Bhat M, Pasini E, Pastrello C, Rahmati S, Angeli M, Kotlyar M, Ghanekar A, Jurisica I. Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies. World J Hepatol 2021; 13(1): 94-108 [PMID: 33584989 DOI: 10.4254/wjh.v13.i1.94]
Corresponding Author of This Article
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. mamatha.bhat@uhn.ca
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Basic Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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 mamatha.bhat@uhn.ca 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: http://creativecommons.org/Licenses/by-nc/4.0/
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. mamatha.bhat@uhn.ca
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 Processing time: 172 Days and 21 Hours
ARTICLE HIGHLIGHTS
Research background
Hepatocellular carcinoma (HCC) is highly heterogeneous, difficult to characterize and the molecular basis of HCC has been elusive.
Research motivation
The Cancer Genome Atlas is a large-scale project that has enabled improved characterization of cancers with several layers of data. Elucidating the layers of data in a disease can provide additional insights into the pathways that drive cancer.
Research objectives
A novel integrative approach of all publicly available high-throughput data from patient HCC tumors was used to delineate critical pathway dependencies in HCC.
Research methods
A comprehensive analysis and characterization of all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC covered 85 studies and 3355 patient sample profiles and identified the key overlapping dysregulated genes and pathways affected.
Research results
We identified the prognostic value of these genes in HCC genes, specifically with Netrin and Slit3 being novel proteins of prognostic importance to HCC.
Research conclusions
Our large integrative analysis of all publicly available data in HCC and our pathway enrichment analysis has elucidated epidermal growth factor, β1-integrin, and axon guidance as pathway dependencies in HCC.
Research perspectives
Based on our integrative analysis, epidermal growth factor, and β1-integrin are master regulators that could be considered as potential therapeutic targets in HCC.
