Abstract：
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.
摘要：
鼻咽癌（NPC）是一种在东南亚地区患病率高，具有高侵袭性和转移性特征的恶性肿瘤。放射治疗是鼻咽癌治疗的主要策略,然而，仍然缺乏预测治疗失败的主要原因辐射抗性的效果方法。在这里，放疗敏感和耐药组和健康组的鼻咽癌患者血浆的分子谱，分别,首次采用基于表面等离子体共振的无标记表面增强拉曼光谱(SERS)进行了研究。尤其是,通过分离过程分析了不同分子量大小的组分，有助于避免由于竞争性吸附而可能丢失的诊断信息。在此之后,采用基于主成分分析和线性判别分析的鲁棒机器学习算法(PCA-LDA)对血液SERS数据进行特征提取,建立了有效的预测模型,准确率达96.7%,100％用于识别健康的NPC受试者。这项工作证明了分子分离辅助无标记SERS结合机器学习在临床场景中用于NPC筛查和治疗策略指导的潜力。
