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利用基因表达谱和监督式机器学习预测乙型肝炎病毒阳性转移性肝细胞癌

Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.

作者信息

Ye Qing-Hai, Qin Lun-Xiu, Forgues Marshonna, He Ping, Kim Jin Woo, Peng Amy C, Simon Richard, Li Yan, Robles Ana I, Chen Yidong, Ma Zeng-Chen, Wu Zhi-Quan, Ye Sheng-Long, Liu Yin-Kun, Tang Zhao-You, Wang Xin Wei

机构信息

Liver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.

出版信息

Nat Med. 2003 Apr;9(4):416-23. doi: 10.1038/nm843. Epub 2003 Mar 17.

DOI:10.1038/nm843
PMID:12640447
Abstract

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.

摘要

肝细胞癌(HCC)是最常见且侵袭性最强的人类恶性肿瘤之一。其高死亡率主要是肝内转移所致。我们分析了有无肝内转移的HCC样本的表达谱。使用监督式机器学习算法,我们首次生成了一种分子特征,可对转移性HCC患者进行分类,并鉴定出与转移及患者生存相关的基因。我们发现,伴有转移的原发性HCC的基因表达特征与其相应转移灶的基因表达特征非常相似,这意味着有利于转移进展的基因在原发性肿瘤中就已启动。骨桥蛋白被鉴定为该特征中的主导基因,在转移性HCC中过度表达;一种骨桥蛋白特异性抗体可有效阻断体外HCC细胞的侵袭,并抑制HCC细胞在裸鼠体内的肺转移。因此,骨桥蛋白既是转移性HCC的诊断标志物,也是潜在的治疗靶点。

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