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Raman spectroscopic analysis differentiates between breast cancer cell lines
Authors:A C S Talari  C A Evans  I Holen  R E Coleman  Ihtesham Ur Rehman
Abstract:Breast cancer incident rates are increasing in women worldwide with the highest incidence rates reported in developing countries. Major breast cancer screening approaches like mammography, ultrasound, clinical breast examination (CBE) and magnetic resonance imaging (MRI) are currently used but have their own limitations. Optical spectroscopy has attained great attention from biomedical researchers in recent years due to its non‐invasive and non‐destructive detection approach. Chemometrics is one of the powerful tools used in spectroscopic research to enhance its sensitivity. Raman spectroscopy, a vibrational spectroscopic approach, has been used to explore the chemical fingerprints of different biological tissues including normal and malignant types. This approach was used to characterize and differentiate two breast cancer and one normal breast cell lines (MDA‐MB‐436, MCF‐7 and MCF‐10A) using dispersive Raman spectroscopy. Raman spectra of the cell lines have revealed that basic differences in the concentration of biochemical compounds such as lipids, nucleic acids and protein Raman peaks were found to differ in intensity, and principal component analysis (PCA) was able to identify variations that lead to accurate and reliable separation of the three cell lines. Linear discriminant analysis (LDA) model of three cell lines was predicted with 100% sensitivity and 91% specificity. We have shown that a combination of Raman spectroscopy and chemometrics are capable of differentiation between breast cancer cell lines. These variations may be useful in identifying new spectral markers to differentiate different subtypes of breast cancer although this needs confirmation in a larger panel of cell lines as well as clinical material. Copyright © 2015 John Wiley & Sons, Ltd.
Keywords:breast cancer  breast cancer cell lines: Raman spectroscopy  principal component analysis  linear discriminant analysis
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