Prognostic Values of the Systemic Immune Inflammation Index and Ferritin Level Combined with Pathological Indicators for the Patients with Colon Cancer After Radical Resection

系统性免疫炎症指数和铁蛋白水平联合病理指标对结肠癌根治术后患者预后的预测价值

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Abstract

BACKGROUND: The systemic immune-inflammation index (SII) has become a new inflammatory biomarker in recent years. However, ferritin, as a readily measurable marker, is elevated in cancer patients. Therefore, in this study, we determined the predictive value of SII combined with ferritin (FS) and the pathological features associated with the prognosis of patients with colon cancer after radical resection. METHODS: Univariate and multivariate Cox regression analyses were performed to analyze the independent factors affecting the prognosis of patients with colon cancer. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves were plotted to evaluate the predictive efficacy of the SII, F, and FS for the overall survival of patients with colon cancer after radical resection. A nomogram prediction model was constructed, and the consistency index (C-index) and calibration curve were used to evaluate the performance of the nomogram. RESULTS: The five-year overall survival rates of patients in the training set and validation set were 56.8% and 57.3%, respectively. The results of multivariate Cox regression analysis revealed that the FS grade (grade 2 vs grade 1: hazard ratio (HR) = 6.375, 95% CI = 2.725-14.911; grade 3 vs grade 1: HR = 21.623, 95% CI = 9.897-47.240); vascular cancer thrombus (HR = 1.706, 95% CI = 1.082-2.689); nerve infiltration (HR = 1.634, 95% CI = 1.070-2.495), etc. were independent influencing factors for overall survival after surgery (all P < 0.05). Kaplan-Meier survival curve analysis showed that the SII, ferritin level, and FS were independent influencing factors for overall survival after radical resection in patients with colon cancer (all P < 0.05). CONCLUSION: Preoperative FS grading is an effective and practical method to predict the overall survival of patients with colon cancer after radical resection. Moreover, its combination with pathological indicators can achieve a better prediction effect.

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