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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 histogram of discrimination scores of OSCC and normal group
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Fig5: The histogram of discrimination scores of OSCC and normal group

Mentions: In the first analytical step to discriminate the OSCC spectra from the normal ones, the OSCC spectra were selected as the positive group and the normal ones were selected as the negative group. The process of PCA extracted 55 principle components(PCs) from the raw spectral data, which captured about 95% of the cumulative variance of the raw data and were input as variables for the LDA process. As the result of the LDA, 109 of 135 OSCC spectra and 122 of 145 normal spectra were classified into the accurate group successfully (Table 3). The sensitivity and specificity were 80.7 and 84.1% respectively, and the total accuracy of this diagnostic classification was 82.5%. The histogram of discrimination scores demonstrated a clear classification of the two groups (Fig. 5). In order to test the results of the classification, the ‘leave-one-out’ method was employed in the cross validation process. And the result of the cross validation shown that 107/135 of OSCC spectra and 120/145 of normal spectra were diagnosed correctly (Table 4). The sensitivity and specificity of the diagnosis were 79.3 and 82.8%, and the total accuracy was 81.1%.Table 3


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

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

Fig5: The histogram of discrimination scores of OSCC and normal group
Mentions: In the first analytical step to discriminate the OSCC spectra from the normal ones, the OSCC spectra were selected as the positive group and the normal ones were selected as the negative group. The process of PCA extracted 55 principle components(PCs) from the raw spectral data, which captured about 95% of the cumulative variance of the raw data and were input as variables for the LDA process. As the result of the LDA, 109 of 135 OSCC spectra and 122 of 145 normal spectra were classified into the accurate group successfully (Table 3). The sensitivity and specificity were 80.7 and 84.1% respectively, and the total accuracy of this diagnostic classification was 82.5%. The histogram of discrimination scores demonstrated a clear classification of the two groups (Fig. 5). In order to test the results of the classification, the ‘leave-one-out’ method was employed in the cross validation process. And the result of the cross validation shown that 107/135 of OSCC spectra and 120/145 of normal spectra were diagnosed correctly (Table 4). The sensitivity and specificity of the diagnosis were 79.3 and 82.8%, and the total accuracy was 81.1%.Table 3

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.