Methylome analysis and integrative profiling of human HCCs identify novel protumorigenic factors

Abstract

To identify new tumor-suppressor gene candidates relevant for human hepatocarcinogenesis, we performed genome-wide methylation profiling and vertical integration with array-based comparative genomic hybridization (aCGH), as well as expression data from a cohort of well-characterized human hepatocellular carcinomas (HCCs). Bisulfite-converted DNAs from 63 HCCs and 10 healthy control livers were analyzed for the methylation status of more than 14,000 genes. After defining the differentially methylated genes in HCCs, we integrated their DNA copy-number alterations as determined by aCGH data and correlated them with gene expression to identify genes potentially silenced by promoter hypermethylation. Aberrant methylation of candidates was further confirmed by pyrosequencing, and methylation dependency of silencing was determined by 5-aza-2'-deoxycytidine (5-aza-dC) treatment. Methylation profiling revealed 2,226 CpG sites that showed methylation differences between healthy control livers and HCCs. Of these, 537 CpG sites were hypermethylated in the tumor DNA, whereas 1,689 sites showed promoter hypomethylation. The hypermethylated set was enriched for genes known to be inactivated by the polycomb repressive complex 2, whereas the group of hypomethylated genes was enriched for imprinted genes. We identified three genes matching all of our selection criteria for a tumor-suppressor gene (period homolog 3 [PER3], insulin-like growth-factor-binding protein, acid labile subunit [IGFALS], and protein Z). PER3 was down-regulated in human HCCs, compared to peritumorous and healthy liver tissues. 5-aza-dC treatment restored PER3 expression in HCC cell lines, indicating that promoter hypermethylation was indeed responsible for gene silencing. Additionally, functional analysis supported a tumor-suppressive function for PER3 and IGFALS in vitro.

Conclusion: The present study illustrates that vertical integration of methylation data with high-resolution genomic and transcriptomic data facilitates the identification of new tumor-suppressor gene candidates in human HCC.