Proteomic expression signature distinguishes cancerous and nonmalignant tissues in hepatocellular carcinoma.
Proteomic expression signature distinguishes cancerous and nonmalignant tissues in hepatocellular carcinoma.
复制标题
蛋白质组学表达的特征区分肝细胞癌中的癌性和非恶性组织。
DOI：
10.1021/pr800637z
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发表时间：
2009-03
影响因子：
4.4
通讯作者：
Luk JM
中科院分区：
文献类型：
--
作者：
Lee NP;Chen L;Lin MC;Tsang FH;Yeung C;Poon RT;Peng J;Leng X;Beretta L;Sun S;Day PJ;Luk JM
肝细胞癌（HCC）是一种侵袭性肝癌，但缺乏可以预测恶性进展自然史的临床验证生物标志物。本研究探索了 HCC 的蛋白质组模式，以确定可以区分癌性和非恶性肝组织的生物标志物特征。纳入了 80 例 HBV 相关 HCC 的回顾性队列，并通过 2-DE 方法对肿瘤和邻近非肿瘤组织进行了全蛋白质组表达谱分析。将受试者随机分为训练（n=55）和验证（n=25）子集，并通过分类回归树算法对数据进行分析。蛋白质标记物通过 MALDI-ToF/MS 进行表征，并通过免疫组织化学、蛋白质印迹和 qPCR 测定进行确认。由六种生物标志物（触珠蛋白、细胞色素 b5、孕酮受体膜成分 1、热休克 27 kDa 蛋白 1、溶酶体蛋白酶组织蛋白酶 B、角蛋白 I）组成的蛋白质组表达特征被开发为预测 HCC 的分类模型。我们使用留一法和独立验证进一步评估了该模型，HCC 的总体敏感性和特异性分别为 92.5%。临床相关分析显示，这些生物标志物与血清 AFP、总蛋白水平和 Ishak 评分显着相关。所描述的使用生物标志物特征的模型可以准确地区分 HCC 与非恶性组织，这也可能为正常肝细胞在肿瘤进展过程中如何转变为恶性状态提供提示和指导。
Hepatocellular carcinoma (HCC) is an aggressive liver cancer but clinically validated biomarkers that can predict natural history of malignant progression are lacking. The present study explored the proteome-wide patterns of HCC to identify biomarker signature that could distinguish cancerous and non-malignant liver tissues. A retrospective cohort of 80 HBV-associated HCC was included and both the tumor and adjacent non-tumor tissues were subjected to proteome-wide expression profiling by 2-DE method. The subjects were randomly divided into the training (n=55) and validation (n=25) subsets, and the data analyzed by classification-and-regression tree algorithm. Protein markers were characterized by MALDI-ToF/MS and confirmed by immunohistochemistry, western blotting and qPCR assays. Proteomic expression signature composed of six biomarkers (haptoglobin, cytochrome b5, progesterone receptor membrane component 1, heat shock 27 kDa protein 1, lysosomal proteinase cathepsin B, keratin I) was developed as a classifier model for predicting HCC. We further evaluated the model using both leave-one-out procedure and independent validation, and the overall sensitivity and specificity for HCC both are 92.5% respectively. Clinical correlation analysis revealed that these biomarkers were significantly associated with serum AFP, total protein levels and the Ishak’s score. The described model using biomarker signatures could accurately distinguish HCC from non-malignant tissues, which may also provide hints and guidance on how normal hepatocytes are transformed to malignant state during tumor progression.