Research Paper Volume 11, Issue 24 pp 12375—12411
Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells
- 1 Translational Medicine Center, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang 310015, P.R. China
- 2 Department of Occupational Medicine, Zhejiang Provincial Integrated Chinese and Western Medicine Hospital, Hangzhou, Zhejiang 310003, P.R. China
received: June 28, 2019 ; accepted: November 26, 2019 ; published: December 18, 2019 ;https://doi.org/10.18632/aging.102579
How to Cite
Copyright © 2019 Li 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.
Identification of master regulator (MR) genes offers a relatively rapid and efficient way to characterize disease-specific molecular programs. Since strong consensus regarding commonly altered MRs in hepatocellular carcinoma (HCC) is lacking, we generated a compendium of HCC datasets from 21 studies and identified a comprehensive signature consisting of 483 genes commonly deregulated in HCC. We then used reverse engineering of transcriptional networks to identify the MRs that underpin the development and progression of HCC. After cross-validation in different HCC datasets, systematic assessment using patient-derived data confirmed prognostic predictive capacities for most HCC MRs and their corresponding regulons. Our HCC signature covered well-established liver cancer hallmarks, and network analyses revealed coordinated interaction between several MRs. One novel MR, SEC14L2, exerted an anti-proliferative effect in HCC cells and strongly suppressed tumor growth in a mouse model. This study advances our knowledge of transcriptional MRs potentially involved in HCC development and progression that may be targeted by specific interventions.
HCC: hepatocellular carcinoma; TF: Transcription factor; TN: Transcription network; ARACNe: Algorithm for the reconstruction of accurate cellular networks; MR: Master regulator; MRA: Master regulator analysis; PVCA: Principal variance component analysis; PT: Tumor tissues; NT: Adjacent non-tumor tissues; EMT: Epithelial-to-mesenchymal transition; TCGA-LIHC: The Cancer Genome Atlas-Liver Hepatocellular Carcinoma data; GSEA: Gene Set Enrichment Analysis; dES: Differential enrichment score; JC: Jaccard similarity coefficient; CHC: ConsensusClusterPlus; NR: orphan nuclear receptor.