Detection of Nasopharyngeal Carcinoma Based on Human Saliva Surface-Enhanced Raman Spectroscopy
LIN Xue-liang1, LIN Duo1, 2, QIU Su-fang3, GE Xiao-song1, PAN Jian-ji3, WU Qiong1, LIN Hui-jing1, HUANG Hao2*
1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Normal University, Fuzhou 350007, China
2. College of Integrated Traditional Chinese and Western Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
3. Fujian Provincial Cancer Hospital, Fujian Medical University, Fuzhou 350014, China
Abstract：Surface Enhanced Raman Spectroscopy (SERS) technique is a fast, convenient and nondestructive optical diagnose method that can provide specific fingerprints of biomolecular compositions and structures. Human saliva contains many biomarkers that can reflect health condition. In this paper, we employed SERS methods based on nanometer silver colloid to obtain high quality SERS of 208 saliva samples from 46 patients with nasopharyngeal carcinoma (NPC), 30 patients with rhinitis and 48 healthy volunteers. The results showed there were great differences in SERS pattern among these three subjects. Principal component analysis-linear discriminant analysis (PCA-LDA) was utilized to analyze and classify the saliva spectra from NPC, rhinitis and healthy subjects. The diagnostic sensitivity was 89.5%, 93.0% and 100% respectively, and meanwhile the specificity was 94.6%, 96.7% and 97.7% respectively. The research showed that saliva SERS combined with PCA-LDA can rapidly and effectively distinguish three saliva groups, and this technique has great potential to be a non-destructive and clinical screening method for nasopharyngeal carcinoma detection.
林学亮，林 多，邱素芳，戈小松，潘建基，吴 琼，林慧晶，黄 浩. 鼻咽癌患者唾液的表面增强拉曼光谱研究[J]. 光谱学与光谱分析, 2018, 38(08): 2430-2434.
LIN Xue-liang, LIN Duo, QIU Su-fang, GE Xiao-song, PAN Jian-ji, WU Qiong, LIN Hui-jing, HUANG Hao. Detection of Nasopharyngeal Carcinoma Based on Human Saliva Surface-Enhanced Raman Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2430-2434.
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