Integration of gene expression and methylation to unravel biological networks in glioblastoma patients

Gadaleta, Francesco; Bessonov, Kyrylo; Steen, Kristel Van

doi:10.1002/gepi.22028

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…For example, a previous study used a joint analysis of DNA methylation and gene expression data of GBM to demonstrate that changes in DNA methylation can be associated with survival outcome (15). In addition, a recent study used a computational approach to integrate gene expression and genomic or methylation data to investigate biological networks in GBM (16). The current study used bioinformatics approaches to reanalyze the DNA methylation data deposited by Lai et al (17).…”

Section: Introductionmentioning

confidence: 99%

DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

et al. 2019

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Glioblastoma multiforme (GBM) is one of the most lethal and damaging types of human cancer. The current study was conducted to identify differentially methylated genes (DMGs) between GBM and normal controls, and to improve our understanding of GBM at the epigenetic level. The DNA methylation profile of GBM was downloaded from the Gene Expression Omnibus (GEO) database using the accession number GSE50923. The MethyAnalysis package was applied to identify DMGs between GBM and controls, which were then analyzed by functional enrichment analysis. Protein-protein interaction (PPI) networks were constructed using the hypermethylated and hypomethylated genes. Finally, transcription factors (TFs) that can regulate the hypermethylated and hypomethylated genes were predicted, followed by construction of transcriptional regulatory networks. Furthermore, another relevant dataset, GSE22867, was downloaded from the GEO database for data validation. A total of 476 hypermethylated and 850 hypomethylated genes were identified, which were mainly associated with the functions of ‘G-protein-coupled receptors ligand binding’, ‘cytokine activity’, ‘cytokine-cytokine receptor interaction’, and ‘D-glutamine and D-glutamate metabolism’. The hypermethylated gene neuropeptide Y ( NPY ) and the hypomethylated gene tumor necrosis factor ( TNF ) demonstrated high degrees in the PPI network. Forkhead box protein A1 ( FOXA1 ), potassium voltage-gated channel subfamily C member 3 ( KCNC3 ) and caspase-8 ( CASP8 ) exhibited high degrees in the transcriptional regulatory networks. In addition, the methylation profiles of NPY, TNF, FOXA1, KCNC3 and CASP8 were confirmed by another dataset. In summary, the present study systematically analyzed the DNA methylation profile of GBM using bioinformatics approaches and identified several abnormally methylated genes, providing insight into the molecular mechanism underlying GBM.

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Section: Introductionmentioning

confidence: 99%

DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

et al. 2019

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“…In addition to contending with immune cells for metabolites, certain glioblastoma cells can also avail distinctive metabolic pathways to produce unique metabolites such as 2-Hydroxyglutarate (2HG) and extracellular adenosine, which can directly suppress the immune system. While there are multiple mechanisms by which tumors can alter their metabolism and influence the immune system, we have utilized large-scale omics analysis to selectively highlight pathways that are critical to glioblastoma pathogenesis (2)(3)(4)(5).…”

Section: Introductionmentioning

confidence: 99%

Targeting Immunometabolism in Glioblastoma

et al. 2021

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We have only recently begun to understand how cancer metabolism affects antitumor responses and immunotherapy outcomes. Certain immunometabolic targets have been actively pursued in other tumor types, however, glioblastoma research has been slow to exploit the therapeutic vulnerabilities of immunometabolism. In this review, we highlight the pathways that are most relevant to glioblastoma and focus on how these immunometabolic pathways influence tumor growth and immune suppression. We discuss hypoxia, glycolysis, tryptophan metabolism, arginine metabolism, 2-Hydroxyglutarate (2HG) metabolism, adenosine metabolism, and altered phospholipid metabolism, in order to provide an analysis and overview of the field of glioblastoma immunometabolism.

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“…The extracted dense subgraphs spoke for densely connected and highly co-expressed clusters from biological networks. Gadaleta et al (2017) integrated gene expression data and DNA methylation data for glioblastoma multiforme using Regression2Net and identified potential candidate genes showing significant over representation in different cancer related pathways. Eight most connected hub genes and candidate differentially expressed genes were identified in Liu et al (2018) by integrating multiple cohort profile data sets of B-cell lymphoma.…”

Section: Introductionmentioning

confidence: 99%

Integrated multiplex network based approach for hub gene identification in oral cancer

Mahapatra

Bhuyan

Das

et al. 2021

Heliyon

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Background:The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

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Integration of gene expression and methylation to unravel biological networks in glioblastoma patients

Cited by 5 publications

References 38 publications

DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

Targeting Immunometabolism in Glioblastoma

Integrated multiplex network based approach for hub gene identification in oral cancer

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