Exosome Classification by Pattern Analysis of Surface-Enhanced Raman Spectroscopy Data for Lung Cancer Diagnosis

Jaena Park, Miyeon Hwang, Byeonghyeon Choi, Hyesun Jeong, Jik Han Jung, Hyun Koo Kim, Sunghoi Hong, Ji Ho Park, Yeonho Choi

Research output: Contribution to journalArticlepeer-review

172 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)6695-6701
Number of pages7
JournalAnalytical chemistry
Issue number12
Publication statusPublished - 2017 Jun 20

Bibliographical note

Publisher Copyright:
© 2017 American Chemical Society.

ASJC Scopus subject areas

  • Analytical Chemistry


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