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

Ge, Yanshan; He, Zhengxi; Xiang, Yanqi; Wang, Dawei; Yang, Yuping; Qiu, Jian; Zhou, Yanhong

doi:10.1007/s11033-019-04729-3

Cited by 25 publications

(19 citation statements)

References 61 publications

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“…The genes, with a consistent expression profile and concordant biological role, were assigned to the same module [5]. WGCNA also has been used to detect co-expression gene modules and biomarkers, which usually be applied to cancer diagnosis [6][7][8][9], prognosis [10] and treatment [11,12]. Similarly, it has been successfully applied in other diseases to identify the genetic determinants, such as osteoporosis [13,14], obesity [14] and neurodegenerative disease [15].…”

Section: Introductionmentioning

confidence: 99%

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

Gao¹,

Sun²,

Hu³

et al. 2020

Pharmacogenomics J

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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 coexpression 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.

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Section: Introductionmentioning

confidence: 99%

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

Gao¹,

Sun²,

Hu³

et al. 2020

Pharmacogenomics J

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“…Finally, we built a weighted correlation network composed of 14 modules. Through enrichment analysis, we found that the modules were related to some terms and pathways that had been previously reported by us or other researchers, such as "Wnt signaling pathway", "Small cell lung cancer", "PI3K−Akt signaling pathway", "Epstein−Barr virus infection", "Cell cycle", and "DNA replication" [18,28,29].…”

Section: Discussionmentioning

confidence: 60%

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

Zou

Huang

2020

Preprint

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Background: Increasing evidence indicated that aberrant expression of long noncoding RNAs (lncRNAs) are involved in tumorigenesis and progression of nasopharyngeal carcinoma (NPC). The purpose of this study is to construct a lncRNA-mediated ceRNA network based on Weighted correlation network analysis (WGCNA) and explore the relationship between the constituents of the ceRNA network and the prognosis of patients with NPC.Methods: In this study, WGCNA was performed on the GSE102349 to find modules of highly correlated genes. The preservation of the modules was examined by GSE68799. GSE12452 was utilized to identify the differentially expressed lncRNAs and mRNAs. GSE32960 was utilized to identify the differentially expressed miRNAs. Through annotation and intersection, the lncRNAs and mRNAs in the same WGCNA modules and the miRNAs were used to construct a ceRNA network. Next, the prognostic value of the constituents of the ceRNA 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 based on the differentially expressed genes, and the differences in the expression of the lncRNAs and mRNAs in the ceRNA network between high- and low-risk groups were investigated.Results: Fourteen stable modules were identified using GSE102349 based on WGCNA. A ceRNA network composed of 11 lncRNAs, 15 miRNAs and 40 mRNAs was established. The lncRNAs and mRNAs in this network were from turquoise and salmon modules. Survival analysis indicated that ZNF667-AS1, four mRNAs (LDHA, LMNB2, TPI1, and UNG), and hsa-miR-142-3p in the ceRNA network were significantly correlated with the prognosis of NPC. Gene set enrichment analysis indicated that the up-regulation 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 in NPC patients. Among the 11 lncRNAs in the ceRNA network, SNHG16, SNHG17, and THAP9-AS1 were up-regulated in the high-risk group of NPC, while ZNF667-AS1 was down-regulated in the high-risk group of NPC.Conclusions: These results will promote our understanding of the crosstalk among lncRNAs, miRNAs, and mRNAs in the tumorigenesis and progression of NPC.

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“…In addition, the leucine‐rich repeat kinase 2 depletion leads to apoptotic cell death, α‐synuclein accumulation, and protein degradation pathway damage in aged mice . Ge et al . 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.…”

Section: Discussionmentioning

confidence: 99%

“…33 In addition, the leucine-rich repeat kinase 2 depletion leads to apoptotic cell death, α-synuclein accumulation, and protein degradation pathway damage in aged mice. 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.…”

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

Cited by 25 publications

References 61 publications

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

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

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

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

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