Early Detection of Serum Protein Biomarker of Lung Cancer by Surface-enhanced Laser Desorption/ionization Time-of-flight Mass Spectrometry Combining with Magnetic Bead
-
摘要:
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。 -
关键词:
- 肺癌 /
- 肿瘤标志物 /
- 诊断 /
- 蛋白质组学 /
- 表面增强激光解吸电离飞行时间质谱
Abstract: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. -
表 1 肺癌组与肺部良性肿瘤及正常对照组的蛋白质峰表达差异(x±s)
质荷比 肺癌组 肺部良性肿瘤
及正常对照组t值 P值 1115.37 0.36±0.51 0.07±0.63 2.248 0.027 1929.70 0.63±1.58 0.02±1.74 2.462 0.016 3217.57 1.46±1.72 2.81±2.28 -2.897 0.004 3246.34 1.79±1.93 0.97±1.74 2.022 0.047 3318.57 6.90±5.44 9.78±5.47 -2.374 0.002 11 508.90 0.91±1.64 0.32±0.96 -2.032 0.045 表 2 肺癌组和肺部良性肿瘤及正常对照组蛋白质谱差异模型敏感性及特异性[例(%)]
类别 组别 对照组 肺癌组 建模 对照组(n=44) 40(90.91) 4(9.09) 肺癌组(n=38) 3(7.90) 35 (92.11) 验证 对照组(n=15) 13(86.67) 2(13.33) 肺癌组(n=15) 2(13.33) 13(86.67) 表 3 肺癌患者手术前后蛋白质谱表达的差异(n=38,x±s)
质荷比 治疗前 治疗后 t值 P值 1115.37 0.36±0.51 0.23±0.61 -3.195 0.003 1929.70 0.63±1.58 0.36±1.29 -2.861 0.006 3217.57 1.46±1.72 0.97±1.53 -3.396 0.001 3246.34 1.79±1.93 1.63±2.30 -2.777 0.008 3318.57 6.90±5.44 3.78±3.20 -3.466 0.001 11 508.90 0.91±1.64 0.74±1.74 -4.195 0.000 -
[1] Radziszewska A, Karczmarek-Borowska B, Gradalska-Lampart M, et al. Epidemiology, prevention and risk morbidity factors for lung cancer[J]. Pol Merkur Lekarski, 2015, 38:113-118. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/14034948000280040401 [2] Carlile PV. Lung cancer screening:where have we been? Where are we going?[J]. J Okla State Med Assoc, 2015, 108:14-18. [3] 张国强, 杜杰, 庞达.表面增强激光解吸电离飞行时间质谱技术筛查乳腺癌血清特异性蛋白质及其临床意义[J].中华肿瘤杂志, 2006, 28:204-207. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zhzl200603011 [4] Adam BL, Qu Y, Davis JW, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men[J]. Cancer Res, 2002, 62:3609-3614. http://biomet.oxfordjournals.org/cgi/ijlink?linkType=ABST&journalCode=canres&resid=62/13/3609 [5] 张锐强, 谢静, 刘振元, 等.血清蛋白质指纹图谱模型在肾癌诊断中的应用[J].中华泌尿外科杂志, 2006, 27:527-529. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zhmnwk200608006 [6] Simsek C, Sonmez O, Keyf AI, et al. Importance of serum SELDI-TOF-MS analysis in the diagnosis of early lung cancer[J]. Asian Pac J Cancer Prev, 2013, 14:2037-2042. doi: 10.7314/APJCP.2013.14.3.2037 [7] 金川, 张永晖, 胡晓晔, 等.应用SELDI-TOF-MS技术预测非小细胞肺癌患者对TC方案化疗的敏感性[J].当代医学, 2011, 17:58-59. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ddyx201133038 [8] Jiang M, Gu G, Ni B, et al. Detection of serum protein biomarkers by surface enhanced laser desorption/ionization in patients with adenocarcinoma of the lung[J]. Asia Pac J Clin Oncol, 2014, 10:e7-e12. doi: 10.1111/ajco.12057