Research Paper Advance Articles
Integrative analysis reveals novel driver genes and molecular subclasses of hepatocellular carcinoma
- 1 Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- 2 State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Shanghai 200031, China
- 3 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- 4 Department of Pathology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- 5 Anhui Engineering Laboratory for Big Data of Precision Medicine, Anhui 234000, China
- 6 Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai 200433, China
- 7 Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
Received: February 17, 2020 Accepted: August 25, 2020 Published: November 20, 2020https://doi.org/10.18632/aging.104047
How to Cite
Copyright © 2020 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Hepatocellular carcinoma (HCC) is a heterogeneous disease with various genetic and epigenetic abnormalities. Previous studies of HCC driver genes were primarily based on frequency of mutations and copy number alterations. Here, we performed an integrative analysis of genomic and epigenomic data from 377 HCC patients to identify driver genes that regulate gene expression in HCC. This integrative approach has significant advantages over single-platform analyses for identifying cancer drivers. Using this approach, HCC tissues were divided into four subgroups, based on expression of the transcription factor E2F and the mutation status of TP53. HCC tissues with E2F overexpression and TP53 mutation had the highest cell cycle activity, indicating a synergistic effect of E2F and TP53. We found that overexpression of the identified driver genes, stratifin (SFN) and SPP1, correlates with tumor grade and poor survival in HCC and promotes HCC cell proliferation. These findings indicate SFN and SPP1 function as oncogenes in HCC and highlight the important role of enhancers in the regulation of gene expression in HCC.