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Analytical and Bioanalytical Chemistry

, Volume 411, Issue 24, pp 6377–6386 | Cite as

A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data

  • Benzhe Su
  • Ping Luo
  • Zhao Yang
  • Pei Yu
  • Zaifang Li
  • Peiyuan YinEmail author
  • Lina Zhou
  • Jinhu FanEmail author
  • Xin Huang
  • Xiaohui LinEmail author
  • Youlin Qiao
  • Guowang Xu
Research Paper

Abstract

Omics techniques develop quickly and have made a great contribution to disease study. Omics data are usually complex. How to analyze the data and mine important information has been a key part in omics research. To study the nature of disease mechanisms systematically, we propose a new data analysis method to define the network biomarkers based on horizontal comparison (DNB-HC). DNB-HC performs molecule horizontal relationships to characterize the physiological status and differential network analysis to screen the biomarkers. We applied DNB-HC to analyze a large-scale metabolomics data, which contained 550 samples from a nested case-control hepatocellular carcinoma (HCC) study. A network biomarker was defined, and its areas under curves (AUC) in the receiver-operating characteristic (ROC) analysis for HCC discrimination were larger than those defined by six efficient feature selection methods in most cases. The effectiveness was further corroborated by another nested HCC dataset. Besides, the performance of the defined biomarkers was better than that of α-fetoprotein (AFP), a commonly used clinical biomarker for distinguishing HCC from high-risk population of liver cirrhosis in other two independent metabolomics validation sets. All and 90.3% of the AFP false-negative patients with HCC were correctly diagnosed in these two sets, respectively. The experimental results illustrate that DNB-HC can mine more important information reflecting the nature of the research problems by studying the feature horizontal relationship systematically and identifying effective disease biomarkers in clinical practice.

Graphical abstract

Keywords

LC-MS/MS Biomarker identification Networks Metabolomics HCC 

Notes

Acknowledgments

The authors thank all the people who participated in the study, and the many individuals not specifically mentioned in the paper who have supported the study.

Funding information

The study has been supported by the National Natural Science Foundation of China (No. 21375011) and Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS, No. 2017-I2M-B&R-03).

Compliance with ethical standards

The study was approved by the ethics committee of Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) according to the Declaration of Helsinki.

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

216_2019_2011_MOESM1_ESM.pdf (264 kb)
ESM 1 (PDF 264 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science & TechnologyDalian University of TechnologyDalianChina
  2. 2.CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalianChina
  3. 3.Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingChina

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