Recurrence prediction in oral cancers: a serum Raman spectroscopy study. Issue 7 (26th January 2015)

• Record Type: Journal Article
• Authors: Sahu, Aditi; Nandakumar, Nikhila; Sawant, Sharada; Krishna, C. Murali
• Is Part Of: Analyst. Volume 140:Issue 7(2015)
• Page Start: 2294
• Page End: 2301
• Publication Date: 2015-01-26
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c4an01860e
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Serum Raman spectroscopy was explored for prediction of oral cancer recurrence in before surgery and after surgery blood samples. Findings suggest RS of post-surgery samples may help in prediction of recurrence. Abstract : High mortality rates associated with oral cancers can be primarily attributed to the failure of current histological procedures in predicting recurrence. Identifying recurrence related factors can lead to improved prognosis, optimized treatment and enhanced overall outcomes. Serum Raman spectroscopy has previously shown potential in the diagnosis of cancers, such as head and neck, cervix, breast, oral cancers, and also in predicting treatment response. In the present study, serum was collected from 22 oral cancer subjects [with recurrence ( n = 10) and no-recurrence ( n = 12)] before and after surgery and spectra were acquired using a Raman microprobe coupled with a 40× objective. Spectral acquisition parameters were as follows: λ ex = 785 nm, laser power = 30 mW, integration time: 12 s and averages: 3. Data was analyzed in a patient-wise approach using unsupervised PCA and supervised PC-LDA, followed by LOOCV. PCA and PC-LDA findings suggest that recurrent and non-recurrent cases cannot be classified in before surgery serum samples; an average classification efficiency of ∼78% was obtained in after-surgery samples. Mean and difference spectra and PCA loadings indicate that DNA and protein markers may be potential spectral markers for recurrence. RS of post surgery serum samples may have the potential to predict the probability of recurrence in clinics, after prospective large-scale validation.