Gu Liangyou, Ma Xin, Li Hongzhao, Chen Luyao, Xie Yongpeng, Zhao Chaofei, Luo Guoxiong, Zhang Xu
Department of Urology, Chinese PLA General Hospital, Beijing, China.
School of Medicine, Nankai University, Tianjin, China.
Sci Rep. 2016 Mar 31;6:23846. doi: 10.1038/srep23846.
To examine the prognostic role of inflammatory response biomarkers in sarcomatoid renal cell carcinoma (sRCC). From January 2004 to May 2015, 103 patients with sRCC were enrolled in this study. Preoperative neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR) were analyzed. Besides well-established clinicopathological prognostic factors, we evaluated the prognostic value of this four markers using Kaplan-Meier method and Cox regression models. Additionally, a nomogram was established to predict the prognosis of sRCC patients. Elevated NLR, dNLR and PLR were significantly associated with worse overall survival (OS), nevertheless, elevated LMR showed an adverse effect on reduced OS. Multivariate analysis revealed that NLR (HR = 4.07, 95% CI = 1.50-11.00, P = 0.006) retained as independent factor. Incorporation of the NLR into a prognostic model including T stage, M stage, tumor necrosis and percentage of sarcomatoid generated a nomogram, which accurately predicted OS for sRCC patients. Preoperative NLR may serve as a potential prognostic biomarker in patients with sRCC and may help with clinical decisions about treatment intervention in clinical practice. The proposed nomogram can be used for the prediction of OS in patients with sRCC.
探讨炎症反应生物标志物在肉瘤样肾细胞癌（sRCC）中的预后作用。2004年1月至2015年5月，103例sRCC患者纳入本研究。分析术前中性粒细胞与淋巴细胞比值（NLR）、衍生中性粒细胞与淋巴细胞比值（dNLR）、血小板与淋巴细胞比值（PLR）及淋巴细胞与单核细胞比值（LMR）。除了公认的临床病理预后因素外，我们采用Kaplan-Meier法和Cox回归模型评估这四种标志物的预后价值。此外，建立了一个列线图来预测sRCC患者的预后。NLR、dNLR和PLR升高与较差的总生存期（OS）显著相关，然而，LMR升高对OS降低有不利影响。多因素分析显示NLR（HR = 4.07，95%CI = 1.50-11.00，P = 0.006）仍为独立因素。将NLR纳入包括T分期、M分期、肿瘤坏死和肉瘤样成分百分比的预后模型中生成了一个列线图，该列线图可准确预测sRCC患者的OS。术前NLR可能作为sRCC患者潜在的预后生物标志物，并可能有助于临床实践中关于治疗干预的临床决策。所提出的列线图可用于预测sRCC患者的OS。
