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FGB and FGG derived from plasma exosomes as potential biomarkers to distinguish benign from malignant pulmonary nodules

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Previous proteomic analysis (label-free) of plasma exosomes revealed that the expression of FGG and FGB was significantly higher in the malignant pulmonary nodules group, compared to the benign pulmonary nodules group. The present study was performed to evaluate the role of plasma exosomal proteins FGB and FGG in the diagnosis of benign and malignant pulmonary nodules. We examined the expression levels of FGB and FGG in plasma exosomes from 63 patients before surgery. Postoperative pathological diagnosis confirmed that 43 cases were malignant and 20 cases were benign. The ROC curve was used to describe the sensitivity, specificity, area under the curve (AUC) of the biomarker and the corresponding 95% confidence interval. We confirmed that the expression levels of FGB and FGG were higher in the plasma exosomes of malignant group than in the benign group. The sensitivity and AUC of FGB combined with FGG detection to determine the nature of pulmonary nodules are superior to single FGB or FGG detection. FGB and FGG might represent novel and sensitive biomarker to distinguish benign from malignant pulmonary nodules.

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Pulmonary nodules


Fibrinogen beta chain


Fibrinogen gamma chain


Receiver operating characteristic


Area under curve


Computed tomography


Positron emission computed tomography


  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. 2018;68(1):7–30.

    Article  Google Scholar 

  2. Henschke CI, Yankelevitz DF, Libby DM, Pasmantier MW, Smith JP, Miettinen OS. Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med. 2006;355(17):1763–71.

    Article  PubMed  Google Scholar 

  3. Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395–409.

    Article  PubMed  Google Scholar 

  4. Peters S, Adjei AA, Gridelli C, Reck M, Kerr K, Felip E. Metastatic non-small-cell lung cancer (NSCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2012;23(Suppl 7):56–64.

    Article  Google Scholar 

  5. Wender R, Fontham ET, Barrera E Jr, Colditz GA, Church TR, Ettinger DS, et al. American Cancer Society lung cancer screening guidelines. CA: Cancer J Clin. 2013;63(2):107–17.

    Article  Google Scholar 

  6. Hassanein M, Callison JC, Callaway-Lane C, Aldrich MC, Grogan EL, Massion PP. The state of molecular biomarkers for the early detection of lung cancer. Cancer Prev Res. 2012;5(8):992–1006.

    Article  CAS  Google Scholar 

  7. Luo L, Dong LY, Yan QG, Cao SJ, Wen XT, Huang Y, et al. Research progress in applying proteomics technology to explore early diagnosis biomarkers of breast cancer, lung cancer and ovarian cancer. Asian Pac J Cancer Prev. 2014;15(20):8529–38.

    Article  Google Scholar 

  8. Hudler P, Kocevar N, Komel R. Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics. Sci World J. 2014;2014:260348.

    Article  CAS  Google Scholar 

  9. Klupczynska A, Plewa S, Kasprzyk M, Dyszkiewicz W, Kokot ZJ, Matysiak J. Serum lipidome screening in patients with stage I non-small cell lung cancer. Clin Exp Med. 2019.

    Article  PubMed  Google Scholar 

  10. Qian C, Wu S, Chen H, Zhang X, Jing R, Shen L, et al. Clinical significance of circulating tumor cells from lung cancer patients using microfluidic chip. Clin Exp Med. 2018;18(2):191–202.

    Article  CAS  PubMed  Google Scholar 

  11. Ansari J, Yun JW, Kompelli AR, Moufarrej YE, Alexander JS, Herrera GA, et al. The liquid biopsy in lung cancer. Genes Cancer. 2016;7(11–12):355–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Crowley E, Di Nicolantonio F, Loupakis F, Bardelli A. Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol. 2013;10(8):472–84.

    Article  CAS  PubMed  Google Scholar 

  13. Lu L, Sun C, Su Q, Wang Y, Li J, Guo Z, et al. Radiation-induced lung injury: latest molecular developments therapeutic approaches and clinical guidance. Clin Exp Med. 2019.

    Article  PubMed  Google Scholar 

  14. Toss A, Mu Z, Fernandez S, Cristofanilli M. CTC enumeration and characterization: moving toward personalized medicine. Ann Transl Med. 2014;2(11):108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Heitzer E, Ulz P, Geigl JB. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem. 2015;61(1):112–23.

    Article  CAS  PubMed  Google Scholar 

  16. Brinton LT, Sloane HS, Kester M, Kelly KA. Formation and role of exosomes in cancer. Cell Mol Life Sci. 2015;72(4):659–71.

    Article  CAS  Google Scholar 

  17. Zhang X, Yuan X, Shi H, Wu L, Qian H, Xu W. Exosomes in cancer: small particle, big player. J Hematol Oncol. 2015;8:83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kuang M, Tao X, Peng Y, Zhang W, Pan Y, Cheng L, et al. Proteomic analysis of plasma exosomes to differentiate malignant from benign pulmonary nodules. Clin Proteomics. 2019;16:5.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13.

    Article  CAS  PubMed  Google Scholar 

  20. Daly S, Rinewalt D, Fhied C, Basu S, Mahon B, Liptay MJ, et al. Development and validation of a plasma biomarker panel for discerning clinical significance of indeterminate pulmonary nodules. J Thorac Oncol. 2013;8(1):31–6.

    Article  CAS  PubMed  Google Scholar 

  21. Lin J, Li J, Huang B, Liu J, Chen X, Chen XM, et al. Exosomes: novel biomarkers for clinical diagnosis. Sci World J. 2015;2015:657086.

    Article  CAS  Google Scholar 

  22. Farjah F, Madtes DK, Wood DE, Flum DR, Zadworny ME, Waworuntu R, et al. Vascular endothelial growth factor C complements the ability of positron emission tomography to predict nodal disease in lung cancer. J Thorac Cardiovasc Surg. 2015;150(4):796–8031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Patz EF Jr, Campa MJ, Gottlin EB, Trotter PR, Herndon JE 2nd, Kafader D, et al. Biomarkers to help guide management of patients with pulmonary nodules. Am J Respir Crit Care Med. 2013;188(4):461–5.

    Article  PubMed  Google Scholar 

  24. Grenier J, Pujol JL, Guilleux F, Daures JP, Pujol H, Michel FB. Cyfra 21-1, a new marker of lung cancer. Nucl Med Biol. 1994;21(3):471–6.

    Article  CAS  Google Scholar 

  25. Okamura K, Takayama K, Izumi M, Harada T, Furuyama K, Nakanishi Y. Diagnostic value of CEA and CYFRA 21-1 tumor markers in primary lung cancer. Lung Cancer (Amsterdam, Netherlands). 2013;80(1):45–9.

    Article  Google Scholar 

  26. Whiteside TL. The potential of tumor-derived exosomes for noninvasive cancer monitoring. Expert Rev Mol Diagn. 2015;15(10):1293–310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Milane L, Singh A, Mattheolabakis G, Suresh M, Amiji MM. Exosome mediated communication within the tumor microenvironment. J Controlled Release. 2015;219:278–94.

    Article  CAS  Google Scholar 

  28. Sandfeld-Paulsen B, Aggerholm-Pedersen N, Baek R, Jakobsen KR, Meldgaard P, Folkersen BH, et al. Exosomal proteins as prognostic biomarkers in non-small cell lung cancer. Mol Oncol. 2016;10(10):1595–602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. de Moerloose P, Casini A, Neerman-Arbez M. Congenital fibrinogen disorders: an update. Semin Thromb Hemost. 2013;39(6):585–95.

    Article  CAS  PubMed  Google Scholar 

  30. Rotstein OD. Role of fibrin deposition in the pathogenesis of intraabdominal infection. Eur J Clin Microbiol Infect Dis. 1992;11(11):1064–8.

    Article  CAS  Google Scholar 

  31. Rijneveld AW, Weijer S, Florquin S, Esmon CT, Meijers JC, Speelman P, et al. Thrombomodulin mutant mice with a strongly reduced capacity to generate activated protein C have an unaltered pulmonary immune response to respiratory pathogens and lipopolysaccharide. Blood. 2004;103(5):1702–9.

    Article  CAS  PubMed  Google Scholar 

  32. Liu A, Tanaka N, Sun L, Guo B, Kim JH, Krausz KW, et al. Saikosaponin d protects against acetaminophen-induced hepatotoxicity by inhibiting NF-kappaB and STAT3 signaling. Chem Biol Interact. 2014;223:80–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sahni A, Simpson-Haidaris PJ, Sahni SK, Vaday GG, Francis CW. Fibrinogen synthesized by cancer cells augments the proliferative effect of fibroblast growth factor-2 (FGF-2). J Thromb Haemost. 2008;6(1):176–83.

    Article  CAS  PubMed  Google Scholar 

  34. Wang H, Meyer CA, Fei T, Wang G, Zhang F, Liu XS. A systematic approach identifies FOXA1 as a key factor in the loss of epithelial traits during the epithelial-to-mesenchymal transition in lung cancer. BMC Genom. 2013;14:680.

    Article  CAS  Google Scholar 

  35. Linden M, Segersten U, Runeson M, Wester K, Busch C, Pettersson U, et al. Tumour expression of bladder cancer-associated urinary proteins. BJU Int. 2013;112(3):407–15.

    Article  CAS  PubMed  Google Scholar 

  36. Zha C, Jiang XH, Peng SF. iTRAQ-based quantitative proteomic analysis on S100 calcium binding protein A2 in metastasis of laryngeal cancer. PLoS ONE. 2015;10(4):e0122322.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhang C, Leng W, Sun C, Lu T, Chen Z, Men X, et al. Urine proteome profiling predicts lung cancer from control cases and other tumors. EBioMedicine. 2018;30:120–8.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Zhao J, Cheng W, He X, Liu Y, Li J, Sun J, et al. Construction of a specific SVM classifier and identification of molecular markers for lung adenocarcinoma based on lncRNA-miRNA-mRNA network. OncoTargets Ther. 2018;11:3129–40.

    Article  CAS  Google Scholar 

  39. Gao HJ, Chen YJ, Zuo D, Xiao MM, Li Y, Guo H, et al. Quantitative proteomic analysis for high-throughput screening of differential glycoproteins in hepatocellular carcinoma serum. Cancer Biol Med. 2015;12(3):246–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chen C, Zhang LG, Liu J, Han H, Chen N, Yao AL, et al. Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data. OncoTargets Ther. 2016;9:1545–57.

    Article  CAS  Google Scholar 

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This work was supported by the National Natural Science Foundation of China (No. 81572264).

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Authors and Affiliations



MK performed data analysis and wrote the manuscript; YP and XT carried out extraction and identification of exosomes. ZZ helped to perform bioinformatics analysis. LZ and HM collected samples and information of clinical cases. YS and HZ conceived of the study and participated in its designation and helped to draft the manuscript. All authors read and approved the final manuscript.

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Correspondence to Huibiao Zhang.

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The authors declare that they have no conflict of interest.

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All procedures performed in the study involving human participants were in accordance with the ethical standards of the Committee for Ethical Review of Research of Fudan University and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Kuang, M., Peng, Y., Tao, X. et al. FGB and FGG derived from plasma exosomes as potential biomarkers to distinguish benign from malignant pulmonary nodules. Clin Exp Med 19, 557–564 (2019).

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