ArticleCancer management and research2021
Comprehensive Bioinformatics Analysis of mRNA Expression Profiles and Identification of a miRNA-mRNA Network Associated with the Pathogenesis of Low-Grade Gliomas.
Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- The role and therapeutic value of NUSAP1 in human cancers.Journal of translational medicine · 2025Review
- Network pharmacology, bioinformatics and in vitro/in vivo validation elucidate the anti-lung cancer activities and potential targets of Rhoifolin.Frontiers in pharmacology · 2025Article
- A Four-Gene Panel for the Prediction of Prognosis and Immune Cell Enrichment in Gliomas.Molecular biotechnology · 2024Article
- Important role and underlying mechanism of non‑SMC condensin I complex subunit G in tumours (Review).Oncology reports · 2024Review
- Glioma Subtypes Based on the Activity Changes of Immunologic and Hallmark Gene Sets in Cancer.Frontiers in endocrinology · 2022Article
- Comprehensive Pan-Cancer Analysis of IRAK Family Genes Identifies IRAK1 as a Novel Oncogene in Low-Grade Glioma.Journal of oncology · 2022Article
- The Role and Mechanism of microRNA-1224 in Human Cancer.Frontiers in oncology · 2022Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLow-grade glioma is the most common type of primary intracranial tumour, and the overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past few decades. Therefore, it is crucial to understand the precise molecular mechanisms involved in the carcinogenesis of LGG.
methodsTo investigate the regulatory mechanisms of mRNA-miRNA networks related to LGG, in the present study, a comprehensive analysis of the genomic landscape between low-grade gliomas and normal brain tissues from the GEO and TCGA datasets was first conducted to identify differentially expressed genes (DEGs) and differentially expressed miRNAs in LGG. Following a series of analyses, including WGCNA, GO and KEGG analyses, PPI and key model analyses, and survival analysis of the DEGs with clinical phenotypes, the potential key genes were screened and identified, and the related miRNA-mRNA networks were subsequently constructed through miRWalk 3.0. Finally, the potential miRNA-mRNA networks were further validated in CGGA (Chinese Glioma Genome Atlas) datasets and clinical specimens by qRT-PCR.
resultsIn our results, six hub genes, MELK, NCAPG, KIF4A, NUSAP1, CEP55, and TOP2A, were ultimately identified. Two regulatory pathways, miR-495-3p-TOP2A and miR-1224-3p-MELK, that regulate the pathogenesis of LGG were ultimately identified. Furthermore, the expression of miR-495-3p-TOP2A and miR-1224-3p-MELK in solid tissues was validated by qRT-PCR.
conclusionOur study identified hub genes and related miRNA-mRNA regulatory pathways that contribute to the carcinogenesis of LGG, which may help us reveal the mechanisms underlying the development of LGG.
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