TY - JOUR
T1 - Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma
AU - Pan, Yuan
AU - Duron, Christina
AU - Bush, Erin C.
AU - Ma, Yu
AU - Sims, Peter A.
AU - Gutmann, David H.
AU - Radunskaya, Ami
AU - Hardin, Johanna
N1 - Funding Information:
This work was funded by grants from the James S. McDonnell Foundation (to DHG); and the National Cancer Institute (1R01-CA195692-01 to DHG, AR, and JH). YP was supported by a McDonnell Center fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Courtney Corman for expert technical assistance. We thank Mariano Alvarez for providing us with the glioma ARACNe network. All mice were maintained on a C57Bl/6 background and procedures performed in accordance with an approved Animal Studies Committee protocol at Washington University.
Publisher Copyright:
© 2018 Pan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/5
Y1 - 2018/5
N2 - Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated mouse optic glioma models relative to non-neoplastic (normal healthy) optic nerves, as well as in the cognate human tumors (pilocytic astrocytoma) relative to normal human brain. Importantly, differential Etv5 and Etv5 network expression was not directly the result of Nf1 gene dysfunction in specific cell types, but rather reflects a property of the tumor as an aggregate tissue. Moreover, this differential Etv5 expression was independently validated at the RNA and protein levels. Taken together, the combined use of network analysis, differential RNA expression findings, and experimental validation highlights the potential of the computational network approach to provide new insights into tumor biology.
AB - Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated mouse optic glioma models relative to non-neoplastic (normal healthy) optic nerves, as well as in the cognate human tumors (pilocytic astrocytoma) relative to normal human brain. Importantly, differential Etv5 and Etv5 network expression was not directly the result of Nf1 gene dysfunction in specific cell types, but rather reflects a property of the tumor as an aggregate tissue. Moreover, this differential Etv5 expression was independently validated at the RNA and protein levels. Taken together, the combined use of network analysis, differential RNA expression findings, and experimental validation highlights the potential of the computational network approach to provide new insights into tumor biology.
UR - http://www.scopus.com/inward/record.url?scp=85047393070&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0190001
DO - 10.1371/journal.pone.0190001
M3 - Article
C2 - 29787563
AN - SCOPUS:85047393070
VL - 13
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 5
M1 - e0190001
ER -