Molecular Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: The Potential of Gene Expression Profiling

ABSTRACT

Gene expression profile analysis through DNA microarrays and other high-throughput technologies permit simultaneous investigation of all genes within a biologic sample, providing a snapshot of the transcriptional state of healthy or diseased tissue. Although most of the current applications are still geared toward research (study of disease mechanisms/signaling pathways involved, identification of novel oncogenes/tumor suppressor genes), clinical diagnostic applications of this approach are beginning to enter more routine molecular usage. There are clear examples where a group of genes, or “signature,” can provide clinically useful information (e.g., cancer prognosis and response to treatment). Importantly, the identification of genes that are up-regulated during tumorigenesis is heralding a new era of targeted molecular therapies in oncology. In addition, the high capacity of the technologies available allow for the identification of new biomarkers for early diagnosis. In the future, gene expression profiling (“disease fingerprinting”) is likely to complement liver biopsy in the molecular differential diagnosis of chronic liver diseases and hepatocellular carcinoma (HCC). There has already been some success in the identification of subtypes of HCC based on derived sets of signature gene clusters. Data recently reported have been able to provide the first molecular classification of HCC, although more comprehensive studies are required to confirm these results and translate them into the clinical practice. In fact, variations in gene expression in normal and diseased livers, as well technical factors, are obstacles to the routine use of microarray-based methods in the liver clinic. Continued progress is anticipated in the practical application of gene array methods to refine diagnosis and therapy of HCC.

KEYWORDS

Gene expression profiles - transcriptome - microarrays - integrative genomic analysis - biomarkers - liver diseases - hepatocellular carcinoma

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