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目的:应用多因子固相抗体芯片筛选免疫细胞治疗肺癌患者血清的标志性蛋白作为临床评价指标,建立肺癌疗效评价模型。方法:采用多因子Proteome Profiler~(TM)固相抗体芯片和Image J软件对5例肺癌恶化患者(恶化组)、5例肺癌治疗有效患者(有效组)及10例健康体检者(正常个体对照组)进行血清蛋白的灰度/光密度分析,将得到的蛋白灰度值采用SPSS软件进行分析,建立肺癌疗效评价的Fisher模型。结果 :对比肺癌治疗恶化组、有效组患者和正常对照组健康个体的共有标志性蛋白,筛选得到8个有显著差异(P<0.05)的蛋白(二肽基肽酶Ⅳ、生长激素、IL-4、髓过氧化物酶、骨桥蛋白、晚期糖基化终产物受体、肿瘤坏死因子-α和尿激酶纤维蛋白溶酶原激活物受体)。对所有试验者进行聚类分析,发现这8个蛋白能区分恶化组和有效组患者及正常组健康个体。肺癌的Fisher模型得到验证。结论:多因子固相抗体芯片技术和优化统计方法能够筛选出与肺癌的发生发展及疗效有关的血清生物标志物,为肺癌的发病机制研究及临床诊断和治疗奠定一定的临床试验基础,对肺癌的个体化治疗具有重要指导意义。
OBJECTIVE: To screen the immune cells for the treatment of lung cancer patients with markers of multi-factor solid-phase antibody as a clinical evaluation index to establish the evaluation model of lung cancer efficacy. Methods: Five patients with malignant lung cancer (deteriorating group), 5 patients with effective lung cancer (effective group) and 10 healthy subjects (normal control group) were treated with Proteome Profiler ~ (TM) solid phase antibody chip and Image J software. Group), the gray level / optical density of serum protein was analyzed. The gray value of protein was analyzed by SPSS software to establish the Fisher model of lung cancer efficacy evaluation. Results: Eight common proteins with significant difference (P <0.05) (dipeptidyl peptidase Ⅳ, growth hormone, IL- 4, myeloperoxidase, osteopontin, advanced glycation end-products receptor, tumor necrosis factor-alpha and urokinase plasminogen activator receptor). All subjects were clustered and found that the eight proteins were able to distinguish between the worsened and the effective group of patients and the normal group of healthy individuals. The Fisher model of lung cancer has been validated. Conclusion: Multi-factor solid-phase antibody chip technology and optimized statistical methods can screen out serum biomarkers related to the occurrence, development and therapeutic effect of lung cancer, lay a foundation for the clinical research and clinical diagnosis and treatment of lung cancer, The individualized treatment has important guiding significance.