Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.
Smart Healthcare Research Institute, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.
Anal Chem. 2021 Mar 2;93(8):3778-3785. doi: 10.1021/acs.analchem.0c04200. Epub 2021 Feb 12.
Metabolomics shows tremendous potential for the early diagnosis and screening of cancer. For clinical application as an effective diagnostic tool, however, improved analytical methods for complex biological fluids are required. Here, we developed a reliable rapid urine analysis system based on surface-enhanced Raman spectroscopy (SERS) using 3D-stacked silver nanowires (AgNWs) on a glass fiber filter (GFF) sensor and applied it to the diagnosis of pancreatic cancer and prostate cancer. Urine samples were pretreated with centrifugation to remove large debris and with calcium ion addition to improve the binding of metabolites to AgNWs. The label-free urine-SERS detection using the AgNW-GFF SERS sensor showed different spectral patterns and distinguishable specific peaks in three groups: normal control ( = 30), pancreatic cancer ( = 22), and prostate cancer ( = 22). Multivariate analyses of SERS spectra using unsupervised principal component analysis and supervised orthogonal partial least-squares discriminant analysis showed excellent discrimination between the pancreatic cancer group and the prostate cancer group as well as between the normal control group and the combined cancer groups. The results demonstrate the great potential of the urine-SERS analysis system using the AgNW-GFF SERS sensor for the noninvasive diagnosis and screening of cancers.
代谢组学在癌症的早期诊断和筛查方面显示出巨大的潜力。然而，为了将其作为一种有效的诊断工具应用于临床，需要改进用于复杂生物流体的分析方法。在这里，我们使用玻璃纤维滤器（GFF）传感器上的三维堆叠银纳米线（AgNWs）开发了一种基于表面增强拉曼光谱（SERS）的可靠快速尿液分析系统，并将其应用于胰腺癌和前列腺癌的诊断。尿液样本先用离心法去除大的碎片，然后加入钙离子以提高代谢物与 AgNWs 的结合。使用 AgNW-GFF SERS 传感器进行的无标记尿液-SERS 检测显示，在三组（正常对照组 = 30，胰腺癌组 = 22，前列腺癌组 = 22）中具有不同的光谱模式和可区分的特征峰。使用无监督主成分分析和有监督正交偏最小二乘判别分析对 SERS 光谱进行多元分析，结果表明，AgNW-GFF SERS 传感器的尿液-SERS 分析系统在胰腺癌组和前列腺癌组以及正常对照组和合并癌症组之间具有很好的区分能力。这些结果表明，使用 AgNW-GFF SERS 传感器的尿液-SERS 分析系统在癌症的非侵入性诊断和筛查方面具有巨大的潜力。
