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Meningioma is a prevalent type of brain tumor. However, the initiation and progression mechanisms involved in the meningioma are mostly unknown. This study aimed at exploring the potential transcription factors\/micro(mi)RNAs\/genes and biological pathways associated with meningioma. <jats:italic>Methods<\/jats:italic>. mRNA expressions from GSE88720, GSE43290, and GSE54934 datasets, containing data from 83 meningioma samples and eight control samples, along with miRNA expression dataset GSE88721, which had 14 meningioma samples and one control sample, were integrated analyzed. The bioinformatics approaches were used for identifying differentially expressed genes and miRNAs, as well as predicting transcription factor targets related to the differentially expressed genes. The approaches were also used for gene ontology term analysis and biological pathway enrichment analysis, construction, and analysis of protein\u2010protein interaction network, and transcription factor\u2010miRNA\u2010gene coregulation network construction. <jats:italic>Results<\/jats:italic>. Fifty\u2010six upregulated and 179 downregulated genes were identified. Thirty transcription factors able to target the differentially expressed genes were predicted and selected based on public databases. One hundred seventeen overlapping genes were identified from the differentially expressed genes and the miRNAs predicted by miRWalk. Furthermore, NF\u2010<jats:italic>\u03ba<\/jats:italic>B\/IL6, PTGS2, MYC\/hsa\u2010miR\u2010574\u20105p, hsa\u2010miR\u201026b\u20105p, hsa\u2010miR\u2010335\u20105p, and hsa\u2010miR\u201098\u20105p, which are involved in the transcription factor\u2010miRNA\u2010mRNA coregulation network, were found to be associated with meningioma. <jats:italic>Conclusion<\/jats:italic>. The bioinformatics analysis identified several potential molecules and relevant pathways that may represent critical mechanisms involved in the progression and development of meningioma. This work provides new insights into meningioma pathogenesis and treatments.<\/jats:p>","DOI":"10.1155\/2020\/6353814","type":"journal-article","created":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T00:01:10Z","timestamp":1596844870000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of a Transcription Factor\u2010microRNA\u2010Gene Coregulation Network in Meningioma through a Bioinformatic Analysis"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0291-9558","authenticated-orcid":false,"given":"Juan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3835-2370","authenticated-orcid":false,"given":"Yan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0121-5615","authenticated-orcid":false,"given":"Hui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-8128","authenticated-orcid":false,"given":"Jian-Huang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.2176\/nmc.ra.2014-0233"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/onc.2010.609"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(06)70625-1"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11060-010-0386-3"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.3945\/an.116.012211"},{"key":"e_1_2_10_6_2","volume-title":"Ferri\u2032s clinical advisor 2017 : 5 books in 1","author":"Ferri F. 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