Journal
SCIENTIFIC REPORTS
Volume 5, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep09582
Keywords
-
Categories
Funding
- Medical Research Foundation of Guangdong Province [A2014466]
- Doctor Start Fund of Guangdong Medical College [XB1407]
- National Natural Science Foundation of China [61335011, 61275187, 31300691]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20114407110001]
- Natural Science Foundation of Guangdong Province [9251063101000009]
- Cooperation Project in Industry, Education and Research of Guangdong province
- Ministry of Education of China [2011A090200011]
Ask authors/readers for more resources
This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481-486, 682-687, 1018-1034, 1313-1323, 1450-1459 and 1582-1587 cm(-1) related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available