鼻咽癌（NPC）是东南亚地区高发的恶性肿瘤，具有高度侵袭性和转移性的特点。放射治疗是鼻咽癌治疗的主要策略，但目前仍缺乏有效的方法来预测放射抵抗，这是治疗失败的主要原因。在此，首次利用基于表面等离振子共振的无标记表面增强拉曼光谱（SERS）分别探索了放疗敏感组和耐药组以及健康组的鼻咽癌患者血浆的分子谱。特别是，通过分离过程对不同分子量大小的组分进行分析，有助于避免由于竞争吸附而可能丢失的诊断信息。随后，采用基于主成分分析和线性判别分析（PCA-LDA）的鲁棒机器学习算法提取血液SERS数据特征，建立有效的预测模型，准确率高达96.7%，用于识别放疗抵抗受试者100% 区分鼻咽癌受试者和健康受试者。这项工作展示了分子分离辅助无标记 SERS 与机器学习相结合在临床场景中用于鼻咽癌筛查和治疗策略指导的潜力。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.Copyright © 2024 Elsevier B.V. All rights reserved.