应用表面增强激光解吸电离飞行时间质谱联合磁珠技术寻找肺癌血清蛋白标志物

  目的  建立肺癌蛋白质指纹图谱诊断模型, 探讨用于肺癌早期诊断及手术疗效评估的血清蛋白标志物。  方法  收集38例肺癌患者、12例肺部良性肿瘤患者及32名正常对照者的血清标本, 应用表面增强激光解吸电离飞行时间质谱(surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, SELDI-TOF-MS)联合磁珠技术, 获得蛋白质指纹图谱, 采用BPS分析软件对数据分组及相关性进行分析, 初步建立肺癌的血清蛋白质指纹图谱诊断模型, 并验证其诊断效率; 同时对比肺癌患者手术前后的差异蛋白质谱, 结合肺癌的诊断模型, 选取合适的蛋白作为肺癌手术疗效的观察指标。  结果  在质荷比为1000~50 000范围内, 肺癌患者、肺部良性肿瘤患者和正常对照者之间共检测到215个蛋白质峰。其中, 质荷比为1115.37、1929.70、3217.57、3246.34、3318.57、11 508.90的6个蛋白质峰表达差异具有统计学意义(P < 0.05)。决策树模型对肺癌的原始判别敏感性为92.11%(35/38), 特异性为90.91%(40/44);交叉验证敏感性为86.67%(13/15), 特异性为86.67%(13/15)。其中质荷比为1115.37、1929.70、3246.34和11 508.90的蛋白质峰在肺癌患者中明显升高(P < 0.05), 当肺癌患者手术治疗后表达量较术前明显降低(P < 0.01), 表明这4个蛋白质峰对肺癌的诊断及疗效判定具有潜在应用价值。  结论  应用SELDI-TOF-MS技术建立的肺癌血清蛋白质指纹图谱诊断模型具有较高的敏感性和特异性, 为发现肺癌早期生物标志物并判断疗效奠定基础。

Early Detection of Serum Protein Biomarker of Lung Cancer by Surface-enhanced Laser Desorption/ionization Time-of-flight Mass Spectrometry Combining with Magnetic Bead

  Objective  To establish the diagnostic model for lung cancer by protein fingerprint techniques and to further explore the serum protein biomarker for early diagnosis and surgical effect assessment of lung cancer.  Methods  Serum samples from 32 healthy controls, 38 lung cancer patients, and 12 lung benign tumor patients were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combining with magnetic bead technique to establish proteomic spectra. The data were categorized and analyzed with Biomarker Patterns Software (BPS) to develop a preliminary diagnostic model of serum protein fingerprint of lung cancer. Efficiency of this diagnostic model was tested. Combining with the diagnostic model, the proteomic spectra before and after surgery were compared to identify the proteins suitable as indicators of surgical effect.  Results  Within the mass-to-charge ratio (m/z) range of 1000-50 000, 215 protein peaks were detected and marked in the enrolled patients and healthy controls. Of these protein peaks, 6 peaks were identified as showing statistically significant difference in expression (m/z 1115.37, 1929.70, 3217.57, 3246.34, 3318.57, 11 508.90, P < 0.05). The primary sensitivity for diagnosing lung cancer was 92.11% (35/38) and its corresponding specificity was 90.91% (40/44). The cross validation suggested that the sensitivity and specificity were both 86.67% (13/15). The protein peaks with m/z being 1115.37, 1929.70, 3246.34, and 11508.90 were significantly increased in lung cancer patients(P < 0.05), and significantly reduced after surgery compared with before surgery in these patients (P < 0.01), suggesting potential value of these 4 protein peaks in diagnosis and treatment effect assessment of lung cancer.  Conclusion  The serum protein fingerprint diagnostic model for lung cancer based on SELDI-TOF-MS technique yields fairly high sensitivity and specificity, which may provide innovative thoughts for identification of biomarker for early diagnosis and treatment effect assessment of lung cancer.