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Molecular Diagnosis & Therapy

, Volume 22, Issue 4, pp 493–502 | Cite as

Evaluation of Serum Paired MicroRNA Ratios for Differential Diagnosis of Non-Small Cell Lung Cancer and Benign Pulmonary Diseases

  • Lihong Fan
  • Jie Sha
  • Junliang Teng
  • Dan Li
  • Changhui Wang
  • Qing Xia
  • Hao Chen
  • Bo SuEmail author
  • Huiwei QiEmail author
Original Research Article
First Online:
  • 55 Downloads

Abstract

Background and Objective

To clarify whether there are different expressions between lung cancer and benign pulmonary diseases, we studied seven microRNAs (miRNAs) in serum from patients with non-small cell lung cancer (NSCLC), benign pulmonary nodules and four pulmonary inflammation diseases.

Methods

We detected the expression of miRNAs using quantitative reverse transcriptase–polymerase chain reaction (qRT–PCR).

Results

We found that five miRNA ratios—miR-15b-5p/miR-146b-3p, miR-20a-5p/miR-146b-3p, miR-19a-3p/miR-146b-3p, miR-92a-3p/miR-146b-3p, and miR-16-5p/miR-146b-3p—show higher expression in the NSCLC group than the benign pulmonary nodule group, and 13 ratios of miRNAs were significantly upregulated in the NSCLC group compared with the pulmonary inflammation diseases group. Receiver operating characteristic (ROC) curve analysis was performed. For NSCLC and benign pulmonary nodules, the sensitivity and specificity were 0.70 and 0.90, respectively. For NSCLC and pulmonary inflammation diseases, the sensitivity and specificity were 0.81 and 0.71, respectively.

Conclusion

The ratios of miRNAs can be used as potential non-invasive biomarkers for diagnosis of early-stage NSCLC and benign pulmonary diseases.

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Notes

Compliance with Ethical Standards

Conflict of interest

All authors (LF, JS, JT, DL, CW, QX, HC, BS and HQ) declare that they have no conflicts of interest.

Funding

This study was funded by the Fundamental Research Funds for the Central Universities (no. 20153590) and the Chinese National Natural Science Foundation (nos. 81703847, 81473469).

Ethical Standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

References

  1. 1.
    Crino L, Weder W, van Meerbeeck J, Felip E. Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2010;21(Suppl 5):v103–15.  https://doi.org/10.1093/annonc/mdq207.CrossRefPubMedGoogle Scholar
  2. 2.
    Howlader N, Noone AM, Krapcho M, Neyman N, Aminou R, Waldron W, et al. SEER Cancer Statistics Review, 1975-2009 (Vintage 2009 Populations) [based on November 2011 SEER data submission, posted to the SEER website April 2012]. Bethesda: National Cancer Institute; 2012. https://seer.cancer.gov/csr/1975_2009_pops09/. Accessed 1 May 2017.
  3. 3.
    Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 2008;18(10):997–1006.  https://doi.org/10.1038/cr.2008.282.CrossRefPubMedGoogle Scholar
  4. 4.
    Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA. 2008;105(30):10513–8.  https://doi.org/10.1073/pnas.0804549105.CrossRefPubMedGoogle Scholar
  5. 5.
    Turchinovich A, Weiz L, Langheinz A, Burwinkel B. Characterization of extracellular circulating microRNA. Nucleic Acids Res. 2011;39(16):7223–33.  https://doi.org/10.1093/nar/gkr254.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Wozniak MB, Scelo G, Muller DC, Mukeria A, Zaridze D, Brennan P. Circulating MicroRNAs as non-invasive biomarkers for early detection of non-small-cell lung cancer. PLoS One. 2015;10(5):e0125026.  https://doi.org/10.1371/journal.pone.0125026.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Boeri M, Verri C, Conte D, Roz L, Modena P, Facchinetti F, et al. MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. Proc Natl Acad Sci USA. 2011;108(9):3713–8.  https://doi.org/10.1073/pnas.1100048108.CrossRefPubMedGoogle Scholar
  8. 8.
    Sozzi G, Boeri M, Rossi M, Verri C, Suatoni P, Bravi F, et al. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol. 2014;32(8):768–73.  https://doi.org/10.1200/jco.2013.50.4357.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Fan L, Qi H, Teng J, Su B, Chen H, Wang C, et al. Identification of serum miRNAs by nano-quantum dots microarray as diagnostic biomarkers for early detection of non-small cell lung cancer. Tumour Biol. 2016;37(6):7777–84.  https://doi.org/10.1007/s13277-015-4608-3.CrossRefPubMedGoogle Scholar
  10. 10.
    Le CH, Ko YC, Cheng LS, Lin YC, Lin HJ, Huang MS, et al. The heterogeneity in risk factors of lung cancer and the difference of histologic distribution between genders in Taiwan. Cancer Causes Control. 2001;12(4):289–300.CrossRefPubMedGoogle Scholar
  11. 11.
    Rosenberger A, Bickeboller H, McCormack V, Brenner DR, Duell EJ, Tjonneland A, et al. Asthma and lung cancer risk: a systematic investigation by the International Lung Cancer Consortium. Carcinogenesis. 2012;33(3):587–97.  https://doi.org/10.1093/carcin/bgr307.CrossRefPubMedGoogle Scholar
  12. 12.
    Khoury S, Tran N. Circulating microRNAs: potential biomarkers for common malignancies. Biomark Med. 2015;9(2):131–51.  https://doi.org/10.2217/bmm.14.102.CrossRefPubMedGoogle Scholar
  13. 13.
    Ost D, Fein AM, Feinsilver SH. Clinical practice. The solitary pulmonary nodule. N Engl J Med. 2003;348(25):2535–42.  https://doi.org/10.1056/NEJMcp012290.CrossRefPubMedGoogle Scholar
  14. 14.
    Kahn N, Kuner R, Eberhardt R, Meister M, Muley T, Winteroll S, et al. Gene expression analysis of endobronchial epithelial lining fluid in the evaluation of indeterminate pulmonary nodules. J Thorac Cardiovasc Surg. 2009;138(2):474–9.  https://doi.org/10.1016/j.jtcvs.2009.04.024.CrossRefPubMedGoogle Scholar
  15. 15.
    Li CR, Li YZ, Li YM, Zheng YS. Dynamic and contrast enhanced CT imaging of lung carcinoma, pulmonary tuberculoma, and inflammatory pseudotumor. Eur Rev Med Pharmacol Sci. 2017;21(7):1588–92.PubMedGoogle Scholar
  16. 16.
    Basu S, Saboury B, Werner T, Alavi A. Clinical utility of FDG-PET and PET/CT in non-malignant thoracic disorders. Mol Imaging Biol. 2011;13(6):1051–60.  https://doi.org/10.1007/s11307-010-0459-x.CrossRefPubMedGoogle Scholar
  17. 17.
    Miyashita N, Akaike H, Teranishi H, Nakano T, Ouchi K, Okimoto N. Chest computed tomography for the diagnosis of Mycoplasma pneumoniae infection. Respirology. 2014;19(1):144–5.  https://doi.org/10.1111/resp.12218.CrossRefPubMedGoogle Scholar
  18. 18.
    Yeom SK, Kim HJ, Byun JH, Kim AY, Lee MG, Ha HK. Abdominal aspergillosis: CT findings. Eur J Radiol. 2011;77(3):478–82.  https://doi.org/10.1016/j.ejrad.2009.08.016.CrossRefPubMedGoogle Scholar
  19. 19.
    Kuper H, Adami HO, Trichopoulos D. Infections as a major preventable cause of human cancer. J Intern Med. 2000;248(3):171–83.CrossRefPubMedGoogle Scholar
  20. 20.
    Wahidi MM, Herth FJ, Ernst A. State of the art: interventional pulmonology. Chest. 2007;131(1):261–74.  https://doi.org/10.1378/chest.06-0975.CrossRefPubMedGoogle Scholar
  21. 21.
    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.  https://doi.org/10.1056/NEJMoa1102873.CrossRefPubMedGoogle Scholar
  22. 22.
    Welch HG, Woloshin S, Schwartz LM, Gordis L, Gotzsche PC, Harris R, et al. Overstating the evidence for lung cancer screening: the International Early Lung Cancer Action Program (I-ELCAP) study. Arch Intern Med. 2007;167(21):2289–95.  https://doi.org/10.1001/archinte.167.21.2289.CrossRefPubMedGoogle Scholar
  23. 23.
    Reich JM. A critical appraisal of overdiagnosis: estimates of its magnitude and implications for lung cancer screening. Thorax. 2008;63(4):377–83.  https://doi.org/10.1136/thx.2007.079673.CrossRefPubMedGoogle Scholar
  24. 24.
    Beigelman-Aubry C, Hill C, Grenier PA. Management of an incidentally discovered pulmonary nodule. Eur Radiol. 2007;17(2):449–66.  https://doi.org/10.1007/s00330-006-0399-7.CrossRefPubMedGoogle Scholar
  25. 25.
    Belinsky SA. Gene-promoter hypermethylation as a biomarker in lung cancer. Nat Rev Cancer. 2004;4(9):707–17.  https://doi.org/10.1038/nrc1432.CrossRefPubMedGoogle Scholar
  26. 26.
    Van Pottelberge GR, Mestdagh P, Bracke KR, Thas O, van Durme YM, Joos GF, et al. MicroRNA expression in induced sputum of smokers and patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2011;183(7):898–906.  https://doi.org/10.1164/rccm.201002-0304OC.CrossRefPubMedGoogle Scholar
  27. 27.
    Lewis A, Riddoch-Contreras J, Natanek SA, Donaldson A, Man WD, Moxham J, et al. Downregulation of the serum response factor/miR-1 axis in the quadriceps of patients with COPD. Thorax. 2012;67(1):26–34.  https://doi.org/10.1136/thoraxjnl-2011-200309.CrossRefPubMedGoogle Scholar
  28. 28.
    Sato T, Liu X, Nelson A, Nakanishi M, Kanaji N, Wang X, et al. Reduced miR-146a increases prostaglandin E(2)in chronic obstructive pulmonary disease fibroblasts. Am J Respir Crit Care Med. 2010;182(8):1020–9.  https://doi.org/10.1164/rccm.201001-0055OC.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Ezzie ME, Crawford M, Cho JH, Orellana R, Zhang S, Gelinas R, et al. Gene expression networks in COPD: microRNA and mRNA regulation. Thorax. 2012;67(2):122–31.  https://doi.org/10.1136/thoraxjnl-2011-200089.CrossRefPubMedGoogle Scholar
  30. 30.
    Wang K, Yuan Y, Cho JH, McClarty S, Baxter D, Galas DJ. Comparing the MicroRNA spectrum between serum and plasma. PLoS One. 2012;7(7):e41561.  https://doi.org/10.1371/journal.pone.0041561.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Zampetaki A, Mayr M. Analytical challenges and technical limitations in assessing circulating miRNAs. Thromb Haemost. 2012;108(4):592–8.  https://doi.org/10.1160/TH12-02-0097.PubMedCrossRefGoogle Scholar
  32. 32.
    Kim DJ, Linnstaedt S, Palma J, Park JC, Ntrivalas E, Kwak-Kim JY, et al. Plasma components affect accuracy of circulating cancer-related microRNA quantitation. J Mol Diagn. 2012;14(1):71–80.  https://doi.org/10.1016/j.jmoldx.2011.09.002.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Cheng HH, Yi HS, Kim Y, Kroh EM, Chien JW, Eaton KD, et al. Plasma processing conditions substantially influence circulating microRNA biomarker levels. PLoS One. 2013;8(6):e64795.  https://doi.org/10.1371/journal.pone.0064795.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Molina-Pinelo S, Suarez R, Pastor MD, Nogal A, Marquez-Martin E, Martin-Juan J, et al. Association between the miRNA signatures in plasma and bronchoalveolar fluid in respiratory pathologies. Dis Markers. 2012;32(4):221–30.  https://doi.org/10.3233/DMA-2011-0882.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Respiration Medicine, Shanghai Tenth People’s HospitalTongji University School of MedicineShanghaiChina
  2. 2.Department of Respiration Medicine, Shanghai Tenth People’s HospitalNanjing Medical UniversityNanjingChina
  3. 3.School of Information Management and EngineeringShanghai University of Finance and EconomicsShanghaiChina
  4. 4.Department of Nuclear Medicine, Shanghai Tenth People’s HospitalTongji University School of MedicineShanghaiChina
  5. 5.Central Laboratory, Shanghai Pulmonary HospitalTongji University School of MedicineShanghaiChina
  6. 6.Department of Oncology, Shanghai Pulmonary HospitalTongji University School of MedicineShanghaiChina

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