Document Type
Article
Publication Date
7-28-2020
Abstract
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.
Recommended Citation
Sun, Lidan; Jiang, Libo; Grant, Christa N; Wang, Hong-Gang; Gragnoli, Claudia; Liu, Zhenqiu; and Wu, Rongling, "Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk" (2020). Division of Endocrinology, Diabetes and Metabolic Diseases Faculty Papers. Paper 4.
https://jdc.jefferson.edu/endocrinologyfp/4
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
32731407
Language
English
Comments
This article is the author’s final published version in Cancers, Volume 12, Issue 8, August 2020, Article number 2086
The published version is available at https://doi.org/10.3390/cancers12082086. Copyright © Sun et al.