Predictive value of NRI, NLR, PLR and LMR for therapeutic efficacy and prognosis in advanced rectal cancer patients treated with PD-1/PD-L1 immunotherapy

Objective The purpose of the study was to investigate the predictive value of the Nutritional Risk Index (NRI), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Lymphocyte-to-Monocyte Ratio (LMR) for the prognosis and effectiveness of immunotherapy in patients with advanced rectal cancer. Methods Retrospective analysis of 172 advanced rectal cancer patients undergoing immunotherapy (Feb 2020 - Feb 2023) was performed. The optimal cut-off values of each indicator were determined by receiver operating characteristic (ROC) curves. The χ 2 test was used to analyze the correlations between indicators and clinicopathological characteristics, objective response rate (ORR), and disease control rate (DCR). Kaplan–Meier method was used to draw survival curves, and Log-rank test was used to compare the differences in progression-free survival (PFS) and overall survival (OS). Cox proportional hazards regression model was used to screen independent influencing factors for efficacy and prognosis. Results The optimal cut-off values of NLR, PLR, LMR, NRI were 2.50, 142.23, 3.92, 50.45. Efficacy analysis revealed that the ORR and DCR in the low NLR, low PLR, high LMR, and high NRI groups were significantly higher than those in the corresponding groups (all P 0.05). Survival analysis indicated that patients with high NLR and high PLR had significantly shorter median PFS and OS (all P 0.001). Multivariate Cox regression analysis confirmed that TNM stage Ⅳ (HR = 2.014) and high NLR (HR = 2.689) were independent risk factors for shortened PFS; TNM stage Ⅳ (HR = 1.923), poorly-undifferentiated tumor (HR = 1.786), and high NLR (HR = 2.519) were independent risk factors for shortened OS. Conclusion These indicators correlate with immunotherapy efficacy/prognosis. Among them, NLR≥2.50 is an independent biomarker for poor efficacy and prognosis, providing references for clinical treatment individualization.