Expression Patterns of Nrf2 and Keap1 in Ovarian Cancer Cells and their Prognostic Role in Disease Recurrence and Patient Survival.

Abstract

Objective: This study evaluated the expression patterns of nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) and assessed their clinical value as prognostic indicators in ovarian cancer.

Methods: The expression patterns of Nrf2 and Keap1 were determined in 100 epithelial ovarian cancers by immunohistochemistry analyses. The associations of Nrf2 and Keap1 expression with clinicopathological characteristics of patients were evaluated. All patients received platinum-based chemotherapy. Chemoresistance was defined as recurrence within 6 months of first-line chemotherapy.

Results: Cytoplasmic expression of Nrf2 and Keap1 was observed in 95% and 72%, respectively, of all 100 epithelial ovarian cancers examined. Low Keap1 expression (intensity < 1) was strongly associated with disease recurrence (P = 0.046) and death (P = 0.002). Chemoresistance was associated with high Nrf2 expression (intensity = 3) (P = 0.833; hazard ratio [HR], 1.202; 95% confidence interval [CI], 0.217-6.667) and low Keap1 expression (P = 0.862; HR, 0.899; 95% CI, 0.270-2.994). However, these associations were not statistically significant. Survival analysis indicated that high Keap1 expression (intensity ≥ 1) was strongly predictive of better overall survival (P = 0.049) and disease-free survival (P = 0.004). Cox regression analysis indicated that Keap1 expression was an independent prognostic factor for overall survival (P = 0.012; HR, 0.349; 95% CI, 0.153-0.797). Although patients with high Nrf2 expression displayed better overall survival and disease-free survival, the association was not statistically significant.

Conclusions: High cytoplasmic Keap1 expression, which might prevent nuclear translocation of Nrf2 in ovarian cancer cells, was associated with lower disease recurrence and death rate. Survival analysis suggested a probable role of Keap1 expression in predicting the prognosis of ovarian cancer.