Identification of genes and pathways in nasopharyngeal carcinoma by bioinformatics analysis

Chen, Fang; Shen, Changwei; Wang, Xiaoqi; Wang, Qianqian; Liu, Qing; Yu, Chaosheng; Lv, Jieyu; He, Jingjing; Wen, Zhong

doi:10.18632/oncotarget.19478

Cited by 25 publications

(22 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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“…revealed that GSE12452 from the GEO database included 31 NPC samples and 10 normal samples and highlighted MAD2L1, CCNB1, PCNA, ALDH1A1, and MUC1 as the genes that were correlated with NPC. In addition, the DEGs retrieved from the expression data sets (GSE12452 and GSE13597) and hub genes identified from PPI were reported to highly participate in biological processes of NPC and these molecular factors could improve the understanding of the underlying mechanism of NPC, thus providing novel insights into the diagnosis and treatment of NPC . Similarly, Jiang et al .…”

Section: Discussionmentioning

confidence: 98%

“…In addition, the DEGs retrieved from the expression data sets (GSE12452 and GSE13597) and hub genes identified from PPI were reported to highly participate in biological processes of NPC and these molecular factors could improve the understanding of the underlying mechanism of NPC, thus providing novel insights into the diagnosis and treatment of NPC. 37 Similarly, Jiang et al 38 used the expression data set GSE12452 to investigate the underlying mechanism of NPC and highlighted the DEGs (PTGS2, FN1, CXCL9, CXCL10, ZIC2, and OVOL1) as potential therapeutic targets for NPC. Figure 4 The intersection of nasopharyngeal carcinoma-related DEGs and genes from the blue module.…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Therapeutic Gene Markers in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

Xue

Cao

Wang

et al. 2019

Clinical Translational Sci

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Nasopharyngeal carcinoma (NPC) is a common cancer found in the nasopharynx with high metastatic and invasive nature. Increasing evidences have identified the critical role of gene therapy in NPC treatment. Hence, this study was designed to identify specific gene markers that affected NPC progression through gene expression profile analysis. NPC‐related gene expression data set gene set enrichment (GSE)53819 were retrieved and analyzed to screen out differentially expressed genes (DEGs), followed by determination of their expression in noncancerous tissues and NPC specimens. Next, weighted gene co‐expression network analysis (WGCNA) was conducted on DEGs to obtain tumor‐associated gene modules. Genes in those modules were intersected with DEGs for gene ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Then protein‐protein interaction network of tumor‐associated genes was constructed to select genes most closely linked to NPC. Afterward, expression of chromosome 9 open reading frame 24 (c9orf24), primary ciliary dyskinesia protein 1 (PCDP1), and leucine‐rich repeat‐containing protein 46 (LRRC46) was detected in GSE53819 and further verified in GSE12452 and GSE64634. Differential analysis on GSE53819 found that 2,173 genes were aberrantly expressed in NPC, among which 917 genes are upregulated and 1,256 genes are downregulated. WGCNA showed that genes were enriched in 17 modules and 727 genes exhibited ectopic expression in NPC and enriched in cytokine‐cytokine receptor interaction, cytochrome P450, and chemical carcinogenesis signaling pathways, among which c9orf24, PCDP1, and LRRC46 were poorly expressed in NPC. Therefore, c9orf24, PCDP1, and LRRC46 might serve as prominent diagnostic markers for NPC, which presents new insights for NPC therapy.

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“…Integration and analysis of gene chip data to obtain more consistent results has become problematic. Some researchers choose to do meta-analysis of multiple gene expression profiles based on different platforms or from a number of types of cells, whereas others chose to integrate data based on the same platform (25)(26)(27). Additional molecular biology experiments may be needed to conclude which method provides more accurate results.…”

Section: Discussionmentioning

confidence: 99%

Investigation of differentially expressed genes in nasopharyngeal carcinoma by integrated bioinformatics analysis

et al. 2019

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Nasopharyngeal carcinoma (NPC) is a common malignancy of the head and neck. The aim of the present study was to conduct an integrated bioinformatics analysis of differentially expressed genes (DEGs) and to explore the molecular mechanisms of NPC. Two profiling datasets, GSE12452 and GSE34573, were downloaded from the Gene Expression Omnibus database and included 44 NPC specimens and 13 normal nasopharyngeal tissues. R software was used to identify the DEGs between NPC and normal nasopharyngeal tissues. Distributions of DEGs in chromosomes were explored based on the annotation file and the CYTOBAND database of DAVID. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied. Additionally, a protein-protein interaction (PPI) network, constructed using the STRING database and visualized by Cytoscape, was used to identify hub genes, key modules and important transcription factors (TFs). A total of 906 DEGs were identified; 434 (47.90%) DEGs were upregulated and 472 (52.10%) were downregulated. The DEGs were demonstrated to be enriched in chromosome 7p15-p14, 2q31, 1q21-q22, 1q21, 4q21 and 1p31-p22. DEGs were mainly enriched for the following GO terms: 'Cilium movement', 'microtubule bundle formation' and 'axoneme assembly'. KEGG pathway enrichment analysis revealed that pathways for 'cell cycle', 'DNA replication', 'interleukin-17 signaling', 'amoebiasis' and 'glutathione metabolism' were enriched. In addition, a PPI network comprising 867 nodes and 1,241 edges was constructed. Finally, five hub genes (aurora kinase A, cell division cycle 6, mitotic arrest deficient 2-like 1, DNA topoisomerase 2α and TPX2 microtubule nucleation factor), 8 modules, and 14 TFs were identified. Modules analysis revealed that cyclin-dependent kinase 1 and exportin 1 were involved in the pathway of Epstein-Barr virus infection. In summary, the hub genes, key modules and TFs identified in this study may promote our understanding of the pathogenesis of NPC and require further in-depth investigation.

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Identification of genes and pathways in nasopharyngeal carcinoma by bioinformatics analysis

Cited by 25 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 Potential Therapeutic Gene Markers in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

Investigation of differentially expressed genes in nasopharyngeal carcinoma by integrated bioinformatics analysis

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