Label-free detection of nasopharyngeal and liver cancer using surface-enhanced Raman spectroscopy and partial lease squares combined with support vector machine.

In this paper, we investigated the feasibility of using surface enhanced Raman spectroscopy (SERS) and multivariate analysis method to discriminate liver cancer and nasopharyngeal cancer from healthy volunteers. SERS measurements were performed on serum protein samples from 104 liver cancer patients, 100 nasopharyngeal cancer patients, and 95 healthy volunteers. Two dimensionality reduction methods, principal component analysis (PCA) and partial least square (PLS) were compared, and the results indicated that the performance of PLS is superior to that of PCA. When the number of components was compressed to 3 by PLS, support vector machine (SVM) with a Gaussian radial basis function (RBF) was employed to classify various cancers simultaneously. Based on the PLS-SVM algorithm, high diagnostic accuracies of 95.09% and 90.67% were achieved from the training set and the unknown testing set, respectively. The results of this exploratory work demonstrate that serum protein SERS technology combined with PLS-SVM diagnostic algorithm has great potential for the noninvasive screening of cancer.