Compact Superlattice as Label-free Surface-Enhanced Raman Scattering Substrate for Noninvasive Urine Test in the Diagnosis of Lung Cancer

Abstract

The development of noninvasive cancer diagnosis may provide the convenience of remote painless diagnosis, instant healthcare and postoperative follow-up. Label-free surface-enhanced Raman spectroscopy (SERS) is becoming as a powerful approach for the detection of various potential biomarkers in cancer diagnosis, which avoids focusing on one or several specific targets, thus it may achieve a comprehensive and accurate diagnosis of cancer. In this work, compact nanosuperlattice is constructed as a label-free SERS substrate for the Raman measurements of urine samples from healthy persons, lung cancer (LC) patients before and after surgery for noninvasive diagnosis and postoperative monitoring of LC.Multiple chemometric methods including principal component analysis (PCA), linear discrimination analysis (LDA), and orthogonal partial least-squares discrimination analysis (OPLS-DA) are applied for the classification of SERS spectra measured from different samples. LDA and OPLS-DA outperform PCA to discriminate lung cancer preoperative patients from postoperative and healthy persons with higher efficiency. The high accuracy based on LDA and OPLS-DA is evaluated by calculating the area under the curve (AUC) of receiver operating characteristic (ROC) curves, which demonstrate the AUC values of 0.926 and 0.986 for LDA and OPLS-DA, respectively. In addition, the noninvasive SERS measurement in urine samples shows significant superiority on LC diagnosis over typical clinical biomarker tests such as carcinoembryonic antigen (CEA) test in serum. These results demonstrate that label-free SERS associated with multivariate analysis is a promising tool for noninvasive diagnosis and postoperative monitoring of LC patient.