Identification of key genes in glioblastoma-associated stromal cells using bioinformatics analysis

Sun, Chong; Tang, Dong; Yang, Guoqiang; Zhou, Xuanjun; Wang, Donghai

doi:10.3892/ol.2016.4526

Cited by 10 publications

(6 citation statements)

References 58 publications

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“…Cdk1, Ccnb1, Plk1, Top2a, and Ccna2 were determined and confirmed as hub–bottleneck nodes that serve significant roles in cell cycle function. These central genes are in accordance with previous findings from PPI network analysis (Chen et al, ; Chen et al, ). Besides, five modules were selected in the PPI network, genes of which were mainly downregulated in these modules and enriched in cell cycle and DNA replication process.…”

Section: Discussionsupporting

confidence: 92%

Systems toxicology assessment revealed the impact of graphene‐based materials on cell cycle regulators

Farahani

Rezaei‐Tavirani

Zali

et al. 2020

J Biomedical Materials Res

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Understanding the cellular and molecular toxicity of graphene and its derivatives is essential for their biomedical applications. Herein, gene expression profile of graphene-exposed cells was retrieved from the Gene expression omnibus database.Differentially expressed genes and their functional roles were then investigated through the pathway, protein-protein interaction (PPI) network, and module analysis.High degree (hub) and high betweenness centrality (bottleneck) nodes were subsequently identified. The functional analysis of central genes indicated that these graphene-gene interactions could be of great value for further investigation. Accordingly, we also followed the expression of five hub-bottleneck genes in graphenetreated murine peritoneal macrophages and human breast cancer cell line by realtime PCR. The five hub-bottleneck genes related to graphene cytotoxicity; CDK1, CCNB1, PLK1, TOP2A, and CCNA2 were identified through network analysis, which were highly correlated with regulation of cell cycle processes. The module analysis indicated the cell cycle pathway to be the predominant one. Gene expression evaluation showed downregulation of these genes in the macrophages and cancer cells treated with graphene. These results provided some new intuitions concerning the graphene-cell interactions and unveiled targeting critical cell cycle regulators. The present study indicated some toxic effects of graphene-based materials through systems toxicology assessment. Integrating gene expression and PPI network may help explaining biological responses of graphene and lead to beneficial impacts in nanomedicine. K E Y W O R D Scell cycle, gene expression, graphene, protein-protein interaction network, systems toxicology

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Section: Discussionsupporting

confidence: 92%

Systems toxicology assessment revealed the impact of graphene‐based materials on cell cycle regulators

Farahani

Rezaei‐Tavirani

Zali

et al. 2020

J Biomedical Materials Res

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“…Protein-protein interaction network analysis has been a useful tool to identify key regulators in human diseases, such as laryngeal carcinoma [20], colorectal cancer [21,22] and glioma [23]. Here, we for the first time constructed NAFLD associated PPI networks.…”

Section: Discussionmentioning

confidence: 99%

Identification of key pathways and genes in nonalcoholic fatty liver disease using bioinformatics analysis

Liu¹,

Lin²,

Chen³

et al. 2020

aoms

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A b s t r a c t Introduction: Nonalcoholic fatty liver disease (NAFLD) is one of the most common types of liver disease in the world. However, the molecular mechanisms regulating the development of NAFLD have remained unclear. Material and methods: In the present study, we analyzed two public datasets (GSE48452 and GSE89632) to identify differentially expressed mRNAs in the progression of NAFLD. Next, we performed bioinformatics analysis to explore key pathways underlying NAFLD development. Results: Gene Ontology (GO) analysis showed that differentially expressed genes (DEGs) were mainly involved in regulating a series of metabolism-related pathways (including proteolysis and lipid metabolism), cell proliferation and adhesion, the inflammatory response, and the immune response. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that DEGs in NAFLD were mainly enriched in the insulin signaling pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway, and p53 signaling pathway. We also constructed protein-protein interaction (PPI) networks for these DEGs. Interestingly, we observed that key hub nodes in PPI networks were also associated with the progression of hepatocellular carcinoma (HCC). Conclusions: Taken together, our analysis revealed that a series of pathways, such as metabolism and PPAR signaling pathways, were involved in NAFLD development. Moreover, we observed that many DEGs in NAFLD were also dysregulated in HCC. Although further validation is still needed, we believe this study could provide useful information to explore the potential candidate biomarkers for diagnosis, prognosis, and drug targets of NAFLD.

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“…According to the biological process enrichment results, CCNA2 was enriched in cell cycle and oocyte meiosis pathways, in which CDK1, BUB1 and CDC20 were also involved. These genes are also known as key genes playing a crucial role in promoting GBM growth (Chen et al, 2016). Repression of these genes when the cells were treated with THTMP indicates that this compound strongly inhibits the growth of GBM throughout cell cycle arrest.…”

Section: Discussionmentioning

confidence: 99%

Alkylaminophenol Induces G1/S Phase Cell Cycle Arrest in Glioblastoma Cells Through p53 and Cyclin-Dependent Kinase Signaling Pathway

et al. 2019

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Glioblastoma (GBM) is the most common type of malignant brain tumor in adults. We show here that small molecule 2-[(3,4-dihydroquinolin-1(2H)-yl)(p-tolyl)methyl]phenol (THTMP), a potential anticancer agent, increases the human glioblastoma cell death. Its mechanism of action and the interaction of selective signaling pathways remain elusive. Three structurally related phenolic compounds were tested in multiple glioma cell lines in which the potential activity of the compound, THTMP, was further validated and characterized. Upon prolonged exposer to THTMP, all glioma cell lines undergo p53 and cyclin-dependent kinase mediated cell death with the IC 50 concentration of 26.5 and 75.4 μM in LN229 and Snb19, respectively. We found that THTMP strongly inhibited cell growth in a dose and in time dependent manner. THTMP treatment led to G1/S cell cycle arrest and apoptosis induction of glioma cell lines. Furthermore, we identified 3,714 genes with significant changes at the transcriptional level in response to THTMP. Further, a transcriptional analysis (RNA-seq) revealed that THTMP targeted the p53 signaling pathway specific genes causing DNA damage and cell cycle arrest at G1/S phase explained by the decrease of cyclin-dependent kinase 1, cyclin A2, cyclin E1 and E2 in glioma cells. Consistently, THTMP induced the apoptosis by regulating the expression of Bcl-2 family genes and reactive oxygen species while it also changed the expression of several anti-apoptotic genes. These observations suggest that THTMP exerts proliferation activity inhibition and pro-apoptosis effects in glioma through affecting cell cycle arrest and intrinsic apoptosis signaling. Importantly, THTMP has more potential at inhibiting GBM cell proliferation compared to TMZ, the current chemotherapy treatment administered to GBM patients; thus, we propose that THTMP may be an alternative therapeutic option for glioblastoma.

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Identification of key genes in glioblastoma-associated stromal cells using bioinformatics analysis

Cited by 10 publications

References 58 publications

Systems toxicology assessment revealed the impact of graphene‐based materials on cell cycle regulators

Systems toxicology assessment revealed the impact of graphene‐based materials on cell cycle regulators

Identification of key pathways and genes in nonalcoholic fatty liver disease using bioinformatics analysis

Alkylaminophenol Induces G1/S Phase Cell Cycle Arrest in Glioblastoma Cells Through p53 and Cyclin-Dependent Kinase Signaling Pathway

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