Identification of genes involved in Epstein-Barr virus-associated nasopharyngeal carcinoma

Abstract

Nasopharyngeal carcinoma (NPC) is the most common cancer originating from the nasopharynx, and can be induced by infection with Epstein-Barr virus (EBV). To study the mechanisms of EBV-associated NPC, a microarray of the GSE12452 dataset was analyzed. GSE12452 was downloaded from Gene Expression Omnibus and consisted of 31 NPC samples and 10 normal healthy nasopharyngeal tissue samples. The differentially-expressed genes (DEGs) were screened using the linear models for microarray data package in R. Using Database for Annotation, Visualization and Integrated Discovery software, potential functions of the DEGs were predicted by Gene Ontology and pathway enrichment analyses. With the information from the Search Tool for the Retrieval of Interacting Genes/Proteins database, the protein-protein interaction (PPI) network was visualized by Cytoscape. Furthermore, modules of the PPI network were searched using ClusterONE in Cytoscape. A total of 951 DEGs were screened in the NPC samples compared with the normal healthy nasopharyngeal tissue samples. Function enrichment indicated that the upregulated genes were associated with the cell cycle, cytoskeleton organization and DNA metabolism. Meanwhile, the downregulated genes were mainly associated with cell differentiation, hormone metabolism, inflammatory response and immune response. PPI networks for the DEGs suggested that upregulated mitotic arrest deficient 2-like 1 (MAD2L1; degree=133), proliferating cell nuclear antigen (PCNA; degree=125) and cyclin B1 (CCNB1; degree=115), and downregulated member A1 of aldehyde dehydrogenase 1 (ALDH1A1; degree=15) may be of great importance as they exhibited higher degrees on interaction. Mucin 1 (MUC1) was a key node of module 4. Overall, the study indicated that MAD2L1, CCNB1, PCNA, ALDH1A1 and MUC1 may have a correlation with EBV-associated NPC.

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

“…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).…”

“…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.…”

“…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.…”

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

“…performed a systematic bioinformatics analysis to retrieve the NPC‐related DEGs from GSE12452 and found the DEGs were closely associated with many cellular processes of NPC, including cell cycle and immune response metabolic process of DNA. Moreover, the study conducted by Wang et al . 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.…”

“…34 Ge et al 35 performed a systematic bioinformatics analysis to retrieve the NPC-related DEGs from GSE12452 and found the DEGs were closely associated with many cellular processes of NPC, including cell cycle and immune response metabolic process of DNA. Moreover, the study conducted by Wang et al 36 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.…”