Predictive Value of Label-Free Surface-Enhanced Raman Spectroscopy for Locally Advanced Gastric Cancer Following Neoadjuvant Chemoimmunotherapy

Xinran Wang¹Bowen Shi¹Jinling Jiang²Wenfang Wang³Kangning Wang¹Luke Zhang¹Fei Yuan^4*Weiwu Yao^5*Huan Zhang^1,6*

• ¹Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ²Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ³Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
• ⁴Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ⁵Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ⁶Shanghai Key Laboratory of Gastric Neoplasms, Shanghai, China

Background Although neoadjuvant chemoimmunotherapy (NACI) is increasingly applied in clinical settings, its therapeutic efficacy and prognostic significance remain unclear. This study sought to establish a surface-enhanced Raman spectroscopy (SERS)-based approach for assessing treatment efficacy and predicting prognosis in patients with locally advanced gastric cancer (LAGC) undergoing NACI. In addition, the utility of SERS for molecular and pathological profiling was investigated. Methods This retrospective study enrolled 31 patients with LAGC treated with anti-PD-1 inhibitors plus chemotherapy before gastrectomy (May 2018–December 2022). A Raman score (RS) was established from SERS spectral features to predict overall survival (OS). The area under the time-dependent receiver operating characteristic curve (AUC), Cox proportional hazards regression, and concordance index (C-index) were used to evaluate model performance. A nomogram combining RS and ypTNM stage was constructed. Kaplan-Meier analysis assessed the risk stratification capacity. Key spectral bands were analyzed for biomarker identification, and machine learning (ML) models were used for histopathological and molecular classification. Results A total of 3,670 spectra from 31 patients were analyzed. The RS, based on Raman spectral features, achieved AUCs of 0.854 (1-year OS) and 0.920 (3-year OS). Lower RS correlated with longer OS (p<0.05). RS served as an independent prognostic factor in multivariable analysis. The nomogram incorporating RS and ypTNM improved prediction for 3-year OS (AUC=0.955) while maintaining 1-year accuracy. Kaplan–Meier analysis confirmed effective risk stratification (P=0.01). Nine significant Raman bands were linked to nucleotides, collagen, and proteins. ML models achieved >0.85 accuracy in classifying microsatellite instability (MSI), combined positive score (CPS) of programmed cell death ligand-1 (PD-L1), and tumor regression grade (TRG) based on SERS data. Conclusions: This study demonstrates that label-free SERS can effectively predict prognosis in NACI-treated LAGC patients and shows promise in molecular and pathological profiling, supporting its potential for clinical application.