Metabolomic profiling reveals potential biomarkers in esophageal cancer progression using liquid chromatography-mass spectrometry platform

Zhang, Haiping; Wang, Lei; Hou, Zhichao; Ma, Hong; Mamtimin, Batur; Hasim, Ayshamgul; Sheyhidin, Ilyar

doi:10.1016/j.bbrc.2017.07.060

Cited by 37 publications

(31 citation statements)

References 28 publications

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“…Most studies have noticed that enzymes associated with the purine biosynthetic pathway are enhanced in tumor cells because purine nucleotides are essential for tumor cell proliferation [33]. Decreased serum levels of hypoxanthine and inosine have been reported in gastric cancer and other diseases [34,35]. It has been suggested that the higher propagation rate of tumor cells leads to decreased serum levels of inosine and hypoxanthine.…”

Section: Discussionmentioning

confidence: 99%

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Zhu¹,

Zheng²,

Zhang³

et al. 2020

J. Cancer

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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.

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Section: Discussionmentioning

confidence: 99%

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Zhu¹,

Zheng²,

Zhang³

et al. 2020

J. Cancer

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show abstract

Section: Discussionmentioning

confidence: 99%

“…It is well recognized that metabolomic can provide snapshots of physiological conditions in biological samples by acquiring qualitative and quantitative information of low-molecular-mass endogenous metabolites, 18 and it has been applied to identify potential molecular biomarkers of various cancers. [19][20][21] A previous study suggested that malignant cancers had markedly altered intermediate metabolites in metabolic pathways compared with nontumor tissue. 22 In addition, Budhu et al 23 found that metabolic profiles were significantly different among several cancer types, and those distinct metabolites or metabolic pathways in particular cancer types might be helpful to However, an effective integration of metabolomic with transcriptomic data could better interpret results that could not be obtained from one single metabolomic dataset.…”

Section: Discussionmentioning

confidence: 99%

Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

Cai

Yong

et al. 2018

Cancer Science

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To identify metabolic pathways that were perturbed in pancreatic cancer (PC), we investigated gene‐metabolite networks by integration of metabolomic and transcriptomic. In this research, we undertook the metabolomic study of 43 paired human PC samples, aiming to identify key metabolic alterations in PC. We also carried out in vitro experiments to validate that the key metabolite cytidine and its related gene ENTPD8 played an important role in PC cell proliferation. We screened out 13 metabolites differentially expressed in PC tissue (PCT) by liquid chromatography/mass spectrometry analysis on 34 metabolites, and the partial least square discrimination analysis results revealed that 9 metabolites among them were remarkably altered in PCT compared to adjacent noncancerous tissue (variable importance in projection >1, P < .05). Among the 9 metabolites, 7 might be potential biomarkers. The most significantly enriched metabolic pathway was pyrimidine metabolism. We analyzed 351 differentially expressed genes from The Cancer Genome Atlas and intersected them with Kyoto Encyclopedia of Genes and Genomes metabolic pathways. We found that ENTPD8 had a gene‐metabolite association with cytidine in the CTP dephosphorylation pathway. We verified by in vitro experiments that the CTP dephosphorylation pathway was changed in PCT compared with adjacent noncancerous tissue. ENTPD8 was downregulated in PCT, causing a reduction in cytidine formation and hence weakened CTP dephosphorylation in pyrimidine metabolism.

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“…The GO-BP pathway was only visualized by selecting the pathway with pvalue <0.01. It can be seen that for the KEGG pathway results, these significantly enriched pathways were mainly concentrated in the FoxO signaling pathway and related pathways such as glutamate synapses and EGFR tyrosine kinases, and these pathways have been reported to play an important role in ESCC (Zhang et al, 2017;Kashyap & Abdel-Rahman, 2018;Hong et al, 2015). For the bar graph of GO-BP, these mRNAs were mainly involved in the biological processes of the corresponding cascade of calcineurin.…”

Section: Analysis Of Cerna Combined With Dna Methylationmentioning

confidence: 98%

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

Guan

Yao

Bao

et al. 2020

PeerJ

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Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

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Metabolomic profiling reveals potential biomarkers in esophageal cancer progression using liquid chromatography-mass spectrometry platform

Cited by 37 publications

References 28 publications

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

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