A serum metabolomics analysis reveals a panel of screening metabolic biomarkers for esophageal squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Lv J
PROVIDER: S-EPMC8101533 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is one of the most common malignancies with poor diagnosis. Esophageal squamous dysplasia (ESD) is considered as an immediate precancerous lesion of ESCC. Lack of biomarkers for discriminating ESCC and ESD from healthy subjects limits the early diagnosis and treatment of ESCC. Therefore, a serum metabolomic strategy was conducted to identify and validate potential metabolic markers for the screening of ESCC and ESD subjects.MethodsA total of 74 patients with ESCC, 72 patients with ESD, and 75 normal control (NC) subjects were enrolled in this study. Gas chromatography-mass spectrometry was used to acquire serum metabolic profiles. Pathway analysis was conducted to uncover the fluctuated metabolic pathways during ESCC. Multivariate analyses were used to screen and validate the biomarkers.ResultsESCC, ESD, and NC subjects revealed progressively altered metabolic profiles, in which amino acids globally increased, while fatty acids decreased in ESCCs compared with the control groups. Pathway analysis demonstrated the activated biosynthesis of amino acids and inhibited desaturation of saturated fatty acids. The panel constructed with propanoic acid, linoleic acid, glycerol-3-phosphate, and l-glutamine showed the area under the curve (AUC), sensitivity, and specificity of 0.817, 0.75, and 0.74, respectively, in the discrimination of ESCC/ESD patients from NC subjects. The panel constructed by propanoic acid, l-leucine, and hydroxyproline revealed the AUC, sensitivity, and specificity of 0.819, 0.76, and 0.72, respectively, in the discrimination of ESD from NC subjects. The combination of hypoxanthine, 2-ketoisocaproic acid, l-glutamate, and l-aspartate showed the AUC, sensitivity, and specificity of 0.818, 0.83, and 0.74, respectively, in the discrimination of ESCC patients from ESD subjects.ConclusionsOur study revealed the systematic landscape for metabolic alterations in sera of ESD and ESCC patients. The defined metabolite markers showed reasonable performance in the discrimination of ESCC and ESD patients, and may provide helpful reference for clinicians and biologists.
| S-EPMC7581083 | biostudies-literature
Project description:We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid ?-oxidation and the metabolism of amino acids, purines, and pyrimidines.
| S-EPMC5057114 | biostudies-literature
Project description:Background: Esophageal squamous cell carcinoma (ESCC) is one of the most fatal diseases worldwide. Because early diagnosis is difficult, ESCC is mostly diagnosed at an advanced stage, leading to a poor overall prognosis. The purpose of this study was to explore the differences between plasma metabolic profiles in ESCC patients and healthy controls and to establish a diagnostic model of ESCC. Methods: In this study, a cohort of 310 subjects, containing 140 ESCC patients and 170 healthy controls (HC), was recruited. Participants were randomly separated into a training set (80 ESCCs, 80 HCs) and a validation set (60 ESCCs, 90 HCs) and their plasma metabolomics profiles were analyzed by ultra-performance liquid chromatography-tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF/MS) technique. Univariate statistical analysis and multivariate analysis (MVA) methods were used to identify differential metabolites. Finally, the dysregulated pathways associated with ESCC were further explored and the diagnostic performance of the biomarker panel was evaluated. Results: Metabolic analyses identified 34 significant metabolites involved in the metabolism of amino acids, phospholipids, fatty acids, purine, and choline. Farthermore, an effective diagnostic model for ESCC was constructed based on eight metabolites. This panel of biomarkers consisted of hypoxanthine, proline betaine, indoleacrylic acid, inosine, 9-decenoylcarnitine, tetracosahexaenoic acid, LPE (20:4), and LPC (20:5). The model was verified and evaluated in the validation set. The AUC value of the ROC curve was 0.991(95% CI: 0.981-1.000, CI, Confidence interval), with a sensitivity (SE) of 98.8% and a specificity (SP) of 94.9% for the training set and 0.965(95% CI: 0.936-0.993), with a SE of 88.3% and a SP of 88.9% for the validation set. Among them, three biomarkers, indoleacrylic acid, LPC (20:5), and LPE (20:4), exhibited a trend associated with the ESCC progression. Conclusions: Our study identified a novel plasma biomarker panel, which clearly distinguishes ESCC patients and provides insight into the mechanisms of ESCC. This finding may form the basis for the development of a minimally invasive method for ESCC detection.
| S-EPMC7171502 | biostudies-literature
Project description:BACKGROUND: Previous metabolomics studies have found differences in metabolic characteristics between the healthy and ESCC patients. However, few of these studies concerned the whole process of the progression of ESCC. This study aims to explore serum metabolites associated with the progression of ESCC. METHODS: Serum samples from 653 participants (305 normal, 77 esophagitis, 228 LGD, and 43 HGD/ESCC) were examined by ultra-high performance liquid chromatography quadruple time-of-flight mass spectrometry (UHPLC-QTOF/MS). Principal component analysis (PCA) was first applied to obtain an overview of the clustering trend for the multidimensional data. Fuzzy c-means (FCM) clustering was then used to screen metabolites with a changing tendency in the progression of ESCC. Univariate ordinal logistic regression analysis and multiple ordinal logistic regression analysis were applied to evaluate the association of metabolites with the risk of ESCC progression, and adjusted for age, gender, BMI, tobacco smoking, and alcohol drinking status. RESULTS: After FCM clustering analysis, a total of 38 metabolites exhibiting changing tendency among normal, esophagitis, LGD, and HGD/ESCC patients. Final results showed 15 metabolites associated with the progression of ESCC. Ten metabolites (dopamine, L-histidine, 5-hydroxyindoleacetate, L-tryptophan, 2'-O-methylcytidine, PC (14:0/0:0), PC (O-16:1/0:0), PE (18:0/0:0), PC (16:1/0:0), PC (18:2/0:0)) were associated with decreased risk of developing ESCC. Five metabolites (hypoxanthine, inosine, carnitine (14:1), glycochenodeoxycholate, PC (P-18:0/18:3)) were associated with increased risk of developing ESCC. CONCLUSIONS: These results demonstrated that serum metabolites are associated with the progression of ESCC. These metabolites are capable of potential biomarkers for the risk prediction and early detection of ESCC.
| S-EPMC8100812 | biostudies-literature
Project description:Diagnostic and therapeutic biomarkers useful for esophageal squamous cell carcinoma (ESCC) have the ability to increase the long term survival of cancer patients. A metabolomics study, using plasma from four groups including ESCC patients before, during, and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by LC-MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects. It is worth pointing out that a clear clustering and separation of metabolic data from the four groups was observed, which indicated that disease status and treatment intervention resulted in specific metabolic perturbations in the patients. A series of metabolites were found to be significantly altered in ESCC patients versus healthy controls and in pre- versus post-treatment patients based on multivariate statistical data analysis (MVDA). To further validate the reliability of these potential biomarkers, an independent validation was performed by using the selected reaction monitoring (SRM) based targeted approach. Finally, 18 most significantly altered plasma metabolites in ESCC patients, relative to healthy controls, were tentatively identified as lysophosphatidylcholines (lysoPCs), fatty acids, l-carnitine, acylcarnitines, organic acids, and a sterol metabolite. The classification performance of these metabolites were analyzed by receiver operating characteristic (ROC)(1) analysis and a biomarker panel was generated. Together, biological significance of these metabolites was discussed. Comparison between pre- and post-treatment patients generated 11 metabolites as potential therapeutic biomarkers that were tentatively identified as amino acids, acylcarnitines, and lysoPCs. Levels of three of these (octanoylcarnitine, lysoPC(16:1), and decanoylcarnitine) were closely correlated with treatment effect. Moreover, variation of these three potential biomarkers was investigated over the treatment course. The results suggest that these biomarkers may be useful in diagnosis, as well as in monitoring therapeutic responses and predicting outcomes of the ESCC.
| S-EPMC3650341 | biostudies-literature
Project description:BackgroundThe incidence of esophageal squamous cell carcinoma in China ranks first in the world. The early diagnosis technology is underdeveloped, and the prognosis is poor, which seriously threatens the quality of life of the Chinese people. Epidemiological findings are related to factors such as diet, living habits, and age. The specific mechanism is not clear yet. Metabolomics is a kind of omics that simultaneously and quantitatively analyzes the comprehensive profile of metabolites in living systems. It has unique advantages in the study of the diagnosis and pathogenesis of tumor-related diseases, especially in the search for biomarkers. Therefore, it is desirable to perform metabolic profiling analysis of cancer tissues through metabolomics to find potential biomarkers for the diagnosis and treatment of esophageal squamous cell carcinoma.MethodsHPLC-TOF-MS/MS technology and Illumina Hiseq Xten Sequencing was used for the analysis of 210 pairs of matched esophageal squamous cell carcinoma tissues and normal tissues in Zhenjiang City, Jiangsu Province, a high-incidence area of esophageal cancer in China. Bioinformatics analysis was also performed.ResultsThrough metabolomic and transcriptomic analysis, this study found that a total of 269 differential metabolites were obtained in esophageal squamous cell carcinoma and normal tissues, and 48 differential metabolic pathways were obtained through KEGG enrichment analysis. After further screening and identification, 12 metabolites with potential biomarkers to differentiate esophageal squamous cell carcinoma from normal tissues were obtained.ConclusionsFrom the metabolomic data, 4 unknown compounds were found to be abnormally expressed in esophageal squamous cell carcinoma for the first time, such as 9,10-epoxy-12,15-octadecadienoate; 3 metabolites were found in multiple abnormal expression in another tumor, but upregulation or downregulation was found for the first time in esophageal cancer, such as oleoyl glycine; at the same time, it was further confirmed that five metabolites were abnormally expressed in esophageal squamous cell carcinoma, which was similar to the results of other studies, such as PE.
| S-EPMC9246618 | biostudies-literature
Project description:Background:DNA methylation has been implicated as a promising biomarker for precise cancer diagnosis. However, limited DNA methylation-based biomarkers have been described in esophageal squamous cell carcinoma (ESCC). Methods:A high-throughput DNA methylation dataset (100 samples) of ESCC from The Cancer Genome Atlas (TCGA) project was analyzed and validated along with another independent dataset (12 samples) from the Gene Expression Omnibus (GEO) database. The methylation status of peripheral blood mononuclear cells and peripheral blood leukocytes from healthy controls was also utilized for biomarker selection. The candidate CpG sites as well as their adjacent regions were further validated in 94 pairs of ESCC tumor and adjacent normal tissues from the Chinese Han population using the targeted bisulfite sequencing method. Logistic regression and several machine learning methods were applied for evaluation of the diagnostic ability of our panel. Results:In the discovery stage, five hyper-methylated CpG sites were selected as candidate biomarkers for further analysis as shown below: cg15830431, P?=?2.20?×?10-4; cg19396867, P?=?3.60?×?10-4; cg20655070, P?=?3.60?×?10-4; cg26671652, P?=?5.77?×?10-4; and cg27062795, P?=?3.60?×?10-4. In the validation stage, the methylation status of both the five CpG sites and their adjacent genomic regions were tested. The diagnostic model based on the combination of these five genomic regions yielded a robust performance (sensitivity?=?0.75, specificity?=?0.88, AUC?=?0.85). Eight statistical models along with five-fold cross-validation were further applied, in which the SVM model reached the best accuracy in both training and test dataset (accuracy?=?0.82 and 0.80, respectively). In addition, subgroup analyses revealed a significant difference in diagnostic performance between the alcohol use and non-alcohol use subgroups. Conclusions:Methylation profiles of the five genomic regions covering cg15830431 (STK3), cg19396867, cg20655070, cg26671652 (ZNF418), and cg27062795 (ZNF542) can be used for effective methylation-based testing for ESCC diagnosis.
| S-EPMC5732523 | biostudies-literature
Project description:The causes and etiology of Crohn's disease (CD) are currently unknown although both host genetics and environmental factors play a role. Here we used non-targeted metabolic profiling to determine the contribution of metabolites produced by the gut microbiota towards disease status of the host. Ion Cyclotron Resonance Fourier Transform Mass Spectrometry (ICR-FT/MS) was used to discern the masses of thousands of metabolites in fecal samples collected from 17 identical twin pairs, including healthy individuals and those with CD. Pathways with differentiating metabolites included those involved in the metabolism and or synthesis of amino acids, fatty acids, bile acids and arachidonic acid. Several metabolites were positively or negatively correlated to the disease phenotype and to specific microbes previously characterized in the same samples. Our data reveal novel differentiating metabolites for CD that may provide diagnostic biomarkers and/or monitoring tools as well as insight into potential targets for disease therapy and prevention.
| S-EPMC2713417 | biostudies-literature
Project description:Introduction: Circulating microRNAs (miRNAs) are promising molecular biomarkers for the early detection of esophageal squamous cell carcinoma (ESCC). We investigated the serum miRNA expression profiles from microarray-based technologies and evaluated the diagnostic value of serum miRNAs as potential biomarkers for ESCC by using feature selection algorithms. Methods: Serum miRNA expression profiles were obtained from 52 ESCC patients and 52 age- and sex-matched controls via performing a high-throughput microarray assay. Five representative feature selection algorithms including the false discovery rate procedure, family-wise error rate procedure, Lasso logistic regression, hybrid huberized support vector machine (SVM), and SVM using the squared-error loss with the elastic-net penalty were jointly carried out to select the significantly differentially expressed miRNAs based on the miRNA profiles. Results: Three miRNAs including miR-16-5p, miR-451a, and miR-574-5p were identified as the powerful biomarkers for the diagnosis of ESCC. The diagnostic accuracy of the combination of these three miRNAs was evaluated by using logistic regression and the SVM. The averages of the area under the receiver operating curve and classification accuracies based on different classifiers were more than 0.80 and 0.79, respectively. The cross-validation results suggested that the three-miRNA-based classifiers could clearly distinguish ESCC patients from healthy controls. Moreover, the classifying performance of the miRNA panel persisted in discriminating the healthy group from patients with ESCC stage I-II (AUC > 0.76) and patients with ESCC stage III-IV (AUC > 0.80). Conclusions: These results in this study have moved forward the identification of novel biomarkers for the diagnosis of ESCC.
| S-EPMC6348251 | biostudies-literature
Project description:Esophageal cancer (EC) is a common malignant disease in eastern countries. However, a study of the metabolomic characteristics associated with other biological factors in esophageal squamous cell carcinoma (ESCC) is limited. Interleukin enhancer binding factor 2 (ILF2) and ILF3, double-stranded RNA-binding proteins, have been reported to contribute to the occurrence and development of various types of malignancy. Nevertheless, the underlying functions of ILF2 and ILF3 in ESCC metabolic reprogramming have never been reported. This study aimed to contribute to the metabolic characterization of ESCC and to investigate the metabolomic alterations associated with ILF2 and ILF3 in ESCC tissues. Here, we identified 112 differential metabolites, which were mainly enriched in phosphatidylcholine biosynthesis, fatty acid metabolism, and amino acid metabolism pathways, based on liquid chromatography-mass spectrometry and capillary electrophoresis-mass spectrometry approaches using ESCC tissues and paired para-cancer tissues from twenty-eight ESCC patients. In addition, ILF2 and ILF3 expression were significantly elevated in EC tissues compared to the histologically normal samples, and closely associated with PI3K/AKT and MAPK signaling pathways in ESCC. Moreover, in ESCC tissues with a high ILF2 expression, several short-chain acyl-carnitines (C3:0, C4:0, and C5:0) related to the BCAA metabolic pathway and long-chain acyl-carnitines (C14:0, C16:0, C16:0-OH, and C18:0) involved in the oxidation of fatty acids were obviously upregulated. Additionally, a series of intermediate metabolites involved in the glycolysis pathway, including G6P/F6P, F1,6BP, DHAP, G3P, and 2,3BPG, were remarkably downregulated in highly ILF3-expressed ESCC tissues compared with the corresponding para-cancer tissues. Overall, these findings may provide evidence for the roles of ILF2 and ILF3 during the process of ESCC metabolic alterations, and new insights into the development of early diagnosis and treatment for ESCC. Further investigation is needed to clarify the underlying mechanism of ILF2 and ILF3 on acyl-carnitines and the glycolysis pathway, respectively.
| S-EPMC8459612 | biostudies-literature