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目的寻找可能与食管癌患者食管癌家族史相关的血清标志物。方法收集林州及其周边食管癌高发区食管癌家族史阳性食管癌患者28例和食管癌家族史阴性食管癌患者38例血清样本,经Clinprot磁珠纯化、基质辅助激光解析电离飞行时间质谱(MALDI-TOF-MS)分析建立相应的蛋白质指纹图谱,应用ClinProTools生物信息学方法研究其血清蛋白表达谱。比较食管癌家族史阳性和阴性的食管癌患者血清蛋白表达谱,筛选出可能与食管癌家族史相关的差异蛋白。每组样品采用sigma血清进行批间重复性检测。结果平均CV值为0.232;在相对分子质量800～10 000范围内,共检测到家族史阳性与阴性食管癌患者血清差异蛋白101个。选取组合分组能力最好的5个质荷比(M/Z)分别为5 248.24、5 905.05、2 023.12、2 953.07、4 467.16的蛋白峰建立模型并验证,显示食管癌家族史阳性组和阴性组的血清差异蛋白表达无显著性差异(P>0.05)。对食管癌家族史阳性组与阴性组进行分析,采用GA(遗传算法)模型算法选取差异蛋白,识别率为82.62%;准确性为家族史阳性组69.23%,家族史阴性组96%;预测能力为61.89%;采用SNN(遗传算法)选取差异蛋白,食管癌家族史阳性组及阴性组识别率和准确性均为100%,预测能力为53.25%。结论应用ClinProt系统未检测到与食管癌家族史相关的特异性蛋白质,推测食管发生癌变后所产生的反应蛋白可能与食管癌家族史无明显相关性。要寻找食管癌发生及家族聚集的原因,还需要对更多因素进行进一步分析、检测以及大样本的分子流行病学研究。
Objective To search for serum markers that may be associated with family history of esophageal cancer in esophageal cancer patients. Methods Serum samples from 28 esophageal cancer patients with positive family history of esophageal cancer and 38 esophageal cancer patients with negative family history of esophageal cancer were collected from Linzhou and its adjacent areas. Clinprot magnetic beads purification, matrix-assisted laser desorption ionization time of flight mass spectrometry MALDI-TOF-MS) analysis of the corresponding protein fingerprinting, ClinProTools bioinformatics method to study the serum protein profile. The serum protein profiles of esophageal cancer patients with positive and negative family history of esophageal cancer were compared to screen out the differential proteins that may be related to the family history of esophageal cancer. Each group of samples using sigma serum for inter-assay repeatability. Results The average CV value was 0.232. There were 101 serum differential proteins between family history positive and negative esophageal cancer patients in the range of 800 ~ 10 000. The five mass-to-charge ratios (M / Z) with the best combinatorial grouping ability were respectively selected as 5 248.24, 5 905.05, 2 023.12, 2 953.07 and 4 467.16 to establish the model and verify that the positive group and negative of esophageal cancer family history There was no significant difference in serum differential protein expression (P> 0.05). The positive and negative family members of esophageal cancer were analyzed. GA (genetic algorithm) model algorithm was used to select the differential proteins, the recognition rate was 82.62%; the accuracy was 69.23% in family history and 96% in family history negative; (61.89%). Using SNN (Genetic Algorithm) to select differential proteins, the positive rate and accuracy of esophageal cancer family history positive group and negative group were 100% and the predictive ability was 53.25%. Conclusion ClinProt system can not detect the specific proteins associated with family history of esophageal cancer. It is speculated that the esophageal cancers may have no correlation with the family history of esophageal cancer. Looking for the cause of esophageal cancer and familial aggregation, more factors need further analysis, testing and large sample of molecular epidemiological studies.