The goal of our study was to determine the extent to which several commonly used mechanisms of transcription regulation contribute to gene expression changes that occur during the development of a human liver tumor. Accordingly, we began our studies by identifying a set of transcripts that were disregulated in both of two independent liver tumors. We then examined changes in transcription initiation complex formation, gene silencing, and gene copy number for these deregulated genes. We found that, of these mechanisms, amplification and deletion of chromosomal regions was most often used to confer changes in mRNA expression in the liver tumor.
Although our study was not meant to be a comprehensive analysis of RNA changes in HCC, it is critical to note that we did identify sets of deregulated genes that a) fit into several interesting gene ontology categories and b) correspond to genes identified in previous studies of tumors. For example, we found that the most commonly downregulated functional categories of genes were those from the metallotheionein (e.g MT1A, MT1F, MT1G, MT1M, MT1X and MT2A) and cytochrome P45 (e.g CYP1A2, CYP2C9, CYP21 CYP3A5, and CYP3A7) families. This suggests that a dedifferentiation of the liver has occurred in the tumor cells. Similarly, Xu et al (3
) performed an analysis of ESTs from Hepatitis B positive HCC samples and found that genes involved in liver function were downregulated. More specifically, Tao et al (20
) performed a direct analysis of the metallotheionein family in HCC and found that many metallotheionein family members were downregulated and suggested that downregulated expression of this family may be a marker for hepatocarcinogenesis. We found that one of the most commonly upregulated categories of genes included ribonucleoproteins involved in protein synthesis and transport. Similarly, Kondoh et al (19
) used differential display to identify 5 cDNAs that were upregulated in HCC; all of these corresponded to ribosomal protein mRNAs. Interestingly, they also demonstrated that the expression of these ribosomal protein mRNAs was high in three HCC cells lines irrespective of the growth state of the cell and suggested that activation of these genes is an important manifestation of the HCC phenotype. The other main category of genes that we discovered to be upregulated in the HCC samples was nuclear proteins (see Supplementary Figure S7
for categorized list of the nuclear proteins). Of the 194 nuclear proteins that were upregulated in the HCC samples, 25 are involved in chromosomal biology and biogenesis, 12 are involved in nucleocytoplasmic transport, 24 are involved in DNA replication and repair, 32 are involved in RNA metabolism, and 67 are transcriptional regulators. Of the 67 transcriptional regulators, 38 (> 50%) fit into the zinc ion binding/zinc finger category (Supplementary Figure S7
). Several of the upregulated transcription factors have been previously associated with cancers. For example, FoxQ1 has been shown to be overexpressed in pancreatic cancer (28
); Sox18 is upregulated in gastric, pancreatic, breast, and embryonal tumor cell lines (29
), and DP1 has been previously shown to be increased in expression in HCCs (30
As noted above, gankyrin mRNA is overexpressed in most HCC samples (9
); we also observed increased gankyrin mRNA in both of the HCC samples we analyzed. Detailed analysis of the gankryin gene revealed that it was not increased in the tumor samples due to changes in RNAPII, H3me3K27, or H3me3K9 binding. Rather, the gankryin gene is amplified. In fact, the amplification ratio of the gankyrin gene was in the top 3% of all amplified promoters. Overexpression of gankyrin can lead to degradation of p53 and Rb, contributing to genomic instability and oncogenic transformation. Rb functions to suppress the E2F family, a key regulator of cell proliferation. Interestingly, we also observed an increase in levels of DP1, the obligate heterodimeric partner of E2F1-E2F6, in the tumor samples. Expression analysis indicates that E2F3 and E2F4 are expressed at similar levels in the normal and tumor tissues, whereas the other E2Fs are not expressed in any of the liver samples. The increased expression of DP1 and decreased levels of Rb protein (due to gankyrin) may synergize to allow enhanced E2F3- or E2F4-mediated activation of key proliferation-responsive genes in the liver tumors.
In summary, we found that changes in preinitiation complex formation and changes in gene silencing do not play major roles in the development of the tested liver tumors. Rather, many gene expression changes in the tumors were a direct result of changes in gene copy number. We also note that 38% and 49% of the up and down regulated genes, respectively, were not regulated by any of the mechanisms. Recent estimates suggest that ~50% of human genes have alternative promoters (32
). The set of alternative promoters for human genes is not well characterized and thus these promoters are not well represented on the arrays. Therefore, it is possible that some changes in RNA levels in the liver tumors were due to differential usage of novel alternative promoters. It is also possible that changes in transcription elongation and/or transcript stability contribute to the regulation of the set of genes in the “other” category.
Although our analysis was limited to a small number of liver tumors, our results suggest that clinical treatments with chromatin modifying drugs, which have shown promise in the treatment of cancers of hematologic origin, may not be effective for HCC (see (12
) for a review of the clinical applications of epigenetic drugs). The mechanisms regulating the tumor transcriptome may differ for different tumor types and therefore RNA levels, chromatin structure, and gene copy number should all be analyzed to provide the required information to allow a rationale design of tumor type-specific chemotherapeutic regimens. Finally, we have outlined an efficient and cost-effective method to analyze copy number changes at all the known promoters regions in the human genome; this analysis can be performed at no extra cost using the data from ChIP-chip experiments.