1 research outputs found
Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis
Owing
to the role of exosome as a cargo for intercellular communication,
especially in cancer metastasis, the evidence has been consistently
accumulated that exosomes can be used as a noninvasive indicator of
cancer. Consequently, several studies applying exosome have been proposed
for cancer diagnostic methods such as ELISA assay. However, it has
been still challenging to get reliable results due to the requirement
of a labeling process and high concentration of exosome. Here, we
demonstrate a label-free and highly sensitive classification method
of exosome by combining surface-enhanced Raman scattering (SERS) and
statistical pattern analysis. Unlike the conventional method to read
different peak positions and amplitudes of a spectrum, whole SERS
spectra of exosomes were analyzed by principal component analysis
(PCA). By employing this pattern analysis, lung cancer cell derived
exosomes were clearly distinguished from normal cell derived exosomes
by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing
the PCA result, we could suggest that this difference was induced
by 11 different points in SERS signals from lung cancer cell derived
exosomes. This result paved the way for new real-time diagnosis and
classification of lung cancer by using exosome as a cancer marker