ABSTRACT
BACKGROUND: Early detection of breast cancer reduces breast cancer-related mortality. Breast cancer biomarkers offer a promising means of detecting this disease at the earliest and most treatable stages. PATIENTS AND METHODS: The aim of this study was to generate a protein biomarker profile in tear fluid for breast cancer patients. This established biomarker profile was then used to discriminate between cancer patients and healthy controls. Potential biomarkers were screened in tear fluid from 50 women with breast cancer and 50 healthy women, matched for age. Tear fluid was drawn prior to surgery. Surface-enhanced laser desorption-ionisation time-of-flight mass spectrometry was used for protein profiling with two different active surfaces on the protein chips: a cationic exchanger (CM-10) and a reverse-phase surface (H50). The data were analyzed by multivariate statistical techniques and artificial neural networks. RESULTS: A total of 404 peaks were found with different molecular weights at different laser intensities and a statistically significant (p<0.05) panel with 20 biomarkers was generated. Use of the biomarker panel resulted in 71.19% of the samples being correctly classified as cancer samples (42 out of 59) and 70.69% as control samples (41 out of 58), thus overall 70.94% were correctly classified. The diagnostic pattern was able to differentiate cancer patients from healthy women with a specificity and sensitivity of approximately 70% using tear fluid. CONCLUSION: In this study a biomarker panel in tear fluid was successfully generated to allow breast cancer patients to be discriminated from healthy women. The study suggests that the proteomic pattern of tear fluid may be useful in the diagnosis of breast cancer and for high-throughput biomarker discovery.
