Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma

Abstract

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.

Panoramic Visualization of Circulating MicroRNAs Across Neurodegenerative Diseases in Humans

“…ETV5 (targeted by miR-219) is a transcription factor (TF) that has been shown to be part of the molecular and functional signature of microglia that is dependent on TGFβ [96]. This gene is also suspected to be a central regulator in neurofibromatosis type 1 and low-grade optic gliomas [97]. Certain PD-associated mutations are suspected to allow binding of the ETV5 TF to the promoter for ATG12, which interferes with autophagy function and promotes the development of PD [98].…”

Gene Regulatory Network of ETS Domain Transcription Factors in Different Stages of Glioma

“…In addition to their role in tumor vascularization, ETS proteins can also regulate other aspects of tumorigenesis. In a network analysis based on complexity, as measured by betweenness, Etv5 was identified as a regulator in low-grade optic gliomas in Nf1 mutant mice, and experiments validated the increased expression of both Etv5 and its target genes in optic gliomas [ 27 ].…”

“…Gliomas can be broadly classified as diffuse and non-diffuse (circumscribed) gliomas. Diffuse gliomas, namely oligodendrogliomas and astrocytomas, exhibit similarities to glial precursors, and are identified and categorized based on the WHO classification of CNS tumors [ 28 ]. Due to their rather heterogeneous nature, the reproducibility in diagnosis of low-grade (WHO grade II/III) diffuse gliomas can be a challenge.…”

The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer‐aided detection

“…There is an increasing emergence of network theory methods in biology . In cancer, graph theory (see Glossary) is commonly used to analyze gene networks . In the case of histopathological images, three reconstructions have recurrently been used to obtain a graph that connects the elements of the image: Delaunay triangulation, Voronoi diagram (see Glossary) and minimum spanning tree .…”

“…[17][18][19][20] In cancer, graph theory (see Glossary) is commonly used to analyze gene networks. 21,22 In the case of histopathological images, three reconstructions have recurrently been used to obtain a graph that connects the elements of the image: Delaunay triangulation, Voronoi diagram (see Glossary) and minimum spanning tree. [23][24][25] However, these approaches did not consider the importance of the spatial context of the extracellular matrix (ECM) for patient outcomes.…”