Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy

Abstract

Objective To explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC). Patients and Methods SERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms. Results A total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm −1 , 1328 cm −1 and 1455 cm −1 . The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm −1 , which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm −1 to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%. Conclusion We show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.