Wang Binshuai, Zhang Shudong, Meng Jingxin, Min Li, Luo Jing, Zhu Zhongpeng, Bao Han, Zang Ruhua, Deng Shaohui, Zhang Fan, Ma Lulin, Wang Shutao
Department of Urology, Peking University Third Hospital, Beijing, 100191, P. R. China.
CAS Key Laboratory of Bio-inspired Materials and Interfacial Science, CAS Center for Excellence in Nanoscience, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, 100190, P. R. China.
Adv Mater. 2021 Oct;33(40):e2103999. doi: 10.1002/adma.202103999. Epub 2021 Aug 16.
The prostate-specific antigen (PSA) has been widely used for the early diagnosis of prostate cancer during routine check-ups. However, the low sensitivity of regular PSA tests in the PSA gray zone often means that patients are required to undergo further invasive needle biopsy for the diagnosis of prostate cancer, which may lead to potential overdiagnosis and overtreatment. In this study, a circulating tumor cell (CTC)-chip based on an evaporation-induced reduced graphene oxide (rGO) coating is presented, which enables a highly specific and non-invasive diagnosis of prostate cancer in the PSA gray zone. During the evaporation process of the rGO dispersion, the Marangoni effect induces the self-assembly of a hierarchical micro/nanowrinkled rGO coating, which can capture CTCs after subsequent surface modification of capture agents. Compared to the low diagnostic sensitivity (58.3%) of regular PSA tests, a combination of CTC detection and PSA-based hematological tests via machine-learning analysis can greatly upgrade the diagnostic sensitivity of this disease to 91.7% in clinical trial. Therefore, this study provides a non-invasive alternative with high sensitivity for the diagnosis of prostate cancer in the PSA gray zone.
前列腺特异性抗原(PSA)已在常规体检中广泛用于前列腺癌的早期诊断。然而,常规PSA检测在PSA灰色区域的低敏感性通常意味着患者需要接受进一步的侵入性穿刺活检来诊断前列腺癌,这可能导致潜在的过度诊断和过度治疗。在本研究中,提出了一种基于蒸发诱导还原氧化石墨烯(rGO)涂层的循环肿瘤细胞(CTC)芯片,其能够在PSA灰色区域对前列腺癌进行高度特异性和非侵入性诊断。在rGO分散体的蒸发过程中,马兰戈尼效应诱导形成分级微/纳米皱纹rGO涂层的自组装,在随后用捕获剂进行表面修饰后,该涂层可以捕获CTC。与常规PSA检测的低诊断敏感性(58.3%)相比,通过机器学习分析将CTC检测与基于PSA的血液学检测相结合,在临床试验中可将该疾病的诊断敏感性大幅提高至91.7%。因此,本研究为PSA灰色区域前列腺癌的诊断提供了一种具有高敏感性的非侵入性替代方法。