The identification of key genes in nasopharyngeal carcinoma by bioinformatics analysis of high-throughput data

Novel prognostic biomarkers in nasopharyngeal carcinoma unveiled by mega-data bioinformatics analysis

Nasopharyngeal carcinoma (NPC) is commonly diagnosed at an advanced stage with a high incidence rate in Southeast Asia and Southeast China. However, the limited availability of NPC patient survival data in public databases has resulted in less rigorous studies examining the prediction of NPC survival through construction of Kaplan-Meier curves. These studies have primarily relied on small samples of NPC patients with progression-free survival (PFS) information or data from head and neck squamous cell carcinoma (HNSCC) studies almost without NPC patients. Thus, we coanalyzed RNA expression profiles in eleven datasets (46 normal (control) vs 160 tumor (NPC)) downloaded from the Gene Expression Omnibus (GEO) database and survival data provided by Jun Ma from Sun Yat-sen University. Then, differential analysis, gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and network analysis were performed using STRING database. After that, 2142 upregulated differentially expressed genes (DEGs) and 3857 downregulated DEGs were screened. Twenty-five of them were identified as hub genes, which were enriched in several pathways (cilium movement, extracellular matrix structural constituent, homologous recombination and cell cycle). Utilizing the comprehensive dataset we amassed from GEO database, we conducted a survival analysis of DEGs and subsequently constructed survival models. Seven DEGs (RASGRP2, MOCOS, TTC9, ARHGAP4, DPM3, CD37, and CD72) were identified and closely related to the survival prognosis of NPC. Finally, qRT-PCR, WB and IHC were performed to confirm the elevated expression of RASGRP2 and the decreased expression of TTC9, CD37, DPM3 and ARHGAP4, consistent with the DEG analysis. Conclusively, our findings provide insights into the novel prognostic biomarkers of NPC by mega-data bioinformatics analysis, which suggests that they may serve special targets in the treatment of NPC.

The Diagnostic and Prognostic Value of Plasma Circulating CircNUP98 for Nasopharyngeal Carcinoma

BACKGROUND

Our preliminary sequencing analysis revealed increased expression levels of circNUP98 in nasopharyngeal carcinoma (NPC). This study was therefore carried out to explore the role of circNUP98 in NPC.

METHODS

The present study enrolled 56 patients with NPC, 44 patients with cervical lymphadenitis (CL), 50 patients with nose bleeding (NB), 50 patients with chronic sinusitis (CS), 50 patients with lymph node tuberculosis (LNT), and 50 healthy controls (Control). Plasma samples were obtained from all patients and the controls. In addition, NPC and paired non-tumor tissue samples were collected from the 56 NPC patients. The expression of circNUP98 in both tissue and plasma samples was determined by RT-qPCR. The 56 NPC patients were followed up for 5 years to analyze the associations between plasma expression of circNUP98 and the survival of patients. The diagnostic value of circNUP98 for NPC was analyzed through ROC curve analysis.

RESULTS

The plasma expression levels of circNUP98 were only increased in NPC, but not in CL, NB, CS and LNT groups compared to that in the Control group. In addition, increased expression levels of circNUP98 were observed in NPC tissues compared to that in non-tumor tissues. Plasma circNUP98 was closely correlated with circNUP98 in NPC tissues, but not circNUP98 in non-tumor tissues. With plasma circNUP98 as a biomarker, NPC patients were separated from CL, NB, CS, LNT, and the Control groups. The Plasma expression of circNUP98 was found to be positively correlated with the poor survival of patients. Moreover, plasma circNUP98 was only closely associated with tumor metastasis, but not tumor size.

CONCLUSION

The expression of circNUP98 in plasma may be used to diagnose NPC and predict patients' survival.

Role of Receptor Interacting Protein (RIP) kinases in cancer

The Receptor Interacting Protein (RIP) kinase family consists of seven Serine/Threonine kinases, which plays a key signaling role in cell survival and cell death. Each RIP family member contains a conserved kinase domain and other domains that determine the specific kinase function through protein-protein interactions. RIP1 and RIP3 are best known for their critical roles in necroptosis, programmed necrosis and a non-apoptotic inflammatory cell death process. Dysregulation of RIP kinases contributes to a variety of pathogenic conditions such as inflammatory diseases, neurological diseases, and cancer. In cancer cells, alterations of RIP kinases at genetic, epigenetic and expression levels are frequently found, and suggested to promote tumor progression and metastasis, escape of antitumor immune response, and therapeutic resistance. However, RIP kinases can be either pro-tumor or anti-tumor depending on specific tumor types and cellular contexts. Therapeutic agents for targeting RIP kinases have been tested in clinical trials mainly for inflammatory diseases. Deregulated expression of these kinases in different types of cancer suggests that they represent attractive therapeutic targets. The focus of this review is to outline the role of RIP kinases in cancer, highlighting potential opportunities to manipulate these proteins in cancer treatment.

Disease-related mutations in PI3Kγ disrupt regulatory C-terminal dynamics and reveal a path to selective inhibitors

Class I Phosphoinositide 3-kinases (PI3Ks) are master regulators of cellular functions, with the class IB PI3K catalytic subunit (p110γ) playing key roles in immune signalling. p110γ is a key factor in inflammatory diseases and has been identified as a therapeutic target for cancers due to its immunomodulatory role. Using a combined biochemical/biophysical approach, we have revealed insight into regulation of kinase activity, specifically defining how immunodeficiency and oncogenic mutations of R1021 in the C-terminus can inactivate or activate enzyme activity. Screening of inhibitors using HDX-MS revealed that activation loop-binding inhibitors induce allosteric conformational changes that mimic those in the R1021C mutant. Structural analysis of advanced PI3K inhibitors in clinical development revealed novel binding pockets that can be exploited for further therapeutic development. Overall, this work provides unique insights into regulatory mechanisms that control PI3Kγ kinase activity and shows a framework for the design of PI3K isoform and mutant selective inhibitors.

Screening and bioinformatical analysis of differentially expressed genes in nasopharyngeal carcinoma

Objective: To identify differentially expressed genes via bioinformatical analysis for nasopharyngeal carcinoma (NPC) and explore potential biomarkers for NPC.

Methods: We downloaded the NPC gene expression datasets (GSE40290, GSE53819) and obtained differentially expressed genes (DEGs) via GEO2R. Functional analysis of DEGs was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In order to explore the interaction of DEGs and screen the core genes, we established protein-protein interaction (PPI) network. Then the expression level, prognostic and diagnostic analysis of the core genes in NPC were performed to reveal their potential effects on NPC. Furthermore, we obtained the transcription factors (TF) and microRNAs of core genes to construct the coregulatory network.

Results: We obtained 124 up-regulated genes and 190 down-regulated genes in total. These genes were found to be related to signal transduction, extracellular matrix organization and cell adhesion based on GO analysis. KEGG analysis revealed that the NF-kappa B (NF-κB) signaling pathway, pathways in cancer were mainly enriched signaling pathways. 25 core genes were obtained by constructing PPI network. Then the high expression of 10 core genes in NPC were verified via GEPIA, Oncomine databases and laboratory experiments. The TF-microRNA coregulatory network of the 10 core genes was built. Survival and diagnostic analysis indicated that SPP1 had negative influence on the prognosis of NPC patients based on two datasets and nine up-regulated core genes (FN1, MMP1, MMP3, PLAU, PLAUR, SERPINE1, SPP1, COL8A1, COL10A1) might be diagnostic markers for NPC.

Conclusions: Core genes of NPC were screened out by bioinformatical analysis in the present study and these genes may serve as prognostic and diagnostic biomarkers for NPC.

Transcriptomic Analyses Reveal Gene Expression Profiles and Networks in Nasopharyngeal Carcinoma

Background

Nasopharyngeal carcinoma (NPC) is a rare but highly aggressive tumor that is predominantly encountered in Southeast Asia and China in particular. Aside from radiotherapy, no effective therapy that specifically treats NPC is available, including targeted drugs. Finding more sensitive biomarkers is important for new drug discovery and for evaluating patient prognosis.

Methods

mRNA expression datasets from the Gene Expression Omnibus database (GSE53819, GSE64634, and GSE40290) were selected. After all samples in each dataset were subjected to quality control using principal component analyses, the qualified samples were used for additional analyses. The genes that were significantly expressed in each dataset were intersected to identify the most significant of these. Gene functional enrichment analyses were performed on these genes, using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. The protein–protein interaction network of selected genes was analyzed using the Search Tool for the Retrieval of Interacting Genes database. Significantly, differentially expressed genes were further verified with two RNA-seq datasets (GSE68799 and GSE12452), as well as in clinical samples.

Results

In all, 34 (8 upregulated genes and 26 downregulated) genes were identified as significantly differentially expressed. The immune response and the regulation of cell proliferation were the most enriched biological GO terms. Using reverse transcription quantitative real-time PCR (RT-qPCR), the genes MMP1, AQP9, and TNFAIP6 were detected to be upregulated, and FAM3D, CR2, and LTF were downregulated in NPC tissue samples.

Conclusion

This study provides information on the genes that may be involved in the development of NPC and suggests possible druggable targets and biomarkers for diagnosing and evaluating the prognosis of NPC.

The change in tumor volume after induction chemotherapy with docetaxel plus cisplatin in 259 nasopharyngeal carcinoma patients

PURPOSE

To assess the tumor volume change after induction chemotherapy with docetaxel plus cisplatin (DP) in nasopharyngeal carcinoma (NPC).

METHODS

A total of 259 non-metastatic NPC patients who received DP induction chemotherapy were retrospectively reviewed. Gross tumor volume of nasopharynx and lymph nodes (GTVnx and GTVnd) were contoured before and after chemotherapy. Univariate and multivariate analyses were performed to identify factors associated with tumor volume reduction.

RESULTS

For GTVnx, the mean volume before and after chemotherapy were 43.2 cm³ vs 37.4 cm³ (p < 0.001) and the mean relative volume reduction was 12.7%. For GTVnd, the mean volume before and after chemotherapy were 24.6 cm³ vs 17.6 cm³ (p < 0.001) and the mean relative volume reduction was 13.9%. There was a positive linear correlation between the reduction of GTVnx and GTVnd (r = 0.351, p  < 0.001). For GTVnx volume reduction, pretreatment GTVnx volume (≤ 20 cm³ vs > 20 cm³, OR = 4.644, p = 0.001) and chemotherapy cycle (2 cycles vs 3 cycles, OR = 2.418, p = 0.009) were independently associated factors. For GTVnd volume reduction, pretreatment GTVnd volume (≤ 8 cm³ vs > 8 cm³, OR = 7.472, p < 0.001) and chemotherapy cycle (2 cycles vs 3 cycles, OR = 2.621, p = 0.007) were independently associated factors.

CONCLUSION

DP induction chemotherapy reduces tumor volumes moderately in NPC. Larger pre-treatment tumor volumes and three cycles of chemotherapy are associated with higher likelihood of tumor volume reduction.

Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression

In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification performances in multiple datasets. It shows that elastic net, lasso,L 1 / 2 and SCAD penalties get the better performance than others and can be successfully used to identify biomarkers of SPTB. Particularly, we make a functional enrichment analysis on these biomarkers and construct a logistic regression classifier based on them. The classifier generates an indicator of preterm risk score (PRS) for predicting SPTB. Based on the trained predictor, we verify the identified biomarkers on an independent dataset. The biomarkers achieve the AUC value of 0.933 in the SPTB classification. The results demonstrate the effectiveness and efficiency of the built-up strategy of biomarker discovery with regularized logistic regression. Obviously, the proposed method of discovering biomarkers for SPTB can be easily extended for other complex diseases.

Identified IGSF9 association with prognosis and hypoxia in nasopharyngeal carcinoma by bioinformatics analysis

Background

Despite improvements in nasopharyngeal carcinoma (NPC) treatment, patients with recurrence and metastasis still have a poor prognosis. Thus, the identification of novel biomarkers is urgently needed to predict outcomes and tailor treatment for NPC.

Methods

Four data sets were downloaded from Gene Expression Omnibus, and one data set GSE68799 of which was applied to filtrate key modules and hub genes by construction of a co-expression network. Other data sets (GSE12452 and GSE53819) were used to verify hub genes. The data set GSE102349 was devoted to identify prognostic hub genes by survival analysis. To explored whether prognostic hub genes are related to hypoxia signatures in NPC, correlation analysis was carried out, and followed by functional verification experiments of those genes in vitro.

Results

By co-expression network analysis, blue module was regarded as a key module in the benign and malignant group, and IGSF9 of the blue module was identified as a prognostic hub gene. Moreover, IGSF9 is expected to be a innovative hypoxia-related gene in NPC based on the strong associativity between expression of IGSF9 and hypoxia scores of three signatures (99-gene, 26-gene and 15-gene). Further functional studies verified that down-regulated expression of IGSF9 could reduce the proliferation, migration and invasion ability of NPC cells, and hypoxia could induce the expression of IGSF9.

Conclusion

IGSF9 was identified to be relevant to prognosis and involved in hypoxia in NPC. IGSF9 might serve as one novel prognostic indicator of NPC in the future.

Construction and Analysis of lncRNA-Mediated ceRNA Network in Nasopharyngeal Carcinoma Based on Weighted Correlation Network Analysis

Increasing evidence indicated that aberrant expression of long noncoding RNAs (lncRNAs) are involved in tumorigenesis of nasopharyngeal carcinoma (NPC). The purpose of this study was to construct a lncRNA-mediated ceRNA network based on weighted correlation network analysis (WGCNA). First, modules with highly correlated genes were identified from GSE102349 via WGCNA, and the preservation of the modules was evaluated by GSE68799. Then, the differentially expressed lncRNAs and mRNAs identified from GSE12452 which belonged to the same WGCNA modules and the differentially expressed miRNAs identified from GSE32960 were used to construct a ceRNA network. The prognostic value of the network was evaluated by survival analysis. Furthermore, a risk score model for predicting progression-free survival (PFS) of NPC patients was established via LASSO-penalized Cox regression, and the differences in the expression of the lncRNAs between high- and low-risk groups were investigated. Finally, 14 stable modules were identified, and a ceRNA network composed of 11 lncRNAs, 15 miRNAs, and 40 mRNAs was established. The lncRNAs and mRNAs in the network belonged to the turquoise and salmon modules. Survival analysis indicated that ZNF667-AS1, LDHA, LMNB2, TPI1, UNG, and hsa-miR-142-3p were significantly correlated with the prognosis of NPC. Gene set enrichment analysis indicated that the upregulation of ZNF667-AS1 was associated with some immune-related pathways. Besides, a risk score model consisting of 12 genes was constructed and showed a good performance in predicting PFS for NPC patients. Among the 11 lncRNAs in the ceRNA network, SNHG16, SNHG17, and THAP9-AS1 were upregulated in the high-risk group of NPC, while ZNF667-AS1 was downregulated in the high-risk group of NPC. These results will promote our understanding of the crosstalk among lncRNAs, miRNAs, and mRNAs in the tumorigenesis and progression of NPC.

DNA methylation-based diagnostic and prognostic biomarkers of nasopharyngeal carcinoma patients

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor with a remarkable racial and geographical distribution including people in southern China, South East Asia, and the Middle East/North Africa. DNA methylation is an important manifestation of epigenetic modification, has been studied over several decades, and by regulating and controlling the expression of cancer-related genesits, abnormal DNA methylation can influence in a variety of human malignancy tumors.Until now, there is no analysis focus on differentially methylated, differential expressed genes (MDEGs) study, so we make a joint analysis for both gene methylation profiling microarray and gene expression profiling microarray in NPC. Two gene expression datasets (GSE64634 and GSE12452) and gene methylation profiling data set (GSE62336) were downloaded from GEO and analyzed using the online tool GEO2R to identify MDEGs. Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the differentially methylated genes were performed. The STRING database was used to evaluate the interactions of MDEGs and to construct a protein-protein interaction (PPI) network using Cytoscape software. Hub genes were validated with the cBioPortal database.The overlap among the 3 datasets contained 135 hypermethylation genes and 541 hypomethylation genes between NPC and non-NPC samples. A total of 4 genes (TROAP, PCOLCE2, HOXA4, and C1QB) in Hyper-LGs and 14 genes (DYNC1H1, LNX1, RAB37, ALDH3A1, SLC24A4, CP, CEP250, ANK2, DNAI2, MUC13, ACACB, GABRP, STX7, and TTC9) in Hypo-HGs were identified as hub genes.The study of DNA methylation and gene expression provides us a strong support as well as new comprehensive information of MDEGs to the revelation of nasopharyngeal carcinoma's complex pathogenesis. However, further studies are needed to elucidate the biological function of these genes in NPC in the future.

Identification of hub genes in papillary thyroid carcinoma: robust rank aggregation and weighted gene co-expression network analysis

BACKGROUND

Papillary thyroid carcinoma (PTC), which is the most common endocrine malignancy, has been steadily increasing worldwide in incidence over the years, while mechanisms underlying the pathogenesis and diagnostic for PTC are incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of PTC, and provide new insights into pathogenesis of PTC.

METHODS

Based on weighted gene co-expression network analysis, Robust Rank Aggregation, functional annotation, GSEA and DNA methylation, were employed for investigating potential biomarkers for diagnosis of PTC.

RESULTS

Black and turquoise modules were identified in the gene co-expression network constructed by 1807 DEGs that from 6 eligible gene expression profiles of Gene Expression Omnibus database based on Robust Rank Aggregation and weighted gene co-expression network analysis. Hub genes were significantly down-regulated and the expression levels of the hub genes were different in different stages in hub gene verification. ROC curves indicated all hub genes had good diagnostic value for PTC (except for ABCA6 AUC = 89.5%, the 15 genes with AUC > 90%). Methylation analysis showed that hub gene verification ABCA6, ACACB, RMDN1 and TFPI were identified as differentially methylated genes, and the decreased expression level of these genes may relate to abnormal DNA methylation. Moreover, the expression levels of 8 top hub genes were correlated with tumor purity and tumor-infiltrating immune cells. These findings, including functional annotations and GSEA provide new insights into pathogenesis of PTC.

CONCLUSIONS

The hub genes and methylation of hub genes may as potential biomarkers provide new insights for diagnosis of PTC, and all these findings may be the direction to study the mechanisms underlying of PTC in the future.

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10^-2), CD2 (HR = 0.87, p = 6.31 × 10^-2), CD3D (HR = 0.83, p = 6.94 × 10^-3), FYB (HR = 0.82, p = 1.40 × 10^-2), GZMK (HR = 0.92, p = 1.19 × 10^-1), FN1 (HR = 0.88, p = 7.06 × 10^-2), SPARC (HR = 0.82, p = 2.06 × 10^-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

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

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.

Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Background

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC.

Methods

We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA.

Results

In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research.

Screening and identification of key biomarkers in nasopharyngeal carcinoma: Evidence from bioinformatic analysis

As for the lack of simple and effective diagnostic methods at the early of the nasopharyngeal carcinoma (NPC), the mortality rate of NPC still remains high. Therefore, it is meaningful to explore the precise molecular mechanisms involved in the proliferation, carcinogenesis, and recurrence of NPC and thus find an effective diagnostic way and make a better therapeutic strategy.Three gene expression data sets (GSE64634, GSE53819, and GSE12452) were downloaded from Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEGs were performed in Database for Annotation, Visualization and Integrated Discovery. The Search Tool for the Retrieval of Interacting Genes database was used to evaluate the interactions of DEGs and to construct a protein-protein interaction network using Cytoscape software. Hub genes were validated with the cBioPortal database.The overlap among the 3 data sets contained 306 genes were identified to be differentially expressed between NPC and non-NPC samples. A total of 13 genes (DNAAF1, PARPBP, TTC18, GSTA3, RCN1, MUC5AC, POU2AF1, FAM83B, SLC22A16, SPEF2, ERICH3, CCDC81, and IL33) were identified as hub genes with degrees ≥10.The present study was attempted to identify and functionally analyze the DEGs that may be involved in the carcinogenesis or progression of NPC by using comprehensive bioinformatics analyses and unveiled a series of hub genes and pathways. A total of 306 DEGs and 13 hub genes were identified and may be regarded as diagnostic biomarkers for NPC. However, more experimental studies are needed to carried out elucidate the biologic function of these genes results for NPC.