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Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis.

ABSTRACT: Background:Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC. Methods:We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba. Results:We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network. Conclusions:Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.

SUBMITTER: Xu Y

PROVIDER: S-EPMC7448472 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis.

Xu Yuanji Y Huang Xinyi X Ye Wangzhong W Zhang Yangfan Y Li Changkun C Bai Penggang P Lin Zhizhong Z Chen Chuanben C

Cancer cell international 20200826

<h4>Background</h4>Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC.<h4>Methods</h4>We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets ...[more]

PMID: 32863767

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Project description: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.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Although multiple efforts have been made to understand the development of HCC, morbidity, and mortality rates remain high. In this study, we aimed to discover the mRNAs and long non-coding RNAs (lncRNAs) that contribute to the progression of HCC. We constructed a lncRNA-related competitive endogenous RNA (ceRNA) network to elucidate the molecular regulatory mechanism underlying HCC.MethodsA microarray dataset (GSE54238) containing information about both mRNAs and lncRNAs was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) in tumor tissues and non-cancerous tissues were identified using the limma package of the R software. The miRNAs that are targeted by DElncRNAs were predicted using miRcode, while the target mRNAs of miRNAs were retrieved from miRDB, miRTarBas, and TargetScan. Functional annotation and pathway enrichment of DEGs were performed using the EnrichNet website. We constructed a protein-protein interaction (PPI) network of DEGs using STRING, and identified the hub genes using Cytoscape. Survival analysis of the hub genes and DElncRNAs was performed using the gene expression profiling interactive analysis database. The expression of molecules with prognostic values was validated on the UALCAN database. The hepatic expression of hub genes was examined using the Human Protein Atlas. The hub genes and DElncRNAs with prognostic values as well as the predictive miRNAs were selected to construct the ceRNA networks.ResultsWe found that 10 hub genes (KPNA2, MCM7, CKS2, KIF23, HMGB2, ZWINT, E2F1, MCM4, H2AFX, and EZH2) and four lncRNAs (FAM182B, SNHG6, SNHG1, and SNHG3) with prognostic values were overexpressed in the hepatic tumor samples. We also constructed a network containing 10 lncRNA-miRNA-mRNA pathways, which might be responsible for regulating the biological mechanisms underlying HCC.ConclusionWe found that the 10 significantly overexpressed hub genes and four lncRNAs were negatively correlated with the prognosis of HCC. Further, we suggest that lncRNA SNHG1 and the SNHG3-related ceRNAs can be potential research targets for exploring the molecular mechanisms of HCC.

Project description:ObjectiveThe incidence of papillary thyroid cancer (PTC) concomitant with Hashimoto's thyroiditis (HT) is gradually increasing over the past decades. This study aims to identify differentially expressed lncRNAs between tumor tissues of PTC with or without HT and further to confer a better understanding of lncRNA-based competing endogenous RNA (ceRNA) network in PTC with HT.MethodsGSE138198 containing tissue mRNA data and GSE192560 containing lncRNA data were utilized to perform differentially expression analysis. The ceRNA network was constructed based on miRNA-mRNA interactions merging with lncRNA-microRNA interactions. Functional enrichment analysis and protein-protein interaction (PPI) analysis were performed. The mRNA levels of core genes in the PPI analysis in tumor tissues collected from 112 PTC patients including 35 cases coexistent with HT were determined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 57 genes and 40 lncRNAs, with value of |log2 fold change (FC)|≥ 1 and the adjusted P-value < 0.05, were deemed as differentially expressed genes and lncRNAs between PTC with and without HT. The pathways most significantly enriched by differentially expressed genes between PTC with and without HT were viral protein interaction with cytokine and cytokine receptor and cytokine-cytokine receptor interaction. CXCL10, CXCL9, CCL5, FCGR3A, and CCR2 owned degree values not less than 10 were deemed as core genes differentially expressed between PTC with and without HT. A total of 76 pairs of lncRNA-miRNA-mRNA ceRNA were obtained. Results of qRT-PCR partially demonstrated the bioinformatics results that the mRNA levels of CXCL10, CXCL9, CCL5, and CCR2 were remarkably elevated in tumor tissues collected from PTC patients coexistent with HT than those without HT (P < 0.001).ConclusionOur study offers a better understanding of the lncRNA-related ceRNA network involved in PTC with HT, providing novel key genes associated with PTC coexistent with HT.

Project description:Background: Nasopharyngeal carcinoma (NPC) represents a highly aggressive malignant tumor. Competing endogenous RNAs (ceRNA) regulation is a common regulatory mechanism in tumors. The ceRNA network links the functions between mRNAs and ncRNAs, thus playing an important regulatory role in diseases. This study screened the potential key genes in NPC and predicted regulatory mechanisms using bioinformatics analysis. Methods: The merged microarray data of three NPC-related mRNA expression microarrays from the Gene Expression Omnibus (GEO) database and the expression data of tumor samples or normal samples from the nasopharynx and tonsil in The Cancer Genome Atlas (TCGA) database were both subjected to differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The results from two different databases were intersected with WGCNA results to obtain potential regulatory genes in NPC, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The hub-gene in candidate genes was discerned through Protein-Protein Interaction (PPI) analysis and its upstream regulatory mechanism was predicted by miRwalk and circbank databases. Results: Totally 68 upregulated genes and 96 downregulated genes in NPC were screened through GEO and TCGA. According to WGCNA, the NPC-related modules were screened from GEO and TCGA analysis results, and the genes in the modules were obtained. After the results of differential analysis and WGCNA were intersected, 74 differentially expressed candidate genes associated with NPC were discerned. Finally, fibronectin 1 (FN1) was identified as a hub-gene in NPC. Prediction of upstream regulatory mechanisms of FN1 suggested that FN1 may be regulated by ceRNA mechanisms involving multiple circRNAs, thereby influencing NPC progression through ceRNA regulation. Conclusion: FN1 is identified as a key regulator in NPC development and is likely to be regulated by numerous circRNA-mediated ceRNA mechanisms.

Project description:Background: Acute ischemic stroke (AIS) is the second leading cause of death and the third leading cause of disability worldwide. Long noncoding RNAs (lncRNAs) are promising biomarkers for the early diagnosis of AIS and closely participate in the mechanism of stroke onset. However, studies focusing on lncRNAs functioning as microRNA (miRNA) sponges to regulate the mRNA expression are rare and superficial. Methods: In this study, we systematically analyzed the expression profiles of lncRNA, mRNA (GSE58294), and miRNA (GSE110993) from the GEO database. Gene ontology (GO) analysis was performed to reveal the functions of differentially expressed genes (DEGs), and we used weighted gene co-expression network analysis (WGCNA) to investigate the relationships between clinical features and expression profiles and the co-expression of miRNA and lncRNA. Finally, we constructed a lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network with selected DEGs using bioinformatics methods and obtained ROC curves to assess the diagnostic efficacy of differentially expressed lncRNAs (DElncRNAs) and differentially expressed mRNAs (DEmRNAs) in our network. The GSE22255 dataset was used to confirm the diagnostic value of candidate genes. Results: In total, 199 DElncRNAs, 2068 DEmRNAs, and 96 differentially expressed miRNAs were detected. The GO analysis revealed that DEmRNAs primarily participate in neutrophil activation, neutrophil degranulation, vacuolar transport, and lysosomal transport. WGCNA screened out 16 lncRNAs and 195 mRNAs from DEGs, and only eight DElncRNAs maintained an area under the curve higher than 0.9. By investigating the relationships between lncRNAs and mRNAs, a ceRNA network containing three lncRNAs, three miRNAs, and seven mRNAs was constructed. GSE22255 confirmed that RP1-193H18.2 is more advantageous for diagnosing stroke, whereas no mRNA showed realistic diagnostic efficacy. Conclusion: The ceRNA network may broaden our understanding of AIS pathology, and the candidate lncRNA from the ceRNA network is assumed to be a promising therapeutic target and diagnostic biomarker for AIS.

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Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis.

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Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis.

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