Label-free diagnosis of lung cancer with tissue-slice surface-enhanced Raman spectroscopy and statistical analysis

  • Kun Zhang
  • Chunyan Hao
  • Yanyan Huo
  • Baoyuan Man
  • Chao Zhang
  • Cheng Yang
  • Mei Liu
  • Chuansong ChenEmail author
Original Article


Despite the rapid development of medical science, the diagnosis of lung cancer is still quite challenging. Due to the ultrahigh detection sensitivity of surface-enhanced Raman spectroscopy (SERS), SERS has a broad application prospect in biomedicine, especially in the field of tumor blood detection. Although Raman spectroscopy can diagnose lung cancer through tissue slices, its weak cross sections are problematic. In this study, silver nanoparticles (AgNPs) were added to the surface of lung tissue slices to enhance the Raman scattering signals of biomolecules. The electromagnetic field distribution of AgNPs prepared was simulated using the COMSOL software. SERS obtained from the slices reflected the difference in biochemical molecules between normal (n = 23) and cancerous (n = 23) lung tissues. Principal component-linear discriminate analysis (PCA-LDA) was utilized to classify lung cancer and healthy lung tissues. The receiver operating characteristic curve gave the sensitivity (95.7%) and specificity (95.7%) of the PCA-LDA method. This study sheds new light on the general applicability of SERS analysis of tissue slices in clinical trials.


Surface-enhanced Raman spectroscopy (SERS) Silver nanoparticles Tissue slice Lung cancer PCA-LDA 


Funding information

This work was supported by the National Natural Science Foundation of China (11774208, 11674199, and 11604040), Shandong Province Natural Science Foundation (ZR2018MA040, ZR2017BA004, ZR2016AM19), China Postdoctoral Science Foundation (2016M602716).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethics statement

The sample collection was approved by the medical ethics committee of Qilu Hospital of Shandong University.

Informed consent

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

Supplementary material

10103_2019_2781_MOESM1_ESM.docx (19 kb)
ESM 1 (DOCX 19 kb)


  1. 1.
    Bird RE, Wallace TW, Yankaskas BC (1992) Analysis of cancers missed at screening mammography. Radiology 184(3):613–617CrossRefGoogle Scholar
  2. 2.
    Berrington de Gonzalez A, Berg CD, Visvanathan K et al (2009) Estimated risk of radiation-induced breast cancer from mammographic screening for young BRCA mutation carriers. J Natl Cancer I 101(3):205–209CrossRefGoogle Scholar
  3. 3.
    Kudelski A (2008) Analytical applications of Raman spectroscopy. Talanta 76(1):1–8CrossRefGoogle Scholar
  4. 4.
    Shao J, Tong L, Tang S et al (2015) PLLA nanofibrous paper-based plasmonic substrate with tailored hydrophilicity for focusing SERS detection. ACS Appl Mater Inter 7(9):5391–5399CrossRefGoogle Scholar
  5. 5.
    Li Z, Wang M, Jiao Y et al (2018) Different number of silver nanoparticles layers for surface enhanced raman spectroscopy analysis. Sensor Actuat B Chem 255:374–383CrossRefGoogle Scholar
  6. 6.
    Zhang C, Jiang S, Huo Y et al (2015) SERS detection of R6G based on a novel graphene oxide/silver nanoparticles/silicon pyramid arrays structure. Opt Express 23(19):24811–24821CrossRefGoogle Scholar
  7. 7.
    Huang X, El-Sayed IH, Qian W et al (2007) Cancer cells assemble and align gold nanorods conjugated to antibodies to produce highly enhanced, sharp, and polarized surface Raman spectra: a potential cancer diagnostic marker. Nano Lett 7(6):1591–1597CrossRefGoogle Scholar
  8. 8.
    Petersen D, Mavarani L, Niedieker D et al (2017) Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy. Analyst 142(8):1207–1215CrossRefGoogle Scholar
  9. 9.
    Zhou H, Yang D, Ivleva NP et al (2015) Label-free in situ discrimination of live and dead bacteria by surface-enhanced Raman scattering. Anal Chem 87(13):6553–6561CrossRefGoogle Scholar
  10. 10.
    Wang X, Qian X, Beitler JJ et al (2011) Detection of circulating tumor cells in human peripheral blood using surface-enhanced Raman scattering nanoparticles. Cancer Res 71(5):1526–1532CrossRefGoogle Scholar
  11. 11.
    Dina N, Zhou H, Colniţă A et al (2017) Rapid single-cell detection and identification of pathogens by using surface-enhanced Raman spectroscopy. Analyst 142(10):1782–1789CrossRefGoogle Scholar
  12. 12.
    Park J, Hwang M, Choi B et al (2017) Exosome classification by pattern analysis of surface-enhanced Raman spectroscopy data for lung cancer diagnosis. Anal Chem 89(12):6695–6701CrossRefGoogle Scholar
  13. 13.
    Li X, Yang T, Lin J (2012) Spectral analysis of human saliva for detection of lung cancer using surface-enhanced Raman spectroscopy. J Biomed Opt 17(3):0370031–0370035CrossRefGoogle Scholar
  14. 14.
    Huang Z, McWilliams A, Lui H et al (2003) Near-infrared Raman spectroscopy for optical diagnosis of lung cancer. Int J Cancer 107(6):1047–1052CrossRefGoogle Scholar
  15. 15.
    Oshima Y, Shinzawa H, Takenaka T et al (2010) Discrimination analysis of human lung cancer cells associated with histological type and malignancy using Raman spectroscopy. J Biomed Opt 15(1):017009CrossRefGoogle Scholar
  16. 16.
    Zheng XS, Jahn IJ, Weber K et al (2018) Label-free SERS in biological and biomedical applications: recent progress, current challenges and opportunities. Spectrochim Acta A 197:56–77CrossRefGoogle Scholar
  17. 17.
    Zhang F, Braun GB, Shi Y et al (2010) Fabrication of Ag@ SiO2@ Y2O3: Er nanostructures for bioimaging: tuning of the upconversion fluorescence with silver nanoparticles. J Am Chem Soc 132(9):2850–2851CrossRefGoogle Scholar
  18. 18.
    Brozek-Pluska B, Kopec M, Surmacki J et al (2018) Histochemical analysis of human breast tissue samples by IR and Raman spectroscopies. Protocols discussion. Infrared Phys Technol 93:247–254CrossRefGoogle Scholar
  19. 19.
    Zhao J, Lui H, McLean DI et al (2007) Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy. Appl Spectrosc 61(11):1225–1232CrossRefGoogle Scholar
  20. 20.
    Hanlon E, Manoharan R, Koo T et al (2000) Prospects for in vivo Raman spectroscopy. Phys Med Biol 45(2):R1CrossRefGoogle Scholar
  21. 21.
    Shetty G, Kendall C, Shepherd N et al (2006) Raman spectroscopy: elucidation of biochemical changes in carcinogenesis of oesophagus. Brit J Cancer 94(10):1460–1464CrossRefGoogle Scholar
  22. 22.
    Krafft C, Neudert L, Simat T et al (2005) Near infrared Raman spectra of human brain lipids. Spectrochim Acta A 61(7):1529–1535CrossRefGoogle Scholar
  23. 23.
    Ruiz-Chica A, Medina M, Sanchez-Jimenez F et al (2004) Characterization by Raman spectroscopy of conformational changes on guanine–cytosine and adenine–thymine oligonucleotides induced by aminooxy analogues of spermidine. J Raman Spectrosc 35(2):93–100CrossRefGoogle Scholar
  24. 24.
    Chan JW, Taylor DS, Zwerdling T et al (2006) Micro-Raman spectroscopy detects individual neoplastic and normal hematopoietic cells. Biophys J 90(2):648–656CrossRefGoogle Scholar
  25. 25.
    Stone N, Kendall C, Smith J et al (2004) Raman spectroscopy for identification of epithelial cancers. Faraday Discuss 126:141–157CrossRefGoogle Scholar
  26. 26.
    Kaminaka S, Yamazaki H, Ito T et al (2001) Near-infrared Raman spectroscopy of human lung tissues: possibility of molecular-level cancer diagnosis. J Raman Spectrosc 32(2):139–141CrossRefGoogle Scholar
  27. 27.
    Wu H, Xue R, Lu C et al (2009) Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry. J Chromatogr B 877(27):3111–3117CrossRefGoogle Scholar
  28. 28.
    Banki F, Yacoub WN, Hagen JA et al (2008) Plasma DNA is more reliable than carcinoembryonic antigen for diagnosis of recurrent esophageal cancer. J Am Coll Surg 207(1):30–35CrossRefGoogle Scholar
  29. 29.
    Bergholt MS, Zheng W, Ho KY et al (2013) Fiber-optic Raman spectroscopy probes gastric carcinogenesis in vivo at endoscopy. J Biophotonics 6(1):49–59CrossRefGoogle Scholar
  30. 30.
    Chowdary M, Kumar KK, Thakur K et al (2007) Discrimination of normal and malignant mucosal tissues of the colon by Raman spectroscopy. Photomed Laser Surg 25(4):269–274CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Kun Zhang
    • 1
  • Chunyan Hao
    • 2
  • Yanyan Huo
    • 1
  • Baoyuan Man
    • 1
  • Chao Zhang
    • 1
  • Cheng Yang
    • 1
  • Mei Liu
    • 1
  • Chuansong Chen
    • 1
    Email author
  1. 1.Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and ElectronicsShandong Normal UniversityJinanChina
  2. 2.Key Laboratory of the Ministry of Education for Experimental Teratology, Department of Histology and Embryology, School of MedicineShandong UniversityJinanChina

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