Innovative model of surface-enhanced Raman spectroscopy for exosomes identification: An approach for the diagnosis of hepatocellular carcinoma

Background: The current hepatocellular carcinoma (HCC) diagnostic approaches lack adequate sensitivity and specificity. So, this study was performed to develop an innovative model of surface-enhanced Raman spectros-copy (SERS) that can detect HCC patients by identifying the circulating tumor-derived exosomes.Methodology: Sixty participants, including normal controls, hepatitis C virus (HCV)-infected patients, and HCV-associated HCC patients, had their whole blood samples and exosomes separated from these samples analyzed using Raman spectroscopy (RS). A revolutionary model of SERS, based on an innovative glass and nano-gold, was designed to directly identify exosomes. Its measurements were simulated by Comsol Multiphysics (5.6).Results: The RS examination of the whole blood samples revealed no Raman peaks. Yet, the isolated exosomes from these samples generated Raman peaks at 400 and 1000 cm-1 wavenumbers in the HCV group. A Raman shift was detected in HCC patients at 812, 852, and 878 cm-1 wavenumbers with intensity ratios of 120, 130, and 60, respectively. The RS had a sensitivity and specificity of 95 % and 100 %, respectively, for detecting HCC. However, the newly-designed SERS was able to identify the HCC-derived exosomes, at 812 and 878 cm-1 wavenumbers, with boosted intensity ratios of 9*106 and 4*106, respectively, in the whole blood samples.Conclusion: The newly-developed SERS model has the potential to detect HCC patients through recognizing the tumor-derived exosomes non-invasively, with high accuracy, and without the need for laborious exosomal separation. Nonetheless, bringing this technology into the clinic demands the establishment of spectral databases and their validation using the current gold standards.

Automated summary

A novel model of surface-enhanced Raman spectroscopy (SERS) was developed to detect hepatocellular carcinoma (HCC) patients by identifying the circulating tumor-derived exosomes. This model can non-invasively and accurately recognize tumor-derived exosomes without the need for laborious exosomal separation.