Surface-enhanced Raman scattering (SERS) spectra of serum proteins purified from human serum samples were employed to detect colorectal cancer. Acetic acid as a new aggregating agent was introduced to increase the magnitude of the SERS enhancement. High-quality SERS spectra of serum proteins were acquired from 103 cancer patients and 103 healthy volunteers. Tentative assignments of SERS bands reflect that some specific biomolecular contents and protein secondary structures change with colorectal cancer progression. Principal component analysis combined with linear discriminant analysis was used to assess the capability of this approach for identifying colorectal cancer, yielding diagnostic accuracies of 100% (sensitivity: 100%; specificity: 100%) based on albumin SERS spectroscopy and 99.5% (sensitivity: 100%; specificity: 99%) based on globulin SERS spectroscopy, respectively. A partial least squares (PLS) approach was introduced to develop diagnostic models. An albumin PLS model successfully predicted the unidentified subjects with a diagnostic accuracy of 93.5% (sensitivity: 95.6%; specificity: 91.3%) and the globulin PLS model gave a diagnostic accuracy of 93.5% (sensitivity: 91.3%; specificity: 95.6%). These results suggest that serum protein SERS spectroscopy can be a sensitive and clinically powerful means for colorectal cancer detection.

Surface-enhanced Raman spectroscopy (SERS) was developed here for the noninvasive detection of the hepatitis B virus (HBV). Chronic hepatitis B virus (HBV) infection is a primary health problem in the world and may further develop into cirrhosis and hepatocellular carcinoma (HCC). SERS measurement was applied to two groups of serum samples. One group included 93 HBV patients and the other group included 94 healthy volunteers as control subjects. Tentative assignments of the Raman bands in the measured SERS spectra have shown the difference of the serum SERS spectra between HBV patients and healthy volunteers. The differences indicated an increase in the relative amounts of Larginine, Saccharide band (overlaps with acyl band), phenylalanine and tyrosine, together with a decrease in the percentage of nucleic acid, valine and hypoxanthine in the serum of HBV patients compared with those of healthy volunteers. For better analysis of the spectral data, the first-order derivation was applied to the SERS data. Furthermore, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were employed to distinguish HBV patients from healthy volunteers and to realize the diagnostic sensitivity of 78.5% and 91.4%, and specificity of 75% and 83% for SERS and the first order derivative SERS spectrum, respectively. These results suggest that derivative analysis could be an effective method to improve the classification of SERS spectra belonging to different groups. This exploratory work demonstrated that first-order derivative serum SERS spectrum combined with PCA-LDA has great potential for improving the detection of HBV.

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Abstract. The present study evaluated the use of saliva surface-enhanced Raman spectroscopy (SERS) for the detection of non-invasive nasopharyngeal carcinoma (NPC). SERS measurements were taken from 62 saliva samples, of which 32 were from NPC patients and 30 from healthy volunteers. Notable biochemical Raman bands in the SERS spectra were tentatively assigned to various saliva components. The saliva SERS spectra obtained from the NPC patients and the healthy volunteers were also analyzed by multivariate statistical techniques based on principal component analysis and linear discriminant analysis (PCA-LDA). Significant differences were observed between the saliva SERS spectral intensities for NPC patients and healthy volunteers, particularly at 447, 496, 635, 729, 1134, 1270 and 1448 cm -1 , which primarily contained signals associated with proteins, nucleic acids, fatty acids, glycogen and collagen. The classification results based on the PCA-LDA method provided a relatively high diagnostic sensitivity of 86.7%, specificity of 81.3% and diagnostic accuracy of 83.9% for NPC identification. The results from the present study demonstrate that saliva SERS analysis used in conjunction with PCA-LDA diagnostic algorithms possesses a promising clinical application for the non-invasive detection of NPC.