The proteins found in tears have an important role in the maintenance of the 
ocular surface and changes in the quality and quantity of tear components 
reflect changes in the health of the ocular surface. In this study, we have used 
quantitative proteomics, iTRAQ technology coupled with 2D-nanoLC-nano-ESI-MS/MS 
and with a statistical model to uncover proteins that are significantly and 
reliably changed in the tears of dry eye patients in an effort to reveal 
potential biomarker candidates. Fifty-six patients with dry eye and 40 healthy 
subjects were recruited for this study. In total, 93 tear proteins were 
identified with a ProtScore >or=2 (>or=99% confidence). Associated with dry eye 
were 6 up-regulated proteins, alpha-enolase, alpha-1-acid glycoprotein 1, S100 
A8 (calgranulin A), S100 A9 (calgranulin B), S100 A4 and S100 A11 (calgizzarin) 
and 4 down-regulated proteins, prolactin-inducible protein (PIP), lipocalin-1, 
lactoferrin and lysozyme. Receiver operating curves (ROC) were evaluated for 
individual biomarker candidates and a biomarker panel. With the use of a 
4-protein biomarker panel, the diagnostic accuracy for dry eye was 96% 
(sensitivity, 91.0%; specificity, 90.0%). Two biomarker candidates 
(alpha-enolase and S100 A4) generated from iTRAQ experiments were successfully 
verified using an ELISA assay. The levels of these 10 tear proteins reflect 
aqueous secretion deficiency by lacrimal gland, inflammatory status of the 
ocular surface. The clinical classification of the severity of the dry eye 
condition was successfully correlated to the proteomics by using three proteins 
that are associated with inflammation, alpha1-acid glycoprotein 1, S100 A8 and 
S100 A9. The nine tear protein biomarker candidates (except alpha1-acid 
glycoprotein 1) were also verified using an independent age-matched patient 
sample set. This study demonstrated that iTRAQ technology combined with 
2D-nanoLC-nanoESI-MS/MS quantitative proteomics is a powerful tool for biomarker 
discovery.