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:Applying solid phase antibody chip against multiple factors to screen serum marker proteins of the patients with lung carcinoma who were treated by immunocytotherapy as clinical diagnosis index and to establish an evaluation model for clinical efficacy of the patients with lung carcinoma.Methods:Using Proteome ProfilerTM solid phase antibody chip against multiple factors and Image J software to analyze gray scale/optical density of the serum marker proteins from 5 patients with advanced lung carcinoma, 5 patients with improved lung carcinoma and 10 healthy individuals. The protein gray values obtained were analyzed by SPSS software, and Fisher model for evaluation of clinical efficacy of the patients with lung carcinoma was established. Results: Analyzing and comparing the common marker proteins among the patients with advanced lung carcinoma, the patients with improved lung carcinoma and the healthy individuals, 8 proteins that are dipeptidyl peptidase Ⅳ, growth hormone, IL-4, myeloperoxidase, osteopontin, receptor of advanced glycation endproducts, TNF-α, and urokinase type plasminogen activator receptor with significant difference (P<0.05) were obtained. Clustering and analyzing data for all of the patients and the healthy individuals in the experiment found that the 8 proteins can very well distinguish the patients with advanced lung carcinoma from the patients with improved lung carcinoma and the healthy individuals. The Fisher model for lung carcinoma was confirmed. Conclusion: The solid phase antibody chip against multiple factors and optimal statistical methods could screen out the serum protein biomarkers related to occurrence, development and efficacy of lung carcinoma, which could establish a certain basis of clinical trail for researches on the mechanism of lung carcinogenesis as well as clinical diagnosis and treatment of the lung carcinoma. It could play an important guiding role in the individualized treatment of the lung carcinoma.

辽宁省教育厅基金资助项目（No. 201610163L28)