Tissue metabolic profiling of lymph node metastasis of colorectal cancer assessed by 1H NMR

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Metabolic Switch Under Glucose Deprivation Leading to Discovery of NR2F1 as a Stimulus of Osteoblast Differentiation

Poor survival of grafted cells is the major impediment of successful cell-based therapies for bone regeneration. Implanted cells undergo rapid death in an ischemic environment largely because of hypoxia and metabolic stress from glucose deficiency. Understanding the intracellular metabolic processes and finding genes that can improve cell survival in these inhospitable conditions are necessary to enhance the success of cell therapies. Thus, the purpose of this study was to investigate changes of metabolic profile in glucose-deprived human bone marrow stromal/stem cells (hBMSCs) through metabolomics analysis and discover genes that could promote cell survival and osteogenic differentiation in a glucose-deprived microenvironment. Metabolomics analysis was performed to determine metabolic changes in a glucose stress metabolic model. In the absence of glucose, expression levels of all metabolites involved in glycolysis were significantly decreased than those in a glucose-supplemented state. In glucose-deprived osteogenic differentiation, reliance on tricarboxylic acid cycle (TCA)-predicted oxidative phosphorylation instead of glycolysis as the main mechanism for energy production in osteogenic induction. By comparing differentially expressed genes between glucose-deprived and glucose-supplemented hBMSCs, NR2F1 (Nuclear Receptor Subfamily 2 Group F Member 1) gene was discovered to be associated with enhanced survival and osteogenic differentiation in cells under metabolic stress. Small, interfering RNA (siRNA) for NR2F1 reduced cell viability and osteogenic differentiation of hBMSCs under glucose-supplemented conditions whereas NR2F1 overexpression enhanced osteogenic differentiation and cell survival of hBMSCs in glucose-deprived osteogenic conditions via the protein kinase B (AKT)/extracellular signal-regulated kinase (ERK) pathway. NR2F1-transfected hBMSCs significantly enhanced new bone formation in a critical size long-bone defect of rats compared with control vector-transfected hBMSCs. In conclusion, the results of this study provide an understanding of the metabolic profile of implanted cells in an ischemic microenvironment and demonstrate that NR2F1 treatment may overcome this deprivation by enhancing AKT and ERK regulation. These findings can be utilized in regenerative medicine for bone regeneration. © 2022 American Society for Bone and Mineral Research (ASBMR).

A miRNA-clinicopathological nomogram for the prediction of central lymph node metastasis in papillary thyroid carcinoma-analysis from TCGA database

It is of significance to evaluate central lymph node status in patients with papillary thyroid carcinoma (PTC), because it can decrease postoperative complications resulting from unnecessary prophylactic central lymph node dissection (CLND). Due to the low sensitivity and specificity of neck ultrasonography in the evaluation of central lymph node metastasis (CLNM), it is urgently required to find alternative biomarkers to predict CLNM in PTC patients, which is the main purpose of this study.RNA-sequencing datasets and clinical data of 506 patients with thyroid carcinoma from the Cancer Genome Atlas (TCGA) database were downloaded and analyzed to identify differentially expressed miRNAs (DEMs), which can independently predict CLNM in PTC. A nomogram predictive of CLNM was developed based on clinical characteristics and the identified miRNAs. Receiver operating characteristics curves were drawn to evaluate the predictive performance of the nomogram. Bioinformatics analyses, including target genes identification, functional enrichment analysis, and protein-protein interaction network, were performed to explore the potential roles of the identified DEMs related to CLNM in PTC.A total of 316 PTC patients were included to identify DEMs. Two hundred thirty-seven (75%) PTC patients were randomly selected from the 316 patients as a training set, while the remaining 79 (25%) patients were regarded as a testing set for validation. Two DEMs, miRNA-146b-3p (HR: 1.327, 95% CI = 1.135-1.551, P = .000) and miRNA-363-3p (HR: 0.714, 95% CI = 0.528-0.966, P = .029), were significantly associated with CLNM. A risk score based on these 2 DEMs and calculating from multivariate logistic regression analysis, was significantly lower in N0 group over N1a group in both training (N0 vs N1a: 2.04 ± 1.01 vs 2.73 ± 0.61, P = .000) and testing (N0 vs N1a: 2.20 ± 0.93 vs 2.79 ± 0.68, P = .003) sets. The nomogram including risk score, age, and extrathyroidal extension (ETE) was constructed in the training set and was then validated in the testing set, which showed better prediction value than the other three predictors (risk score, age, and ETE) in terms of CLNM identification. Bioinformatics analyses revealed that 5 hub genes, SLC6A1, SYT1, COL19A1, RIMS2, and COL1A2, might involve in pathways including extracellular matrix organization, ion transmembrane transporter activity, axon guidance, and ABC transporters.On the basis of this study, the nomogram including risk score, age, and ETE showed good prediction of CLNM in PTC, which has a potential to facilitate individualized decision for surgical plans.

Metabolomic Markers of Colorectal Tumor With Different Clinicopathological Features

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

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

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

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

L-Arginine/NO Pathway Metabolites in Colorectal Cancer: Relevance as Disease Biomarkers and Predictors of Adverse Clinical Outcomes Following Surgery

The L-Arginine/NO pathway is involved in carcinogenesis and immunity. Its diagnostic and prognostic value in colorectal cancer (CRC) was determined using tandem mass spectrometry in 199 individuals (137 with CRC) and, during a three-day follow up, in 60 patients undergoing colorectal surgery. Citrulline was decreased and asymmetric (ADMA) and symmetric (SDMA) dimethylarginines and dimethylamine (DMA) were increased in CRC. The DMA increase corresponded with CRC advancement while arginine, ADMA, and SDMA levels were higher in left-sided cancers. Arginine, citrulline, ADMA, and DMA dropped and SDMA increased post incision. Females experienced a more substantial drop in arginine. The arginine and ADMA dynamics depended on blood loss. The initial SDMA increase was higher in patients requiring transfusions. Postoperative dynamics in arginine and dimethylarginines differed in robot-assisted and open surgery. Concomitant SDMA, citrulline, and DMA quantification displayed a 92% accuracy in detecting CRC. Monitoring changes in arginine, ADMA, and SDMA in the early postoperative period predicted postoperative ileus with 84% and surgical site infections with 90% accuracy. Changes in ADMA predicted operative morbidity with 90% and anastomotic leakage with 77% accuracy. If positively validated, L-arginine/NO pathway metabolites may facilitate CRC screening and surveillance, support differential diagnosis, and assist in clinical decision-making regarding patients recovering from colorectal surgery.

A Comprehensive Repertoire of Transfer RNA-Derived Fragments and Their Regulatory Networks in Colorectal Cancer

To provide systematic insight into the composition and expression of transfer RNA (tRNA) derivatives transcriptome in colorectal cancer (CRC). tRNA derivatives expression profiles in three pairs of CRC and adjacent normal colon tissues were performed by tRNA-derived small RNA fragments (tRFs) and tRNA halves (tiRNA) sequencing, and microarray data of transcriptomes from CRC and paired controls were retrieved from Gene Expression Omnibus database. The differentially expressed tRFs and tiRNAs and differentially expressed genes between CRC and paired normal samples were screened. The functional regulations between tRF and tiRNA and gene were identified. A total of 60 upregulated and 48 downregulated tRNA derivatives and 7373 upregulated and 12,138 downregulated messenger RNA (mRNA) were identified. The tRF and tiRNA-gene regulatory modules were constructed by analyzing computational tRF and tiRNA-target predictions and inverse expression relationships between tRF and tiRNAs and mRNA. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation showed that the function of targets of tiRNA-Tyr-GTA was mainly enriched in negative regulation of epithelial cell apoptotic process and peroxisome proliferator activated-receptors (PPAR) signaling pathway. Cellular response to monoamine stimulus and inflammatory bowel disease was enriched in function of tiRNA-Val-CAC. Two functions, including negative regulation of c-Jun N-terminal kinase (JNK) cascade and choline metabolism in cancer, were enriched in tRF-Gln-CTG. The function of mesenchymal to epithelial transition was enriched in tRF-Leu-TAG. For the first time to our knowledge, our study provided a landscape of tRNA derivatives expression profiles in CRC. Further tRF and tiRNA-gene regulatory modules construction explored the potential functions related to these tRNA derivatives in the pathogenesis of CRC.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Metabonomic Variation of Exopolysaccharide from Rhizopus nigricans on AOM/DSS-Induced Colorectal Cancer in Mice

BACKGROUND

Colorectal cancer (CRC), which occurs at the junction of the rectum and sigmoid colon, is a common malignancy associated with poor prognosis and high mortality worldwide. The exopolysaccharide (EPS1-1), isolated from the fermentation broth of Rhizopus nigricans (R. nigricans), has been reported to possess anti-CRC properties. However, the metabolic alterations caused by azoxymethane (AOM) and dextran sulfate sodium (DSS) are still unknown.

METHODS

In the present study, a mice colon cancer model was established by treatment with AOM/DSS. LC-MS/MS-based metabolomics studies were performed to analyze metabolic alterations at the tissue level. Partial least squares discriminant analysis (PLS-DA) was used to identify differentially expressed metabolites.

RESULTS

Nineteen distinct metabolites were identified that were associated with disruptions in the following pathways: biosynthesis of unsaturated fatty acids, pyrimidine metabolism, phenylalanine metabolism, fatty acid metabolism, folate biosynthesis, and inositol phosphate metabolism. Furthermore, six significantly altered metabolites were involved in these six pathways. Compared with the Model group, the expression of cytosine, deoxyuridine, 20-hydroxy-leukotriene E4, and L-homocysteic acid was lower, whereas that of 2-dehydro-3-deoxy-6-phospho-D-gluconic acid and hematoporphyrin was higher in the EPS1-1 group.

CONCLUSION

The results of multivariate statistical analysis demonstrate a promising application of the above metabolites by EPS1-1 in CRC therapy. Deeper understanding of the related mechanism warrants further investigation.

1H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

INTRODUCTION

Gastric cancer (GC) is a malignant tumor worldwide. As primary pathway for metastasis, the lymphatic system is an important prognostic factor for GC patients. Although the metabolic changes of gastric cancer have been investigated in extensive studies, little effort focused on the metabolic profiling of lymph node metastasis (LNM)-positive or negative GC patients.

OBJECTIVES

We performed ¹H NMR spectrum of GC tissue samples with and without LNM to identify novel potential metabolic biomarkers in the process of LNM of GC.

METHODS

¹H NMR-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of tissue samples from LNM-positive GC patients (n = 40), LNM-negative GC patients (n = 40) and normal controls (n = 40).

RESULTS

There was a clear separation between GC patients and normal controls, and 33 differential metabolites were identified in the study. Moreover, GC patients were also well-classified according to LNM-positive or negative. Totally eight distinguishing metabolites were selected in the metabolic profiling of GC patients with LNM-positive or negative, suggesting the metabolic dysfunction in the process of LNM. According to further validation and analysis, especially BCAAs metabolism (leucine, isoleucine, valine), GSH and betaine may be as potential factors of diagnose and prognosis of GC patients with or without LNM.

CONCLUSION

To our knowledge, this is the first metabolomics study focusing on LNM of GC. The identified distinguishing metabolites showed a promising application on clinical diagnose and therapy prediction, and understanding the mechanism underlying the carcinogenesis, invasion and metastasis of GC.