Label‐Free Discrimination of Liver Cancer Using Plasma Proteins Surface‐Enhanced Raman Spectroscopy and Multivariate Statistical Algorithm

• DOI: 10.1002/jrs.6809
• Publication Date: 2025-04-08T00:55:18Z
• Authors: Cong Li; Xianqiong Gong; Xingen Gao; Houyang Ge; Hongyi Zhang; Zhenyu Yin; Fuqiang Wang; Juqiang Lin
• Keywords: Surface-Enhanced Raman Spectroscopy; Liver Cancer

ABSTRACT Plasma proteins are considered an important indicator for cancer diagnosis and prognosis. Currently, commonly used detection methods unable to achieve rapid highly sensitive of plasma proteins, making them insufficient meet the demands high‐sensitivity screening. To address this issue, we propose a method using surface‐enhanced Raman spectroscopy (SERS) based on particulate triacetate cellulose (TCA), referred as TCA‐SERS. verify feasibility TCA‐SERS liver (LC) screening, analyzed SERS spectra samples from 60 LC patients healthy individuals two multivariate statistical algorithms: PCA‐LDA PLS‐SVM. The classification accuracy in distinguishing was 84.2%, with sensitivity 81.7%, specificity 86.7%, AUC value 0.911. In contrast, PLS‐SVM 95.8%, 96.7%, 95%, 0.999. diagnostic performance surpassed that PCA‐LDA, indicating demonstrates excellent potential detection. Moreover, results highlight greater algorithm analyzing These findings great significance developing label‐free, noninvasive screening methods.