原发性肝癌血清蛋白质谱图人工神经网络诊断模型研究
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摘要
目的：原发性肝癌是由肝细胞或肝内胆管细胞发生的癌肿,是我国常见的恶性肿瘤之一,其死亡率在消化系统肿瘤中列第三位,仅次于胃癌和食道癌,临床上以肝细胞癌(Hepatocellular carcinoma,HCC)最为多见。肝细胞癌是世界上最常见恶性肿瘤之一,总的5年生存率低于5%。目前HCC发病率在我国呈日渐上升趋势,造成其高病死率的主要原因是目前尚缺乏特异性高的早期诊断方法,由于HCC病人早期常无症状,发现时已属中、晚期,丧失了治疗的最佳时机。因此,迫切需要探索一种快速、简单、灵敏度高、特异性好的早期诊断方法。在众多的检测手段中,表面增强激光解析电离飞行时间质谱技术(Surface enhanced laser desorption ionization time of flight mass spectrometry, SELDI-TOF-MS)无疑是当今报道最前沿、最热门、检测敏感度和特异度最高的技术。任何疾病在出现病理变化之前,细胞内的蛋白质在成分和数量上都会有相应改变,并通过血清中的蛋白质模式反映出来。因此,通过比较不同疾病人群血清中差异蛋白质的表达,可能筛选出肿瘤相关的标志分子。本研究采用差异蛋白组学的方法,利用SELDI-TOF-MS技术检测乙肝、肝硬化、原发性肝癌患者、其他消化系统肿瘤与健康人血清蛋白指纹图谱,并筛选原发性肝癌患者特异表达的血清蛋白标志物,结合人工神经网络技术(artificial neural network,ANN)建立预测模型,探索其用于原发性肝癌实验诊断的临床价值,以建立早期有效检测原发性肝癌的客观实验指标。方法：利用SELDI-TOF-MS技术及其配套的金芯片(Gold Chip)检测435份血清标本得到相应的蛋白指纹图谱,经Biomarker Wizard分析软件找出差异蛋白,采用人工神经网络,建立诊断原发性肝癌实验诊断模型。将其中75例乙肝和68例肝硬化、100例原发性肝癌、91例其他消化系统肿瘤和101例健康人血清随机分为训练集(乙肝35例,肝硬化33例,原发性肝癌50例,其他消化系统肿瘤41例,健康人51例)结合到金芯片上,检测血清蛋白质谱数据,将获得的蛋白质谱图用Ciphergen ProteinChip 3.0软件进行数据的校正和分析,采用Ciphergen Biomaker Wizard 3.1软件筛选肝炎、肝硬化、原发性肝癌和其他消化系统肿瘤的差异蛋白。此数据将用于筛选肝癌的差异蛋白标志物并建立人工神经网络诊断模型。验证集(乙肝40例,肝硬化35例,原发性肝癌50例,其他消化系统肿瘤50例,健康人50例)进行相同的处理后数据用于模型诊断效度的盲法验证。结果：原发性肝癌患者与对照组血清蛋白指纹图谱筛选出75个差异表达的蛋白质荷比峰(P<0.05),利用其中7个有明显表达差异的标志蛋白(P<0.01)建立人工神经网络诊断模型,.其质荷比(m／z)分别为4207、6604、7734、8106、8545、8599、8894。经Swiss-Prot蛋白数据库检索,初步鉴定为Peptide YY-like,50S ribosomal protein L30,50S ribosomal protein L35, Neutrophil-activating peptide 2 (74), Acyl carrier protein,30S ribosomal protein S21, UPF0330 protein TK1752。利用该模型对原发性肝癌进行盲法预测,对原发性肝癌的诊断灵敏度和特异度分别为84.00%和81.25%,受试者工作特征曲线(ROC曲线)下面积(AUC)为0.847,阴性预测值94.20%,阳性预测值58.33%,准确度为81.90%。结论：原发性肝癌患者血清具有明显表达差异的特征蛋白,建立的人工神经网络模型为原发性肝癌的诊断提供了的新方法,,对原发性肝癌的鉴别诊断、治疗和基因研究具有重要意义。
Objective:primary hepatic carcinoma (PHC) is the cancer of hepatocyte or Intrahepatic bile duct, which is frequent malignant tumor and has the third death rate in alimentary system tumor, and second only to gastric cancer and esophageal cancer. Hepatocellular carcinoma (HCC) is the most frequent occurrence in PHC. And total five survival rate is lower than 5% in the world wide. Because of lacking of early diagnosis methods, the case fatality rate becomes more and more highly in China. Many patients loss their best opportunity to cure. We need exploring a new quick, simple, high sensitivity, good specificity early diagnosis method. Surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) is the most popular technology with high sensitivity and specificity in those test facilities. When any disease showes the pathological change, the composition and quantity of proteins will change in cells, and pattern of proteins can reflect the changing. So we could screen these relative proteins of tumor with comparing on different patients protein in serum. In this study, using SELDI-TOF-MS to detect hepatitis B, liver cirrhosis, primary liver cancer, other gastrointestinal tumors and health human serum protein profiling with proteomics approach, and screening in patients with primary liver cancer-specific expression of serum protein markers, combined with artificial neural network (ANN) to establish prediction model for primary liver cancer and explored the clinical value of laboratory diagnosis in order to establish an effective early detection of hepatocellular carcinoma objective experimental index. Methods:The 435 serum samples were tested by SELDI-TOF-MS matched with Gold Chip and finded different proteins through Biomarker Wizard software. Using ANN to develop a model of diagnosis of PHC.75 patients with type B hepatitis,68 patients with hepatic cirrhosis,100 patients with PHC,91 patients with other alimentary system tumor and 101 health controls were included. Samples were randomly assigned into two subsets, the training set (35 patients with type B hepatitis,33 patients with hepatic cirrhosis,50 patients with PHC,41 patients with alimentary system and 51 health controls), and the testing set (40 patients with type B hepatitis,35 patients with hepatic cirrhosis,50 patients with PHC, 50 patients with alimentary system and 50 health controls). The serum samples were binded to the gold chip and detected serum protein profiling data, and corrected and analysed protein profiling data by Ciphergen ProteinChip 3.0 software. The training set was used for identifying the statistically significant peaks as well as for developing ANN model. And the testing set was used for blind test to validate the diagnostic efficiency of ANN model. Results:Serum protein fingerprint selected 75 differentially expressed proteins than the peak load (P<0.05) in PHC and control groups. The use of which 7 are significant differences in the expression of marker proteins (P<0.01) to establish artificial neural networks diagnosis model, the mass charge ratio (m/z) were 4207,6604,7734,8106,8545,8599,8894. And they correspond to Peptide YY-like, 50S ribosomal protein L30,50S ribosomal protein L35, Neutrophil-activating peptide 2(74), Acyl carrier protein,30S ribosomal protein S21, UPF0330 protein TK1752 respectively by Swiss-Prot roughly. Using blind method to predict the model of PHC, we could get the sensitivity and specificity were 84.00% and 81.25% respectively, and the area under (AUC) of receiver operating characteristic curve (ROC curve) was 0.847, negative predictive value(NPV) was 94.20%, positive predictive value (PPV) was 58.33%, accuracy(ACC) was 81.90%. Conclusions:There were significantly different proteins in PHC, and the ANN model provides a new method of differential diagnosis of PHC. The method is important for differential diagnosis and treating PHC.
Objective:primary hepatic carcinoma (PHC) is the cancer of hepatocyte or Intrahepatic bile duct, which is frequent malignant tumor and has the third death rate in alimentary system tumor, and second only to gastric cancer and esophageal cancer. Hepatocellular carcinoma (HCC) is the most frequent occurrence in PHC. And total five survival rate is lower than 5% in the world wide. Because of lacking of early diagnosis methods, the case fatality rate becomes more and more highly in China. Many patients loss their best opportunity to cure. We need exploring a new quick, simple, high sensitivity, good specificity early diagnosis method. Surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) is the most popular technology with high sensitivity and specificity in those test facilities. When any disease showes the pathological change, the composition and quantity of proteins will change in cells, and pattern of proteins can reflect the changing. So we could screen these relative proteins of tumor with comparing on different patients protein in serum. In this study, using SELDI-TOF-MS to detect hepatitis B, liver cirrhosis, primary liver cancer, other gastrointestinal tumors and health human serum protein profiling with proteomics approach, and screening in patients with primary liver cancer-specific expression of serum protein markers, combined with artificial neural network (ANN) to establish prediction model for primary liver cancer and explored the clinical value of laboratory diagnosis in order to establish an effective early detection of hepatocellular carcinoma objective experimental index. Methods:The 435 serum samples were tested by SELDI-TOF-MS matched with Gold Chip and finded different proteins through Biomarker Wizard software. Using ANN to develop a model of diagnosis of PHC.75 patients with type B hepatitis,68 patients with hepatic cirrhosis,100 patients with PHC,91 patients with other alimentary system tumor and 101 health controls were included. Samples were randomly assigned into two subsets, the training set (35 patients with type B hepatitis,33 patients with hepatic cirrhosis,50 patients with PHC,41 patients with alimentary system and 51 health controls), and the testing set (40 patients with type B hepatitis,35 patients with hepatic cirrhosis,50 patients with PHC, 50 patients with alimentary system and 50 health controls). The serum samples were binded to the gold chip and detected serum protein profiling data, and corrected and analysed protein profiling data by Ciphergen ProteinChip 3.0 software. The training set was used for identifying the statistically significant peaks as well as for developing ANN model. And the testing set was used for blind test to validate the diagnostic efficiency of ANN model. Results:Serum protein fingerprint selected 75 differentially expressed proteins than the peak load (P<0.05) in PHC and control groups. The use of which 7 are significant differences in the expression of marker proteins (P<0.01) to establish artificial neural networks diagnosis model, the mass charge ratio (m/z) were 4207,6604,7734,8106,8545,8599,8894. And they correspond to Peptide YY-like, 50S ribosomal protein L30,50S ribosomal protein L35, Neutrophil-activating peptide 2(74), Acyl carrier protein,30S ribosomal protein S21, UPF0330 protein TK1752 respectively by Swiss-Prot roughly. Using blind method to predict the model of PHC, we could get the sensitivity and specificity were 84.00% and 81.25% respectively, and the area under (AUC) of receiver operating characteristic curve (ROC curve) was 0.847, negative predictive value(NPV) was 94.20%, positive predictive value (PPV) was 58.33%, accuracy(ACC) was 81.90%. Conclusions:There were significantly different proteins in PHC, and the ANN model provides a new method of differential diagnosis of PHC. The method is important for differential diagnosis and treating PHC.
引文
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10.胡琼英,王开正,丁银环,等.胰腺癌血清差异蛋白的筛选及临床应用[J].中华消化杂志,2009：29(11)：767-768
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14. Schmid O,Ball G,Lancashire L,et al. New approches to identification of bacterial pathogens by surface enhanced laser desorption/ionization time of flight mass spectrometry in concert with artificial neural networks,with special reference to Neisseria gonorrhoeae[J]. J Med Microbiol,2005;54 (12):1205-1211
15.Yu JK,Yang MQ,Jiang TJ, et al. Application of artificial neural network combining serum tumor markers in colorectal carcinoma diagnosis[J]. J Zhejiang univ (Med Sci),2004;33:407-410
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