微小核糖核酸-125b、微小核糖核酸-142-5p和微小核糖核酸-140-3p与非小细胞肺癌患者程序性死亡受体1抗体治疗敏感性的关系

Relationships of microRNA-125b, microRNA-142-5pand microRNA-140-3p with sensitivity to programmed death-1 antibody therapy in patients with non-small cell lung cancer

  • 摘要:
    目的 探讨微小核糖核酸-125b (miR-125b)、微小核糖核酸-142-5p (miR-142-5p)和微小核糖核酸-140-3p (miR-140-3p)与非小细胞肺癌(NSCLC)患者程序性死亡受体1(PD-1)抗体治疗敏感性的关系及临床意义。
    方法 选取219例NSCLC患者作为研究对象, 根据PD-1抗体治疗敏感性分为敏感组92例和非敏感组127例。比较2组患者血清miR-125b、miR-142-5p、miR-140-3p水平,通过Logistic回归模型拟合miR-125b、miR-142-5p、miR-140-3p构建新的联合预测因子,采用受试者工作特征(ROC)曲线评估预测效能,最后将数据代入方程中进行预测验证。
    结果 非敏感组血清miR-125b水平高于敏感组,血清miR-142-5p、miR-140-3p水平低于敏感组,差异有统计学意义(P < 0.05)。Logistic回归模型分析结果显示, miR-125b升高是NSCLC患者PD-1抗体治疗敏感性的独立危险因素(P < 0.05), 而miR-142-5p升高、miR-140-3p升高则是独立保护因素(P < 0.05); 联合预测因子的最佳临界值为0.117, 敏感度为90.22%, 特异度为85.04%, 准确度为87.21%。ROC曲线分析结果显示,联合预测因子预测PD-1抗体治疗敏感性的曲线下面积为0.928, 显著大于miR-125b、miR-142-5p、miR-140-3p的曲线下面积0.825、0.817、0.772(P < 0.05)。将原始Logistic回归方程变形后得到新方程,随机抽取3例患者数据代入方程进行计算,预测结果均与临床实际情况相符。
    结论 miR-125b、miR-142-5p和miR-140-3p均与NSCLC患者PD-1抗体治疗敏感性相关,可作为预测PD-1抗体治疗敏感性的标志物。基于三者生成的联合预测因子能够进一步提高预测价值,为临床治疗决策提供更可靠的参考信息。

     

    Abstract:
    Objective To explore the relationships of microRNA-125b (miR-125b), microRNA-142-5p (miR-142-5p) and microRNA-140-3p (miR-140-3p) with sensitivity to programmed death receptor-1 (PD-1) antibody therapy in patients with non-small cell lung cancer (NSCLC) and their clinical significance.
    Methods A total of 219 NSCLC patients were selected and divided into sensitive group (n=92) and non-sensitive group (n=127) based on their sensitivity to PD-1 antibody therapy. Serum levels of miR-125b, miR-142-5p and miR-140-3p were compared between the two groups. A new combined predictor was constructed using miR-125b, miR-142-5p, and miR-140-3p through a Logistic regression model. The predictive performance was evaluated using the receiver operating characteristic (ROC) curve, and data were substituted into the equation forpredictive validation.
    Results The serum level of miR-125b was higher in the non-sensitive group than that in the sensitive group, while the serum levels of miR-142-5pand miR-140-3p were lower in the non-sensitive group (P < 0.05). Logistic regression analysis showed that an increased level of miR-125b was an independent risk factor for sensitivity to PD-1 antibody therapy in NSCLC patients (P < 0.05), while increased levels of miR-142-5p and miR-140-3p were independent protective factors (P < 0.05). The optimal cut-off value for the combined predictor was 0.117, with a sensitivity of 90.22%, a specificity of 85.04%, and an accuracy of 87.21%. ROC curve analysis revealed that the area under the curve (AUC) for the combined predictor in predicting sensitivity to PD-1 antibody therapy was 0.928, which was significantly larger than the AUCs of 0.825, 0.817 and 0.772 for miR-125b, miR-142-5p and miR-140-3p, respectively (P < 0.05). A new equation was obtained by transforming the original Logistic regression equation, and data from three randomly selected patients were substituted into the equation for calculation, with predictive results being consistent with clinical reality.
    Conclusion The miR-125b, miR-142-5p and miR-140-3p are all associated with sensitivity to PD-1 antibody therapy in NSCLC patients and can serve as biomarkers for predicting sensitivity to PD-1 antibody therapy. The combined predictor based on these three microRNAs can further enhance predictive value and provide more reliable reference information for clinical treatment decisions.

     

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