Xing Wei1,2, Qing-Mei Zhang1,3, Chang Liu4, Song Wu4, Wei-Xia Nong1, Ying-Ying Ge1, Li-Na Lin1, Feng Li1, Xiao-Xun Xie5,6, Bin Luo7,8. 1. Department of Histology and Embryology, School of Pre-clinical Medicine, Guangxi Medical University, Nanning, 530021, China. 2. Department of Neurology, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, China. 3. Central Laboratory of Pre-Clinical Medicine (Key Laboratory of Guangxi Colleges and Universities), Guangxi Medical University, Nanning, 530021, China. 4. Department of Neurosurgery, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, China. 5. Department of Histology and Embryology, School of Pre-clinical Medicine, Guangxi Medical University, Nanning, 530021, China. xxiaoxun@163.com. 6. Key Laboratory of Early Prevention and Treatment of Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021, China. xxiaoxun@163.com. 7. Department of Histology and Embryology, School of Pre-clinical Medicine, Guangxi Medical University, Nanning, 530021, China. glbinbin@sr.gxmu.edu.cn. 8. Central Laboratory of Pre-Clinical Medicine (Key Laboratory of Guangxi Colleges and Universities), Guangxi Medical University, Nanning, 530021, China. glbinbin@sr.gxmu.edu.cn.
Abstract
OBJECTIVE: Glioblastoma (GBM) is the most common, invasive, and malignant primary brain tumor with a poor prognosis and high recurrence rate. It's known that some microRNAs (miRNAs) which are associated with tumorigenesis and progression can be considered as prognostic and therapeutic targets in tumors including GBM. This study aims to highlight the potential role of the core miRNAs in GBM and their potential use as a prognostic and therapeutic biomarker. METHODS: Differentially expressed miRNAs (DEmiRNAs) were identified in GBM by integrating miRNA-sequencing results and a GBM microarray dataset from the Gene Expression Omnibus (GEO) database through bioinformatics tools. The dysregulated miRNAs were identified by survival analysis through Chinese Glioma Genome Atlas (CGGA). Target genes of the dysregulated miRNAs were predicted on MiRWalk and miRTarBase database. TAM2.0 database, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were used to analyze the function of the dysregulated miRNAs. Subsequently, protein-protein interaction (PPI) network analysis was used to identify the top 20 hub targets of the up-regulated and down-regulated miRNAs, respectively. Then, core miRNAs in GBM were identified by constructing dysregulated miRNA-differentially expressed hub gene networks. Validation of the core miRNAs expression was detected in 41 GBM tissues compared to 8 normal brain tissues. Furthermore, the potential biomarkers were identified by clinical correlation analysis and survival analysis. RESULTS: Totally, 68 intersecting DEmiRNAs were identified, 40 of which were upregulated and the other 28 miRNAs were downregulated. Two upregulated and 4 downregulated miRNAs showed prognostic significance. Most differentially expressed hub genes were regulated by the miR-28-5p and miR-1224-5p, which were respectively upregulated and downregulated in GBM. The correlation between miR-1224-5p level and recurrence was statistically significant (P=0.011). Survival analysis showed that high miR-28-5p level and high miR-1224-5p level were both associated with better prognosis. Moreover, high miR-1224-5p level was an independent prognosis factor for GBM patients according to the cox regression analysis. CONCLUSION: MiRNA-1224-5p could be a potential target for the prognosis and treatment in GBM.
OBJECTIVE: Glioblastoma (GBM) is the most common, invasive, and malignant primary brain tumor with a poor prognosis and high recurrence rate. It's known that some microRNAs (miRNAs) which are associated with tumorigenesis and progression can be considered as prognostic and therapeutic targets in tumors including GBM. This study aims to highlight the potential role of the core miRNAs in GBM and their potential use as a prognostic and therapeutic biomarker. METHODS: Differentially expressed miRNAs (DEmiRNAs) were identified in GBM by integrating miRNA-sequencing results and a GBM microarray dataset from the Gene Expression Omnibus (GEO) database through bioinformatics tools. The dysregulated miRNAs were identified by survival analysis through Chinese Glioma Genome Atlas (CGGA). Target genes of the dysregulated miRNAs were predicted on MiRWalk and miRTarBase database. TAM2.0 database, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were used to analyze the function of the dysregulated miRNAs. Subsequently, protein-protein interaction (PPI) network analysis was used to identify the top 20 hub targets of the up-regulated and down-regulated miRNAs, respectively. Then, core miRNAs in GBM were identified by constructing dysregulated miRNA-differentially expressed hub gene networks. Validation of the core miRNAs expression was detected in 41 GBM tissues compared to 8 normal brain tissues. Furthermore, the potential biomarkers were identified by clinical correlation analysis and survival analysis. RESULTS: Totally, 68 intersecting DEmiRNAs were identified, 40 of which were upregulated and the other 28 miRNAs were downregulated. Two upregulated and 4 downregulated miRNAs showed prognostic significance. Most differentially expressed hub genes were regulated by the miR-28-5p and miR-1224-5p, which were respectively upregulated and downregulated in GBM. The correlation between miR-1224-5p level and recurrence was statistically significant (P=0.011). Survival analysis showed that high miR-28-5p level and high miR-1224-5p level were both associated with better prognosis. Moreover, high miR-1224-5p level was an independent prognosis factor for GBM patients according to the cox regression analysis. CONCLUSION: MiRNA-1224-5p could be a potential target for the prognosis and treatment in GBM.
Authors: Hiroko Ohgaki; Pierre Dessen; Benjamin Jourde; Sonja Horstmann; Tomofumi Nishikawa; Pier-Luigi Di Patre; Christoph Burkhard; Danielle Schüler; Nicole M Probst-Hensch; Paulo César Maiorka; Nathalie Baeza; Paola Pisani; Yasuhiro Yonekawa; M Gazi Yasargil; Urs M Lütolf; Paul Kleihues Journal: Cancer Res Date: 2004-10-01 Impact factor: 12.701