Basic Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Jan 27, 2021; 13(1): 94-108
Published online Jan 27, 2021. doi: 10.4254/wjh.v13.i1.94
Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies
Mamatha Bhat, Elisa Pasini, Chiara Pastrello, Sara Rahmati, Marc Angeli, Max Kotlyar, Anand Ghanekar, Igor Jurisica
Mamatha Bhat, Elisa Pasini, Marc Angeli, Multi Organ transplant Program, University Health Network, Toronto M5G2N2, Canada
Chiara Pastrello, Sara Rahmati, Max Kotlyar, Igor Jurisica, Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health NetworkandKrembil Research Institute, University Health Network, Toronto M5T 0S8, Canada
Anand Ghanekar, Surgery, University Health Network, Toronto M5G 2C4, Canada
Igor Jurisica, Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto M5T 0S8, Canada
Author contributions: Bhat M, Pasini E, Kotlyar M and Jurisica I study design, and writing of the manuscript; Bhat M, Pasini E, and Angeli M data collection, analysis and compilation; Ghanekar A, Jurisica I and Bhat M input into study design, data interpretation and final manuscript. All authors approved the final version of the manuscript.
Institutional review board statement: All data was from publicly available sources, no animal or human studies where done by the authors. No approval was needed. 
Conflict-of-interest statement: The authors do not have any conflict of interest to declare.
Data sharing statement: Technical appendix, statistical code available from the corresponding author at all data sets are publicly available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Mamatha Bhat, MD, MSc, PhD, FRCPC(C) Assistant Professor, Staff Physician, Multi Organ transplant Program, University Health Network, 585 University avenue 11th floor, PMB, rm 183, Toronto M5G2N2, Canada.
Received: August 6, 2020
Peer-review started: August 6, 2020
First decision: September 21, 2020
Revised: November 19, 2020
Accepted: December 4, 2020
Article in press: December 4, 2020
Published online: January 27, 2021

The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC). 


To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect. 


We collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.


We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and β1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately.


By analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, β1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and β1-integrin are master regulators that could serve as potential therapeutic targets in HCC.

Keywords: Hepatocellular carcinoma, Gene expression, miRNA, Methylation, Proteomics, High throughput data

Core Tip: Analyzing all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human hepatocellular carcinoma tissue, we identified master regulators of key pathways in hepatocellular carcinoma, such as the mTOR, Ras/Raf/MAPK and p53 pathways.