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   <ref-type name="Journal Article">17</ref-type>
   <contributors>
    <authors>
     <author>Dong Xingli</author>
     <author>Gareev, I.F.</author>
     <author>Roumiantsev, S.</author>
     <author>Pavlov, V.N.</author>
     <author>Beylerli, O.</author>
     <author>Shiguang Zhao</author>
     <author>Jianing Wu</author>
    </authors>
   </contributors>
   <titles>
    <title>Identify Key Genes and Construct the lncRNA-miRNA-mRNA Regulatory Networks Associated with Glioblastoma by Bioinformatics Analysis</title>
   </titles>
   <keywords>
    <keyword>glioblastoma</keyword>
    <keyword>oncogenesis</keyword>
    <keyword>key genes</keyword>
    <keyword>bioinformatics</keyword>
    <keyword>small molecules</keyword>
    <keyword>lncRNA-miRNA-mRNA network</keyword>
    <keyword>therapy</keyword>
    <keyword>Scopus</keyword>
    <keyword>Web of Science</keyword>
    <keyword>Белый список</keyword>
   </keywords>
   <dates>
    <year>2025</year>
    <pub-dates>
     <date>2026-05-03</date>
    </pub-dates>
   </dates>
   <doi>10.2174/0109298673372488250530173615</doi>
   <journal>CURRENT MEDICINAL CHEMISTRY</journal>
   <abstract>Introduction: Glioblastoma is the most common and aggressive brain tumor,&#13;
with low survival rates and high recurrence rates. Therefore, it is crucial to understand&#13;
the precise molecular mechanisms involved in the oncogenesis of glioblastoma.&#13;
Material and methods: To investigate the regulatory mechanisms of long non-coding&#13;
RNA (lncRNA)-microRNA (miRNA)-messenger RNA (miRNA) network related to&#13;
glioblastoma, in the present study, a comprehensive analysis of the genomic landscape&#13;
between glioblastoma and normal brain tissues from the Gene Expression Omnibus&#13;
(GEO) dataset was first conducted to identify differentially expressed genes (DEGs) in&#13;
glioblastoma. Following a series of analyses, including Gene Ontology (GO) and Kyoto&#13;
Encyclopedia of Genes and Genomes (KEGG) analyses, protein-protein interaction (PPI), and key model analyses. In addition, we used the L1000CDS2 database bioinformatic tool to identify candidates for therapy based on glioblastoma specific genetic profile.&#13;
Results: In our results, 100 key genes, 50 upregulated and 50 downregulated, were ultimately identified. The results of KEGG pathway enrichment gene analysis showed that&#13;
the five regulatory pathways. Furthermore, 3 small molecule signatures (trichostatin A,&#13;
TG-101348, and vorinostat) were recommended as the top-ranked candidate therapeutic&#13;
agents. Nevertheless, the constructed miRNA-mRNA network revealed a convergence&#13;
on 40 miRNAs. We found that dysregulation of lncRNAs such as KCNQ1OT1 and&#13;
RP11-13N13.5 could sequester several miRNAs such as hsa-miR-27a-3p, hsamiR-27b-3p, hsa-miR-106a-5p, etc., and promote the development and progression of&#13;
glioblastoma.&#13;
Conclusions: Our study identified key genes and related lncRNA-miRNA-mRNA network that contribute to the oncogenesis of glioblastoma.</abstract>
   <urls>
    <web-urls>
     <url>https://repo.bashgmu.ru/publication/5249</url>
    </web-urls>
    <pdf-urls>
     <url>https://repo.bashgmu.ru/files/5435</url>
    </pdf-urls>
   </urls>
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