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

• Published in: International journal of nanomedicine 2015-01, Vol.10 (default), p.537-547
• Main Authors: Feng, Shangyuan, Huang, Shaohua, Lin, Duo, Chen, Guannan, Xu, Yuanji, Li, Yongzeng, Huang, Zufang, Pan, Jianji, Chen, Rong, Zeng, Haishan
• Format: Article
• Language: English
• Subjects: Biomarkers; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - metabolism; Case-Control Studies; Diagnosis; Discriminant Analysis; Disease; Female; Genetic aspects; Hospitals; Humans; Lung cancer; Mammography; Medical diagnosis; Medical imaging; Medical screening; Methods; Original Research; Physiological aspects; Predictive Value of Tests; Proteins; Raman spectroscopy; Saliva - chemistry; Salivary Proteins and Peptides - analysis; Salivary Proteins and Peptides - chemistry; Spectrum analysis; Spectrum Analysis, Raman - methods; Tumors
• ISSN: 1178-2013; 1176-9114; 1178-2013
• DOI: 10.2147/IJN.S71811

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.