@article{246249a5188b497ab16f20aa9e12e936,
title = "A compiled dataset of molecular pathways associated with fusion genes identified in pediatric cancers",
abstract = "Fusion genes can serve as actionable biomarkers for diagnosis, prognosis or therapeutic stratification in the clinic. Pathways associated with fusion genes identified in different pediatric cancers are compiled in this article. Fusion genes reported in each cancer were collected using the PubMed search option with the keywords {\textquoteleft}fusion transcript{\textquoteright}, {\textquoteleft}fusion gene,{\textquoteright} {\textquoteleft}chromosomal translocation,{\textquoteright} or {\textquoteleft}DNA translocation{\textquoteright} along with the corresponding pediatric cancer type. Research articles that identified fusion genes using conventional Fluorescence in situ hybridization (FISH) or quantitative real-time polymerase chain reaction (RT-PCR) methods or high-throughput RNA or DNA sequencing were included. The collected fusion gene data were compiled for each cancer and analyzed to identify their functions related to cancer and associated pathways using Ingenuity Pathway Analysis (IPA) and ClueGO software programs. Similarities in associated pathways across different cancers were also analyzed using IPA to identify commonly affected genes and pathways. This value-added and functionally annotated dataset will be an excellent resource for pediatric cancer researchers and clinicians interested in exploring fusion genes in different cancers. This article is a companion article to {\textquoteleft}Fusion genes as biomarkers in pediatric cancers: A review of the current state and applicability in diagnostics and personalized therapy'[1].",
keywords = "Fusion gene, Gene networks, Molecular pathways, Pediatric cancers",
author = "Vellichirammal, {Neetha N.} and Chittibabu Guda",
note = "Funding Information: This work has been supported by the National Institutes of Health awards [ 5P20GM103427 , 1P30GM127200 , 5P30CA036727 ] and National Science Foundation's EPSCoR Award [Grant No. OIA-1557417 ] to CG, and the Fred & Pamela Buffett Cancer Center , which is supported by the National Cancer Institute under award number P30 CA036727 , in conjunction with the UNMC/Children's Hospital & Medical Center Child Health Research Institute Pediatric Cancer Research Group to NNV. The authors are grateful to the Bioinformatics and Systems Biology Core at the University of Nebraska Medical Center (UNMC) for providing access to the computational infrastructure. Authors also acknowledge the Holland Computing Center of the University of Nebraska-Lincoln for high-performance computational resources. Funding Information: This work has been supported by the National Institutes of Health awards [5P20GM103427, 1P30GM127200, 5P30CA036727] and National Science Foundation's EPSCoR Award [Grant No. OIA-1557417] to CG, and the Fred & Pamela Buffett Cancer Center, which is supported by the National Cancer Institute under award number P30 CA036727, in conjunction with the UNMC/Children's Hospital & Medical Center Child Health Research Institute Pediatric Cancer Research Group to NNV. The authors are grateful to the Bioinformatics and Systems Biology Core at the University of Nebraska Medical Center (UNMC) for providing access to the computational infrastructure. Authors also acknowledge the Holland Computing Center of the University of Nebraska-Lincoln for high-performance computational resources. Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2021",
month = apr,
doi = "10.1016/j.dib.2021.106780",
language = "English (US)",
volume = "35",
journal = "Data in Brief",
issn = "2352-3409",
publisher = "Elsevier Inc.",
}