Journal article
Identification of novel prognosis-related genes associated with cancer using integrative network analysis
Scientific Reports, Vol.8(1), 3233
2018
Abstract
Prognosis identifies the seriousness and the chances of survival of a cancer patient. However, it remains a challenge to identify the key cancer genes in prognostic studies. In this study, we collected 2064 genes that were related to prognostic studies by using gene expression measurements curated from published literatures. Among them, 1820 genes were associated with copy number variations (CNVs). The further functional enrichment on 889 genes with frequent copy number gains (CNGs) revealed that these genes were significantly associated with cancer pathways including regulation of cell cycle, cell differentiation and mitogen-Activated protein kinase (MAPK) cascade. We further conducted integrative analyses of CNV and their target genes expression using the data from matched tumour samples of The Cancer Genome Atlas (TCGA). Ultimately, 95 key prognosis-related genes were extracted, with concordant CNG events and increased up-regulation in at least 300 tumour samples. These genes, and the number of samples in which they were found, included: ACTL6A (399), ATP6V1C1 (425), EBAG9 (412), FADD (308), MTDH (377), and SENP5 (304). This study provides the first observation of CNV in prognosis-related genes across pan-cancer. The systematic concordance between CNG and up-regulation of gene expression in these novel prognosis-related genes may indicate their prognostic significance. © 2018 The Author(s).
Details
- Title
- Identification of novel prognosis-related genes associated with cancer using integrative network analysis
- Authors
- Yong Kiat Wee (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringYining Liu (Author) - Guangzhou Medical University, ChinaJ Lu (Author) - Guangzhou Medical University, ChinaX Li (Author) - Capital Medical University, ChinaMin Zhao (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
- Publication details
- Scientific Reports, Vol.8(1), 3233
- Publisher
- Nature Publishing Group
- Date published
- 2018
- DOI
- 10.1038/s41598-018-21691-5
- ISSN
- 2045-2322
- Copyright note
- Copyright © The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; GeneCology Research Centre - Legacy; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99450772702621
- Output Type
- Journal article
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