Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and the Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, China; Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China.
Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Feb 15;267(Pt 2):120605. doi: 10.1016/j.saa.2021.120605. Epub 2021 Nov 11.
Surface-enhanced Raman spectroscopy (SERS) is considered as an ultrasensitive, non-invasive as well as rapid detection technology for cancer diagnosis. In this study, we developed a novel blood serum analysis strategy using coffee ring effect-assisted label-free SERS for different types of cancer screening. Additionally, the pretreated Ag nanoparticles (Ag NPs) were mixed with the serum from liver cancer patients (n = 40), prostate cancer patients (n = 32) and healthy volunteers (n = 30) for SERS measurement. The droplets of Ag NPs-serum mixture formed the coffee ring on the peripheral after air-drying, and thus extremely enhancing Raman signal and ensuring the stability and reliability of SERS detection. Partial least square (PLS) and support vector machine (SVM) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying, yielding the high diagnostic accuracy of 98.04% for normal group and two types of cancers simultaneously distinguishing. More importantly, for the unknown testing set, an ideal diagnostic accuracy of 100% could be achieved by PLS-SVM algorithm for differentiating cancers from the normal group. The results from this exploratory work demonstrate that serum SERS detection combined with PLS-SVM diagnostic algorithm and coffee ring effect has great potential for the noninvasive and label-free detection of cancer.
表面增强拉曼光谱（SERS）被认为是一种超灵敏、非侵入式以及快速的癌症诊断检测技术。在这项研究中，我们开发了一种新颖的血清分析策略，利用咖啡环效应辅助的无标记 SERS 进行不同类型癌症的筛查。此外，预处理后的银纳米颗粒（Ag NPs）与肝癌患者（n=40）、前列腺癌患者（n=32）和健康志愿者（n=30）的血清混合进行 SERS 测量。Ag NPs-血清混合物的液滴在空气干燥后在边缘形成咖啡环，从而极大地增强了拉曼信号，并确保了 SERS 检测的稳定性和可靠性。偏最小二乘（PLS）和支持向量机（SVM）算法被用于建立 SERS 光谱数据分类的诊断模型，从而实现了对正常组和两种癌症的高诊断准确率，达到 98.04%。更重要的是，对于未知的测试集，通过 PLS-SVM 算法可以实现 100%的理想诊断准确率，用于区分癌症与正常组。这项探索性工作的结果表明，血清 SERS 检测结合 PLS-SVM 诊断算法和咖啡环效应具有用于癌症的非侵入式和无标记检测的巨大潜力。
