Abstract
Inflammatory cells can both suppress and stimulate tumor growth, and the influence of inflammatory cells on clinical outcome has been the focus of many studies. The purpose of this study was to evaluate the effectiveness of the neutrophil to lymphocyte ratio (NLR), a measure of the systemic inflammatory response, as an additional discriminative biomarker in epithelial ovarian cancer and to determine whether it predicts survival and recurrence. We studied 192 patients with epithelial ovarian cancer, 173 with benign ovarian tumors, 229 with benign gynecologic disease, and 405 healthy controls. Serum CA125 levels and leukocyte counts according to subtypes were recorded prior to treatment in all study subjects. In epithelial ovarian cancer, the diagnostic usefulness of NLR, in combination with CA125, was evaluated. The correlation between NLR and overall and disease-free survival was analyzed using both univariate and multivariate analyses adjusting for the known prognostic factors (age, stage, cell type, and grade). Preoperative NLR in ovarian cancer subjects (mean 6.02) was significantly higher than that in benign ovarian tumor subjects (mean 2.57), benign gynecologic disease subjects (mean 2.55), and healthy controls (mean 1.98) (P < 0.001). The sensitivity and specificity of NLR in detecting ovarian cancer was 66.1% (95% CI, 59.52-72.68%) and 82.7% (95% CI, 79.02-86.38%), respectively (cutoff value: 2.60). In early stage ovarian cancer, CA125 was not elevated in 19 out of 49 patients. Seven (36.8%) of these 19 patients were NLR positive. On Cox multivariate analysis, NLR positive, stage III/IV, and older age were independent poor prognostic factors, and being NLR positive was the most powerful predictive variable (Hazard Ratio = 8.42 [95% CI: 1.09-64.84], P = 0.041). Our findings provide evidence for the association between NLR and epithelial ovarian cancer. Preoperative NLR, in combination with CA125, may represent a simple and cost-effective method of identifying ovarian cancers, and an elevated NLR may predict an adverse outcome in ovarian cancer.
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