Circularly Polarized SE-SERDS of Circulating cfDNA for Pretreatment Prediction of Nasopharyngeal Carcinoma Recurrence
Jinyong Lin, Lingna Wang, Yuduo Wu, Xueliang Lin, Weilin Wu, Yuanji Xu, Chaobin Huang, Yu Lin, Junxin Wu, Shangyuan FengAbstract
Precise pretreatment prediction of nasopharyngeal carcinoma (NPC) recurrence remains a clinical challenge. This study develops a circularly polarized surface-enhanced shifted-excitation Raman difference spectroscopy (SE-SERDS) system for label-free analysis of circulating cell-free DNA (cfDNA). Spectra were acquired from 90 recurrent and 90 non-recurrent NPC patients under non-polarized (NP), left-handed circularly polarized (LHCP), and right-handed circularly polarized (RHCP) excitations, alongside a fused LHCP + RHCP dataset. While the combination of circular polarization and surface-enhanced Raman scattering (SERS) uncovers hidden chiroptical signatures of cfDNA, the instrumentation-based SERDS method effectively eliminates residual fluorescence. In contrast to traditional polynomial-fitting (PF) methods, this hardware-driven approach replaces subjective algorithmic assumptions with physical wavelength modulation, improving apparent spectral resolution and enabling the extraction of subtle prognostic features at 1074 and 1147 cm–1 with a higher signal-to-background contrast. Furthermore, circularly polarized excitation yields more statistically significant spectral differences between groups than NP excitation, with fused LHCP + RHCP spectra providing higher discriminability by capturing complementary conformational information of the cfDNA double helix than standalone polarization data. Using 5-fold cross-validated linear discriminant analysis, SE-SERDS consistently outperformed PF-SERS across all excitation modes, with prediction accuracies rising from 71.1 vs 66.7% (NP) to a maximum of 93.9 vs 87.8% (LHCP + RHCP). Integration with a support vector machine (SVM) further enhanced the accuracy to 95.6%. These results indicate that the fused dual-polarization SE-SERDS strategy provides a powerful tool for pretreatment NPC recurrence prediction to support clinical intervention.