Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 240; p. 118543
• Main Authors: Cui, Xiaoyu, Liu, Tao, Xu, Xiaosong, Zhao, Zeyin, Tian, Ye, Zhao, Yue, Chen, Shuo, Wang, Zhe, Wang, Yiding, Hu, Dayu, Fu, Shui, Shan, Guangyi, Sun, Jiarun, Song, Kaixin, Zeng, Yu
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.10.2020
• Subjects: Bladder cancer; Discriminant Analysis; Humans; Male; Metal Nanoparticles; Neoplasms; PCA-LDA; Principal Component Analysis; Prostate cancer; Raman spectroscopy; Silver; Spectrum Analysis, Raman; Urine; Urine; SERS; LDA; PCA-LDA; Raman spectroscopy; Bladder cancer; Prostate cancer; PCA
• ISSN: 1386-1425; 1873-3557
• DOI: 10.1016/j.saa.2020.118543

Detecting cancers through testing biological fluids, namely, “liquid biopsy”, is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500–1800 cm−1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine. [Display omitted] •Detecting cancers through testing biological fluids is noninvasive.•Raman spectroscopy is a fast and lossless analytical tool.•The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA).