Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer

• Published in: Analytical and bioanalytical chemistry 2015-10, Vol.407 (26), p.7935-7943
• Main Authors: Banerjee, Satarupa, Pal, Mousumi, Chakrabarty, Jitamanyu, Petibois, Cyril, Paul, Ranjan Rashmi, Giri, Amita, Chatterjee, Jyotirmoy
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Biochemistry; Biomarkers; Biomarkers, Tumor - analysis; Bisphenol A; carcinoma; Carcinoma, Squamous Cell - diagnosis; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Classification; Colorectal carcinoma; Data base management systems; Diagnosis; Diagnostic systems; Differentiation; Disease; Feature selection; Food Science; Fourier transform infrared spectroscopy; glycogen; histopathology; Humans; keratin; Laboratory Medicine; Lesions; Leukoplakia, Oral - diagnosis; Methods; Monitoring/Environmental Analysis; Mouth - pathology; Mouth Neoplasms - diagnosis; Oral cancer; Oral leukoplakia; Pathology; Research Paper; Sensitivity and Specificity; Spectra; Spectroscopy; Spectroscopy, Fourier Transform Infrared - methods; Squamous cell carcinoma; Support Vector Machine; Support vector machines
• ISSN: 1618-2642; 1618-2650
• DOI: 10.1007/s00216-015-8960-3

In search of specific label-free biomarkers for differentiation of two oral lesions, namely oral leukoplakia (OLK) and oral squamous-cell carcinoma (OSCC), Fourier-transform infrared (FTIR) spectroscopy was performed on paraffin-embedded tissue sections from 47 human subjects (eight normal (NOM), 16 OLK, and 23 OSCC). Difference between mean spectra (DBMS), Mann–Whitney’s U test, and forward feature selection (FFS) techniques were used for optimising spectral-marker selection. Classification of diseases was performed with linear and quadratic support vector machine (SVM) at 10-fold cross-validation, using different combinations of spectral features. It was observed that six features obtained through FFS enabled differentiation of NOM and OSCC tissue (1782, 1713, 1665, 1545, 1409, and 1161 cm⁻¹) and were most significant, able to classify OLK and OSCC with 81.3 % sensitivity, 95.7 % specificity, and 89.7 % overall accuracy. The 43 spectral markers extracted through Mann–Whitney’s U Test were the least significant when quadratic SVM was used. Considering the high sensitivity and specificity of the FFS technique, extracting only six spectral biomarkers was thus most useful for diagnosis of OLK and OSCC, and to overcome inter and intra-observer variability experienced in diagnostic best-practice histopathological procedure. By considering the biochemical assignment of these six spectral signatures, this work also revealed altered glycogen and keratin content in histological sections which could able to discriminate OLK and OSCC. The method was validated through spectral selection by the DBMS technique. Thus this method has potential for diagnostic cost minimisation for oral lesions by label-free biomarker identification.