ArticleInvestigational new drugs2021
Identify potential miRNA-mRNA regulatory networks contributing to high-risk neuroblastoma.
Article in Investigational new drugs, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- MDM2 promotes CELF6 ubiquitination-dependent degradation to promote neuroblastoma cell proliferation.Cell death & disease · 2025Article
- Neoadjuvant PD-1 blockade induces the autophagy of immune cells: a new target for synergistic therapy of recurrent glioblastoma.Biochemistry and biophysics reports · 2025Article
- Regulation and dysregulation of microRNA - transcription factor axes in differentiation and neuroblastoma.Cellular and molecular life sciences : CMLS · 2025Review
- Comprehensive analysis of lipid metabolic signatures identified CEBPD promotes breast cancer cell proliferation.Scientific reports · 2025Article
- Comprehensive analysis reveals the tumor suppressor role of macrophage signature gene FCER1G in hepatocellular carcinoma.Scientific reports · 2025Article
- Comprehensive analysis identifies ubiquitin ligase FBXO42 as a tumor-promoting factor in neuroblastoma.Scientific reports · 2024Article
- miRNA326-5p Targets DKC1 Gene to Regulate Apoptosis-Related Proteins and Intervene in the Development of Neuroblastoma.Analytical cellular pathology (Amsterdam) · 2023Article
- Expression of ADRB2 in children with neuroblastoma and its influence on prognosis.Frontiers in surgery · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroblastoma (NB) is a common tumor in children, usually in the retroperitoneum. After various treatments, low- and intermediate-risk patients have achieved good results, but the prognosis of high-risk patients is still very poor. Therefore, it is necessary to find new effective targets for the treatment of high-risk patients. In this study, comprehensive bioinformatics analysis was used to identify the differentially expressed genes (DEG and DEM) between high-risk patients and non-high-risk patients, and it was identified that ADRB2 may affect the survival status of high-risk patients due to miR -30a-5p regulation. The GSE49710, GSE73517, and GSE121513 datasets were downloaded from the Gene Expression Synthesis (GEO) database, and DEG and DEM were selected. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the selected DEGs. The STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) networks and perform modular analysis of the DEGs. The TARGET data set containing information on overall survival days were used for the prognostic analysis of central genes. We identified a total of 255 DEGs from GSE49710 and GSE73517, and 193 DEMs from GSE121513. We identified the 5 most important central genes from the PPI network, performed a prognostic analysis in the target data set, and verified their expression using RT-qPCR to select the most important ADRB2 gene to predict miRNA. Integrating the differential miRNA predicted by miRDB and miRSystem and GSE121513 between the targeted miRNA and the prognosis, miR-30a-5p was finally identified as the targeted miRNA of ADRB2.
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Registered trials
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