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. 2017 Aug 10;8(41):70874-70882.
doi: 10.18632/oncotarget.20147. eCollection 2017 Sep 19.

Prognostic role of pretreatment platelet to lymphocyte ratio in urologic cancer

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Prognostic role of pretreatment platelet to lymphocyte ratio in urologic cancer

Jianfeng Wang et al. Oncotarget. .

Abstract

The prognostic value of platelet to lymphocyte ratio (PLR) in urologic cancer does not reach a consensus. Herein, we performed the meta-analysis to determine the prognostic role of PLR in patients with urologic cancer. A literature search was performed in the PubMed, Embase, and Web of Science databases. Hazard ratios (HRs) were extracted to estimate the association between PLR and prognosis. A total of 20 articles comprising 6079 patients were included in this study. The pooled results showed that a high PLR was significantly associated with worse prognosis of overall survival (OS) in urologic cancer [HR=1.65, 95% confidence interval (CI) =1.37-1.99, P<0.01]. The result also indicated that an elevated PLR was significantly associated with poor OS in renal cancer (HR=1.88, 95% CI=1.39-2.55, P<0.01). In addition, the significant association between poor OS and elevated PLR in renal cancer was consistent regardless of treatment, cut-off value, sample size and study quality. Our result also indicated that an elevated PLR predicted shorter OS (HR=1.78, 95% CI=1.38-2.30, P<0.01) and cancer-specific survival (HR=2.02, 95% CI=1.24-3.29, P<0.01) in prostate cancer. In conclusion, an elevated PLR was a predictive indicator of poor survival in renal cancer and prostate cancer.

Keywords: meta-analysis; platelet to lymphocyte ratio; prognosis; urologic cancer.

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Conflict of interest statement

CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1. Flow diagram of article selection procedure
Figure 2
Figure 2. Forest plot of the hazard ratio for the association between an elevated platelet to lymphocyte ratio and overall survival in patients with urologic cancer
Figure 3
Figure 3. Assessment of publication bias using funnel plot analysis

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