基于低丰度蛋白质的无标记 SERS 方法，用于高精度检测不同阶段的肝癌。
Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.
机构信息
School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, 361024, China.
MOE Key Laboratory of Opto Electronic Science and Technology for Medicine and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350007, China.
出版信息
Anal Chim Acta. 2024 May 22;1304:342518. doi: 10.1016/j.aca.2024.342518. Epub 2024 Mar 21.
BACKGROUND
Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer.
RESULTS
Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set.
SIGNIFICANCE
The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.
背景
表面增强拉曼散射（SERS）技术在血清检测中具有快速、简单、高度敏感的独特优势，可用于肝癌检测。然而，体液中的一些蛋白质生物标志物通常存在于超低浓度，并且严重受到高丰度蛋白质（HAPs）的干扰，这将影响基于 SERS 免疫测定的癌症筛查的特异性和准确性。显然，需要一种基于低丰度蛋白质的无标记 SERS 方法，该方法快速、非侵入性，能够高精度检测和筛选肝癌。
结果
收集了 60 例肝癌患者（27 例 T1 和 T2 期肝癌患者，33 例 T3 和 T4 期肝癌患者）和 40 名健康志愿者的血清样本。在此，通过免疫吸附和 Cohn 乙醇分级分离分离免疫球蛋白和白蛋白。然后，对低丰度蛋白（LAPs）进行富集，并检测和获得高质量的 SERS 光谱信号。最后，结合主成分分析-线性判别分析（PCA-LDA）算法，能够很好地区分早期肝癌（T1-T2）和晚期肝癌（T3-T4）的 SERS 光谱，准确率分别为 98.5%和 100%。此外，基于血清 LAPs 提取的 SERS 技术结合偏最小二乘支持向量机（PLS-SVM）成功实现了正常志愿者和不同肿瘤（T）期肝癌患者的分类和预测，PLS-SVM 在未知测试集中的诊断准确率达到 87.5%。
意义
实验结果表明，血清 LAPs SERS 检测结合多元统计算法可有效区分肝癌患者和健康志愿者，甚至实现了高精度（T1 和 T2 期）的早期肝癌筛查。这些结果表明，血清 LAPs SERS 检测结合多元统计诊断算法在早期癌症筛查中具有一定的应用潜力。
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