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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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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.

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References

  1. Ghosh D, Chinnaiyan AM. Classification and selection of biomarkers in genomic data using LASSO. J Biomed Biotechnol. 2005;2:147–54.

    Article  CAS  Google Scholar 

  2. Luo P, Yin PY, Hua R, Tan YX, Li ZF, Qiu GK, et al. A large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology. 2018;67(2):662–75.

    Article  CAS  PubMed  Google Scholar 

  3. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46(1–3):389–422.

    Article  Google Scholar 

  4. Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5(2):101–13.

    Article  CAS  PubMed  Google Scholar 

  5. Sa RN, Zhang WW, Ge J, Wei XB, Zhou YH, Landzberg DR, et al. Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers. J Mol Cell Biol. 2016;8(3):195–206.

    Article  CAS  PubMed  Google Scholar 

  6. Sakaue S, Hirata J, Maeda Y, Kawakami E, Nii T, Kishikawa T, et al. Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity. Nucleic Acids Res. 2018;46(22):11898–909.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bø TH, Jonassen I. New feature subset selection procedures for classification of expression profiles. Genome Biol. 2002;3(4):0017.1–11.

    Article  Google Scholar 

  8. Geman D, d'Avignon C, Naiman DQ, Winslow RL. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol. 2004;3:19.

    Article  Google Scholar 

  9. Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D. Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics. 2005;21(20):3896–904.

    Article  CAS  PubMed  Google Scholar 

  10. Furlong LI. Human diseases through the lens of network biology. Trends Genet. 2013;29(3):150–9.

    Article  CAS  PubMed  Google Scholar 

  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.

  12. Yang BW, Li MY, Tang WQ, Liu WX, Zhang S, Chen LN, et al. Dynamic network biomarker indicates pulmonary metastasis at the tip** point of hepatocellular carcinoma. Nat Commun. 2018;9:678.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Reverter A, Chan EKF. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics. 2008;24(21):2491–7.

    Article  CAS  PubMed  Google Scholar 

  14. Krumsiek J, Mittelstrass K, Do KT, Stuckler F, Ried J, Adamski J, et al. Gender-specific pathway differences in the human serum metabolome. Metabolomics. 2015;11(6):1815–33.

    Article  CAS  PubMed  PubMed Central  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.

  16. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al. Large-scale map** and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):e8.

    Article  CAS  PubMed  PubMed Central  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.

  18. Baumgartner C, Lewis GD, Netzer M, Pfeifer B, Gerszten RE. A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury. Bioinformatics. 2010;26(14):1745–51.

    Article  CAS  PubMed  PubMed Central  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.

  20. Huang X, Zeng J, Zhou LN, Hu CX, Yin PY, Lin XH. A new strategy for analyzing time-series data using dynamic networks: identifying prospective biomarkers of hepatocellular carcinoma. Sci Rep. 2016;6:32448.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Song XP, Gong M, Chen YP, Liu H, Zhang J. Nine hub genes as the potential indicator for the clinical outcome of diabetic nephropathy. J Cell Physiol. 2019;234(2):1461–8.

    Article  CAS  PubMed  Google Scholar 

  22. Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT. BioNet: an R-package for the functional analysis of biological networks. Bioinformatics. 2010;26(8):1129–30.

    Article  CAS  PubMed  Google Scholar 

  23. Dittrich MT, Klau GW, Rosenwald A, Dandekar T, Muller T. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics. 2008;24(13):i223–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Breitling R, Amtmann A, Herzyk P. Graph-based iterative group analysis enhances microarray interpretation. BMC Bioinform. 2004;5:100.

  25. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.

    Article  PubMed  PubMed Central  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.

  27. Harrigan GG, Goodacre R. Metabolic profiling: its role in biomarker discovery and gene function analysis. 1st ed. Boston: Kluwer Academic Publisher; 2003.

    Book  Google Scholar 

  28. Park KS, Xu CL, Cui X, Tsang SH. Reprogramming the metabolome rescues retinal degeneration. Cell Mol Life Sci. 2018;75(9):1559–66.

    Article  CAS  PubMed  Google Scholar 

  29. Lecuyer L, Bala AV, Deschasaux M, Bouchemal N, Triba MN, Vasson MP, et al. NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of develo** breast cancer. Int J Epidemiol. 2018;47(2):484–94.

    Article  PubMed  Google Scholar 

  30. Bharti SK, Wildes F, Hung CF, Wu TC, Bhujwalla ZM, Penet MF. Metabolomic characterization of experimental ovarian cancer ascitic fluid. Metabolomics. 2017;13(10):113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Marrero JA, Kulik LM, Sirlin CB, Zhu AX, Finn RS, Abecassis MM, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68(2):723–50.

    Article  PubMed  Google Scholar 

  32. Bertuccio P, Turati F, Carioli G, Rodriguez T, La Vecchia C, Malvezzi M, et al. Global trends and predictions in hepatocellular carcinoma mortality. J Hepatol. 2017;67(2):302–9.

    Article  PubMed  Google Scholar 

  33. Stefaniuk P, Cianciara J, Wiercinska-Drapalo A. Present and future possibilities for early diagnosis of hepatocellular carcinoma. World J Gastroenterol. 2010;16(4):418–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fu J, Wang HY. Precision diagnosis and treatment of liver cancer in China. Cancer Lett. 2018;412:283–8.

    Article  CAS  PubMed  Google Scholar 

  35. Han ML, **e MY, Han J, Yuan DY, Yang T, **e 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.

    Article  CAS  PubMed  Google Scholar 

  36. Dai WD, Yin PY, Chen P, Kong HW, Luo P, Xu ZL, et al. Study of urinary steroid hormone disorders: difference between hepatocellular carcinoma in early stage and cirrhosis. Anal Bioanal Chem. 2014;406(18):4325–35.

    Article  CAS  PubMed  Google Scholar 

  37. Yu CY, Liu R, **e C, Zhang Q, Yin YD, Bi KS, et al. Quantification of free polyamines and their metabolites in biofluids and liver tissue by UHPLC-MS/MS: application to identify the potential biomarkers of hepatocellular carcinoma. Anal Bioanal Chem. 2015;407(22):6891–7.

    Article  CAS  PubMed  Google 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.

    Article  CAS  PubMed  Google Scholar 

  39. Qu CX, Kamangar F, Fan JH, Yu BB, Sun XD, Taylor PR, et al. Chemoprevention of primary liver cancer: a randomized, double-blind trial in Linxian, China. J Natl Cancer Inst. 2007;99(16):1240–7.

    Article  PubMed  Google Scholar 

  40. Fan JH, Wang JB, Jiang Y, **ang W, Liang H, Wei WQ, et al. Attributable causes of liver cancer mortality and incidence in China. Asian Pac J Cancer Prev. 2013;14(12):7251–6.

    Article  PubMed  Google 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.

    Article  CAS  PubMed  PubMed Central  Google 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.

    Article  CAS  Google Scholar 

  43. Kamburov A, Cavill R, Ebbels TMD, Herwig R, Keun HC. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics. 2011;27(20):2917–8.

    Article  CAS  PubMed  Google Scholar 

  44. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–55.

    Article  CAS  PubMed  Google Scholar 

  45. Kanehisa M, Goto SKEGG. Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2004;6:R2.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, et al. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. 2018;46(D1):D661–7.

    Article  CAS  PubMed  Google Scholar 

  48. Ma HW, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O, et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol. 2007;3:135.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Jewison T, Su YL, Disfany FM, Liang YJ, Knox C, Maciejewski A, et al. SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 2014;42(D1):D478–84.

    Article  CAS  PubMed  Google Scholar 

  50. Yamamoto S, Sakai N, Nakamura H, Fukagawa H, Fukuda K, Takagi T. INOH: ontology-based highly structured database of signal transduction pathways. Database. 2011;2011:bar052.

    PubMed  PubMed Central  Google 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.

    Article  PubMed  Google Scholar 

  52. Wang XN, **e GX, Zhao AH, Zheng XJ, Huang FJ, Wang YX, et al. Serum bile acids are associated with pathological progression of hepatitis B-induced cirrhosis. J Proteome Res. 2016;15(4):1126–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kuang Y, Wang FJ, Corn DJ, Tian HB, Lee ZH. In vitro characterization of uptake mechanism of L-[methyl-3H]-methionine in hepatocellular carcinoma. Mol Imaging Biol. 2014;16(4):459–68.

    Article  PubMed  PubMed Central  Google 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 

  55. Tan YX, Yin PY, Tang L, **ng WB, Huang Q, Cao D, et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol Cell Proteomics. 2012;11(2):M111.010694.

    Article  CAS  PubMed  Google Scholar 

  56. Liu Y, Li YH, Guo FJ, Wang JJ, Sun RL, Hu JY, et al. Gamma-aminobutyric acid promotes human hepatocellular carcinoma growth through overexpressed gamma-aminobutyric acid a receptor α3 subunit. World J Gastroenterol. 2008;14(47):7175–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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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

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).

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Author notes

  1. Benzhe Su and ** Luo contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science & Technology, Dalian University of Technology, Dalian, 116024, China

    Benzhe Su, **n Huang & **aohui Lin

  2. CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China

    ** Luo, Zaifang Li, Peiyuan Yin, Lina Zhou & Guowang Xu

  3. Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Bei**g, 100021, China

    Zhao Yang, Pei Yu, **hu Fan & Youlin Qiao

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  1. Benzhe Su
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  3. Zhao Yang
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Correspondence to Peiyuan Yin, **hu Fan or **aohui Lin.

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

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Su, B., Luo, P., Yang, Z. et al. A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data. Anal Bioanal Chem 411, 6377–6386 (2019). https://doi.org/10.1007/s00216-019-02011-w

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