Institute of Photonics, Faculty of Science, Ningbo University, Ningbo 315211, Zhejiang, China.
Wolfson School of Mechanical, Manufacturing and Electrical Engineering, Loughborough University, Leicestershire LE11 3TU, United Kingdom.
Talanta. 2018 Oct 1;188:238-244. doi: 10.1016/j.talanta.2018.05.070. Epub 2018 May 23.
Prostate cancer (PCa) is a leading cause of cancer-related death among males globally. To date, prostate-specific antigen (PSA), as a typical tumour marker, has been widely used in the early diagnosis of PCa. However, in practical clinical tests, high serum levels of PSA show a high probability for false-positive results, leading to misdiagnoses. In this study, we developed a new classification system for PCa, benign prostate hyperplasia (BPH) and healthy subjects by using a surface-enhanced Raman scattering (SERS)-based immunoassay of multiple tumour markers along with a support vector machine (SVM) algorithm. Silver nanoparticles (AgNPs) as immune probes and SiC@Ag@Ag-NPs SERS as immune substrates were constructed into a sandwich structure to serve as an ultrasensitive SERS-based immunoassay platform of tumour markers. With this assay, the limits of detection for PSA, prostate-specific membrane antigen (PSMA) and human kallikrein 2 (hK2) were as low as 0.46 fg mL, 1.05 fg mL and 0.67 fg mL, respectively. Furthermore, the serum levels of PSA, PSMA and hK2 in clinical samples were successfully detected using the SERS-based immunoassay platform, and correct classifications of PCa, BPH and healthy subjects were feasible with help of the linear SVM algorithm. These results demonstrate the potential for improving the diagnostic accuracy of PCa. Overall, the linear SVM classification model with multiple tumour markers exhibited good classifications of PCa, BPH and healthy subjects, with a PCa diagnostic accuracy of 70% that was significantly superior to that of the linear SVM classification model based only on the serum level of PSA (50%). Therefore, combining the SERS-based immunoassay with pattern recognition technology can allow for comprehensive analyses of the serum levels of multiple tumour markers to effectively improve the diagnostic accuracy of cancer with potential applications in point-of-care testing.
前列腺癌（PCa）是全球男性癌症相关死亡的主要原因。迄今为止，前列腺特异性抗原（PSA）作为一种典型的肿瘤标志物，已广泛应用于 PCa 的早期诊断。然而，在实际临床检测中，血清中 PSA 水平高的情况下，假阳性结果的概率很高，导致误诊。在这项研究中，我们开发了一种新的前列腺癌、良性前列腺增生（BPH）和健康受试者的分类系统，该系统使用基于表面增强拉曼散射（SERS）的多种肿瘤标志物免疫分析和支持向量机（SVM）算法。将银纳米粒子（AgNPs）作为免疫探针，SiC@Ag@Ag-NPs SERS 作为免疫基底构建成三明治结构，作为肿瘤标志物的超灵敏 SERS 免疫分析平台。通过该检测，PSA、前列腺特异性膜抗原（PSMA）和人激肽释放酶 2（hK2）的检测限低至 0.46 fg mL、1.05 fg mL 和 0.67 fg mL。此外，还成功地使用 SERS 免疫分析平台检测了临床样本中 PSA、PSMA 和 hK2 的血清水平，并借助线性 SVM 算法实现了 PCa、BPH 和健康受试者的正确分类。这些结果表明，提高 PCa 诊断准确性是可行的。总体而言，基于多个肿瘤标志物的线性 SVM 分类模型对 PCa、BPH 和健康受试者的分类效果良好，PCa 的诊断准确率为 70%，明显优于仅基于 PSA 血清水平的线性 SVM 分类模型（50%）。因此，将基于 SERS 的免疫分析与模式识别技术相结合，可以对多种肿瘤标志物的血清水平进行综合分析，有效提高癌症的诊断准确性，具有在即时检测中应用的潜力。
