A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data
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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.
KeywordsLC-MS/MS Biomarker identification Networks Metabolomics HCC
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.
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.
- 11.Schaefer RJ, Michno JM, Myers CL. Unraveling gene function in agricultural species using gene co-expression networks. Biochim Biophys Acta, Gene Regul Mech. 2017;1860(1):53–63.Google Scholar
- 15.Banf M, Rhee SY. Computational inference of gene regulatory networks: approaches, limitations and opportunities. Biochim Biophys Acta, Gene Regul Mech. 2017;1860(1):41–52.Google Scholar
- 17.Meyer PE, Marbach D, Roy S, Kellis M. Information-theoretic inference of gene networks using backward elimination. In: BIOCOMP, international conference bioinformatics computational biology CSREA press. 2010;700–5.Google Scholar
- 19.Netzer M, Weinberger KM, Handler M, Seger M, Fang XC, Kugler KG, et al. Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers. J Clin Bioinform. 2011;1:34.Google Scholar
- 24.Breitling R, Amtmann A, Herzyk P. Graph-based iterative group analysis enhances microarray interpretation. BMC Bioinform. 2004;5:100.Google Scholar
- 26.Batra R, Alcaraz N, Gitzhofer K, Pauling J, Ditzel HJ, Hellmuth M, et al. On the performance of de novo pathway enrichment. npj Syst Biol Appl. 2017;3:6.Google Scholar
- 35.Han ML, Xie MY, Han J, Yuan DY, Yang T, Xie Y. Development and validation of a rapid, selective, and sensitive LC–MS/MS method for simultaneous determination of D- and L-amino acids in human serum: application to the study of hepatocellular carcinoma. Anal Bioanal Chem. 2018;410(10):2517–31.CrossRefPubMedGoogle Scholar
- 38.McGlynn KA, Abnet CC, Zhang MD, Sun XD, Fan JH, O'Brien TR, et al. Serum concentrations of 1,1,1-Trichloro-2,2-bis (p-chlorophenyl) ethane (DDT) and 1,1-Dichloro-2,2-bis (p-chlorophenyl) ethylene (DDE) and risk of primary liver cancer. J Natl Cancer Inst. 2006;98(14):1005–10.CrossRefPubMedGoogle Scholar
- 41.Wang JB, Abnet CC, Chen W, Dawsey SM, Fan JH, Yin LY, et al. Association between serum 25(OH) vitamin D, incident liver cancer and chronic liver disease mortality in the Linxian Nutrition Intervention Trials: a nested case-control study. Br J Cancer. 2013;109(7):1997–2004.CrossRefPubMedPubMedCentralGoogle Scholar
- 42.Chen W, Wang JB, Abnet CC, Dawsey SM, Fan JH, Yin LY, et al. Association between C-reactive protein, incident liver cancer, and chronic liver disease mortality in the Linxian Nutrition Intervention Trials: a nested case-control study. Cancer Epidemiol Biomark Prev. 2015;24(2):386–92.CrossRefGoogle Scholar
- 51.Takahashi T, Deuschle U, Taira S, Nishida T, Fujimoto M, Hijikata T, et al. Tsumura-Suzuki obese diabetic mice-derived hepatic tumors closely resemble human hepatocellular carcinomas in metabolism-related genes expression and bile acid accumulation. Hepatol Int. 2018;12(3):254–61.CrossRefPubMedGoogle Scholar
- 54.Glazer E, Stone E, Cherukuri P, Georgiou G, Curley S. Arginine deprivation via bioengineered arginase produces apoptosis in pancreatic carcinoma, hepatocellular carcinoma, and melanoma. Cancer Res. 2009;69(Suppl 9):1806.Google Scholar