Investigation Of Stratification And Therapeutic Targets In Hepatocellular Carcinoma Based On Integrated Regulatory-metabolic Network Model

Hepatocellular carcinoma(HCC)is one of the deadliest diseases across the world,researchers have spent lot of effort to identify molecular targets available for treatment.However,high tumor heterogeneity makes it difficult to develop effective therapy strategies.Due to this,researchers decided to classify patients on molecular level in order to get more effective therapeutic targets.Previous studies generally focused on single biological level,such as genome,transcriptome,or proteome,systematic study of both regulation and metabolism of HCC is still lacking.Here we established the integrated regulatory-metabolic network model for HCC and applied it to predict potential anticancer targets.We first applied 50 paired HCC-normal expression data on MERLIN and CMIP algorithm to reconstruct HCC-tumor and normal-liver regulatory networks,and then integrated them with genome-scale liver metabolic model on two other independent datasets to identify 6 key TFs common in affecting cancer cell growth.Then,we stratificated 315 TCGA-LIHC samples into 3 significantly differed OS sub-classes.ETV7 and CTBP1 show great influence on tumor cell growth in all three sub-classes,while CREB3L3 and HEY2 are believed to be associated with poor prognosis.After that,we applied metabolic analysis to personalized metabolic models to highlight 18 metabolic genes common in HCC tumorigenesis,besides,ACADSB and CMPK1 seemed to be strongly correlated with lower overall survival rate.Among all thses 20 genese,15 of them have already been targeted by approved anticancer drugs;6of them have been targeted by experimental drugs.The high hit rate make the rest 5 genes worthy further study.In addition,micro RNAs targeting the key essential TFs and genes are also involved in well-known cancer related pathways,which supports that the potential therapeutic targets predicted by the integrated regulatory-metabolic model are highly valuable from the systematic view.By combining muti-scale omics data,we first applied this integrated analysis on tumor research and got a more comprehensive view on the potential mechanism of HCC tumor development.Futhermore,through precise molecular stratification,we were able to identify biomarkers associated with low OS and poor prognosis,which can provide theoretical targets for experimental researches and is meaningful to exploring mechanism of HCC.