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2016 ; 11
(11
): e0165457
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Integrated Multiple "-omics" Data Reveal Subtypes of Hepatocellular Carcinoma
#MMPMID27806083
Liu G
; Dong C
; Liu L
PLoS One
2016[]; 11
(11
): e0165457
PMID27806083
show ga
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.