Preoperative Inflammation-Associated Blood Cell Markers in Patients with Non-Metastatic Clear Cell Renal Cell Carcinoma: A Retrospective Study.

Purpose: This study aimed to investigate the association between preoperative inflammation-associated blood cell markers and the prognosis of patients with non-metastatic clear cell renal cell carcinoma (ccRCC) who underwent nephrectomy.

Patients And Methods: We retrospectively analyzed data from our single-center cohort of patients who underwent radical or partial nephrectomy for non-metastatic ccRCC. The optimal cutoff values for red blood cell distribution width (RDW), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) were determined using X-tile software. We evaluated recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS) using the Kaplann-Meier method. Cox proportional-hazards regression models were utilized to assess predictors of RFS, CSS, and OS. The predictive accuracy was evaluated using Harrell's Concordance Index (C-index).

Results: A total of 444 patients who underwent nephrectomy were included in the study. The optimal cutoff values for RDW, PLR, NLR, and LMR were determined as 13.1, 157.3, 3.4, and 2.7, respectively. On univariate Cox regression analysis, NLR, PLR, and LMR were significant predictors for RFS, CSS, and OS. After adjusting for important prognostic factors, only NLR remained a significant prognostic marker for both CSS and OS. When NLR was added to the stage, size, grade, and necrosis (SSIGN) model, the C-index increased from 0.777 to 0.826 for CSS and from 0.703 to 0.734 for OS. Similarly, when NLR was added to the University of California, Los Angeles, Integrated Staging System (UISS), the C-index increased from 0.796 to 0.811 for CSS and from 0.735 to 0.745 for OS.

Conclusion: NLR is a reliable prognostic biomarker for patients with non-metastatic ccRCC. The prognostic capabilities of UISS and SSIGN models could be improved by adding NLR to UISS and SSIGN models.