Published online Dec 21, 2013. doi: 10.3748/wjg.v19.i47.8873
Revised: November 20, 2013
Accepted: December 5, 2013
Published online: December 21, 2013
High-throughput short-read sequencing of exomes and whole cancer genomes in multiple human hepatocellular carcinoma (HCC) cohorts confirmed previously identified frequently mutated somatic genes, such as TP53, CTNNB1 and AXIN1, and identified several novel genes with moderate mutation frequencies, including ARID1A, ARID2, MLL, MLL2, MLL3, MLL4, IRF2, ATM, CDKN2A, FGF19, PIK3CA, RPS6KA3, JAK1, KEAP1, NFE2L2, C16orf62, LEPR, RAC2, and IL6ST. Functional classification of these mutated genes suggested that alterations in pathways participating in chromatin remodeling, Wnt/β-catenin signaling, JAK/STAT signaling, and oxidative stress play critical roles in HCC tumorigenesis. Nevertheless, because there are few druggable genes used in HCC therapy, the identification of new therapeutic targets through integrated genomic approaches remains an important task. Because a large amount of HCC genomic data genotyped by high density single nucleotide polymorphism arrays is deposited in the public domain, copy number alteration (CNA) analyses of these arrays is a cost-effective way to reveal target genes through profiling of recurrent and overlapping amplicons, homozygous deletions and potentially unbalanced chromosomal translocations accumulated during HCC progression. Moreover, integration of CNAs with other high-throughput genomic data, such as aberrantly coding transcriptomes and non-coding gene expression in human HCC tissues and rodent HCC models, provides lines of evidence that can be used to facilitate the identification of novel HCC target genes with the potential of improving the survival of HCC patients.
Core tip: In addition to detecting somatic mutations in cancer genomes with high-throughput short-read sequencing technologies, analysis of copy number alteration in hepatocellular carcinoma (HCC) cancer genomes genotyped by high density single nucleotide polymorphism arrays is a cost-effective approach to reveal genome-wide somatic alterations accumulated during tumorigenesis. Integration with other genomic data from HCC tissues derived from high-throughput short-read sequencing, proteomics, epigenomics and transcriptomics could provide lines of evidence to identify common and novel HCC genes for potential clinical applications.