Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Cixi Institute of Biomedical Engineering, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, PR China; Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, PR China.
Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Zhejiang International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Cixi Institute of Biomedical Engineering, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, PR China.
Biosens Bioelectron. 2025 Jan 15;268:116897. doi: 10.1016/j.bios.2024.116897. Epub 2024 Oct 30.
Detecting circulating tumor cells (CTCs) from blood has become a promising approach for cancer diagnosis. Surface-enhanced Raman Spectroscopy (SERS) has rapidly developed as a significant detection technology for CTCs, offering high sensitivity and selectivity. Encoded SERS bioprobes have gained attention due to their excellent specificity and ability to identify tumor cells using Raman signals. Machine learning has also made significant contributions to biomedical applications, especially in medical diagnosis. In this study, we developed a detection strategy combining encoded SERS bioprobes and machine learning models to identify CTCs. Dual-modal SERS bioprobes were designed and co-incubated with tumor cells by the "cocktail" method. An identification model for CTCs was constructed using principal component analysis (PCA) and the Random Forest classification algorithm. This innovative strategy endows SERS bioprobes with both effective magnetic separation and highly sensitive identification of CTCs, even at low concentrations of 2 cells/mL. It achieved a high detection rate of 98% for CTCs and effectively eliminated interference from peripheral WBCs. This simple and efficient strategy provides a new approach for CTCs detection and holds important significance for cancer diagnosis.
从血液中检测循环肿瘤细胞（CTC）已成为癌症诊断的一种很有前途的方法。表面增强拉曼光谱（SERS）作为 CTC 的一种重要检测技术得到了快速发展，具有高灵敏度和选择性。编码 SERS 生物探针由于其优异的特异性和使用拉曼信号识别肿瘤细胞的能力而受到关注。机器学习在生物医学应用中也做出了重大贡献，特别是在医学诊断方面。在这项研究中，我们开发了一种结合编码 SERS 生物探针和机器学习模型的检测策略，用于识别 CTC。通过“鸡尾酒”方法设计了双模态 SERS 生物探针，并与肿瘤细胞共孵育。使用主成分分析（PCA）和随机森林分类算法构建了 CTC 的识别模型。这种创新策略使 SERS 生物探针具有有效的磁性分离和对 CTC 的高灵敏度识别能力，即使在低浓度为 2 个细胞/ml 时也是如此。它对 CTC 的检测率达到了 98%，并且有效地消除了外周白细胞的干扰。这种简单高效的策略为 CTC 的检测提供了一种新方法，对癌症诊断具有重要意义。
