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.
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