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Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis

PURPOSE: Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach. METHODS: Five mRNA and two miRNA expression datase...

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Autores principales: Gao, Li, Zhou, Lei, Huang, Xinsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566004/
https://www.ncbi.nlm.nih.gov/pubmed/34744455
http://dx.doi.org/10.2147/IJGM.S327657
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author Gao, Li
Zhou, Lei
Huang, Xinsheng
author_facet Gao, Li
Zhou, Lei
Huang, Xinsheng
author_sort Gao, Li
collection PubMed
description PURPOSE: Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach. METHODS: Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein–protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase–TF–mRNA–miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap. RESULTS: A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase–TF–mRNA–miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted. CONCLUSION: We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.
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spelling pubmed-85660042021-11-05 Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis Gao, Li Zhou, Lei Huang, Xinsheng Int J Gen Med Original Research PURPOSE: Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach. METHODS: Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein–protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase–TF–mRNA–miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap. RESULTS: A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase–TF–mRNA–miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted. CONCLUSION: We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC. Dove 2021-10-30 /pmc/articles/PMC8566004/ /pubmed/34744455 http://dx.doi.org/10.2147/IJGM.S327657 Text en © 2021 Gao et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Gao, Li
Zhou, Lei
Huang, Xinsheng
Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis
title Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis
title_full Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis
title_fullStr Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis
title_full_unstemmed Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis
title_short Identification of Novel Kinase–Transcription Factor–mRNA–miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis
title_sort identification of novel kinase–transcription factor–mrna–mirna regulatory network in nasopharyngeal carcinoma by bioinformatics analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566004/
https://www.ncbi.nlm.nih.gov/pubmed/34744455
http://dx.doi.org/10.2147/IJGM.S327657
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