The Prognostic Value of TNI (Tumor-Nutrition-Inflammation Index) in Patients with Advanced Lung Cancer

Abstract Purpose: As the third most common cancer type behind breast cancer and prostate cancer, lung cancer has the highest mortality rate all over the world. Identification of easily accessible and inexpensive potential biomarkers for advanced lung cancer is necessary. Patients and Methods: A total of 195 patients with advanced lung cancer including non-small lung cancer and small cell lung cancer, who received first-line chemotherapy, were included in this study. Then they were randomized as training set and validation set, and the total population were classified as testing set. The optimized cut-off values of AGR and SIRI (AGR=albumin/globulin; SIRI=neutrophil*monocyte/lymphocyte) were determined by survival function analysis based on R software. COX regression analysis was performed to acquire the independent factors for establishing the nomogram model. The predictive accuracy was verified through ROC curve and calibration curves after index concordance.Results: The optimized cut-off values of AGR and SIRI (AGR=albumin/globulin; SIRI=neutrophil*monocyte/lymphocyte) were 1.22 and 1.60, respectively. Cox analysis revealed that liver metastasis, SCC (squamous cell carcinoma antigen), AGR, and SIRI were independent prognostic factors for advanced lung cancer. A nomogram model comprising these independent prognostic parameters was built for the TNI (tumor-nutrition-inflammation index) score calculation. Patients were divided into four groups based on TNI quartile values. Kaplan-Meier analysis and log-rank test indicated that a higher TNI had a worse OS (P<0.05). The C-index and 1-year AUC areas were 0.756 (0.723-0.788) and 75.62, respectively. High consistency between the predicted and actual survival proportions in the TNI model is shown in the calibration curves. A nomogram prognostic model for predicting survival time rates based on TNI in the total population was established to directly observe the survival rate of patients with advanced lung cancer.Conclusion: TNI may be used as a practical and precise analytical tool for survival prediction in patients with advanced lung cancer.