisms in depression.

Multi-omics analysis of fecal samples in colorectal cancer Egyptians patients: a pilot study

BACKGROUND

Colorectal cancer (CRC) is a public health concern and the second most common disease worldwide. This is due to genetic coding and is influenced by environmental aspects, in which the gut microbiota plays a significant role. The purpose of this study was to compare the microbiota makeup of CRC patients with that of healthy control and to identify upregulated and downregulated proteins and metabolites in CRC patients. Using a next-generation sequencing approach, fecal samples of five females (4 CRC patients and one healthy control) were analyzed by BGI DNBSEQ-T7, Hong Kong, China. Furthermore, proteomics and metabolomics analysis were performed using LC-MS/MS technique.

RESULTS

Dysbiosis of gut microbiota has been observed in patients with CRC, with an increase in microbiota diversity at all taxonomic levels relative to healthy control. Where, at the functional level the bacterial species participate in many different pathways among them de novo nucleotide synthesis and amino acids pathways were aberrantly upregulated in CRC patients. Proteomics and metabolomics profiles of CRC patients showed different proteins and metabolites, a total of 360 and 158 proteins and metabolites, respectively were highly expressed compared to healthy control with fold change ≥ 1.2. Among the highly expressed proteins were transketolase, sushi domain-containing protein, sulfide quinone oxidoreductase protein, AAA family ATPase protein, carbonic anhydrase, IgG Fc-binding protein, nucleoside diphosphate kinase protein, arylsulfatase, alkaline phosphatase protein, phosphoglycerate kinase, protein kinase domain-containing protein, non-specific serine/threonine protein kinase, Acyl-CoA synthetase and EF-hand domain-containing protein. Some of the differential metabolites, Taurine, Taurocholic acid, 7-ketodeoxycholic acid, Glycochenodeoxycholic acid, Glycocholic acid, and Taurochenodeoxycholic acid that belong to bile acids metabolites.

CONCLUSIONS

Some bacterial species, proteins, and metabolites could be used as diagnostic biomarkers for CRC. Our study paves an insight into using multi-omics technology to address the relationship between gut microbiota and CRC.

Effectiveness of Treatments That Alter Metabolomics in Cancer Patients-A Systematic Review

INTRODUCTION

Cancer is the leading cause of death worldwide, with the most frequent being breast cancer in women, prostate cancer in men and colon cancer in both sexes. The use of metabolomics to find new biomarkers can provide knowledge about possible interventions based on the presence of oncometabolites in different cancer types.

OBJECTIVES

The primary purpose of this review is to analyze the characteristic metabolome of three of the most frequent cancer types. We further want to identify the existence and success rate of metabolomics-based intervention in patients suffering from those cancer types. Our conclusions are based on the analysis of the methodological quality of the studies.

METHODS

We searched for studies that investigated the metabolomic characteristics in patients suffering from breast cancer, prostate cancer or colon cancer in clinical trials. The data were analyzed, as well as the effects of specific interventions based on identified metabolomics and one or more oncometabolites. The used databases were PubMed, Virtual Health Library, Web of Science, EBSCO and Cochrane Library. Only nine studies met the selection criteria. Study bias was analyzed using the Cochrane risk of bias tool. This systematic review protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023401474).

RESULTS

Only nine studies about clinical trials were included in this review and show a moderate quality of evidence. Metabolomics-based interventions related with disease outcome were conflictive with no or small changes in the metabolic characteristics of the different cancer types.

CONCLUSIONS

This systematic review shows some interesting results related with metabolomics-based interventions and their effects on changes in certain cancer oncometabolites. The small number of studies we identified which fulfilled our inclusion criteria in this systematic review does not allow us to draw definitive conclusions. Nevertheless, some results can be considered as promising although further research is needed. That research must focus not only on the presence of possible oncometabolites but also on possible metabolomics-based interventions and their influence on the outcome in patients suffering from breast cancer, prostate cancer or colon cancer.

Gut Microbial and Associated Metabolite Markers for Colorectal Cancer Diagnosis

Globally, colorectal cancer (CRC) is the second most common cause of mortality worldwide. Considerable evidence indicates that dysbiosis of the gut microbial community and its metabolite secretions play a fundamental role in advanced adenoma (ADA) and CRC development and progression. This study is a systematic review that aims to assess the clinical association between gut microbial markers and/or gut and circulating metabolites with ADA and CRC. Five electronic databases were searched by four independent reviewers. Only controlled trials that compared ADA and/or CRC with healthy control (HC) using either untargeted (16s rRNA gene or whole genome sequencing) or targeted (gene-based real-time PCR) identification methods for gut microbiome profile, or untargeted or targeted metabolite profiling approaches from the gut or serum/plasma, were eligible. Three independent reviewers evaluated the quality of the studies using the Cochrane Handbook for Systematic Reviews of Interventions. Twenty-four studies were eligible. We identified strong evidence of two microbial markers Fusobacterium and Porphyromonas for ADA vs. CRC, and nine microbial markers Lachnospiraceae-Lachnoclostridium, Ruminococcaceae-Ruminococcus, Parvimonas spp., Parvimonas micra, Enterobacteriaceae, Fusobacterium spp., Bacteroides, Peptostreptococcus-Peptostreptococcus stomatis, Clostridia spp.-Clostridium hylemonae, Clostridium symbiosum, and Porphyromonas-Porphyromonas asaccharolytica for CRC vs. HC. The remaining metabolite marker evidence between the various groups, including ADA vs. HC, ADA vs. HC, and CRC vs. HC, was not of sufficient quality to support additional findings. The identified gut microbial markers can be used in a panel for diagnosing ADA and/or CRC. Further research in the metabolite markers area is needed to evaluate the possibility to use in diagnostic or prognostic markers for colorectal cancer.

Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma

BACKGROUND

Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics.

METHODS

The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC.

RESULTS

Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways.

CONCLUSIONS

Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.

Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

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

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

GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma

Background

Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma.

Methods

By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers.

Results

Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid.

Conclusions

To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

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

BACKGROUND AND AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

MicroRNA-455-3p accelerate malignant progression of tumor by targeting H2AFZ in colorectal cancer

Colorectal cancer (CRC) becomes the second leading cause of cancer-related deaths in 2020. Emerging studies have indicated that microRNAs (miRNAs) play a key role in tumorigenesis and progression. The dysfunctions of miR-455-3p are observed in many cancers. However, its biological function in CRC remains to be confirmed. By sequencing serum sample, miR-455-3p was found to be up-regulated in CRC patients. RT-qPCR demonstrated that the miR-455-3p expression was both higher in the serum and tumor tissues of CRC patients. Furthermore, it indicated that miR-455-3p had the ability in promoting cell proliferation, suppressing cell apoptosis, and stimulating cell migration. In vivo experiments also showed that miR-455-3p promoted tumor growth. Additionally, H2AFZ was proved as the direct gene target of miR-455-3p by dual-luciferase assay. Taken together, miR-455-3p functioned as a tumor promoter in CRC development by regulating H2AFZ directly. Thus, it has enormous potential as a biomarker in the diagnosis of CRC.

The essential roles of FXR in diet and age influenced metabolic changes and liver disease development: a multi-omics study

BACKGROUND

Aging and diet are risks for metabolic diseases. Bile acid receptor farnesoid X receptor (FXR) knockout (KO) mice develop metabolic liver diseases that progress into cancer as they age, which is accelerated by Western diet (WD) intake. The current study uncovers the molecular signatures for diet and age-linked metabolic liver disease development in an FXR-dependent manner.

METHODS

Wild-type (WT) and FXR KO male mice, either on a healthy control diet (CD) or a WD, were euthanized at the ages of 5, 10, or 15 months. Hepatic transcriptomics, liver, serum, and urine metabolomics as well as microbiota were profiled.

RESULTS

WD intake facilitated hepatic aging in WT mice. In an FXR-dependent manner, increased inflammation and reduced oxidative phosphorylation were the primary pathways affected by WD and aging. FXR has a role in modulating inflammation and B cell-mediated humoral immunity which was enhanced by aging. Moreover, FXR dictated neuron differentiation, muscle contraction, and cytoskeleton organization in addition to metabolism. There were 654 transcripts commonly altered by diets, ages, and FXR KO, and 76 of them were differentially expressed in human hepatocellular carcinoma (HCC) and healthy livers. Urine metabolites differentiated dietary effects in both genotypes, and serum metabolites clearly separated ages irrespective of diets. Aging and FXR KO commonly affected amino acid metabolism and TCA cycle. Moreover, FXR is essential for colonization of age-related gut microbes. Integrated analyses uncovered metabolites and bacteria linked with hepatic transcripts affected by WD intake, aging, and FXR KO as well as related to HCC patient survival.

CONCLUSION

FXR is a target to prevent diet or age-associated metabolic disease. The uncovered metabolites and microbes can be diagnostic markers for metabolic disease.

Multiomics integration reveals the effect of Orexin A on glioblastoma

Objectives: This study involved a multi-omics analysis of glioblastoma (GBM) samples to elaborate the potential mechanism of drug treatment. Methods: The GBM cells treated with or without orexin A were acquired from sequencing analysis. Differentially expressed genes/proteins/metabolites (DEGs/ DEPs/ DEMs) were screened. Next, combination analyses were conducted to investigate the common pathways and correlations between the two groups. Lastly, transcriptome-proteome-metabolome association analysis was carried out to determine the common pathways, and the genes in these pathways were analyzed through Kaplan-Meier (K-M) survival analysis in public databases. Cell and animal experiments were performed to investigate the anti-glioma activity of orexin A. Results: A total of 1,527 DEGs, 52 DEPs, and 153 DEMs were found. Moreover, the combination analyses revealed that 6, 4, and 1 common pathways were present in the transcriptome-proteome, proteome-metabolome, and transcriptome-metabolome, respectively. Certain correlations were observed between the two data sets. Finally, 11 common pathways were discovered in association analysis, and 138 common genes were screened out in these common pathways. Six genes showed significant differences in terms of survival in both TCGA and CGGA. In addition, orexin A inhibited the proliferation, migration, and invasion of glioma in vitro and in vivo. Conclusion: Eleven common KEGG pathways with six common genes were found among different omics participations, revealing the underlying mechanisms in different omics and providing theoretical basis and reference for multi-omics research on drug treatment.

GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making.

Integrative analysis of the metabolome and transcriptome reveals the influence of Lactobacillus plantarum CCFM8610 on germ-free mice

This study describes the influence of Lactobacillus plantarum CCFM8610 on the host by employing transcriptome and untargeted metabolomics. According to the enrichment analysis, three pathways, including the complement and coagulation cascade pathway, antigen processing and presentation pathways, and protein processing in the endoplasmic reticulum pathway, were affected by L. plantarum CCFM8610 colonization. According to partial least squares-discriminant analysis, five metabolites, L-methionine, D-tryptophan, indoleacrylic acid, DL-acetylcarnitine, and L-norleucine, were identified as key metabolites in the serum. Furthermore, integrative analysis of the metabolome and transcriptome revealed connections between enriched pathways and differential metabolites, and the regulation strategy of choline by affecting gene expression was proposed. Overall, the effects of L. plantarum CCFM8610 on host health were investigated after excluding the influence of gut microbes, which provides a valuable reference for studying the potential mechanisms of the effect of probiotics on host health.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

Implication of gut microbes and its metabolites in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer with a significant impact on loss of life. In 2020, nearly 1.9 million new cases and over 9,35,000 deaths were reported. Numerous microbes that are abundant in the human gut benefit host physiology in many ways. Although the underlying mechanism is still unknown, their association appears to be crucial in the beginning and progression of CRC. Diet has a significant impact on the microbial composition and may increase the chance of getting CRC. Increasing evidence points to the gut microbiota as the primary initiator of colonic inflammation, which is connected to the development of colonic tumors. However, it is unclear how the microbiota contributes to the development of CRCs. Patients with CRC have been found to have dysbiosis of the gut microbiota, which can be identified by a decline in commensal bacterial species, such as those that produce butyrate, and a concurrent increase in harmful bacterial populations, such as opportunistic pathogens that produce pro-inflammatory cytokines. We believe that using probiotics or altering the gut microbiota will likely be effective tools in the fight against CRC treatment.

PURPOSE

In this review, we revisited the association between gut microbiota and colorectal cancer whether cause or effect. The various factors which influence gut microbiome in patients with CRC and possible mechanism in relation with development of CRC.

CONCLUSION

The clinical significance of the intestinal microbiota may aid in the prevention and management of CRC.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.

Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine Learning Approaches

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg^-1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites

BACKGROUND & AIMS

Studies have reported abnormal gut microbiota or circulating metabolome associated with colorectal cancer (CRC), but it remains a challenge to capture the CRC-relevant features consistent across geographic regions. This is particularly the problem for metabolic traits of CRC because the analyses generally use different platforms and laboratory methods, which poses a barrier to cross-dataset examination. In light of this, we sought to elucidate the microbial and metabolic signatures of CRC with broad population relevance.

METHODS

In this integrated metagenomic (healthy controls [HC], n = 91; colorectal adenoma [CRA], n = 63; CRC, n = 71) and metabolomic (HC, n = 34; CRA, n = 31; CRC, n = 35) analysis, CRC-associated features and microbe-metabolite correlations were first identified from a Shanghai cohort. A gut microbial panel was trained in the in-house cohort and cross-validated in 7 published metagenomic datasets of CRC. The in-house metabolic connections to the cross-cohort microbial signatures were used as evidence to infer serum metabolites with potentially external relevance. In addition, a combined microbe-metabolite panel was produced for diagnosing CRC or adenoma.

RESULTS

CRC-associated alterations were identified in the gut microbiome and serum metabolome. A composite microbe-metabolite diagnostic panel was developed and yielded an area under the curve of 0.912 for adenoma and 0.994 for CRC. We showed that many CRC-associated metabolites were linked to cross-cohort gut microbiome signatures of the disease, including CRC-enriched leucylalanine, serotonin, and imidazole propionate; and CRC-depleted perfluorooctane sulfonate, 2-linoleoylglycerol (18:2), and sphingadienine.

CONCLUSIONS

We generated cross-cohort metagenomic signatures of CRC, some of which linked to in-house CRC-associated serum metabolites. The microbial and metabolic shifts may have wide population relevance.

Liquid chromatography coupled to high-resolution mass spectrometry metabolomics: A useful tool for investigating tumor secretome based on a three-dimensional co-culture model

Three-dimensional (3D) cell culture technologies, which more closely mimic the complex microenvironment of tissue, are being increasingly evaluated as a tool for the preclinical screening of clinically promising new molecules, and studying of tissue metabolism. Studies of metabolites released into the extracellular space (secretome) allow understanding the metabolic dynamics of tissues and changes caused by therapeutic interventions. Although quite advanced in the field of proteomics, studies on the secretome of low molecular weight metabolites (< 1500 Da) are still very scarce. We present an untargeted metabolomic protocol based on the hybrid technique of liquid chromatography coupled with high-resolution mass spectrometry for the analysis of low-molecular-weight metabolites released into the culture medium by 3D cultures and co-culture (secretome model). For that we analyzed HT-29 human colon carcinoma cells and 3T3-L1 preadipocytes in 3D-monoculture and 3D-co-culture. The putative identification of the metabolites indicated a sort of metabolites, among them arachidonic acid, glyceric acid, docosapentaenoic acid and beta-Alanine which are related to cancer and obesity. This protocol represents a possibility to list metabolites released in the extracellular environment in a comprehensive and untargeted manner, opening the way for the generation of metabolic hypotheses that will certainly contribute to the understanding of tissue metabolism, tissue-tissue interactions, and metabolic responses to the most varied interventions. Moreover, it brings the potential to determine novel pathways and accurately identify biomarkers in cancer and other diseases. The metabolites indicated in our study have a close relationship with the tumor microenvironment in accordance with the literature review.

Chronic dietary exposure to a glyphosate-based herbicide results in total or partial reversibility of plasma oxidative stress, cecal microbiota abundance and short-chain fatty acid composition in broiler hens

Glyphosate-based herbicides (GBHs) are massively used in agriculture. However, few studies have investigated the effects of glyphosate-based herbicides on avian species although they are largely exposed via their food. Here, we investigated the potential reversibility of the effects of chronic dietary exposure to glyphosate-based herbicides in broiler hens. For 42 days, we exposed 32-week-old hens to glyphosate-based herbicides via their food (47 mg/kg/day glyphosate equivalent, glyphosate-based herbicides, n = 75) corresponding to half glyphosate’s no-observed-adverse-effect-level in birds. We compared their performance to that of 75 control animals (CT). Both groups (glyphosate-based herbicides and control animals) were then fed for 28 additional days without glyphosate-based herbicides exposure (Ex-glyphosate-based herbicides and Ex-control animals). Glyphosate-based herbicides temporarily increased the plasma glyphosate and AMPA (aminomethylphosphonic acid) concentrations. Glyphosate and aminomethylphosphonic acid mostly accumulated in the liver and to a lesser extent in the leg muscle and abdominal adipose tissue. Glyphosate-based herbicides also temporarily increased the gizzard weight and plasma oxidative stress monitored by TBARS (thiobarbituric acid reactive substances). Glyphosate-based herbicides temporarily decreased the cecal concentrations of propionate, isobutyrate and propionate but acetate and valerate were durably reduced. The cecal microbiome was also durably affected since glyphosate-based herbicides inhibited Barnesiella and favored Alloprevotella. Body weight, fattening, food intake and feeding behavior as well as plasma lipid and uric acid were unaffected by glyphosate-based herbicides. Taken together, our results show possible disturbances of the cecal microbiota associated with plasma oxidative stress and accumulation of glyphosate in metabolic tissues in response to dietary glyphosate-based herbicides exposure in broiler hens. Luckily, glyphosate-based herbicides at this concentration does not hamper growth and most of the effects on the phenotypes are reversible.

Untargeted serum metabolomics reveals specific metabolite abnormalities in patients with Crohn's disease

Crohn's disease (CD) is a subtype of inflammatory bowel disease (IBD) characterized by skip intestinal lesions that can occur in any part of the gastrointestinal tract. Currently, the diagnosis of CD is based on clinical history, physical examination and complementary diagnostic tests. It is challenging for physicians to make a definitive diagnosis. This study aimed to analyze the variation in metabolites in CD serum and identify potential predictive biomarkers of CD diagnosis. We collected serum samples from 316 subjects, including patients with CD and healthy controls (HCs). Serum metabolomics was conducted using liquid chromatography coupled to mass spectrometry. Potential biomarkers were screened and evaluated by univariate and multivariate analyses. A panel of two metabolites (deoxycholic acid and palmitic amide) was identified as a specific biomarker of CD. Receiver operating characteristic analysis (ROC) showed that the panel had a sensitivity of 80.25% with a specificity of 95.54% in discriminating CD patients from healthy controls. The biomarkers identified are increased in CD compared with healthy controls. Our approach successfully identified serum biomarkers associated with CD patients. The potential biomarkers indicated that CD metabolic disturbance might be associated with bile acid biosynthesis, fatty acids and energy metabolism.

Systematic impacts of fluoride exposure on the metabolomics of rats

Fluoride is widely present in the environment. Excessive fluoride exposure leads to fluorosis, which has become a global public health problem and will cause damage to various organs and tissues. Only a few studies focus on serum metabolomics, and there is still a lack of systematic metabolomics associated with fluorosis within the main organs. Therefore, in the current study, a non-targeted metabolomics method using gas chromatography-mass spectrometry (GC-MS) was used to research the effects of fluoride exposure on metabolites in different organs, to uncover potential biomarkers and study whether the affected metabolic pathways are related to the mechanism of fluorosis. Male Sprague-Dawley rats were randomly divided into two groups: a control group and a fluoride exposure group. GC-MS technology was used to identify metabolites. Multivariate statistical analysis identified 16, 24, 20, 20, 24, 13, 7, and 13 differential metabolites in the serum, liver, kidney, heart, hippocampus, cortex, kidney fat, and brown fat, respectively, in the two groups of rats. Fifteen metabolic pathways were affected, involving toxic mechanisms such as oxidative stress, mitochondrial damage, inflammation, and fatty acid, amino acid and energy metabolism disorders. This study provides a new perspective on the understanding of the mechanism of toxicity associated with sodium fluoride, contributing to the prevention and treatment of fluorosis.

[Centromere protein U is highly expressed in colorectal cancer and associated with a poor long-term prognosis]

OBJECTIVE

To analyze the expression of centromere protein U (CENPU) in colorectal cancer and its predictive value for long-term prognosis of the patients.

METHODS

We retrospectively analyzed the data of 102 patients with colorectal cancer undergoing radical resection in our hospital between January, 2005 and December, 2011. The expression level of CENPU in colorectal cancer tissue was detected immunohistochemically, and its association with clinicopathological characteristics of the patients were analyzed. The patients were divided into low expression group (n=51) and high expression group (n=51) based on the median CENPU expression level for analysis the value of CENPU for predicting long-term prognosis of the patients after radical resection of the tumors. In the in vitro study, we constructed colorectal cancer cell lines with CENPU interference and CENPU overexpression by lentiviral transfection and assessed the changes in the proliferation, migration and invasion of the cells using CCK-8 assay and Transwell assay.

RESULTS

The protein expression level of CENPU was significantly higher in colorectal cancer tissues than in the adjacent tissues (P < 0.05) and was positively correlated with the expressions levels of Ki67 (r=0.569, P < 0.05) and VEGF-C (r=0.629, P < 0.05). CENPU expression level in colorectal cancer tissue was closely related with tumor progression and clinicopathological stage of the tumor (P < 0.05). Kaplan-Meier survival analysis showed that the patients with high CENPU expression had significantly decreased postoperative overall survival (χ²=11.155, P < 0.05); Cox multivariate regression analysis suggested that CENPU expression level was an independent risk factor affecting the overall survival of the patients after radical resection (HR=1.848, P < 0.05). The results of cell experiments demonstrated that high CENPU expression significantly promoted the proliferation, migration and invasion of the tumor cells.

CONCLUSION

CENPU is highly expressed in colorectal cancer tissues in closely correlation with tumor progression and may serve as a potential biomarker for evaluating the long-term prognosis of colorectal cancer patients.

Establishment of an early diagnosis model of colon cancerous bowel obstruction based on 1H NMR

Objective

To prospectively establish an early diagnosis model of acute colon cancerous bowel obstruction by applying nuclear magnetic resonance hydrogen spectroscopy(1H NMR) technology based metabolomics methods, combined with machine learning.

Methods

In this study, serum samples of 71 patients with acute bowel obstruction requiring emergency surgery who were admitted to the Emergency Department of Sichuan Provincial People’s Hospital from December 2018 to November 2020 were collected within 2 hours after admission, and NMR spectroscopy data was taken after pretreatment. After postoperative pathological confirmation, they were divided into colon cancerous bowel obstruction (CBO) group and adhesive bowel obstruction (ABO) control group. Used MestReNova software to extract the two sets of spectra bins, and used the MetaboAnalyst5.0 website to perform partial least square discrimination (PLS-DA), combining the human metabolome database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to find possible different Metabolites and related metabolic pathways.

Results

22 patients were classified as CBO group and 30 were classified as ABO control group. Compared with ABO group, the level of Xanthurenic acid, 3-Hydroxyanthranilic acid, Gentisic acid, Salicyluric acid, Ferulic acid, Kynurenic acid, CDP, Mandelic acid, NADPH, FAD, Phenylpyruvate, Allyl isothiocyanate, and Vanillylmandelic acid increased in the CBO group; while the lecel of L-Tryptophan and Bilirubin decreased. There were significant differences between two groups in the tryptophan metabolism, tyrosine metabolism, glutathione metabolism, phenylalanine metabolism and synthesis pathways of phenylalanine, tyrosine and tryptophan (all P<0.05). Tryptophan metabolism pathway had the greatest impact (Impact = 0.19). The early diagnosis model of colon cancerous bowel was established based on the levels of six metabolites: Xanthurenic acid, 3-Hydroxyanthranilic acid, Gentisic acid, Salicylic acid, Ferulic acid and Kynurenic acid (R2 = 0.995, Q2 = 0.931, RMSE = 0.239, AUC = 0.962).

Conclusion

This study firstly used serum to determine the difference in metabolome between patients with colon cancerous bowel obstruction and those with adhesive bowel obstruction. The study found that the metabolic information carried by the serum was sufficient to discriminate the two groups of patients and provided the theoretical supporting for the future using of the more convenient sample for the differential diagnosis of patients with colon cancerous bowel obstruction. Quantitative experiments on a large number of samples were still needed in the future.

Evaluation of the diagnostic value of serum-based proteomics for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a highly malignant cancer with a high incidence and mortality in China. It is urgent to find a diagnostic marker with higher sensitivity and specificity than the traditional approaches for CRC diagnosis.

AIM

To provide new ideas for the diagnosis of CRC based on serum proteomics.

METHODS

Specimens from 83 healthy people, 62 colon polyp (CRP) patients, and 101 CRC patients were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The diagnostic value of the profiles of differentially expressed proteins was then analyzed.

RESULTS

Compared with the healthy control group, CRC patients had elevated expression of 5 proteins and reduced expression of 14 proteins. The area under the curve (AUC) for a differentially expressed protein with a mass-to-charge ratio of 2022.34 was the largest; the AUC was 0.843, which was higher than the AUC of 0.717 observed with carcinoembryonic antigen (CEA), and the sensitivity and specificity of this identified marker were 75.3% and 79.5%, respectively. After cross-validation, the accuracy of diagnosis using levels of this differentially expressed protein was 82.37%. Compared with the CRP group, the expression of 3 proteins in the serum of CRC patients was elevated and 11 proteins were expressed at reduced levels. Proteins possessing mass-to-charge ratio values of 2899.38 and 877.3 were selected to establish a classification tree model. The results showed that the accuracy of CRC diagnosis was 89.5%, the accuracy of CRP diagnosis was 81.6%, and the overall accuracy of this approach was 86.3%. The overall sensitivity and specificity of diagnosis using the proteomics approach were 81.8% and 66.75%, respectively. The sensitivities and specificities of diagnoses based on CEA and carbohydrate antigen 19-9 expression were 55.6% and 91.3% and 65.4% and 65.2%, respectively.

CONCLUSION

We demonstrated that serum proteomics may be helpful for the detection of CRC, and it may assist clinical practice for CRC diagnosis.

Multi-platform-based characterization of ferroptosis in human colorectal cancer

Ferroptosis is a type of programmed cell death potentially playing an important role in colorectal cancer (CRC) development. However, comprehensive investigations toward ferroptosis in human CRC are lacking. Here, we performed multiple investigations on cancer and para-cancer tissues. We demonstrated that the changes of structural variation and chromatin accessibility in CRC were more associated with the altered mRNA expression of ferroptosis-related genes (FRGs), and the expression of CDKN2A, GPX4, ALOXE3, and LINC00336 was related to the overall survival rates. Subsequently, we revealed that CYBB and YAP1 were potentially the hub genes, and that HSF1 and STAT2 were potentially FRGs’ upstream transcription factors. Finally, we depicted the crosstalk between ferroptosis and necrosis, autophagy, and apoptosis. Based on multi-dimensional analyses, we characterized ferroptosis, probable core genes, and the upstream regulators in human CRC. The findings here may improve our understanding of ferroptosis in CRC and provide new opportunities for clinical diagnosis and treatment.

•

Characterize ferroptosis in CRC from DNA, RNA, and protein to epigenetic modification

•

The potentially functional or hub genes in ferroptosis at multiple molecular levels

•

Putative ferroptosis regulators (transcription factors) in human CRC cells

•

The genes that probably link ferroptosis to necrosis, autophagy, and apoptosis

A deep learning method for predicting metabolite-disease associations via graph neural network

Metabolism is the process by which an organism continuously replaces old substances with new substances. It plays an important role in maintaining human life, body growth and reproduction. More and more researchers have shown that the concentrations of some metabolites in patients are different from those in healthy people. Traditional biological experiments can test some hypotheses and verify their relationships but usually take a considerable amount of time and money. Therefore, it is urgent to develop a new computational method to identify the relationships between metabolites and diseases. In this work, we present a new deep learning algorithm named as graph convolutional network with graph attention network (GCNAT) to predict the potential associations of disease-related metabolites. First, we construct a heterogeneous network based on known metabolite-disease associations, metabolite-metabolite similarities and disease-disease similarities. Metabolite and disease features are encoded and learned through the graph convolutional neural network. Then, a graph attention layer is used to combine the embeddings of multiple convolutional layers, and the corresponding attention coefficients are calculated to assign different weights to the embeddings of each layer. Further, the prediction result is obtained by decoding and scoring the final synthetic embeddings. Finally, GCNAT achieves a reliable area under the receiver operating characteristic curve of 0.95 and the precision-recall curve of 0.405, which are better than the results of existing five state-of-the-art predictive methods in 5-fold cross-validation, and the case studies show that the metabolite-disease correlations predicted by our method can be successfully demonstrated by relevant experiments. We hope that GCNAT could be a useful biomedical research tool for predicting potential metabolite-disease associations in the future.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

The study of human serum metabolome on the health effects of glyphosate and early warning of potential damage

Glyphosate is one of the most widely used herbicide with high efficiency, low toxicity and broad-spectrum. In recent decades, increasing evidence suggests that glyphosate may cause adverse health effects on human beings. However, until now, there is little data on the human metabolic changes. Since occupational workers are under greater health risks than ordinary people, the understanding regarding the health effects of glyphosate on occupational workers is very important for the early warning of potential damage. In this study, serum metabolic alterations in workers from three chemical factories were analyzed by gas chromatography-mass spectrometry (GC-MS) to assess the potential health risks caused by glyphosate at the molecular level. It was found that the levels of 27 metabolites changed significantly in the exposed group compared to the controls. The altered metabolic pathways, including amino acid metabolism, energy metabolism (glycolysis and TCA cycle) and glutathione metabolism (oxidative stress), etc., indicated a series of changes occur in health profile of the human body after glyphosate exposure, and the suboptimal health status of human may further evolve into various diseases, such as Parkinson's disease, renal and liver dysfunction, hepatocellular carcinoma, and colorectal cancer. Subsequently, 4 biomarkers (i.e., benzoic acid, 2-ketoisocaproic acid, alpha-ketoglutarate, and monoolein) were identified as potential biomarkers related to glyphosate exposure based on the partial correlation analyses, linear regression analyses, and FDR correction. Receiver-operating curve (ROC) analyses manifested that these potential biomarkers and their combinational pattern had good performance and potential clinical value to assess the potential health risk associated with glyphosate exposure while retaining high accuracy. Our findings provided new insights on mechanisms of health effects probably induced by glyphosate, and may be valuable for the health risk assessment of glyphosate exposure.

Comprehensive fecal metabolomics and gut microbiota for the evaluation of the mechanism of Panax Ginseng in the treatment of Qi-deficiency liver cancer

ETHNOPHARMACOLOGICAL RELEVANCE

Qi deficiency liver cancer (QDLC) is an important part of liver cancer research in traditional Chinese medicine (TCM). In the course of its treatment, Panax ginseng is often selected as the main Chinese herbal medicine, and its function has special significance in the tumor treatment of Qi deficiency constitution. However, its mechanism is not clear.

AIM OF THE STUDY

The research tried to evaluate the mechanism of Panax ginseng in the treatment of QDLC through fecal metabonomics and gut microbiota on the basis of previous pharmacodynamic evaluation.

MATERIALS AND METHODS

Firstly, biomarkers and related metabolic pathways were screened and identified by metabonomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, 16S rRNA sequencing technique was used to investigate the composition, β diversity and key differences of gut microbiota. Finally, the relationship among phenotypes, gut microbiota and fecal metabolites was comprehensively analyzed by spearman correlation coefficient.

RESULTS

31 pharmacodynamic potential biomarkers and 20 synergistic potential biomarkers of effective parts of Panax ginseng on QDLC were screened and identified by fecal metabonomics. And then, 6 major metabolic pathways were searched, including bile acid biosynthesis, unsaturated fatty acid biosynthesis, tryptophan metabolism, arachidonic acid metabolism, pyrimidine metabolism, vitamin B6 metabolism. In the study of gut microbiota, at the genus level, 25 species of bacteria with significant differences of effective parts on QDLC and 23 species of bacteria with significant differences of synergistic action of ginsenosides and polysaccharides were screened. In addition, Spearman correlation analysis showed that there was a complex potential relationship among phenotype, gut microbiota and fecal metabolites during the development of QDLC and Panax ginseng intervention, which was mainly reflected in the close potential relationship between bacteria and fecal metabolites such as bile acids, unsaturated fatty acids and indole compounds.

CONCLUSION

Through the changes of fecal endogenous metabolites and intestinal bacteria, the mechanism of Panax ginseng on QDLC were preliminarily clarified.

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.

Mechanisms of Danggui Buxue Tang on Hematopoiesis via Multiple Targets and Multiple Components: Metabonomics Combined with Database Mining Technology

This study aimed to explore the mechanism of action of Danggui Buxue Tang (DBT) with its multiple components and targets in the synergistic regulation of hematopoiesis. Mouse models of hematopoiesis were established using antibiotics. Metabolomics was used to detect body metabolites and enriched pathways. The active ingredients, targets, and pathways of DBT were analyzed using system pharmacology. The results of metabolomics and system pharmacology were integrated to identify the key pathways and targets. A total of 515 metabolites were identified using metabolomics. After the action of antibiotics, 49 metabolites were markedly changed: 23 were increased, 26 were decreased, and 11 were significantly reversed after DBT administration. Pathway enrichment analysis showed that these 11 metabolites were related to bile secretion, cofactor biosynthesis, and fatty acid biosynthesis. The results of the pharmacological analysis showed that 616 targets were related to DBT-induced anemia, which were mainly enriched in biological processes, such as bile secretion, biosynthesis of cofactors, and cholesterol metabolism. Combined with the results of metabolomics and system pharmacology, we found that bile acid metabolism and biotin synthesis were the key pathways for DBT. Forty-two targets of DBT were related to these two metabolic pathways. PPI analysis revealed that the top 10 targets were CYP3A4, ABCG2, and UGT1A8. Twenty-one components interacted with these 10 targets. In one case, a target corresponds to multiple components, and a component corresponds to multiple targets. DBT acts on multiple targets of ABCG2, UGT1A8, and CYP3A4 through multiple components, affecting the biosynthesis of cofactors and bile secretion pathways to regulate hematopoiesis.

A Metabolomics-Based Screening Proposal for Colorectal Cancer

Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

Metabolomic Profiling Reveals New Insight of Fowl Adenovirus Serotype 4 Infection

Highly pathogenic fowl adenovirus serotype 4 (FAdV-4) is the causative agent of hydropericardium syndrome (HPS), which is characterized by pericardial effusion and hepatitis, and is one of the foremost causes of economic losses to the poultry industry over the last 30 years. However, the metabolic changes in cells in response to FAdV-4 infection remain unclear. In order to understand the metabolic interactions between the host cell and virus, we utilized ultra-high-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry to analyze the metabolic profiles with hepatocellular carcinoma cell line (LMH) infected with FAdV-4. The results showed that FAdV-4 could restore metabolic networks in LMH cells and tricarboxylic acid cycle, glycolysis, and metabolism of purines, pyrimidines, alanine, aspartate, glutamate, and amino sugar and nucleotide sugar moieties. Moreover, FAdV-4 production was significantly reduced in LMH cells cultured in glucose or glutamine-deficient medium. These observations highlighted the importance of host cell metabolism in virus replication. Therefore, similarities and disparities in FAdV-4-regulation of the metabolism of host cells could help improve targeted drug and reduce infection.

Study of microRNA expression profiling as biomarkers for colorectal cancer patients in Lebanon

The high incidence and mortality rates of colorectal cancer (CRC) reveal its hazardous effect globally. Thus, it is important to diagnose CRC at an early stage to decrease its burden and improve survival rates. Previous studies have investigated the role of short non-coding microRNAs (miRNAs or miRs) in numerous types of cancer, including CRC. Previous studies have been performed to investigate the role of miRNAs as biomarkers in diagnosis, prognosis and prediction of CRC development. The aim of the present retrospective study was to identify the expression levels of miR-31, miR-145, miR-146b and miR-186 to highlight their role in CRC diagnosis and progression at different stages of the disease (precancerous polyp, adenoma and adenocarcinoma) in a Lebanese population. The expression levels of miRNAs was revealed using TaqMan reverse transcription-quantitative PCR on formalin-fixed paraffin-embedded tissues from Lebanese patients at different stages; their diagnostic value was determined using a receiver operating characteristics curve. Compared with healthy controls, miR-31 was upregulated (P<0.0001) at all stages. By contrast, miR-145, miR-186, and miR-146b were significantly downregulated at all stages (P<0.0001, P=0.0009 and P=0.0241, respectively). Of the four miRNAs studied, miR-31 and miR-145 were identified as potentially useful diagnostic factors, with an area under the curve of 0.7771 and 0.8269 and diagnostic accuracy of 71.3 and 78.5%, respectively. These data suggested that miR-31 and miR-145, upon further clinical validation, may be used as potential diagnostic biomarkers for the early detection of CRC at the polyp stage.

Effects of ESCO2 or its methylation on the prognosis, clinical characteristics, immune microenvironment, and pathogenesis of low-grade glioma

The establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) has an important regulatory effect on cell proliferation and division, which is closely related to the malignant process of glioma cells. Therefore, this study attempts to provide a target for biologically targeted therapy for low-grade glioma (LGG) by demonstrating the regulatory effect of ESCO2 during the pathological process of LGG. First, the 1064 samples of LGG transcriptomic data and corresponding clinicopathological information obtained from various databases were included in the study. Second, the chi-squared test showed that the expression of ESCO2 was associated with the malignant characteristics of LGG (recurrence and grade), and Kaplan Meier and multivariate analysis suggested that ESCO2 was an independent risk factor, resulting in a significant reduction in the overall duration of survival of patients. Third, co-expression analysis showed that the level of mRNA expression of ESCO2 was negatively regulated by multiple methylation sites (cg04108328, cg12564175, and cg26534677), and the hypermethylation status of cg12564175 could prolong the overall survival of patients. Fourth, the Tumor Immune Estimation Resource (TIMER) database shows that ESCO2 can have a positive regulatory relationship with six different immune cells, such as CD8 + T cells and macrophages, and a positive expression relationship with PD-1 and PD-L1. Finally, Gene Set Enrichment Analysis (GSEA) showed that ESCO2 may play a carcinogenic role by affecting cell replication and DNA repair. In summary, this study confirmed the carcinogenic effect of ESCO2 on LGG for the first time. It is speculated that both the mRNA of ESCO2 and its methylation site (cg12564175) can be useful biological targets for molecular targeted therapy of LGG.

Metabolomic Profiling Identified Serum Metabolite Biomarkers and Related Metabolic Pathways of Colorectal Cancer

Background

The screening and early detection of colorectal cancer (CRC) still remain a challenge due to the lack of reliable and effective serum biomarkers. Thus, this study is aimed at identifying serum biomarkers of CRC that could be used to distinguish CRC from healthy controls.

Methods

A prospective 1 : 2 individual matching case-control study was performed which included 50 healthy control subjects and 98 CRC patients. Untargeted metabolomic profiling was conducted with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify CRC-related metabolites and metabolic pathways.

Results

In total, 178 metabolites were detected, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish CRC patients from healthy controls. Nine metabolites showed significantly differential serum levels in CRC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.2 or ≤0.5. The above nine metabolites were 3-hydroxybutyric acid, hexadecanedioic acid, succinic acid semialdehyde, 4-dodecylbenzenesulfonic acid, prostaglandin B2, 2-pyrocatechuic acid, xanthoxylin, 12-hydroxydodecanoic acid, and formylanthranilic acid. Four potential biomarkers were identified to diagnose CRC through ROC curves: hexadecanedioic acid, 4-dodecylbenzenesulfonic acid, 2-pyrocatechuic acid, and formylanthranilic acid. All AUC values of these four serum biomarkers were above 0.70. In addition, the exploratory analysis of metabolic pathways revealed the activated states for the vitamin B metabolic pathway and the alanine, aspartate, and glutamate metabolic pathways associated with CRC.

Conclusion

The 4 identified potential metabolic biomarkers could discriminate CRC patients from healthy controls, and the 2 metabolic pathways may be activated in the CRC tissues.

Flat Disc-Shaped Sampling Probe and Online Re-extraction Apparatus for Mass Spectrometric Analysis of Skin Metabolites: A Proof of Concept

Sweat analysis provides an alternative and noninvasive way of clinical diagnostics. However, sampling and transferring sweat-derived samples to analytical instruments is challenging. In this report, we demonstrate a method utilizing a flat disc-shaped sampling probe, and a compatible re-extraction apparatus coupled online with extractive electrospray ionization (EESI) mass spectrometry (MS). The probe enables sampling of metabolites from a skin area of ∼2.2 cm². The subsequent online re-extraction and analysis by EESI-MS further mitigates matrix effects caused by sweat components, thus eliminating sample preparation steps. The total analysis time is only 6 min. We have optimized the key parameters of the system, including flow rate of the nebulizing gas in ESI, pressure of the nebulizing gas in pneumatic sample nebulizer, flow rate of the solvent in ESI, and composition of extractant. The standard solutions (0.1 mL) were supplemented with 0.04 M sodium chloride to mimic the matrix effect normally observed in sweat samples. The method has been characterized with four chemical standards (positive-ion mode of histidine, leucine, urocanic acid; negative-ion mode of lactic acid). The limits of detection range from 1.09 to 95.9 nmol. We have further demonstrated the suitability of the method for analysis of sweat. An attempt was made to identify some of the recorded signals by product-ion scan and accurate/exact mass matching.