title: [Using (1)H-nuclear magnetic resonance metabolomics and gene ontology to establish pathological staging model for esophageal cancer patients].
abstract: OBJECTIVE
  By combining the metabolomics and computational biology, to explore the relationship between metabolic phenotype and pathological stage in esophageal cancer patients, to find the mechanism of metabolic network disturbance and develop a new method for fast preoperative clinical staging.
  
  
  METHODS
  A prospective cohort study (from April 2013 to January 2016) was conducted. The preoperative patients from Sichuan Provincial People's Hospital, who were diagnosed with esophageal cancer from May 2013 to April 2014 were included, and their serum samples were collected to detect (1)H-nuclear magnetic resonance (NMR) metabolomics for the purpose of drawing the metabolic fingerprinting in different stages of patients with esophageal cancer. The data were processed with these methods-principal components analysis: partial least squares regression and support vector machine, for the exploration of the enzyme-gene network regulatory mechanism in abnormal esophageal cancer metabolic network regulation and to build the quantitative prediction model of esophageal cancer staging in the end. All data were processed on high-performance computing platforms Matalab. The comparison of data had used Wilcoxon test, variance analysis, χ(2) test and Fisher exact test.
  
  
  RESULTS
  Twenty patients with different stages of esophageal cancer were included; and their serum metabolic fingerprinting could differentiate different tumor stages. There were no difference among the five teams in the age (F=1.086, P>0.05), the body mass index (F=1.035, P>0.05), the distance from the incisors to tumor (F=1.078, P>0.05). Among the patients with different TNM stages, there was a significant difference in plasma metabolome. Compared to ⅡB, ⅢA, Ⅳstage patients, increased levels of butanone, ethanol amine, homocysteine, hydroxy acids and estriol, together with decreased levels of glycoprotein, creatine, choline, isobutyricacid, alanine, leucine, valine, were observed inⅠB, ⅡA stage patients. Four metabolic markers (ethanol amine, hydroxy-propionic acid, homocysteine and estriol) were eventually selected. gene ontology analysis showed that 54 enzymes and genes regulated the 4 key metabolic markers. The quantitative prediction model of esophageal cancer staging based on esophageal cancer NMR spectrum were established. Cross-validation results showed that the predicted effect was good (root mean square error=5.3, R(2)=0.47, P=0.036).
  
  
  CONCLUSIONS
  The systems biology approaches based on metabolomics and enzyme-gene regulatory network analysis can be used to quantify the metabolic network disturbance of patients with advanced esophageal cancer, and to predict preoperative clinical staging of esophageal cancer patients by plasma NMR metabolomics.
authors.affiliation: Department of Computational Mathematics and Biological Statistics, Metabolomics and Multidisciplinary Laboratory for Trauma Research, Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, Sichuan Academy of Medical Sciences, Chengdu 610101, China.
authors.forename: X
authors.initials: X
authors.lastname: Chen
authors.forename: K
authors.initials: K
authors.lastname: Wang
authors.forename: W
authors.initials: W
authors.lastname: Chen
authors.forename: H
authors.initials: H
authors.lastname: Jiang
authors.forename: P C
authors.initials: PC
authors.lastname: Deng
authors.forename: Z J
authors.initials: ZJ
authors.lastname: Li
authors.forename: J
authors.initials: J
authors.lastname: Peng
authors.forename: Z Y
authors.initials: ZY
authors.lastname: Zhou
authors.forename: H
authors.initials: H
authors.lastname: Yang
authors.forename: G X
authors.initials: GX
authors.lastname: Huang
authors.forename: J
authors.initials: J
authors.lastname: Zeng
mesh_terms: D004938:Esophageal Neoplasms; D006801:Humans; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy; D055442:Metabolome; D055432:Metabolomics; D011446:Prospective Studies