International Journal of Nanomedicine (Aug 2018)
Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for tumor stages detection and histologic grades classification of oral squamous cell carcinoma
Abstract
Lili Xue,1 Bing Yan,2 Yi Li,3 Yingyun Tan,4 Xianyang Luo,2 Min Wang1 1State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China; 2Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China; 3Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China; 4Department of Implant, Stomatological Hospital of Chongqing Medical University, Chongqing, China Background: Tumor stages detection and histologic grades classification are essential for the diagnosis and prognosis of oral squamous cell carcinoma (OSCC). In this research, we apply surface-enhanced Raman spectroscopy (SERS) of blood serum to detect the tumor stages and histologic classification of OSCC. Methods: According to TNM classification and World Health Organization histologic grading system, the blood serum samples were collected from a total of 135 OSCC patients in the different tumor stages and histologic grades. Then the SERS spectra of serum samples from OSCC patients were diagnosed and classified into different groups using principal component analysis (PCA) and linear discriminant analysis (LDA) based on the tumor sizes, lymph node metastasis and histologic grades. Results: The SERS spectra of blood serum samples have shown the distinct changes and differences compared with each other, which were assigned to the biomolecule alterations (nucleic acids, proteins, lipids, and so on) in blood serums. And all accuracies of detection and classification reached above 85%. Conclusion: This study demonstrated that the SERS based on blood serum test had an enormous potential to carry out the preoperative assessment and prediction of the OSCC patients in different tumor stages and histologic classification. Keywords: SERS, OSCC, TNM classification, histologic grades, diagnosis
