Guangdong Liu1, Danian Liu2, Jingjing Huang3, Jianxin Li4, Chuang Wang1, Guangyao Liu1, Shiqiang Ge1, Haidong Gong5. 1. Department of Neurosurgery, Hongqi Hospital Affiliated to Mudanjiang Medical University, No. 5, Tongxiang Road, Aimin, MuDanJiang, HeiLongJiang, China. 2. Department of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University, MuDanJiang, China. 3. Department of Infectious Diseases, Hongqi Hospital Affiliated to Mudanjiang Medical University, MuDanJiang, China. 4. Department of Neurosurgery, Jiaozuo People's Hospital, JiaoZuo, China. 5. Department of Neurosurgery, Hongqi Hospital Affiliated to Mudanjiang Medical University, No. 5, Tongxiang Road, Aimin, MuDanJiang, HeiLongJiang, China. 149585079@qq.com.
Abstract
BACKGROUND: Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM). METHODS: We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model. RESULTS: A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. CONCLUSION: We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.
BACKGROUND: Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM). METHODS: We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model. RESULTS: A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. CONCLUSION: We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.
Authors: David N Louis; Arie Perry; Guido Reifenberger; Andreas von Deimling; Dominique Figarella-Branger; Webster K Cavenee; Hiroko Ohgaki; Otmar D Wiestler; Paul Kleihues; David W Ellison Journal: Acta Neuropathol Date: 2016-05-09 Impact factor: 17.088
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