Identification of key genes and pathways in meningioma by bioinformatics analysis

Dai, Junxi; Ma, Yupo; Chu, Sheng-Hua; Le, Nanyang; Cao, Jun; Wang, Yang

doi:10.3892/ol.2018.8376

Cited by 12 publications

(11 citation statements)

References 50 publications

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“…MYC proteins drive an increased cellular proliferation and facilitate multiple aspects of tumor initiation and progression, thereby controlling all hallmarks of cancer [ 38 ]. A previous study has reported that MYC is a hub gene in meningioma, which is consistent with our results [ 39 ]. Another molecule predicted is PTGS2, which encodes the COX-2 enzyme and is expressed in many tumor types [ 40 , 41 ].…”

Section: Discussionsupporting

confidence: 94%

Identification of a Transcription Factor‐microRNA‐Gene Coregulation Network in Meningioma through a Bioinformatic Analysis

Wang

Liang

Yang

et al. 2020

BioMed Research International

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Background. Meningioma is a prevalent type of brain tumor. However, the initiation and progression mechanisms involved in the meningioma are mostly unknown. This study aimed at exploring the potential transcription factors/micro(mi)RNAs/genes and biological pathways associated with meningioma. Methods. mRNA expressions from GSE88720, GSE43290, and GSE54934 datasets, containing data from 83 meningioma samples and eight control samples, along with miRNA expression dataset GSE88721, which had 14 meningioma samples and one control sample, were integrated analyzed. The bioinformatics approaches were used for identifying differentially expressed genes and miRNAs, as well as predicting transcription factor targets related to the differentially expressed genes. The approaches were also used for gene ontology term analysis and biological pathway enrichment analysis, construction, and analysis of protein-protein interaction network, and transcription factor-miRNA-gene coregulation network construction. Results. Fifty-six upregulated and 179 downregulated genes were identified. Thirty transcription factors able to target the differentially expressed genes were predicted and selected based on public databases. One hundred seventeen overlapping genes were identified from the differentially expressed genes and the miRNAs predicted by miRWalk. Furthermore, NF-κB/IL6, PTGS2, MYC/hsa-miR-574-5p, hsa-miR-26b-5p, hsa-miR-335-5p, and hsa-miR-98-5p, which are involved in the transcription factor-miRNA-mRNA coregulation network, were found to be associated with meningioma. Conclusion. The bioinformatics analysis identified several potential molecules and relevant pathways that may represent critical mechanisms involved in the progression and development of meningioma. This work provides new insights into meningioma pathogenesis and treatments.

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Section: Discussionsupporting

confidence: 94%

Identification of a Transcription Factor‐microRNA‐Gene Coregulation Network in Meningioma through a Bioinformatic Analysis

Wang

Liang

Yang

et al. 2020

BioMed Research International

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“…By integrating results from Upstream regulator and Downstream effects tools, we can create hypotheses that explain what is going on upstream to the experimentally measured gene changes that are linked to a phenotype or other functional outcomes. For instance, regulator effects analysis (Fig 9) shows all predicted upstream genes for hippocampal TBI-induced gene changes at 24 hr are key hub genes that have essential roles in cell function and disease [55–57]. Moreover, many of the genes we find significantly altered by TBI, such as Atf3 [58], are also hub genes and/or transcriptional regulators of essential cell functions.…”

Section: Resultsmentioning

confidence: 99%

Traumatic brain injury induces long-lasting changes in immune and regenerative signaling

et al. 2019

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There are no existing treatments for the long-term degenerative effects of traumatic brain injury (TBI). This is due, in part, to our limited understanding of chronic TBI and uncertainty about which proposed mechanisms for long-term neurodegeneration are amenable to treatment with existing or novel drugs. Here, we used microarray and pathway analyses to interrogate TBI-induced gene expression in the rat hippocampus and cortex at several acute, subchronic and chronic intervals (24 hours, 2 weeks, 1, 2, 3, 6 and 12 months) after parasagittal fluid percussion injury. We used Ingenuity pathway analysis (IPA) and Gene Ontology enrichment analysis to identify significantly expressed genes and prominent cell signaling pathways that are dysregulated weeks to months after TBI and potentially amenable to therapeutic modulation. We noted long-term, coordinated changes in expression of genes belonging to canonical pathways associated with the innate immune response (i.e., NF-κB signaling, NFAT signaling, Complement System, Acute Phase Response, Toll-like receptor signaling, and Neuroinflammatory signaling). Bioinformatic analysis suggested that dysregulation of these immune mediators—many are key hub genes—would compromise multiple cell signaling pathways essential for homeostatic brain function, particularly those involved in cell survival and neuroplasticity. Importantly, the temporal profile of beneficial and maladaptive immunoregulatory genes in the weeks to months after the initial TBI suggests wider therapeutic windows than previously indicated.

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“…Recently, with the continuous development of bioinformatics and molecular biology, microarray technology has been widely used for exploring the molecular mechanisms of various diseases (9–11). In previous decades, analysis of the expression profiles of gene microarrays was used to identify several key genes and diagnostic biomarkers of IBDs, including several differentially expressed genes (DEGs) involved in different pathways, biological processes or molecular functions (12,13).…”

Section: Introductionmentioning

confidence: 99%

Identification of differentially expressed genes, associated functional terms pathways, and candidate diagnostic biomarkers in inflammatory bowel diseases by bioinformatics analysis

et al. 2019

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Inflammatory bowel diseases (IBDs), including ulcerative colitis (UC) and Crohn's disease (CD), are chronic inflammatory disorders caused by genetic influences, the immune system and environmental factors. However, the underlying pathogenesis of IBDs and the pivotal molecular interactions remain to be fully elucidated. The aim of the present study was to identify genetic signatures in patients with IBDs and elucidate the potential molecular mechanisms underlying IBD subtypes. The gene expression profiles of the GSE75214 datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in UC and CD patients compared with controls using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs were performed using DAVID. Furthermore, protein-protein interaction (PPI) networks of the DEGs were constructed using Cytoscape software. Subsequently, significant modules were selected and the hub genes were identified. In the GO and KEGG pathway analysis, the top enriched pathways in UC and CD included Staphylococcus aureus infection, rheumatoid arthritis, complement and coagulation cascades, PI3K/Akt signaling pathway and osteoclast differentiation. In addition, the GO terms in the category biological process significantly enriched by these genes were inflammatory response, immune response, leukocyte migration, cell adhesion, response to molecules of bacterial origin and extracellular matrix (ECM) organization. However, several other biological processes (GO terms) and pathways (e.g., ‘chemotaxis’, ‘collagen catabolic process’ and ‘ECM-receptor interaction’) exhibited significant differences between the two subtypes of IBD. The top 10 hub genes were identified from the PPI network using respective DEGs. Of note, the hub genes G protein subunit gamma 11 (GNG11), G protein subunit beta 4 (GNB4), Angiotensinogen (AGT), Phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) and C-C motif chemokine receptor 7 (CCR7) are disease-specific and may be used as biomarkers for differentiating UC from CD. Furthermore, module analysis further confirmed that common significant pathways involved in the pathogenesis of IBD subtypes were associated with chemokine-induced inflammation, innate immunity, adapted immunity and infectious microbes. In conclusion, the present study identified DEGs, key target genes, functional pathways and enrichment analysis of IBDs, enhancing the understanding of the pathogenesis of IBDs and also advancing the clarification of the underlying molecular mechanisms of UC and CD. Furthermore, these results may provide potential molecular targets and diagnostic biomarkers for UC and CD.

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Identification of key genes and pathways in meningioma by bioinformatics analysis

Cited by 12 publications

References 50 publications

Identification of a Transcription Factor‐microRNA‐Gene Coregulation Network in Meningioma through a Bioinformatic Analysis

Identification of a Transcription Factor‐microRNA‐Gene Coregulation Network in Meningioma through a Bioinformatic Analysis

Traumatic brain injury induces long-lasting changes in immune and regenerative signaling

Identification of differentially expressed genes, associated functional terms pathways, and candidate diagnostic biomarkers in inflammatory bowel diseases by bioinformatics analysis

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