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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 scatter plot of discrimination scores of OSCC, MEC and normal group
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Fig6: The scatter plot of discrimination scores of OSCC, MEC and normal group

Mentions: In order to demonstrate the potential more effectively to diagnose the OSCC by SERS, the MEC spectra were selected as the positive control group in the diagnostic classification. The PCA process also extracted 51 PCs from the raw data, which accounted for about 93% of the total variance. Then the PCs were input in the LDA process to classify the three different groups. The result shown that 101/135 of OSCC spectra, 72/90 of MEC spectra and 129/145 of normal spectra were classified into the correct groups successfully (Table 5). The sensitivity and specificity of the diagnosis of OSCC were 74.8 and 89.0%, and the total accuracy was 81.6%. The scatter plot diagram showed the separation of the three groups (Fig. 6). Then the ‘leave-one-out’ method was employed in the cross validation process to test the results of the classification. In the results of the cross validation, 82/135 of OSCC spectra were classified correctly, 64/90 of MEC spectra and 109/145 of normal spectra were also diagnosed correctly. The sensitivity and specificity of the diagnosis of OSCC were 60.7 and 75.2%, and the total accuracy was 68.9% (Table 6).Table 5


Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma
The scatter plot of discrimination scores of OSCC, MEC and normal group
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: The scatter plot of discrimination scores of OSCC, MEC and normal group
Mentions: In order to demonstrate the potential more effectively to diagnose the OSCC by SERS, the MEC spectra were selected as the positive control group in the diagnostic classification. The PCA process also extracted 51 PCs from the raw data, which accounted for about 93% of the total variance. Then the PCs were input in the LDA process to classify the three different groups. The result shown that 101/135 of OSCC spectra, 72/90 of MEC spectra and 129/145 of normal spectra were classified into the correct groups successfully (Table 5). The sensitivity and specificity of the diagnosis of OSCC were 74.8 and 89.0%, and the total accuracy was 81.6%. The scatter plot diagram showed the separation of the three groups (Fig. 6). Then the ‘leave-one-out’ method was employed in the cross validation process to test the results of the classification. In the results of the cross validation, 82/135 of OSCC spectra were classified correctly, 64/90 of MEC spectra and 109/145 of normal spectra were also diagnosed correctly. The sensitivity and specificity of the diagnosis of OSCC were 60.7 and 75.2%, and the total accuracy was 68.9% (Table 6).Table 5

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.