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Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma

BACKGROUND: In children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis...

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Autores principales: Wen, Yanjun, Zhu, Maolin, Zhang, Xuerui, Xiao, Haodong, Wei, Yan, Zhao, Peiquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372260/
https://www.ncbi.nlm.nih.gov/pubmed/35966326
http://dx.doi.org/10.21037/tcr-21-1748
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author Wen, Yanjun
Zhu, Maolin
Zhang, Xuerui
Xiao, Haodong
Wei, Yan
Zhao, Peiquan
author_facet Wen, Yanjun
Zhu, Maolin
Zhang, Xuerui
Xiao, Haodong
Wei, Yan
Zhao, Peiquan
author_sort Wen, Yanjun
collection PubMed
description BACKGROUND: In children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis to build potential miRNA-gene-TF networks. METHODS: We collected three gene expression profiles and one miRNA expression profile from the Gene Expression Omnibus (GEO) database. We used the limma R package to identify overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs in RB tissues compared to noncancer tissues. The robust rank aggregation (RRA) method was implemented to identify key genes among the DEGs. Then, miRNA-key gene-TF networks were built using the online tools TransmiR and miRTarBase. Next, we used RT-qPCR to confirm the results. RESULTS: We identified 180 DEGs in RB tissues compared to nontumor tissues using integrative analysis, among which 109 genes were upregulated and 71 were downregulated. Gene ontology (GO) analysis revealed that these DEGs were primarily involved with chromosome segregation, condensed chromosome and DNA replication origin binding. The most highly enriched pathways obtained in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were cell cycle, DNA replication, homologous recombination, P53 signaling pathway and pyrimidine metabolism. Furthermore, two key differentially expressed miRNAs (DEMs) were also established: let-7a and let-7b. Finally, the potential regulatory networks of miRNA-target gene-TFs were examined. CONCLUSIONS: This study identified key genes and built miRNA-target gene-TF regulatory networks in RB, which will deepen our understanding of the molecular mechanisms involved in the development of RB. These key genes and miRNAs may be potential targets and biomarkers for RB diagnosis and therapy.
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spelling pubmed-93722602022-08-13 Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma Wen, Yanjun Zhu, Maolin Zhang, Xuerui Xiao, Haodong Wei, Yan Zhao, Peiquan Transl Cancer Res Original Article BACKGROUND: In children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis to build potential miRNA-gene-TF networks. METHODS: We collected three gene expression profiles and one miRNA expression profile from the Gene Expression Omnibus (GEO) database. We used the limma R package to identify overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs in RB tissues compared to noncancer tissues. The robust rank aggregation (RRA) method was implemented to identify key genes among the DEGs. Then, miRNA-key gene-TF networks were built using the online tools TransmiR and miRTarBase. Next, we used RT-qPCR to confirm the results. RESULTS: We identified 180 DEGs in RB tissues compared to nontumor tissues using integrative analysis, among which 109 genes were upregulated and 71 were downregulated. Gene ontology (GO) analysis revealed that these DEGs were primarily involved with chromosome segregation, condensed chromosome and DNA replication origin binding. The most highly enriched pathways obtained in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were cell cycle, DNA replication, homologous recombination, P53 signaling pathway and pyrimidine metabolism. Furthermore, two key differentially expressed miRNAs (DEMs) were also established: let-7a and let-7b. Finally, the potential regulatory networks of miRNA-target gene-TFs were examined. CONCLUSIONS: This study identified key genes and built miRNA-target gene-TF regulatory networks in RB, which will deepen our understanding of the molecular mechanisms involved in the development of RB. These key genes and miRNAs may be potential targets and biomarkers for RB diagnosis and therapy. AME Publishing Company 2022-07 /pmc/articles/PMC9372260/ /pubmed/35966326 http://dx.doi.org/10.21037/tcr-21-1748 Text en 2022 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Wen, Yanjun
Zhu, Maolin
Zhang, Xuerui
Xiao, Haodong
Wei, Yan
Zhao, Peiquan
Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma
title Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma
title_full Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma
title_fullStr Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma
title_full_unstemmed Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma
title_short Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma
title_sort integrated analysis of multiple bioinformatics studies to identify microrna-target gene-transcription factor regulatory networks in retinoblastoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372260/
https://www.ncbi.nlm.nih.gov/pubmed/35966326
http://dx.doi.org/10.21037/tcr-21-1748
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