非小细胞肺癌患者奥西替尼治疗前后肿瘤免疫微环境变化及与预后的关系

Changes of tumor immune microenvironment and its relationship with prognosis in patients with non-small cell lung cancer before and after ositinib treatment

目的 探讨非小细胞肺癌(NSCLC)患者奥西替尼治疗前后肿瘤免疫微环境变化及与预后的关系。方法 选取2017年6月至2022年12月于本院收治的125例NSCLC患者为研究对象,纳入训练集。按照相同标准另选取100例NSCLC患者纳入验证集。免疫组化法分析CD3^(+)、CD4^(+)、CD8^(+)、CD10^(+)、CD56^(+)表达情况。根据患者预后情况分为预后良好组(n=65)和预后不良组(n=60),比较两组患者的一般资料及奥西替尼治疗前后肿瘤免疫微环境变化。采用多因素Cox风险比例模型分析临床因素在训练集和验证集上的一致性指数(C指数)。多因素Logistic回归分析影响预后不良发生的危险因素。并建立列线图模型,利用Logistic回归和临床决策曲线(DCA)曲线分析预后影响因素的临床预测价值。并通过X-tile软件进行危险分层。结果 奥西替尼治疗6个月后,CD3^(+)、CD4^(+)、CD8^(+)以及CD56^(+)的表达水平较治疗前明显增加,CD10^(+)表达水平较治疗前明显降低(P<0.05)。重复测量方差分析显示,不同组别和治疗时间对CD3^(+)、CD4^(+)、CD8^(+)、CD10^(+)以及CD56^(+)的交互效应具有统计学意义(均P<0.05)。多因素Cox风险比例模型分析结果显示,吸烟史、鳞癌、淋巴结转移、CD3^(+)、CD4^(+)、CD8^(+)、CD10^(+)及CD56^(+)均具有最大的C指数,组成的多因素模型对评估NSCLC患者预后不良的风险具有较准确的表现。有吸烟史、组织学类型为鳞癌、淋巴结转移、奥西替尼治疗后6个月CD3^(+)、CD4^(+)、CD8^(+)以及CD56^(+)降低,治疗后6个月CD10^(+)水平升高均为NSCLC患者奥西替尼治疗后预后不良的危险因素(P<0.05)。依据独立影响因素构建的列线图预测模型具有较高的区分度、准确性和临床适用性。使用X-tile软件确定最佳临界值(280.4分和345.1分),可将患者分为三个组,依次为<280分(低危),≥280分且小于345分(中危),≥345分(高危)。结论 奥西替尼治疗后,CD3^(+)、CD4^(+)、CD8^(+)、CD56^(+)的平均阳性率较治疗前明显增加,CD10^(+)的平均阳性率较治疗前明显降低,结合患者临床特征构建的预测模型可有效预测NSCLC患者的预后情况。

Objective To investigate the relationship between tumor immune microenvironment and prognosis in patients with non-small cell lung cancer(NSCLC)treated with ositinib.Methods A total of 125 patients with NSCLC admitted to our hospital from June 2017 to December 2022 were selected as study objects and included in the training set.Another 100 patients with NSCLC were included in the validation set according to the same criteria.The expressions of CD3^(+),CD4^(+),CD8^(+),CD10^(+)and CD56^(+)were analyzed by immunohistochemistry.The patients were divided into the good prognosis group(n=65)and the poor prognosis group(n=60)according to the prognosis.The general data and the changes of tumor immune microenvironment before and after ositinib treatment were compared between the two groups.Multivariate Cox risk scale model was used to analyze the consistency index(C index)of clinical factors in the training set and the validation set.Multivariate logistic regression was used to analyze the risk factors affecting the occurrence of poor prognosis.Logistic regression and clinical decision curve(DCA)were used to analyze the clinical predictive value of prognostic factors,and risk stratification through X-tile software.Results After 6 months of ositinib treatment,the expression levels of CD3^(+),CD4^(+),CD8^(+)and CD56^(+)were significantly increased,while the expression level of CD10^(+)was significantly decreased(P<0.05).Repeated measurement ANOVA showed that different groups and treatment time had statistically significant interaction effect on CD3^(+),CD4^(+),CD8^(+)and CD56^(+)(all P<0.05).Multivariate Cox risk proportional model analysis showed that smoking history,squamous cell carcinoma,lymph node metastasis,CD3^(+),CD4^(+),CD8^(+)and CD56^(+)all had the largest C index,and the multi-factor model was more accurate in evaluating the risk of poor prognosis in NSCLC patients.History of smoking,histological type of squamous cell carcinoma,lymph node metastasis,decreased CD3^(+),CD4^(+),CD8^(+)and CD56^(+)6 months after ositinib treatment,and increased CD10^(+)level 6 months after ositinib treatment were all risk factors for poor prognosis in NSCLC patients(P<0.05).The nomogram prediction model based on independent influencing factors had high differentiation,accuracy and clinical applicability.Using the X-tile software to determine the optimal cutoff values(280.4 and 345.1 points),the patients were divided into three groups,the low risk group with scores<280,the medium group with scores≥280 and less than 345,and the high risk group with scores≥345.Conclusion The average positive rates of CD3^(+),CD4^(+),CD8^(+)and CD56^(+)are significantly higher than those before treatment,and the average positive rate of CD10^(+)is significantly lower than that before treatment.The prediction model constructed in combination with the clinical characteristics of patients can effectively predict the prognosis of NSCLC patients.