19 August 2014 Label-free detection of serum proteins using surface-enhanced Raman spectroscopy for colorectal cancer screening
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Surface-enhanced Raman scattering (SERS) spectra of serum proteins purified from human serum samples were employed to detect colorectal cancer. Acetic acid as a new aggregating agent was introduced to increase the magnitude of the SERS enhancement. High-quality SERS spectra of serum proteins were acquired from 103 cancer patients and 103 healthy volunteers. Tentative assignments of SERS bands reflect that some specific biomolecular contents and protein secondary structures change with colorectal cancer progression. Principal component analysis combined with linear discriminant analysis was used to assess the capability of this approach for identifying colorectal cancer, yielding diagnostic accuracies of 100% (sensitivity: 100%; specificity: 100%) based on albumin SERS spectroscopy and 99.5% (sensitivity: 100%; specificity: 99%) based on globulin SERS spectroscopy, respectively. A partial least squares (PLS) approach was introduced to develop diagnostic models. An albumin PLS model successfully predicted the unidentified subjects with a diagnostic accuracy of 93.5% (sensitivity: 95.6%; specificity: 91.3%) and the globulin PLS model gave a diagnostic accuracy of 93.5% (sensitivity: 91.3%; specificity: 95.6%). These results suggest that serum protein SERS spectroscopy can be a sensitive and clinically powerful means for colorectal cancer detection.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jing Wang, Jing Wang, Duo Lin, Duo Lin, Juqiang Lin, Juqiang Lin, Yun Yu, Yun Yu, Zufang Huang, Zufang Huang, Yanping Chen, Yanping Chen, Jinyong Lin, Jinyong Lin, Shangyuan Feng, Shangyuan Feng, Buhong Li, Buhong Li, Nenrong Liu, Nenrong Liu, Rong Chen, Rong Chen, } "Label-free detection of serum proteins using surface-enhanced Raman spectroscopy for colorectal cancer screening," Journal of Biomedical Optics 19(8), 087003 (19 August 2014). https://doi.org/10.1117/1.JBO.19.8.087003 . Submission:

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