Document Type



This item is available under a Creative Commons License for non-commercial use only


Biophysics, Health-related biotechnology

Publication Details

Analyst, 136, 1365-1373 (2011)


Cervical cancer, a potentially preventable disease, has its main aetiology in infection by high risk human papillomavirus (HR-HPV). Approaches to improving cervical cancer screening and diagnostic methodologies include molecular biological analysis, targeting of biomarker proteins, but also exploration and implementation of new techniques such as vibrational spectroscopy. This study correlates the biomarker protein p16INK4A expression levels dependent on HPV copy number with the infrared absorption spectral signatures of the cervical cancer cell lines, HPV negative C33A, HPV-16 positive SiHa and CaSki and HPV-18 positive HeLa. Confocal fluorescence microscopy demonstrated that p16INK4A is expressed in all investigated cell lines in both nuclear and cytoplasmic regions, although predominantly in the cytoplasm. Flow cytometry was used to quantify the p16INK4A expression levels and demonstrated a correlation, albeit nonlinear, between the reported number of integrated HPV copies and p16INK4A expression levels. CaSki cells were found to have the highest level of expression, HeLa intermediate levels, and SiHa and C33A the lowest levels. FTIR spectra revealed differences in nucleic acid, lipid and protein signatures between the cell lines with varying HPV copy number. Peak intensities exhibited increasing tendency in nucleic acid levels and decreasing tendency in lipid levels with increasing HPV copy number, and although they were found to be nonlinearly correlated with the HPV copy number, their dependence on p16INK4A levels was found to be close to linear. Principal Component Analysis (PCA) of the Infrared absorption spectra revealed differences between nuclear and cytoplasmic spectroscopic signatures for all cell lines, and furthermore clearly differentiated the groups of spectra representing each cell line. Finally, Partial Least Squares (PLS) analysis was employed to construct a model which can predict the p16INK4A expression level based on a spectral fingerprint of a cell line, demonstrating the diagnostic potential of spectroscopic techniques.