Novel Blood Indicators of Progression and Prognosis in Renal Cell Carcinoma: Red Cell Distribution Width-to-Lymphocyte Ratio and Albumin-to-Fibrinogen Ratio

Abstract

Objective: To evaluate the value of preoperative red cell distribution width-to-lymphocyte ratio (RLR) and albumin-to-fibrinogen ratio (AFR) to the prognosis of patients after renal cell carcinoma (RCC).

Methods: From 2012 to 2016, a total of 273 RCC patients underwent radical nephrectomy or partial nephrectomy. This study retrospectively analyzed this group of patients. X-tile software was used to determine the optimal values of RLR and AFR in the peripheral blood. The nomogram constructed with independent factors was used to predict the survival outcome of the patients after RCC.

Results: The RLR of the RCC group was higher than that of the normal control group (P=0.002), whereas the AFR of the RCC group was lower than that of the normal control group (P < 0.001). RLR and AFR are related to tumour type and tumour-node-metastasis (TNM) stage (P < 0.05 for all). Cox regression analysis showed that the independent prognostic factors affecting overall survival and disease-free survival in the RCC group were symptom, tumour type, TNM stage, Fuhrman grade, RLR, and AFR (P < 0.05 for all). The nomogram constructed by multiple factors has better predictive power for patients after RCC.

Conclusion: Preoperative RLR and AFR can serve as potential biomarkers to predict the prognosis of postoperative RCC patients and improve the predictability of patient recurrence and survival.

Conflict of interest statement

The authors declared that there are no conflicts of interest regarding the publication of this article.

Copyright © 2020 Chenjun Ma et al.

Figures

Figure 1

The 5-year OS (AF) and DFS (G-L) were x-tile analyzed using patient data to determine the optimal cutoff value for blood RLR and AFR. (a, d, g, j) The data are represented by the panel graph in different colors to indicate possible cutoff values. The best cutting point (8.8 and 9.0, respectively) is determined by the black circle on the x-tile image and shown in the histogram in the middle. (b, e, h, k) The histograms of the distribution of the number of people in RLR and AFR, and the kaplanMeier curves of OS (c, f) and DFS (i, l) show the difference in survival of different groups of RLR and AFR.

Figure 2

Blood cell counts from healthy people and patients with RCC. (a, b) There was no significant difference in age and gender between NVs and patients with RCC (both P > 0.05). (c) Correlations of RLR with AFR in RCC patients. The lymphocyte counts (d), albumin (g), and albumin-to-fibrinogen ratio (i) in patients with RCC were significantly lower than those in healthy people. The red cell distribution width (e), RDW-to-lymphocyte ratio (f), and fibrinogen (h) in RCC patients were significantly higher than those in healthy people.

Figure 3

Forest plot showing OS (a) and DFS (b) according to subgroup effects. HR, hazard ratio; CI, confidence interval.

Figure 4

Cox regression forest plot of circulating inflammatory biomarkers showing OS (a) and DFS (b) in each subgroup. HR, hazard ratio; CI, confidence interval.

Figure 5

Nomogram to estimate the probability of OS (a) and DFS (b) at 3 and 5 years.

Figure 6

Calibration curves of the nomogram for 3-year OS (a), 5-year OS, (b) 3-year DFS, and (c) and 5-year DFS (d).

Figure 7

C index forest plots of different models. C index for predicting the survival probability of (a) OS in 5 years and (b) DFS in 5 years. Model: RLR + AFR + symptom + tumour type + TNM + Fuhrman grade; Model 1: RLR + symptom + tumour type + TNM + Fuhrman grade; Model 2: AFR + symptom + tumour type + TNM + Fuhrman grade; Model 3: symptom + tumour type + TNM + Fuhrman grade; Model 4: TNM + Fuhrman grade; Model 5: symptom + tumor size; Model 6: TNM.