Predictive Genes in Adjacent Normal Tissue Are Preferentially Altered by sCNV during Tumorigenesis in Liver Cancer and May Rate Limiting

Abstract

BackgroundIn hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear.Methodology/Principal FindingsHere we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ∼250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU.Conclusions/SignificanceThis suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types.

CYCLOPS reveals human transcriptional rhythms in health and disease

“…We applied CYCLOPS to expression data from 249 patient biopsies of noncancerous (NC) liver tissue (37). The vast majority (n = 243) were of the "normal margin" adjacent to tumor.…”

Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma

“…Sample collection, gene expression array analysis, SNP array analysis, and whole-genome sequencing have been described in previous publications (Lamb et al 2011;Sung et al 2012). Microarray data have been deposited in the GEO database under accession numbers GSE28127 (for SNP arrays) and GSE25097 (for expression arrays).…”

“…A significant CNV region is defined as having amplification G-score >0.08 or deletion G-score <0.09, corresponding to a P-value threshold of 0.05 from permutation-derived null distribution (Beroukhim et al 2007). For each gene, we calculated the Pearson correlation coefficient between gene-level copy numbers and mRNA gene expression levels previously published (Lamb et al 2011). A gene was defined to be ''cis-correlated'' if its Pearson correlation coefficient is $0.3 and its gene expression fold-change in CNV versus non-CNV tumor samples is consistent with the direction of CNV ($1.25 for amplified and #0.8 for deleted genes).…”

Expression of ankyrin repeat and SOCS box containing 4 (ASB4) confers migration and invasion properties of hepatocellular carcinoma cells

“…Retrospective analysis of published gene expression array database (22)(23)(24) associated high expression of ASB4 with tumor tissues when compared to the non-tumor tissues in 217 HCC cases (p = 0.0013) ( Figure 1A, upper panel). Validation of the gene expression array data was achieved by detecting the level of ASB4 in a separate cohort of 50 cases using qPCR.…”

“…Table 2 shows 11 miRNAs carrying seed-matched sequences for 3'UTR of ASB4. Taking advantage of our miRNA database of 97 HCC cases with matched gene expression profile (21)(22)(23)(24) and by performing correlation analysis using ASB4 expression level, 3 miRNAs negatively correlated with ASB4 expression were identified on top of the list (Table 3). Cross comparison of miRNAs in Table 2 and 3 pointed to miR-200 family members (miR-200a and miR-200b) as potential miRNAs regulating ASB4 in HCC.…”