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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Jun 27, 2021; 13(6): 662-672
Published online Jun 27, 2021. doi: 10.4254/wjh.v13.i6.662
Published online Jun 27, 2021. doi: 10.4254/wjh.v13.i6.662
Role of chromosome 1q copy number variation in hepatocellular carcinoma
Nathan R Jacobs, Pamela A Norton, Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19102, United States
Author contributions: Norton PA designed the project; Jacobs NR and Norton PA performed the research and wrote the paper.
Conflict-of-interest statement: The authors declare no conflicts of interests for this article.
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: Pamela A Norton, PhD, Associate Professor, Department of Microbiology and Immunology, Drexel University College of Medicine, 245 N 15th Street, Room 14307, Philadelphia, PA 19102, United States. pan29@drexel.edu
Received: February 24, 2021
Peer-review started: February 24, 2021
First decision: May 3, 2021
Revised: May 13, 2021
Accepted: June 4, 2021
Article in press: June 4, 2021
Published online: June 27, 2021
Processing time: 118 Days and 8.3 Hours
Peer-review started: February 24, 2021
First decision: May 3, 2021
Revised: May 13, 2021
Accepted: June 4, 2021
Article in press: June 4, 2021
Published online: June 27, 2021
Processing time: 118 Days and 8.3 Hours
Core Tip
Core Tip: A list of candidate chromosome 1q amplification driver genes was compiled from the existing literature by PubMed search. Bioinformatics tools were used to identify additional candidates using publicly available genomics and transcriptomics data. Genes identified this way were largely distinct from those identified from the literature. Thus, these two strategies can be used in a complementary manner.