Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350117, China.
Department of Pathology, Clinical Oncology School of Fujian Medical University, and Fujian Cancer Hospital, Fuzhou, Fujian 350014, China.
Nanoscale Horiz. 2023 May 30;8(6):746-758. doi: 10.1039/d2nh00570k.
The development of a minimally invasive technique for early-stage lung cancer detection is crucial to reducing mortality. Phenotyping of tumor-associated extracellular vesicles (EVs) has the potential for early-stage lung cancer detection, yet remains challenging due to the lack of sensitive, integrated techniques that can accurately detect rare tumor-associated EV populations in blood. Here, we integrated gold core-silver shell nanoparticles and nanoscopic mixing in a microfluidic assay for sensitive phenotypic analysis of EVs directly in plasma without EV pre-isolation. The assay enabled multiplex detection of lung cancer-associated markers PTX3 and THBS1 and canonical EV marker CD63 by surface-enhanced Raman spectroscopy, providing a squared correlation coefficient of 0.97 in the range of 10-10 EVs mL and a limit of detection of 19 EVs mL. Significantly, our machine learning-based nanostrategy provided 92.3% sensitivity and 100% specificity in differentiating early-stage lung cancer from benign lung diseases, superior to the CT scan-based lung cancer diagnosis (92.3% sensitivity and 71.4% specificity). Overall, our integrated nanostrategy achieved an AUC value of 0.978 in differentiating between early-stage lung cancer patients ( = 28) and controls consisting of patients with benign lung diseases ( = 23) and healthy controls ( = 26), which showed remarkable diagnostic performance and great clinical potential for detecting the early occurrence of lung cancer.
开发一种微创技术用于早期肺癌检测对于降低死亡率至关重要。肿瘤相关细胞外囊泡（EVs）表型分析有可能用于早期肺癌检测，但由于缺乏能够准确检测血液中罕见肿瘤相关 EV 群体的敏感、综合技术，因此仍然具有挑战性。在这里，我们在微流控分析中集成了金核-银壳纳米粒子和纳米级混合，可直接在血浆中对 EV 进行灵敏的表型分析，而无需 EV 预分离。该分析通过表面增强拉曼光谱法实现了肺癌相关标志物 PTX3 和 THBS1 以及经典 EV 标志物 CD63 的多重检测，在 10-10 EVs mL 的范围内提供了 0.97 的平方相关系数和 19 EVs mL 的检测限。重要的是，我们基于机器学习的纳米策略在区分早期肺癌与良性肺部疾病方面提供了 92.3%的灵敏度和 100%的特异性，优于基于 CT 扫描的肺癌诊断（92.3%的灵敏度和 71.4%的特异性）。总体而言，我们的集成纳米策略在区分 28 名早期肺癌患者（ = 28）和包括良性肺部疾病患者（ = 23）和健康对照组（ = 26）的对照组方面实现了 0.978 的 AUC 值，显示出出色的诊断性能和用于检测肺癌早期发生的巨大临床潜力。
