Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning
Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning
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DOI：
10.1038/nm843
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发表时间：
2003-04-01
期刊：
影响因子：
82.9
通讯作者：
Wang, XW
中科院分区：
文献类型：
--
作者：
Ye, QH;Qin, LX;Wang, XW
肝细胞癌（Hepatocellular carcinoma，HCC）是人类最常见、最具侵袭性的恶性肿瘤之一。其高死亡率主要是肝内转移的结果。我们分析了无或有肝内转移的HCC样本的表达谱。使用有监督的机器学习算法，我们首次生成了一个分子特征，可以对转移性HCC患者进行分类，并识别出与转移和患者生存相关的基因。我们发现，原发性肝癌伴转移的基因表达特征与其相应的转移灶非常相似，这意味着有利于转移进展的基因在原发性肿瘤中启动。骨桥蛋白在转移性肝癌中过度表达，被鉴定为信号中的前导基因;骨桥蛋白特异性抗体在体外有效地阻断肝癌细胞侵袭，并抑制肝癌细胞在裸鼠中的肺转移。因此，骨桥蛋白作为一个诊断标志物和转移性肝癌的潜在治疗靶点。
Hepatocellular carcinoma (HCC) is one of the most common and aggressive human malignancies. Its high mortality rate is mainly a result of intra-hepatic metastases. We analyzed the expression profiles of HCC samples without or with intra-hepatic metastases. Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival. We found that the gene expression signature of primary HCCs with accompanying metastasis was very similar to that of their corresponding metastases, implying that genes favoring metastasis progression were initiated in the primary tumors. Osteopontin, which was identified as a lead gene in the signature, was over-expressed in metastatic HCC; an osteopontin-specific antibody effectively blocked HCC cell invasion in vitro and inhibited pulmonary metastasis of HCC cells in nude mice. Thus, osteopontin acts as both a diagnostic marker and a potential therapeutic target for metastatic HCC.