术前Th1/Th2细胞因子与宫颈癌预后的预测价值

The Predictive Value of Preoperative Th1/Th2 Cytokines and the Prognosis of Cervical Cancer

摘要: 目的：探讨术前Th1/Th2细胞因子与宫颈癌预后的预测价值。方法：回顾性收集2015年1月至2019年10月临沂市肿瘤医院收入手术治疗的FIGO2009 IB1~IIA2期宫颈癌患者265例临床病理资料。收集术前Th1/Th2细胞因子[γ干扰素(IFN-γ)、白介素-4 (IL-4)]。根据纳入患者随访期间生存、死亡情况，将患者分为生存组(223例)、死亡组(42例)。建立受试者工作特征(ROC)曲线得到上述指标最佳cut-off值，并分为高、低水平组。比较两组间临床病理资料。采用Kaplan-Meier法进行生存分析，采用COX回归分析可能影响患者术后5年生存的因素。结果：在入组的265例宫颈癌患者中，死亡患者共42例，死亡率为15.85%。ROC曲线分析显示，IL-4、IFN-γ的最佳cut-off值分别为3.04、3.83。COX回归分析显示，IL-4、IFN-γ的水平状态与生存率显著相关(P < 0.05)。结论：对于宫颈癌患者而言，Th1/Th2细胞因子水平有一定的生存率预测价值。

Abstract: Objective: To investigate the predictive value of preoperative Th1/Th2 cytokines and the prognosis of cervical cancer. Methods: The clinicopathological data of 265 patients with stage FIGO2009 IB1~IIA2 cervical cancer who were admitted for surgery in Linyi Cancer Hospital from January 2015 to October 2019 were retrospectively collected. Preoperative Th1/Th2 cytokines [γ interferon (IFN-γ), interleukin-4 (IL-4)] were collected. According to the survival and death of the included patients during the follow-up period, the patients were divided into survival group (223 cases) and death group (42 cases). The receiver operating characteristic (ROC) curve was established to obtain the optimal cut-off value of the above indexes, and they were divided into high and low level groups. The clinicopathological data between the two groups were compared. The Kaplan-Meier method was used for survival analysis, and COX regression was used to analyze the factors that may affect the survival of patients at 5 years after surgery. Results: Among the 265 cervical cancer patients enrolled, 42 patients died, with a mortality rate of 15.85%. ROC curve analysis showed that the optimal cut-off values of IL-4 and IFN-γ were 3.04 and 3.83, respectively. COX regression analysis showed that the level status of IL-4 and IFN-γ was significantly correlated with survival rate (P < 0.05). Conclusion: For cervical cancer patients, Th1/Th2 cytokine levels have certain survival prediction value.