Integrated Multiple "-omics" Data Reveal Subtypes of Hepatocellular Carcinoma.

Hepatocellular carcinoma is one of the most heterogeneous cancers, as reflected by its multiple grades and difficulty to subtype. In this study, we integrated copy number variation, DNA methylation, mRNA, and miRNA data with the developed "cluster of cluster" method and classified 256 HCC samples from TCGA (The Cancer Genome Atlas) into five major subgroups (S1-S5). We observed that this classification was associated with specific mutations and protein expression, and we detected that each subgroup had distinct molecular signatures. The subclasses were associated not only with survival but also with clinical observations. S1 was characterized by bulk amplification on 8q24, TP53 mutation, low lipid metabolism, highly expressed onco-proteins, attenuated tumor suppressor proteins and a worse survival rate. S2 and S3 were characterized by telomere hypomethylation and a low expression of TERT and DNMT1/3B. Compared to S2, S3 was associated with less copy number variation and some good prognosis biomarkers, including CRP and CYP2E1. In contrast, the mutation rate of CTNNB1 was higher in S3. S4 was associated with bulk amplification and various molecular characteristics at different biological levels. In summary, we classified the HCC samples into five subgroups using multiple "-omics" data. Each subgroup had a distinct survival rate and molecular signature, which may provide information about the pathogenesis of subtypes in HCC.