» Articles » PMID: 31420939

Integrated Systems-genetic Analyses Reveal a Network Target for Delaying Glioma Progression

Abstract

Objective: To identify a convergent, multitarget proliferation characteristic for astrocytoma transformation that could be targeted for therapy discovery.

Methods: Using an integrated functional genomics approach, we prioritized networks associated with astrocytoma progression using the following criteria: differential co-expression between grade II and grade III IDH1-mutated and 1p/19q euploid astrocytomas, preferential enrichment for genetic risk to cancer, association with patient survival and sample-level genomic features. Drugs targeting the identified multitarget network characteristic for astrocytoma transformation were computationally predicted using drug transcriptional perturbation data and validated using primary human astrocytoma cells.

Results: A single network, M2, consisting of 177 genes, was associated with glioma progression on the basis of the above criteria. Functionally, M2 encoded physically interacting proteins regulating cell cycle processes and analysis of genome-wide gene-regulatory interactions using mutual information and DNA-protein interactions revealed the known regulators of cell cycle processes FoxM1, B-Myb, and E2F2 as key regulators of M2. These results suggest functional disruption of M2 via gene mutation or altered expression as a convergent pathway regulating astrocytoma transformation. By considering M2 as a multitarget drug target regulating astrocytoma transformation, we identified several drugs that are predicted to restore M2 expression in anaplastic astrocytoma toward its low-grade profile and of these, we validated the known antiproliferative drug resveratrol as down-regulating multiple nodes of M2 including at nanomolar concentrations achievable in human cerebrospinal fluid by oral dosing.

Interpretation: Our results identify M2 as a multitarget network characteristic for astrocytoma progression and encourage M2-based drug screening to identify new compounds for preventing glioma transformation.

Citing Articles

Identification of gene regulatory networks affected across drug-resistant epilepsies.

Francois L, Romagnolo A, Luinenburg M, Anink J, Godard P, Rajman M Nat Commun. 2024; 15(1):2180.

PMID: 38467626 PMC: 10928184. DOI: 10.1038/s41467-024-46592-2.


Involvement of Resveratrol against Brain Cancer: A Combination Strategy with a Pharmaceutical Approach.

Karthika C, Najda A, Klepacka J, Zehravi M, Akter R, Akhtar M Molecules. 2022; 27(14).

PMID: 35889532 PMC: 9320031. DOI: 10.3390/molecules27144663.


Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.

Savino A, Provero P, Poli V Int J Mol Sci. 2020; 21(24).

PMID: 33322692 PMC: 7764314. DOI: 10.3390/ijms21249461.


Systematic analysis to identify transcriptome-wide dysregulation of Alzheimer's disease in genes and isoforms.

Fan C, Chen K, Zhou J, Wong P, He D, Huang Y Hum Genet. 2020; 140(4):609-623.

PMID: 33140241 DOI: 10.1007/s00439-020-02230-7.


The Plant-Derived Compound Resveratrol in Brain Cancer: A Review.

Kiskova T, Kubatka P, Busselberg D, Kassayova M Biomolecules. 2020; 10(1).

PMID: 31963897 PMC: 7023272. DOI: 10.3390/biom10010161.

References
1.
Yan J, Enge M, Whitington T, Dave K, Liu J, Sur I . Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell. 2013; 154(4):801-13. DOI: 10.1016/j.cell.2013.07.034. View

2.
Malkki H . Trial Watch: Glioblastoma vaccine therapy disappointment in Phase III trial. Nat Rev Neurol. 2016; 12(4):190. DOI: 10.1038/nrneurol.2016.38. View

3.
Basso K, Margolin A, Stolovitzky G, Klein U, Dalla-Favera R, Califano A . Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005; 37(4):382-90. DOI: 10.1038/ng1532. View

4.
Willsey A, Morris M, Wang S, Willsey H, Sun N, Teerikorpi N . The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders. Cell. 2018; 174(3):505-520. PMC: 6247911. DOI: 10.1016/j.cell.2018.06.016. View

5.
Anders S, Pyl P, Huber W . HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014; 31(2):166-9. PMC: 4287950. DOI: 10.1093/bioinformatics/btu638. View