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

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.

Comparative and integrative functional genomics of HCC

“…As expected, prediction models based on gene expression profiles successfully discriminate HCC from non-tumor livers with expected high accuracy (Kim et al, 2004;Neo et al, 2004;Nam et al, 2005). Many studies have identified differentially expressed gene sets in HCC that differ according to etiological factors (Okabe et al, 2001), mutations of tumor suppressor genes (Chen et al, 2002), rate of recurrence (Iizuka et al, 2003), intrahepatic metastasis (Ye et al, 2003) and different stages (Nam et al, 2005). The results from these studies demonstrate that genomic-scale gene expression profiles recapitulate the well-known morphological distinctions of the tissues and suggest that the differences in gene expression patterns might be biologically relevant.…”

Genes for systemic vascular complications are differentially expressed in the livers of Type 2 diabetic patients

“…All data are expressed as means ± SEM. To test the significance of numbers and frequencies of genes or Cy5:Cy3 ratios, we used supervised analyses with the permutation-based method (BRB-ArrayTools, http://linus.nci.nih.gov/ BRB-ArrayTools.html) [16]. This software for the statistical analysis of cDNA microarray gene expression data was developed by the Biometric Research Branch of the National Cancer Institute (USA).…”

TP53 mutations and hepatocellular carcinoma: insights into the etiology and pathogenesis of liver cancer

“…For example, genomic and gene expression analyses have identified key dysregulated signal transduction pathways involved in liver carcinogenesis (Thorgeirsson and Grisham, 2002;Ye et al, 2003;Budhu et al, 2006;Lee and Thorgeirsson, 2006). These studies and others have generated a new paradigm of metastasis ( Figure 3) that is not mutually exclusive to the hypothesis of multistage carcinogenesis depicted in Figure 2.…”