High O-linked N-acetylglucosamine transferase expression predicts poor survival in patients with early stage lung
Yi-Cheng Lin1, Chia-Hung Lin2, Yi-Chen Yeh1
1Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei 11221, Taiwan.
Abstract:
Tumor cell heterogeneity can make selection of appropriate interventions to lung cancer a challenge. Novel biomarkers predictive of disease risk and treatment response are needed to improve personalized treatment strategies. O-GlcNAcylation, the attachment of β-N-acetylglucosamine (O-GlcNAc) to serine or threonine residues of intracellular proteins, modulates protein functions and is implicated in cancer pathogenesis. O-GlcNAc-transferase (OGT) and O-GlcNAcase (OGA) catalyze O-GlcNAc addition and removal, respectively. We used immunohistochemistry to explore the utility of OGT, OGA, and O-GlcNAc as potential biomarkers for lung adenocarcinoma. We found that high OGT expression is associated with poor overall survival (OS) in both stage I patients (P=0.032) and those at variable stages of disease (P=0.029), and with poor recurrence-free survival (RFS) in stage I patients (P=0.035). High OGT expression is also associated with poorer OS in patients with EGFR wild-type tumors at variable stages (P=0.038). Multivariate analysis indicated that OGT expression is an independent prognostic factor for RFS (HR 2.946, 95% CI: 1.411-6.150, P=0.004) and OS (HR 2.002, 95% CI: 1.183-3.391, P=0.010) in stage I patients. Our findings indicate OGT is a promising biomarker for further classifying early stage lung adenocarcinomas.
More Related Videos
Related Concept Videos
Covalently Linked Protein Regulators
These groups modify specific amino acids in a protein....
Predicting Molecular Geometry
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Survival Tree
Building a Survival Tree
Constructing a...
Lung Capacity
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.


