Artificial neural networks and decision tree model analysis of liver cancer proteomes
Artificial neural networks and decision tree model analysis of liver cancer proteomes
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DOI：
10.1016/j.bbrc.2007.06.172
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发表时间：
2007-09-14
影响因子：
3.1
通讯作者：
Fan, Sheung-Tat
中科院分区：
文献类型：
--
作者：
Luk, John M.;Lam, Brian Y.;Fan, Sheung-Tat
肝细胞癌（HCC）是一种异质性癌症，通常在高致死率的晚期肿瘤阶段被诊断出来。本研究试图对 HCC 与邻近非肿瘤肝组织进行全蛋白质组分析，以促进生物标志物的发现并研究 HCC 发展的机制。纳入了 66 名中国 HCC 患者队列，通过二维凝胶电泳 (2-DE) 分析进行蛋白质组学分析研究。采用人工神经网络 (ANN) 和决策树 (CART) 数据挖掘方法来分析分析数据并描绘区分 HCC 与非恶性肝组织的重要模式和趋势。通过串联 MS/MS 鉴定蛋白质标记。通过 2-DE 表达谱分析总共生成了 132 个蛋白质组数据集，每个数据集都有 230 个整合的蛋白质表达强度。这两种数据挖掘算法都成功地将 HCC 表型与其他非恶性肝脏样本区分开来。 ANN的检测灵敏度和特异度分别为96.97%和87.88%，CART的检测灵敏度和特异度分别为81.82%和78.79%。 CART 模型中的三个生物分类器被鉴定为细胞色素 b5、热休克 70 kDa 蛋白 8 同工型 2 和组织蛋白酶 B。基于 2-DE 的蛋白质组分析方法与 ANN 或 CART 算法相结合，在识别 HCC 方面产生了令人满意的性能，并揭示了潜在的候选癌症生物标志物。 (c) 2007 Elsevier Inc. 保留所有权利。
Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and decision tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers. (c) 2007 Elsevier Inc. All rights reserved.