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Discrimination of gastric cancer from normal by serum RNA based on surface‐enhanced Raman spectroscopy (SERS) and multivariate analysis
Author(s) -
Chen Yanping,
Chen Gang,
Zheng Xiongwei,
He Cheng,
Feng Shangyuan,
Chen Yan,
Lin Xiaoqian,
Chen Rong,
Zeng Haisan
Publication year - 2012
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4747269
Subject(s) - raman spectroscopy , surface enhanced raman spectroscopy , rna , cancer , principal component analysis , multivariate analysis , chemistry , medicine , analytical chemistry (journal) , materials science , pathology , microbiology and biotechnology , biology , biochemistry , raman scattering , chromatography , computer science , optics , artificial intelligence , gene , physics
Purpose: Here, the authors explore the feasibility of discriminating cancer patients from healthy controls by serum RNA detection based on surface‐enhanced Raman spectroscopy (SERS) and multivariate analysis. Methods: MgSO 4 ‐aggregated silver nanoparticles (Ag NP) as the SERS‐active substrate presented strong SERS signals to RNA. SERS measurements were performed on two groups of serum RNA samples: one group from patients ( n = 31) with gastric cancer and the other group from healthy volunteers ( n = 34). Results: Tentative assignments of the Raman bands in the normalized SERS spectra demonstrated that there are differential expressions of circulating RNA between the gastric cancer group and the control group. Principal component analysis (PCA) combined with linear discriminate analysis (LDA) was introduced to differentiate gastric cancer from normal and achieved sensitivity of 100% and specificity of 94.1%. Conclusions: This exploratory study demonstrated potential for developing serum RNA SERS analysis into a useful clinical tool for noninvasive screening and detection of cancer.