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Background Systemic inflammation is associated with the prognosis of clear cell renal cell carcinoma (ccRCC). The C-reactive protein-to-lymphocyte ratio (CLR) combines systemic inflammation and host immune status. However, its prognostic value in non-metastatic ccRCC remains unclear. This study evaluated the association between preoperative CLR and survival outcomes in patients with non-metastatic ccRCC. Methods We retrospectively included 414 patients with non-metastatic ccRCC who underwent partial or radical nephrectomy. The primary Cox analyses treated ln(CLR) as a continuous variable and assessed overall survival (OS) and the cause-specific hazard of ccRCC-related death. Sensitivity analyses included expanded adjustment and time-varying effects. Cut-off-based analyses were exploratory. Time-dependent receiver operating characteristic curves were used to compare CLR with other inflammatory markers. Incremental discrimination and calibration were assessed using bootstrap internal validation. Results After adjustment for age, Fuhrman grade, and T stage, ln(CLR) remained independently associated with OS (HR = 1.589, 95% CI 1.257–2.009; P < 0.001) and ccRCC-related death (HR = 1.612, 95% CI 1.266–2.052; P < 0.001). The associations persisted in sensitivity analyses addressing additional pathological factors and time-varying effects. The 3- and 5-year AUCs of CLR for OS were 0.810 and 0.776, respectively. No statistically significant AUC differences were detected between CLR and CRP, NLR, or CAR. Adding CLR increased the apparent C-index from 0.843 to 0.885. After bootstrap optimism correction, the C-indices were 0.832 and 0.873, respectively (ΔC-index = 0.041). Calibration was assessed at 3 and 5 years. Conclusions Preoperative CLR was independently associated with OS and the cause-specific hazard of ccRCC-related death in this single-center cohort. Adding CLR improved discrimination after internal validation, but its superiority over other inflammatory markers and clinical utility remain unestablished. Independent multicenter validation is needed.
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Qin et al. (2026) studied this question.
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