Cheng Lei, Zhou Liang, Tao Lei, Xiang Cui-qin, Jia Xiao-dong, Lu Ye, Liao Ping
Department of ENT, Fudan Uiversity affliated Eye, Ear, Nose and Throat Hospital, Shanghai 200031, China.
Zhonghua Yi Xue Za Zhi. 2006 Jun 6;86(21):1484-8.
To screen the serum proteome biomarkers of hypopharyngeal squamous cell carcinoma (HSCC) and to establish a predictive model for early detection of HSCC.
Serum samples were collected from 48 HSCC patients before surgery and 52 age and sex-matched individuals without cancer used as controls. The samples were divided into 2 sets: training set (including 36 HSCC patients and 36 controls) and blind testing set (including 12 HSCC patients and 16 controls). With WCX2 and IMAC3 protein chips, surface-enhanced laser desorption/ionization (SELDI) was used to analyze the serum protein profiling. 72 samples of the training set were analyzed by a decision tree algorithm to be able to differentiate HSCC patients from controls. Double-blind test was used to determine the sensitivity and specificity of the classification model.
Ranging from 2000 - 50000 (M/Z), 11 potential biomarkers on WCX2 and 19 biomarkers on IMAC3 protein chip could differentiate HSCC patients from the control set (P < 10(-5)). Among them 4 candidate protein peaks with the m/z values of 7796, 4216, 5927, and 5361 were selected to be used to establish a predictive model by Biomarker Pattern Software. The model separated effectively the HSCC samples from the control samples, achieving a sensitivity of 94.44%, and a specificity of 88.89%. An accuracy of 85.71% (24/28), sensitivity of 91.67% (11/12), specificity of 81.25% (13/16), positive predictive value of 78.57%% (11/14), and negative predictive value of 92.85% (13/14) were validated in the double-blind testing set.
The SELDI-TOF-MS Protein Chip combined with artificial intelligence classification algorithm helps find serum proteome biomarkers and establish predictive model for early diagnosis of HSCC. This technique has potential for the development of a screening test for the detection of HSCC.
筛选下咽鳞状细胞癌（HSCC）的血清蛋白质组生物标志物，并建立用于HSCC早期检测的预测模型。
收集48例HSCC患者术前血清样本以及52例年龄和性别匹配的无癌个体作为对照。样本分为2组：训练集（包括36例HSCC患者和36例对照）和盲测集（包括12例HSCC患者和16例对照）。使用WCX2和IMAC3蛋白质芯片，采用表面增强激光解吸/电离（SELDI）技术分析血清蛋白质谱。通过决策树算法分析训练集的72个样本，以区分HSCC患者和对照。采用双盲试验确定分类模型的敏感性和特异性。
在2000 - 50000（M/Z）范围内，WCX2上的11个潜在生物标志物和IMAC3蛋白质芯片上的19个生物标志物可区分HSCC患者和对照组（P < 10(-5)）。其中选择4个质荷比分别为7796、4216、5927和5361的候选蛋白峰，通过生物标志物模式软件建立预测模型。该模型有效地将HSCC样本与对照样本区分开，敏感性为94.44%，特异性为88.89%。在双盲测试集中验证的准确率为85.71%（24/28），敏感性为91.67%（11/12），特异性为81.25%（13/16），阳性预测值为78.57%（11/14），阴性预测值为92.85%（13/14）。
SELDI-TOF-MS蛋白质芯片结合人工智能分类算法有助于发现血清蛋白质组生物标志物，并建立用于HSCC早期诊断的预测模型。该技术具有开发HSCC检测筛查试验的潜力。
