Label‐free diagnosis for colorectal cancer through coffee ring‐assisted surface‐enhanced Raman spectroscopy on blood serum

• Published in: Journal of biophotonics Vol. 13; no. 4; pp. e201960176 - n/a
• Main Authors: Hong, Yan, Li, Yongqiang, Huang, Libin, He, Wei, Wang, Shouxu, Wang, Chong, Zhou, Guoyun, Chen, Yuanming, Zhou, Xin, Huang, Yifeng, Huang, Wen, Gong, Tianxun, Zhou, Zongguang
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.04.2020; Wiley Subscription Services, Inc
• Subjects: Antigens; Blood; Cancer; Carcinoembryonic antigen; Coffee; Colorectal cancer; colorectal cancer diagnosis; Colorectal carcinoma; Data analysis; Diagnosis; label‐free detection; Learning algorithms; Machine learning; Nanoparticles; Pretreatment; Principal components analysis; Raman spectroscopy; SERS; Spectra; Spectroscopy; Spectrum analysis; label-free detection; nanoparticles; machine learning; SERS; colorectal cancer diagnosis
• DOI: 10.1002/jbio.201960176
• ISSN: 1864-063X; 1864-0648

Surface‐enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle‐serum mixture formed coffee‐ring‐like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support‐vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results. In this work, a simple, fast and inexpensive optical sensing platform is developed by the rational design of SERS sampling and data analysis. The pretreatment of spectral measurement employed AuNP colloid mixing with the blood serum from the patients with CRC. The droplet of particle‐serum mixture on the substrate formed coffee‐ring‐like region at the rim, providing strong and stable SERS profile of the subjects.