Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk
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Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China
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Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA
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Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
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Division of Pediatric Surgery, Department of Surgery, Penn State College of Medicine, Hershey, PA 17033, USA
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Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA
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Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA
7
Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, 00197 Rome, Italy
*
Authors to whom correspondence should be addressed.
Cancers 2020, 12(8), 2086; https://doi.org/10.3390/cancers12082086
Received: 20 June 2020
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Revised: 24 July 2020
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Accepted: 25 July 2020
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Published: 28 July 2020
(This article belongs to the Collection Application of Bioinformatics in Cancers)
Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-risk patients by which to reconstruct large patient-specific idopNetworks. By converting these networks into risk-specific representations, we found that the shift in patients from a low to high risk or from a high to low risk might be due to the reciprocal change of hub regulators. By altering the directions of regulation exerted by these hubs, it may be possible to reduce a high risk to a low risk. Results from a holistic, systems-oriented paradigm through idopNetworks can potentially enable oncologists to experimentally identify the biomarkers of neuroblastoma and other cancers.
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Keywords:
gene regulatory network; neuroblastoma; idopNetwork; hub; gene co-regulation
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MDPI and ACS Style
Sun, L.; Jiang, L.; Grant, C.N.; Wang, H.-G.; Gragnoli, C.; Liu, Z.; Wu, R. Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk. Cancers 2020, 12, 2086. https://doi.org/10.3390/cancers12082086
AMA Style
Sun L, Jiang L, Grant CN, Wang H-G, Gragnoli C, Liu Z, Wu R. Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk. Cancers. 2020; 12(8):2086. https://doi.org/10.3390/cancers12082086
Chicago/Turabian StyleSun, Lidan, Libo Jiang, Christa N. Grant, Hong-Gang Wang, Claudia Gragnoli, Zhenqiu Liu, and Rongling Wu. 2020. "Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk" Cancers 12, no. 8: 2086. https://doi.org/10.3390/cancers12082086
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