Abstract
Many molecular classification and prognostic gene signatures for hepatocellular carcinoma (HCC) patients have been established based on genome-wide gene expression profiling; however, their generalizability is unclear. Herein, we systematically assessed the prognostic effects of these gene signatures and identified valuable prognostic biomarkers by integrating these gene signatures. With two independent HCC datasets (GSE14520, N=242 and GSE54236, N=78), 30 published gene signatures were evaluated, and 11 were significantly associated with the overall survival (OS) of postoperative HCC patients in both datasets. The random survival forest models suggested that the gene signatures were superior to clinical characteristics for predicting the prognosis of the patients. Based on the 11 gene signatures, a functional protein-protein interaction (PPI) network with 1406 nodes and 10,135 edges was established. With tissue microarrays of HCC patients (N=60), we determined the prognostic values of the core genes in the network and found that RAD21, CDK1, and HDAC2 expression levels were negatively associated with OS for HCC patients. The multivariate Cox regression analyses suggested that CDK1 was an independent prognostic factor, which was validated in an independent case cohort (N=78). In cellular models, inhibition of CDK1 by siRNA or a specific inhibitor, RO-3306, reduced cellular proliferation and viability for HCC cells. These results suggest that the prognostic predictive capacities of these gene signatures are reproducible and that CDK1 is a potential prognostic biomarker or therapeutic target for HCC patients.
Highlights
For men and women worldwide, liver cancer ranks as the second and sixth leading cause of cancer deaths, respectively (Torre et al, 2015)
Through the protein-protein interaction (PPI) network topological analysis, we identified those critical molecules in the network and determined their values as biomarkers for prognosis assessment or as therapeutic targets for Hepatocellular carcinoma (HCC) patients
With the nearest-template prediction (NTP) methods, we determined the prognostic effects of the reported gene signatures in tumor tissues and found that the predictive roles of these gene signatures were reproducible between the datasets
Summary
For men and women worldwide, liver cancer ranks as the second and sixth leading cause of cancer deaths, respectively (Torre et al, 2015). Cai et al / EBioMedicine 19 (2017) 18–30 to these tumor characteristics, biomarkers for preserved liver function and the liver damage status of the HCC patients, such as the ChildPugh stage; the α-fetoprotein (AFP), bilirubin, and albumin levels; and ECOG status are associated with the prognosis of HCC patients These markers have led to the establishment of various conventional staging systems, including the Japan Integrated Staging (JIS) system (Kudo et al, 2003), the Barcelona Clinic Liver Cancer (BCLC) classification system (Llovet et al, 1999), the Cancer of the Liver Italian Program (CLIP) scoring system (No-author-listed, 1998) and the Chinese University Prognostic Index (CUPI) scoring system (Leung et al, 2002). Through the PPI network topological analysis, we identified those critical molecules in the network and determined their values as biomarkers for prognosis assessment or as therapeutic targets for HCC patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
