应用表面加强激光解吸电离飞行时间质谱方法检测喉鳞状细胞癌及淋巴结转移的血清蛋白质标志物

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry profiling of serum in detection of laryngeal squamous cell carcinoma and the progression to lymph node metastasis

目的应用表面加强激光解吸电离飞行时间质谱(SELDI-TOF-MS)方法筛选喉鳞癌(LSCC)血清蛋白质标志物,建立喉癌诊断预测模型。并检测喉癌淋巴结转移相关的血清蛋白标志物。方法收集252例血清标本,其中喉癌患者血清142例,正常对照110例。其中89例喉癌血清,30例有淋巴结转移的喉癌血清和65例正常对照为试验组,53例喉癌血清及45例正常对照为盲法验证组。应用弱阳离子交换芯片(WCX2)经 SELDI-TOF-MS 测定得到蛋白质谱,通过 Biomarker Wizard和 Biomarker Pattern(BPS)软件分析喉癌和正常人血清的蛋白质谱差异,建立喉癌诊断预测模型并进行盲法验证。寻找喉癌转移相关蛋白标志物。结果在质荷比(M/Z)2000～50000范围内,弱阳离子(WCX2)芯片共检测出18个差异蛋白峰,可以鉴别喉癌和正常对照。BPS 自动选用其中一个差异蛋白质峰(4176)建立决策树模型,灵敏性为86.52%,特异性为84.62%(32/36)。盲法验证的灵敏性为84.91%,特异性为82.22%。伴或不伴淋巴结转移的喉癌患者之间检测到14个差异蛋白峰。结论应用 SELDI-TOF-MS 方法可以从血清中筛选出喉癌及淋巴结转移相关的标志蛋白。建立的喉癌诊断预测模型可能对早期发现早期诊断喉癌具有重要的临床意义。

Objective To assess the use of surface enhanced laser desorption/ionization time-of- flight mass spectrometry （SELDI-TOF MS） to identify multiple serum protein biomarkers for early detection of laryngeal squamous cell carcinoma （LSCC）, to establish predictive model, and to accurately distinguish LSCC patients with or without lymph node metastasis. Methods Serum samples were collected from 142 LSCC patients and 110 normal controls, totally 252, and randomly divided into 2 groups： model establishment group （including 89 patients with LSCC at the stages I and II , 30 of which had lymph node metastasis, and 65 normal controls） and blind test group （including 53 patients with LSCC at the stages Ill and 1V and 45 normal controls）. Serum protein profiling on weak cationic exchange （WCX2） was performed by SELDI-TOF MS and the results were analyzed by Biomarker Wizard software. The Decision Tree classification algorithm and blind validation were determined by Biomarker Pattern software （BPS）. Results A panel of 18 biomarkers ranging from 2000 to 50000 was selected based on their collective contribution to the optimal separation between the LSCC patients and healthy controls. Among them 1 candidate protein peak with the M/Z value of 4176 was selected to establish predictive model by BPS with the sensitivity of 86.52% and the specificity of 84.62%. The ability to detect LSCC patients was evaluated using blind test data in cancer patients. The sensitivity of the blind test was 84.91%, and the specificity was 82.22%. 14 potentialbiomarkers were found to differentiate the LSCC patients with or without lymph node metastasis （ P 〈 0.05 ）. Conclusion The high sensitivity and specificity achieved by the serum protein biomarkers show great potential for the early detection of LSCC. SELDI-TOF MS serum profiling is able to distinguish LSCC patients with or without lymph node metastasis.