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Inflammatory biomarkers in prognostic analysis for patients with glioma and the establishment of a nomogram.

Being biomarkers that reflect host nutritional and immune status, prognostic nutritional index (PNI) and neutrophil/lymphocyte ratio (NLR) have been identified to be independent prognostic factors in various malignancies. The aim… Click to show full abstract

Being biomarkers that reflect host nutritional and immune status, prognostic nutritional index (PNI) and neutrophil/lymphocyte ratio (NLR) have been identified to be independent prognostic factors in various malignancies. The aim of the present study was to determine the predictive value of these parameters for the prognosis of patients with glioma. Hematological and clinicopathological data were retrospectively analyzed from 128 patients with glioma who underwent brain tumor resection between January 2008 and December 2012. Receiver operating characteristic (ROC) analysis was used to determine the optimal cut-offs for PNI and NLR. Kaplan-Meier survival analysis, and univariate and multivariate analyses based on Cox proportional hazards regression model were used to determine whether NLR and PNI were associated with the prognosis of patients with glioma. R software was used to develop nomograms with all the independent prognostic factors included. Kaplan-Meier analysis followed by log-rank tests indicated that NLR ≥2.8 and PNI <45 were significantly associated with decreased overall survival time. The subsequent multivariate analysis indicated that age ≥50 years [hazard ratio (HR), 2.328; 95% confidence interval (CI), 1.386-3.908; P<0.001], high-grade glioma (HR, 3.088; 95% CI, 1.893-5.037; P<0.001), gross total resection (HR, 0.606; 95% CI, 0.380-0.965; P=0.035) and NLR ≥2.8 (HR, 2.037; 95% CI, 1.264-3.281; P=0.003) were independent prognostic factors. The results of the present study indicated that high NLR was an independent risk factor for overall survival rates in patients with glioma, which indicated its value in improving the current prognostic model.

Keywords: biomarkers prognostic; prognostic factors; analysis; patients glioma; independent prognostic; inflammatory biomarkers

Journal Title: Oncology letters
Year Published: 2019

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