An Investigation into the Viability of Deep-Ultraviolet Raman Spectroscopy for Cancer Diagnosis Using Cell Lines and Tissues
Keywords:
Cancer diagnostics, Deep-ultraviolet resonance Raman spectroscopy, Prostate cancer, Brain cancer, Cancer imaging systemsAbstract
Background
Deep-ultraviolet resonance Raman spectroscopy (UVRS) offers significant advantages over visible and near-infrared Raman spectroscopy for biological applications, including cancer identification. Cancer is the second-leading cause of death in the United States. Early diagnostics plays a crucial role in providing the best chances for an afflicted individual to seek successful treatment opportunities. Current methods for diagnosing various forms of cancer are both expensive and invasive. As such, the objective of this study is to explore the feasibility of UVRS for discrimination of cancerous tissues and cancer cells from normal samples. The safety issues of using ultraviolet light for human applications are analyzed.
Methods
Cancerous brain tissues from nonobese diabetic/severe combined immunodeficiency (NOD-SCID) model mice injected with 435-tdT cells (human adenocarcinoma breast cancer cells) at known locations and adjacent normal brain tissues as well as normal and cancer (adenocarcinoma PC-3) prostate cells were studied using UVRS. The obtained Raman spectra of the healthy and can cerous samples are compared in order to identify biochemical differences between them.
Results
The obtained spectra reflect biochemical differences which occur between the healthy and malignant samples in both brain and prostate cancers. UVRS provides distinctive resonance signatures of major biochemical components, including proteins and nucleic acids, and it does not suffer from fluorescence interference, nor does it require high laser power levels for excitation. These advantages allow for clear and effective spectral discrimination between samples.
Conclusion
Our results suggest UVRS should be considered for cancer identification, and is safe for use within humans. The proposed innovative approach has significant potential for cancer imaging and real-time tissue discrimination during surgery.
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