TY - JOUR
T1 - Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma
AU - Chen, Zhongjian
AU - Dai, Yalan
AU - Huang, Xiancong
AU - Chen, Keke
AU - Gao, Yun
AU - Li, Na
AU - Wang, Ding
AU - Chen, Aiping
AU - Yang, Qingxia
AU - HONG, Yanjun
AU - Zeng, Su
AU - Mao, Weimin
N1 - Funding Information:
We thank Biobank in Zhejiang Cancer Hospital for providing all the samples in the study. We acknowledge technical support of Dr. Junzhou Wu (Core Facility, Cancer Hospital of the University of Chinese Academy of Sciences) and Dr. Jinjin Xu (Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences). Funding. This research was supported by grants from the National Natural Science Foundation of China (No. 81672315, 81302840, 81802472), Zhejiang Provincial Natural Science Foundation of China (LQ18H160020), and the 1022 Talent Training Program of Zhejiang Cancer Hospital. Science, Technology and Innovation Commission of Shenzhen (JCYJ20160531193901593). Hong Kong Baptist University, Faculty Research Grants (2/17-18/088). Leading Talent of Ten Thousand Plan?National High-Level Talents Special Support Plan.
PY - 2020/8/26
Y1 - 2020/8/26
N2 - Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.
AB - Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.
KW - 3-dioxygenase 1 (IDO1)
KW - artificial intelligence
KW - diagnosis
KW - esophageal squamous cell carcinoma (ESCC)
KW - indoleamine 2
KW - metabolomics
KW - prognosis
KW - risk score
UR - http://www.scopus.com/inward/record.url?scp=85090503549&partnerID=8YFLogxK
U2 - 10.3389/fonc.2020.01545
DO - 10.3389/fonc.2020.01545
M3 - Journal article
AN - SCOPUS:85090503549
SN - 2234-943X
VL - 10
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1545
ER -
