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Surface-enhanced Raman spectroscopy of saliva proteins for the noninvasive differentiation of benign and malignant breast tumors

Authors Feng S, Huang S, Lin D, Chen G, Xu Y, Li Y, Huang Z, Pan J, Chen R, Zeng H

Received 26 July 2014

Accepted for publication 25 October 2014

Published 12 January 2015 Volume 2015:10(1) Pages 537—547

DOI https://doi.org/10.2147/IJN.S71811

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 5

Editor who approved publication: Dr Lei Yang

Shangyuan Feng,1 Shaohua Huang,1 Duo Lin,2 Guannan Chen,1 Yuanji Xu,3 Yongzeng Li,1 Zufang Huang,1 Jianji Pan,3 Rong Chen,1 Haishan Zeng4

1Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, 2Fujian University of Traditional Chinese Medicine, 3Fujian Provincial Tumor Hospital, Fuzhou, People’s Republic of China; 4Imaging Unit – Integrative Oncology Department, British Columbia Cancer Agency Research Centre, Vancouver, BC, Canada

Abstract: The capability of saliva protein analysis, based on membrane protein purification and surface-enhanced Raman spectroscopy (SERS), for detecting benign and malignant breast tumors is presented in this paper. A total of 97 SERS spectra from purified saliva proteins were acquired from samples obtained from three groups: 33 healthy subjects; 33 patients with benign breast tumors; and 31 patients with malignant breast tumors. Subtle but discernible changes in the mean SERS spectra of the three groups were observed. Tentative assignments of the saliva protein SERS spectra demonstrated that benign and malignant breast tumors led to several specific biomolecular changes of the saliva proteins. Multiclass partial least squares–discriminant analysis was utilized to analyze and classify the saliva protein SERS spectra from healthy subjects, benign breast tumor patients, and malignant breast tumor patients, yielding diagnostic sensitivities of 75.75%, 72.73%, and 74.19%, as well as specificities of 93.75%, 81.25%, and 86.36%, respectively. The results from this exploratory work demonstrate that saliva protein SERS analysis combined with partial least squares–discriminant analysis diagnostic algorithms has great potential for the noninvasive and label-free detection of breast cancer.

Keywords: SERS, saliva protein purification, PLS-DA, breast cancer, noninvasive detection

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