A novel prognostic score based on systemic inflammatory biomarkers for patients with oral squamous cell carcinoma
Zhaocheng Zhuang, Yanfen Li, Yihong Hong, Lin Chen, Jiawen Qian, Jing Lin, Qing Chen, Yu Qiu, Lisong Lin, Bin Shi, Lizhen Pan, Xiaoyan Zheng, Jing Wang, Fengqiong Liu, Baochang He, Fa Chen
Abstract
OBJECTIVE: To explore the prognostic value of systemic inflammatory biomarkers (albumin/globulin ratio [AGR], neutrophil/lymphocyte ratio [NLR], and platelet/lymphocyte ratio [PLR]) in patients with oral squamous cell carcinoma (OSCC), and further develop a novel prognostic score (AGR-NLR). METHODS: A large-scale prospective study enrolling 792 eligible patients from December 2002 to June 2018 was carried out at the First Affiliated Hospital of Fujian Medical University. Three multivariate Cox regression models were performed to assess the association of overall survival (OS) with systemic inflammatory biomarkers, quantified by Akaike information criterion (AIC). Then, a novel AGR-NLR score was established and incorporated into a prognostic nomogram. RESULTS: In the univariate analysis, the increased AGR was associated with a reduced risk of death. Conversely, the higher NLR and PLR, the worse the OS. In the multivariate Cox regression models, AGR and NLR were stably independent prognostic indicators in all models, with Model 2 showing a lowest AIC (AGR: HR = 0.56, 95%CI: 0.41-0.78; NLR: HR = 1.80, 95%CI: 1.07-3.04). Then, a novel AGR-NLR score was established, which showed a more excellent performance than either AGR or NLR alone (area under curve [AUC]: 0.589, 0.559, and 0.556, respectively). The C-index of the nomogram based on AGR-NLR was superior to that of traditional TNM staging system (C-index: 0.658 versus. 0.596, p < .001). Similar results were also showed by decision curve analysis, indicating the nomogram had more positive net benefit compared to TNM staging system. CONCLUSION: The novel AGR-NLR score is strongly associated with outcome in patients with OSCC and could be serve as a useful tool to accurately predict the OS of OSCC patients.