A serum analysis method combining membrane protein purification with surface-enhanced Raman spectroscopy for noninvasive prostate cancer detection

Prostate cancer is one of the most common malignant tumors in elderly men worldwide with the sixth cause of cancer death in men. It has a high mortality rate, and early diagnosis contributes to a favorable prognosis. Noninvasive detection methods with high sensitivity and specificity are therefore always being sought for prostate cancer diagnosis. In this paper, a method for serum analysis combining membrane protein purification with silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS) for non-invasive prostate cancer detection was present. In this work, total serum proteins were isolated from human serum by cellulose acetate (CA) membrane, and then the purified serum proteins were mixed with silver nanoparticles to perform SERS analysis. This method was evaluated by analyzing serum samples from patients with prostate cancer (n = 10) and healthy volunteers (n = 10). To further investigate the diagnostic ability of proteins isolated from human serum, multivariate statistical analysis was employer to analyze SERS data. Principal component analysis combined with linear discriminant analysis (PCA-LDA) was used to identify the serum protein SERS spectra from prostate cancer patients and healthy volunteers, and the diagnosis sensitivity and specificity of prostate cancer were 90% and 80%, respectively, as compared with the healthy volunteer. Moreover, receiver operating characteristic (ROC) curves further confirmed the effectiveness of PCA-LDA diagnostic algorithm. Notably, this algorithm predicted prostate cancer with an area under the curve (AUC) of 0.940. The results demonstrated that serum protein SERS technology combined with PCA-LDA diagnostic algorithm has great potential for the noninvasive screening of prostate cancer.