机构：[1]School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China. yejian78@sjtu.edu.cn.[2]Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China. panjiahua@renji.com.[3]Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.[4]Shanghai Jiao Tong University Sichuan Research Institute, Chengdu 610213, P. R. China.[5]Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P. R. China.
Early screening and diagnosis of low-grade bladder cancer (LGBC) can help to guide timely clinical treatments before deterioration, reducing relapse rates and improving patient survival and quality of life. However, current clinical technologies are mainly invasive, painful, and lack sensitivity and time efficacy, which cannot always meet clinical needs. Surface-enhanced Raman scattering (SERS) is a label-free detection technique with high sensitivity and can provide molecular-specific information. In this work, we adopt SERSomes, an advanced SERS characterization approach using a SERS spectral set, to comprehensively and accurately profile urine metabolites of LGBC patients and healthy controls. With the help of machine learning, we achieved high accuracy of LGBC diagnosis (89.47%) and LGBC stratification (90%). The entire diagnostic process is very rapid, convenient, non-invasive, and low-cost, holding potential for future use in mass population health screenings. Moreover, we explore the metabolite contribution based on the varying SERSome patterns in LGBC patients, aiming at indicating potential urine biomarkers of LGBC.
基金：
This work was supported by the National Key Research and
Development Program of China (no. 2024YFF1502600), the
National Natural Science Foundation of China (no. 82272054,
82372016, 623B2070 and 82303585), the Science and
Technology Commission of Shanghai Municipality (no.
24DIPA00300, 24490710800 and 24490790900), the Shanghai
Key Laboratory of Gynecologic Oncology, Sichuan Science and
Technology Program (no. 2024ZYD0112), and Shanghai Jiao
Tong University (no. YG2024LC09, YG2024QNA15 and
YG2025ZD25).
通讯机构：[1]School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China. yejian78@sjtu.edu.cn.[3]Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.[4]Shanghai Jiao Tong University Sichuan Research Institute, Chengdu 610213, P. R. China.[5]Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P. R. China.
推荐引用方式(GB/T 7714)：
Lu Yao,Wang Jiayi,Bi Xinyuan,et al.Non-invasive and rapid diagnosis of low-grade bladder cancer via SERSomes of urine[J].Nanoscale.2025,doi:10.1039/d4nr05306k.
APA：
Lu Yao,Wang Jiayi,Bi Xinyuan,Qian Hongyang,Pan Jiahua&Ye Jian.(2025).Non-invasive and rapid diagnosis of low-grade bladder cancer via SERSomes of urine.Nanoscale,,
MLA：
Lu Yao,et al."Non-invasive and rapid diagnosis of low-grade bladder cancer via SERSomes of urine".Nanoscale .(2025)
