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

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

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.

Targeting Immunometabolism in Glioblastoma

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.

VOLARE: visual analysis of disease-associated microbiome-immune system interplay

Background

Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. “Omic” methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between “omes”, and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments.

Results

To support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies—microbiome:cytokine data from fecal samples in human immunodeficiency virus (HIV), microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that users frequently generated detailed regression plots, suggesting that this detail aids the vetting of results.

Conclusions

Systematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3021-0) contains supplementary material, which is available to authorized users.

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

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.

The PI3K/AKT axis modulates AATF activity in Wilms' tumor cells

Previous studies have reported excessive expression of apoptosis‐antagonizing transcription factor (AATF) in various tumors, where it reinforces the generation and development of cancers and is linked to the clinical outcome. Nevertheless, the expression and influence of AATF in Wilms’ tumor (WT) is largely unknown. Here, we discovered that AATF expression was markedly increased in WT tissues as compared to the surrounding normal tissues. Elevated levels of AATF expression were related to tumor relapse and pulmonary metastasis, congruent with it being a predictor of clinical outcome in people suffering from WT. Proliferation, invasion, and migration of WT cells were suppressed by knockdown of AATF and promoted by AATF overexpression in vitro. Furthermore, the tumor generation capability of WT cells noticeably decreased after knockout of AATF in vivo. The phosphoinositide‐3‐kinase (PI3K)/AKT pathway modulated the activity of AATF in WT. The findings of our study indicate that AATF expression is increased in WT and can serve as a predictor of clinical outcome; in addition, it may enhance the development of WT via the PI3K/AKT axis and may be a promising marker for WT diagnosis and therapy.