Label-Free SERS Analysis of Urine Using a 3D-Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer.

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 (n = 30), pancreatic cancer (n = 22), and prostate cancer (n = 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.

Cancer screening through surface-enhanced Raman spectroscopy fingerprinting analysis of urinary metabolites using surface-carbonized silver nanowires on a filter membrane.

BACKGROUND: Label-free surface-enhanced Raman spectroscopy (SERS)-based metabolic profiling has great potential for early cancer diagnosis, but further advancements in analytical methods and clinical evidence studies are required for clinical applications. To improve the cancer diagnostic accuracy of label-free SERS spectral analysis of complex biological fluids, it is necessary to obtain specifically enhanced SERS signals of cancer-related metabolites present at low concentrations. RESULTS: This study presents a novel 3D SERS sensor, comprising a surface-carbonized silver nanowire (AgNW)-stacked filter membrane, alongside an optimized urine/methanol/chloroform extraction technique, which specifically changes the molecular adsorption and orientation of aromatic metabolites onto SERS substrates. By analyzing the pretreated urine samples on the surface-carbonized AgNW 3D SERS sensor, distinct and highly enhanced SERS peaks derived from semi-polar aromatic metabolites were observed for pancreatic cancer and prostate cancer samples compared with normal controls. Urine metabolite analysis using SERS fingerprinting successfully differentiated pancreatic cancer and prostate cancer groups from normal control group: normal control (n = 56), pancreatic cancer (n = 40), and prostate cancer (n = 39). SIGNIFICANCE AND NOVELTY: We confirmed the clinical feasibility of performing fingerprint analysis of urinary metabolites based on the surface-carbonized AgNW 3D SERS sensor and methanol/chloroform extraction for noninvasive cancer screening. This technology holds potential for large-scale screening owing to its high accuracy, and cost effective, simple and rapid detection method.