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Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma

View Article: PubMed Central - PubMed

ABSTRACT

Background: Oral squamous cell carcinoma (OSCC) is becoming more common across the globe. The prognosis of OSCC is largely dependent on the early detection. But the routine oral cavity examination may delay the diagnosis because the early oral malignant lesions may be clinically indistinguishable from benign or inflammatory diseases. In this study, the new diagnostic method is developed by using the surface enhanced Raman spectroscopy (SERS) to detect the serum samples from the cancer patients.

Method: The blood serum samples were collected from the OSCC patients, MEC patients and the volunteers without OSCC or MEC. Gold nanoparticles(NPs) were then mixed in the serum samples to obtain the high quality SERS spectra. There were totally 135 spectra of OSCC, 90 spectra of mucoepidermoid carcinoma (MEC) and 145 spectra of normal control group, which were captured by SERS successfully. Compared with the normal control group, the Raman spectral differences exhibited in the spectra of OSCC and MEC groups, which were assigned to the nucleic acids, proteins and lipids. Based on these spectral differences and features, the algorithms of principal component analysis(PCA) and linear discriminant analysis (LDA) were employed to analyze and classify the Raman spectra of different groups.

Results: Compared with the normal groups, the major increased peaks in the OSCC and MEC groups were assigned to the molecular structures of the nucleic acids and proteins. And these different major peaks between the OSCC and MEC groups were assigned to the special molecular structures of the carotenoids and lipids. The PCA-LDA results demonstrated that OSCC could be discriminated successfully from the normal control groups with a sensitivity of 80.7% and a specificity of 84.1%. The process of the cross validation proved the results analyzed by PCA-LDA were reliable.

Conclusion: The gold NPs were appropriate substances to capture the high-quality SERS spectra of the OSCC, MEC and normal serum samples. The results of this study confirm that SERS combined PCA-LDA had a giant capability to detect and diagnosis OSCC through the serum sample successfully.

No MeSH data available.


The normalized mean Raman spectra of OSCC, MEC and normal serum samples
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Fig3: The normalized mean Raman spectra of OSCC, MEC and normal serum samples

Mentions: A total of 370 SERS spectra were recorded successfully in the Raman shift region from 200 cm−1 to 1800 cm−1. Among these spectra, 135 spectra were obtained from the clinically confirmed OSCC subjects, 90 spectra were obtained from the clinically confirmed MEC subjects and 145 spectra were obtained from the normal control subjects. The clinical data of the subjects participating in this study was shown in the Table 1. The mean spectra of different groups before the spectral normalization were presented in the Fig. 2. And after the spectral normalization, the normalized mean spectra were used to compare the spectral differences of the OSCC, MEC and normal groups (Fig. 3). Compared with the mean spectrum of the normal control group, the OSCC group showed the increase in the peaks at 294, 446, 548, 726, 745, 1136, 1263, 1371, 1445 and 1491 cm−1 but the decrease in the peaks at 1542 and 1602 cm−1, which were shown in the subtracted spectrum (Fig. 4a). Compared with the mean spectrum of the normal control group, the MEC group showed the increase in the peaks at 476, 548, 726, 745, 933, 1328, 1371 and 1445 cm−1 but the decrease in the peaks at 294, 1263, 1541 and 1607 cm−1 (Fig. 4b). The spectral differences were also presented in the subtracted spectrum between the OSCC group and MEC group. The subtracted spectrum showed the increase in the peaks at 294,1139,1263 and 1491 cm−1 but the decrease in the peaks at 1602 cm−1 in the OSCC group compared with the MEC group (Fig. 4c).Fig. 2


Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma
The normalized mean Raman spectra of OSCC, MEC and normal serum samples
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5384146&req=5

Fig3: The normalized mean Raman spectra of OSCC, MEC and normal serum samples
Mentions: A total of 370 SERS spectra were recorded successfully in the Raman shift region from 200 cm−1 to 1800 cm−1. Among these spectra, 135 spectra were obtained from the clinically confirmed OSCC subjects, 90 spectra were obtained from the clinically confirmed MEC subjects and 145 spectra were obtained from the normal control subjects. The clinical data of the subjects participating in this study was shown in the Table 1. The mean spectra of different groups before the spectral normalization were presented in the Fig. 2. And after the spectral normalization, the normalized mean spectra were used to compare the spectral differences of the OSCC, MEC and normal groups (Fig. 3). Compared with the mean spectrum of the normal control group, the OSCC group showed the increase in the peaks at 294, 446, 548, 726, 745, 1136, 1263, 1371, 1445 and 1491 cm−1 but the decrease in the peaks at 1542 and 1602 cm−1, which were shown in the subtracted spectrum (Fig. 4a). Compared with the mean spectrum of the normal control group, the MEC group showed the increase in the peaks at 476, 548, 726, 745, 933, 1328, 1371 and 1445 cm−1 but the decrease in the peaks at 294, 1263, 1541 and 1607 cm−1 (Fig. 4b). The spectral differences were also presented in the subtracted spectrum between the OSCC group and MEC group. The subtracted spectrum showed the increase in the peaks at 294,1139,1263 and 1491 cm−1 but the decrease in the peaks at 1602 cm−1 in the OSCC group compared with the MEC group (Fig. 4c).Fig. 2

View Article: PubMed Central - PubMed

ABSTRACT

Background: Oral squamous cell carcinoma (OSCC) is becoming more common across the globe. The prognosis of OSCC is largely dependent on the early detection. But the routine oral cavity examination may delay the diagnosis because the early oral malignant lesions may be clinically indistinguishable from benign or inflammatory diseases. In this study, the new diagnostic method is developed by using the surface enhanced Raman spectroscopy (SERS) to detect the serum samples from the cancer patients.

Method: The blood serum samples were collected from the OSCC patients, MEC patients and the volunteers without OSCC or MEC. Gold nanoparticles(NPs) were then mixed in the serum samples to obtain the high quality SERS spectra. There were totally 135 spectra of OSCC, 90 spectra of mucoepidermoid carcinoma (MEC) and 145 spectra of normal control group, which were captured by SERS successfully. Compared with the normal control group, the Raman spectral differences exhibited in the spectra of OSCC and MEC groups, which were assigned to the nucleic acids, proteins and lipids. Based on these spectral differences and features, the algorithms of principal component analysis(PCA) and linear discriminant analysis (LDA) were employed to analyze and classify the Raman spectra of different groups.

Results: Compared with the normal groups, the major increased peaks in the OSCC and MEC groups were assigned to the molecular structures of the nucleic acids and proteins. And these different major peaks between the OSCC and MEC groups were assigned to the special molecular structures of the carotenoids and lipids. The PCA-LDA results demonstrated that OSCC could be discriminated successfully from the normal control groups with a sensitivity of 80.7% and a specificity of 84.1%. The process of the cross validation proved the results analyzed by PCA-LDA were reliable.

Conclusion: The gold NPs were appropriate substances to capture the high-quality SERS spectra of the OSCC, MEC and normal serum samples. The results of this study confirm that SERS combined PCA-LDA had a giant capability to detect and diagnosis OSCC through the serum sample successfully.

No MeSH data available.