Towards Tyrosine Metabolism in Esophageal Squamous Cell Carcinoma

Title:Towards Tyrosine Metabolism in Esophageal Squamous Cell Carcinoma

Volume: 20 Issue: 2

Author(s): Jing Cheng, Guangyong Zheng*, Hai Jin and Xianfu Gao*

Affiliation:

• Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031,China

• Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031,China

Keywords: ESCC, metabolomics, LC-MS/MS, tyrosine metabolism, diagnostic biomarker, prediction model.

Abstract: Background: Esophageal Squamous Cell Carcinoma (ESCC) is a common malignant tumor in China, which causes about 200,000 deaths each year. Sensitive biomarkers are helpful to diagnose the disease in early stage.

Methods: To identify biomarkers of ESCC and elucidate underlying mechanism of the disease, a targeted metabolomics strategy based on liquid chromatography-tandem mass spectrometry (LCMS/ MS) has been implemented to explore tyrosine metabolism from 40 ESCC patients and 27 healthy controls.

Results: Four metabolites, i.e. phenylalanine, 4-hydroxyphenyllactic acid, 3,4-dihydroxyphenylalanine, and 3,4-dihydroxyphenylacetic acid were identified as diagnostic biomarkers for ESCC patients. Based on these biomarkers, a prediction model was constructed for ESCC diagnosis. The analysis of receiver operating characteristic (ROC) curve confirmed its effectiveness of the model.

Conclusion: Our results reveal that tyrosine metabolism is disturbed in ESCC patients and the metabolites involved in tyrosine pathway can be used as diagnostic biomarkers of the disease. Findings of this study can help investigate pathogenesis of ESCC and facilitate understanding mechanism of the disease.

Cite this article as:

Cheng Jing, Zheng Guangyong*, Jin Hai and Gao Xianfu*, Towards Tyrosine Metabolism in Esophageal Squamous Cell Carcinoma, Combinatorial Chemistry & High Throughput Screening 2017; 20 (2) . https://dx.doi.org/10.2174/1386207319666161220115409