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

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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.

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Figure 1: Classification of hepatocellular carcinoma, with or without metastasis, by gene expression.
Figure 2: Prediction of metastasis and survival with metastasis predictor model derived from leave-one-out cross-validated CCP classification.
Figure 3: Candidate genes associated with metastatic HCC.
Figure 4: Immunohistochemical analysis of osteopontin in normal liver and hepatocellular carcinoma.
Figure 5: Role of osteopontin in promoting HCC metastasis.


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We thank C.C. Harris and L. Varticovski for comments; D. Dudek and K. MacPherson for editorial assistance; D. Petersen, J. Powell and members of the National Cancer Institute microarray team at the Advanced Technology Center for technical support; C. Drachenberg for pathological diagnosis; and J. Fan and X.D. Zhou for help in preparing human tissues. This work was supported in part by the Intramural Research Program of the US National Cancer Institute. Q.H.Y., L.X.Q., Z.C.M., Z.Q.W., S.L.Y., Y.K.L. and Z.Y.T. were supported by research grants from the State Key Basic Research Program of China (No. G1998051210) and from the key project of the Ministry of Education of China.

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Correspondence to Xin Wei Wang.

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