应用液体芯片-飞行时间质谱技术检测了79例早期矽肺组和25例非暴露正常对照组的血清蛋白质.以贝叶斯判别法的最小错误率为目标函数,借助遗传算法全局优化搜索能力,筛选出能代表早期矽肺病人分类特征的最小最优差异蛋白质谱峰子集.用选定的差异蛋白质谱峰子集建立早期矽肺的神经网络诊断模型,该模型的特异性为96%,敏感性为96.25%,准确率为96.15%.其中,1 777 u蛋白质谱峰经过二级质谱鉴定其氨基酸序列为补体C3的1个片段C3f(complement C3f),该片段在矽肺暴露人群中异常低,具有潜在的诊断意义.%Sera of 79 workers exposed to silica and 25 healthy controls were determined by matrix-assisted laser desorption ionization mass spectrometry (MALDI-TOF MS). Based on the minimum error Bayes decision theory, serum biomarkers of early silicosis patients were selected by making use of the global optimal ability of the genetic algorithm. Mass spectrometric peaks of 22 proteins were selected and used by artificial neural network (ANN) to establish a diagnostic model. A blinded test shows the ratios of correctness, sensitivity and specificity are 96.15％, 96.25％ and 96％, respectively. Search results of tandem mass spectra against a protein database show that the 1 777 u mass spectrometric peak is identified as C3f, which is a fragment of complement C3. The 1 777 u mass spectrometric peak is significantly decreased in silicosis patients. The results indicate that C3f may be the potential biomarkers for the diagnosis of early stage of silicosis.
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