Identification of key genes involved in nasopharyngeal carcinoma

Jiang, Xue; Feng, Lichun; Dai, Baoqiang; Li, Liping; Lu, Weiwei

doi:10.1016/j.bjorl.2016.09.003

Cited by 14 publications

(15 citation statements)

References 47 publications

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“…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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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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“…The analysis for differential gene expression between tumor and normal tissue was performed using GeneSpring software version 11.5 (Agilent Technologies, Inc., Santa Clara, CA, USA). GSE34573, submitted by Hu et al (18), was based on the Affymetrix GPL570 platform [GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array] and consisted of 16 (21). The Database for Annotation, Visualization, and Integrated Discovery (DAVID; david.abcc.ncifcrf.gov) was used to integrate functional genomic annotations (22).…”

Section: Methodsmentioning

confidence: 99%

“…Gene microarrays, which are high-throughput platforms for the analysis of gene expression, allow the identification of hundreds of differentially expressed genes (DEGs) involved in various signaling pathways, molecular functions and biological processes (12)(13)(14). However, only limited overlaps were observed when comparative analysis of the DEGs in independent studies was conducted (15,16). Combining microarray technologies and bioinformatics tools enhances the efficiency and accuracy of analysis (15,16).…”

Section: Introductionmentioning

confidence: 99%

“…However, only limited overlaps were observed when comparative analysis of the DEGs in independent studies was conducted (15,16). Combining microarray technologies and bioinformatics tools enhances the efficiency and accuracy of analysis (15,16). Wang et al (15) and Jiang et al (16) analyzed the GSE12452 dataset, which contained 31 NPC samples and 10 normal control samples, to identify the key genes involved in NPC.…”

Section: Introductionmentioning

confidence: 99%

“…Combining microarray technologies and bioinformatics tools enhances the efficiency and accuracy of analysis (15,16). Wang et al (15) and Jiang et al (16) analyzed the GSE12452 dataset, which contained 31 NPC samples and 10 normal control samples, to identify the key genes involved in NPC. However, the number of samples included in these two studies was relatively small, and the molecular pathways involved in NPC carcinogenesis remain unclear.…”

Section: Introductionmentioning

confidence: 99%

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Identification of key pathways and genes in nasopharyngeal carcinoma using bioinformatics analysis

Zhu

Qian

et al. 2019

Oncol Lett

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Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in the head and neck. The aim of the current study was to identify the key pathways and genes involved in NPC through bioinformatics analysis and to identify potential molecular mechanisms underlying NPC proliferation and progression. Three gene expression profiles (GSE12452, GSE34573 and GSE64634) were downloaded from the Gene Expression Omnibus database. A total of 76 samples were analyzed, of which 59 were NPC samples and 17 were normal samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were subsequently conducted. The protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Analysis of GSE12452, GSE34573 and GSE64634 datasets identified 1,301 (553 upregulated and 748 downregulated), 1,232 (348 upregulated and 884 downregulated) and 1,218 (555 upregulated and 663 downregulated) DEGs, respectively. Using Venn diagram analysis, 268 DEGs (59 upregulated and 209 downregulated) that intersected all three datasets, were selected for further analysis. The results of GO analysis revealed that upregulated DEGs were significantly enriched in biological processes, including 'cell adhesion', 'cell division', 'mitosis' and 'mitotic cell cycle'. The downregulated DEGs were mainly enriched in 'microtubule-based movement', 'cilium movement', 'cilium axoneme assembly' and 'epithelial cell differentiation'. The KEGG pathway analysis results revealed that the upregulated DEGs were highly associated with several pathways, including 'extracellular matrix-receptor interaction', 'human papillomavirus infection', 'arrhythmogenic right ventricular cardiomyopathy' and 'focal adhesion', whereas the downregulated DEGs were enriched in 'metabolic pathways', 'Huntington's disease', 'fluid shear stress and atherosclerosis' and 'chemical carcinogenesis'. On the basis of the PPI network of the DEGs, the following top 10 hub genes were identified: Dynein axonemal light intermediate chain 1, dynein axonemal intermediate chain 2, calmodulin 1, coiled-coil domain containing 114, dynein axonemal heavy chain 5, radial spoke head 9 homolog, radial spoke head component 4A, NDC80 kinetochore complex component, thymidylate synthetase and coiled-coil domain containing 39. In conclusion, by performing a comprehensive bioinformatics analysis of DEGs, putative targets that could be used to elucidate the molecular mechanisms underlying NPC were identified.

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Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis

et al. 2023

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Cervical cancer is the primary cause of mortality among women in developing countries. Preventing cervical cancer is partially possible through early vaccination against the human papillomavirus, the most common cause of the disease. Nevertheless, it is imperative to understand the genetics of the disease progression to develop new therapeutic strategies. The present study aims to identify potential genes and associated pathways associated with cervical squamous cell carcinoma progression. We used an integrative approach by combining differential expression analysis, network biology, and functional enrichment analysis with survival analysis. In the present study, differential expression analysis of the microarray‐based gene expression profiles of cervical cancer resulted in identifying a total of 544 significantly differentially expressed genes (DEGs). Further, centrality and network vulnerability analysis of the protein–protein interaction network (PPIN) and not well documented in cervical cancer, resulted in seven proteins (FN1, MCM5, TRIP13, KIF11, TTK, CDC45, and BUB1B), in which four proteins were vulnerable. These genes are mostly enriched in biological processes of cell division, mitotic nuclear division, cell cycle checkpoint, and cell proliferation in gene ontology analysis. The KEGG pathway enrichment analysis of the proteins lists them as mainly associated with the cell cycle. In the survival analysis, it was found that the genes MCM5, FN1, KIF11, and CDC45 were statistically significant prognostic factors for cervical cancer. The outcome of the current study identifies and explores the key role of the candidate genes involved in the progression of cervical cancer.

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Identification of key genes involved in nasopharyngeal carcinoma

Abstract: PTGS2, FN1, CXCL9, CXCL10, ZIC2 and OVOL1 might play roles in nasopharyngeal carcinoma.

Cited by 14 publications

References 47 publications

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

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

Identification of key pathways and genes in nasopharyngeal carcinoma using bioinformatics analysis

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis

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